High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

test_fmvsmvis_n_1920		99.65 9	99.61 10	99.77 75	99.38 291	99.37 127	99.58 141	99.62 54	99.41 24	99.87 50	99.92 19	98.81 51	100.00 1	99.97 3	99.93 33	99.94 18

test_fmvsm_n_1920		99.69 7	99.66 4	99.78 72	99.84 40	99.44 120	99.58 141	99.69 23	99.43 20	99.98 13	99.91 27	98.62 79	100.00 1	99.97 3	99.95 23	99.90 28

test_vis1_n_1920		98.63 230	98.40 239	99.31 211	99.86 27	97.94 318	99.67 79	99.62 54	99.43 20	99.99 2	99.91 27	87.29 458	100.00 1	99.92 25	99.92 39	99.98 3

fmvsm_l_mol_unc0.5_1		99.77 1	99.70 1	99.97 1	99.88 14	99.92 2	99.36 304	99.67 28	99.51 2	99.96 27	99.97 1	99.01 20	99.99 4	99.98 1	99.99 1	99.99 1

fmvsm_s_conf0.5_n_11		99.32 79	99.16 93	99.80 65	99.83 49	99.70 63	99.57 149	99.56 92	99.45 14	99.99 2	99.93 11	94.18 321	99.99 4	99.96 14	99.98 5	99.73 130

fmvsm_s_conf0.5_n_10		99.41 60	99.24 79	99.92 2	99.83 49	99.84 22	99.53 186	99.56 92	99.45 14	99.99 2	99.92 19	94.92 270	99.99 4	99.97 3	99.97 10	99.95 12

fmvsm_l_conf0.5_n_9		99.58 17	99.47 26	99.92 2	99.85 33	99.82 31	99.47 244	99.63 48	99.45 14	99.98 13	99.89 46	97.02 151	99.99 4	99.98 1	99.96 18	99.95 12

NormalMVS		99.27 89	99.19 89	99.52 144	99.89 9	98.83 239	99.65 92	99.52 136	99.10 49	99.84 58	99.76 199	95.80 230	99.99 4	99.30 99	99.84 104	99.74 120

SymmetryMVS		99.15 119	99.02 132	99.52 144	99.72 114	98.83 239	99.65 92	99.34 331	99.10 49	99.84 58	99.76 199	95.80 230	99.99 4	99.30 99	98.72 277	99.73 130

fmvsm_s_conf0.5_n_5		99.37 69	99.21 85	99.86 35	99.80 66	99.68 67	99.42 272	99.61 63	99.37 27	99.97 25	99.86 86	94.96 265	99.99 4	99.97 3	99.93 33	99.92 26

fmvsm_l_conf0.5_n_3		99.61 11	99.51 20	99.92 2	99.84 40	99.82 31	99.54 177	99.66 34	99.46 10	99.98 13	99.89 46	97.27 136	99.99 4	99.97 3	99.95 23	99.95 12

fmvsm_l_conf0.5_n_a		99.71 2	99.67 2	99.85 44	99.86 27	99.61 89	99.56 157	99.63 48	99.48 4	99.98 13	99.83 117	98.75 63	99.99 4	99.97 3	99.96 18	99.94 18

fmvsm_l_conf0.5_n		99.71 2	99.67 2	99.85 44	99.84 40	99.63 85	99.56 157	99.63 48	99.47 7	99.98 13	99.82 128	98.75 63	99.99 4	99.97 3	99.97 10	99.94 18

test_fmvsmconf_n		99.70 4	99.64 5	99.87 23	99.80 66	99.66 74	99.48 234	99.64 44	99.45 14	99.92 31	99.92 19	98.62 79	99.99 4	99.96 14	99.99 1	99.96 8

patch_mono-2		99.26 92	99.62 9	98.16 379	99.81 60	94.59 467	99.52 188	99.64 44	99.33 30	99.73 105	99.90 37	99.00 25	99.99 4	99.69 35	99.98 5	99.89 31

h-mvs33		97.70 348	97.28 373	98.97 259	99.70 125	97.27 345	99.36 304	99.45 262	98.94 81	99.66 139	99.64 266	94.93 268	99.99 4	99.48 65	84.36 510	99.65 186

xiu_mvs_v1_base_debu		99.29 85	99.27 74	99.34 203	99.63 176	98.97 192	99.12 390	99.51 164	98.86 87	99.84 58	99.47 339	98.18 107	99.99 4	99.50 58	99.31 195	99.08 316

xiu_mvs_v1_base		99.29 85	99.27 74	99.34 203	99.63 176	98.97 192	99.12 390	99.51 164	98.86 87	99.84 58	99.47 339	98.18 107	99.99 4	99.50 58	99.31 195	99.08 316

xiu_mvs_v1_base_debi		99.29 85	99.27 74	99.34 203	99.63 176	98.97 192	99.12 390	99.51 164	98.86 87	99.84 58	99.47 339	98.18 107	99.99 4	99.50 58	99.31 195	99.08 316

EPNet		98.86 194	98.71 202	99.30 216	97.20 498	98.18 297	99.62 111	98.91 434	99.28 33	98.63 383	99.81 143	95.96 217	99.99 4	99.24 115	99.72 151	99.73 130
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
fmvsm_s_conf0.5_n_8		99.54 25	99.42 34	99.89 13	99.83 49	99.74 57	99.51 198	99.62 54	99.46 10	99.99 2	99.90 37	96.60 177	99.98 21	99.95 17	99.95 23	99.96 8

MM		99.40 65	99.28 70	99.74 81	99.67 141	99.31 139	99.52 188	98.87 442	99.55 1	99.74 103	99.80 161	96.47 185	99.98 21	99.97 3	99.97 10	99.94 18

test_cas_vis1_n_1920		99.16 114	99.01 140	99.61 111	99.81 60	98.86 233	99.65 92	99.64 44	99.39 25	99.97 25	99.94 7	93.20 350	99.98 21	99.55 51	99.91 46	99.99 1

test_vis1_n		97.92 305	97.44 348	99.34 203	99.53 232	98.08 305	99.74 49	99.49 204	99.15 39	100.00 1	99.94 7	79.51 503	99.98 21	99.88 27	99.76 143	99.97 5

xiu_mvs_v2_base		99.26 92	99.25 78	99.29 219	99.53 232	98.91 214	99.02 415	99.45 262	98.80 97	99.71 120	99.26 399	98.94 35	99.98 21	99.34 90	99.23 204	98.98 332

PS-MVSNAJ		99.32 79	99.32 55	99.30 216	99.57 216	98.94 206	98.97 429	99.46 251	98.92 84	99.71 120	99.24 401	99.01 20	99.98 21	99.35 85	99.66 162	98.97 334

QAPM		98.67 225	98.30 246	99.80 65	99.20 343	99.67 71	99.77 36	99.72 15	94.74 452	98.73 363	99.90 37	95.78 232	99.98 21	96.96 389	99.88 75	99.76 109

3Dnovator	97.25 9	99.24 97	99.05 116	99.81 61	99.12 365	99.66 74	99.84 12	99.74 14	99.09 56	98.92 332	99.90 37	95.94 221	99.98 21	98.95 159	99.92 39	99.79 94

OpenMVS	96.50 16	98.47 236	98.12 259	99.52 144	99.04 388	99.53 105	99.82 16	99.72 15	94.56 455	98.08 427	99.88 59	94.73 289	99.98 21	97.47 349	99.76 143	99.06 322

fmvsm_s_conf0.5_n_3		99.37 69	99.20 87	99.87 23	99.75 95	99.70 63	99.48 234	99.66 34	99.45 14	99.99 2	99.93 11	94.64 298	99.97 30	99.94 22	99.97 10	99.95 12

reproduce_model		99.63 10	99.54 15	99.90 9	99.78 73	99.88 11	99.56 157	99.55 102	99.15 39	99.90 36	99.90 37	99.00 25	99.97 30	99.11 134	99.91 46	99.86 44

test_fmvsmconf0.1_n		99.55 24	99.45 32	99.86 35	99.44 272	99.65 78	99.50 209	99.61 63	99.45 14	99.87 50	99.92 19	97.31 133	99.97 30	99.95 17	99.99 1	99.97 5

test_fmvs1_n		98.41 242	98.14 256	99.21 232	99.82 55	97.71 330	99.74 49	99.49 204	99.32 31	99.99 2	99.95 4	85.32 477	99.97 30	99.82 30	99.84 104	99.96 8

CANet_DTU		98.97 181	98.87 178	99.25 226	99.33 305	98.42 289	99.08 399	99.30 359	99.16 38	99.43 210	99.75 204	95.27 253	99.97 30	98.56 230	99.95 23	99.36 286

MGCNet		99.15 119	98.96 155	99.73 84	98.92 406	99.37 127	99.37 298	96.92 514	99.51 2	99.66 139	99.78 185	96.69 172	99.97 30	99.84 29	99.97 10	99.84 56

MTAPA		99.52 29	99.39 41	99.89 13	99.90 5	99.86 19	99.66 86	99.47 238	98.79 98	99.68 128	99.81 143	98.43 93	99.97 30	98.88 169	99.90 57	99.83 66

PGM-MVS		99.45 47	99.31 61	99.86 35	99.87 22	99.78 50	99.58 141	99.65 41	97.84 255	99.71 120	99.80 161	99.12 15	99.97 30	98.33 258	99.87 81	99.83 66

mPP-MVS		99.44 51	99.30 63	99.86 35	99.88 14	99.79 44	99.69 65	99.48 216	98.12 202	99.50 193	99.75 204	98.78 55	99.97 30	98.57 227	99.89 68	99.83 66

CP-MVS		99.45 47	99.32 55	99.85 44	99.83 49	99.75 54	99.69 65	99.52 136	98.07 214	99.53 188	99.63 272	98.93 39	99.97 30	98.74 198	99.91 46	99.83 66

SteuartSystems-ACMMP		99.54 25	99.42 34	99.87 23	99.82 55	99.81 36	99.59 131	99.51 164	98.62 115	99.79 83	99.83 117	99.28 6	99.97 30	98.48 237	99.90 57	99.84 56
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3Dnovator+	97.12 13	99.18 105	98.97 151	99.82 58	99.17 357	99.68 67	99.81 20	99.51 164	99.20 35	98.72 364	99.89 46	95.68 237	99.97 30	98.86 177	99.86 89	99.81 81

fmvsm_s_conf0.5_n_9		99.41 60	99.28 70	99.81 61	99.84 40	99.52 109	99.48 234	99.62 54	99.46 10	99.99 2	99.92 19	95.24 257	99.96 42	99.97 3	99.97 10	99.96 8

lecture		99.60 15	99.50 21	99.89 13	99.89 9	99.90 3	99.75 44	99.59 75	99.06 62	99.88 44	99.85 93	98.41 96	99.96 42	99.28 108	99.84 104	99.83 66

KinetiMVS		99.12 141	98.92 164	99.70 88	99.67 141	99.40 125	99.67 79	99.63 48	98.73 105	99.94 29	99.81 143	94.54 304	99.96 42	98.40 249	99.93 33	99.74 120

fmvsm_s_conf0.5_n_7		99.34 76	99.29 67	99.48 167	99.70 125	98.63 261	99.42 272	99.63 48	99.46 10	99.98 13	99.88 59	95.59 240	99.96 42	99.97 3	99.98 5	99.85 48

fmvsm_s_conf0.5_n_2		99.32 79	99.13 96	99.89 13	99.80 66	99.77 51	99.44 259	99.58 80	99.47 7	99.99 2	99.93 11	94.04 326	99.96 42	99.96 14	99.93 33	99.93 23

reproduce-ours		99.61 11	99.52 16	99.90 9	99.76 85	99.88 11	99.52 188	99.54 111	99.13 42	99.89 41	99.89 46	98.96 28	99.96 42	99.04 145	99.90 57	99.85 48

our_new_method		99.61 11	99.52 16	99.90 9	99.76 85	99.88 11	99.52 188	99.54 111	99.13 42	99.89 41	99.89 46	98.96 28	99.96 42	99.04 145	99.90 57	99.85 48

fmvsm_s_conf0.5_n_a		99.56 22	99.47 26	99.85 44	99.83 49	99.64 84	99.52 188	99.65 41	99.10 49	99.98 13	99.92 19	97.35 132	99.96 42	99.94 22	99.92 39	99.95 12

fmvsm_s_conf0.5_n		99.51 30	99.40 39	99.85 44	99.84 40	99.65 78	99.51 198	99.67 28	99.13 42	99.98 13	99.92 19	96.60 177	99.96 42	99.95 17	99.96 18	99.95 12

mvsany_test1		99.50 32	99.46 30	99.62 110	99.61 197	99.09 172	98.94 435	99.48 216	99.10 49	99.96 27	99.91 27	98.85 45	99.96 42	99.72 33	99.58 172	99.82 74

test_fmvs1		98.88 188	98.79 192	99.16 237	99.69 131	97.61 334	99.55 172	99.49 204	99.32 31	99.98 13	99.91 27	91.41 401	99.96 42	99.82 30	99.92 39	99.90 28

DVP-MVS++		99.59 16	99.50 21	99.88 17	99.51 241	99.88 11	99.87 8	99.51 164	98.99 71	99.88 44	99.81 143	99.27 7	99.96 42	98.85 179	99.80 128	99.81 81

MSC_two_6792asdad				99.87 23	99.51 241	99.76 52		99.33 339					99.96 42	98.87 172	99.84 104	99.89 31

No_MVS				99.87 23	99.51 241	99.76 52		99.33 339					99.96 42	98.87 172	99.84 104	99.89 31

ZD-MVS					99.71 120	99.79 44		99.61 63	96.84 366	99.56 179	99.54 307	98.58 81	99.96 42	96.93 392	99.75 145

SED-MVS		99.61 11	99.52 16	99.88 17	99.84 40	99.90 3	99.60 120	99.48 216	99.08 57	99.91 33	99.81 143	99.20 9	99.96 42	98.91 166	99.85 96	99.79 94

test_241102_TWO								99.48 216	99.08 57	99.88 44	99.81 143	98.94 35	99.96 42	98.91 166	99.84 104	99.88 37

ZNCC-MVS		99.47 41	99.33 53	99.87 23	99.87 22	99.81 36	99.64 100	99.67 28	98.08 213	99.55 185	99.64 266	98.91 40	99.96 42	98.72 201	99.90 57	99.82 74

DVP-MVS		99.57 21	99.47 26	99.88 17	99.85 33	99.89 7	99.57 149	99.37 317	99.10 49	99.81 74	99.80 161	98.94 35	99.96 42	98.93 163	99.86 89	99.81 81
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test_0728_THIRD									98.99 71	99.81 74	99.80 161	99.09 16	99.96 42	98.85 179	99.90 57	99.88 37

test_0728_SECOND				99.91 7	99.84 40	99.89 7	99.57 149	99.51 164					99.96 42	98.93 163	99.86 89	99.88 37

SR-MVS		99.43 54	99.29 67	99.86 35	99.75 95	99.83 25	99.59 131	99.62 54	98.21 178	99.73 105	99.79 178	98.68 73	99.96 42	98.44 244	99.77 140	99.79 94

DPE-MVS		99.46 43	99.32 55	99.91 7	99.78 73	99.88 11	99.36 304	99.51 164	98.73 105	99.88 44	99.84 108	98.72 70	99.96 42	98.16 273	99.87 81	99.88 37
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
UA-Net		99.42 56	99.29 67	99.80 65	99.62 186	99.55 100	99.50 209	99.70 19	98.79 98	99.77 92	99.96 2	97.45 127	99.96 42	98.92 165	99.90 57	99.89 31

HFP-MVS		99.49 34	99.37 45	99.86 35	99.87 22	99.80 41	99.66 86	99.67 28	98.15 187	99.68 128	99.69 238	99.06 18	99.96 42	98.69 206	99.87 81	99.84 56

region2R		99.48 38	99.35 49	99.87 23	99.88 14	99.80 41	99.65 92	99.66 34	98.13 194	99.66 139	99.68 246	98.96 28	99.96 42	98.62 215	99.87 81	99.84 56

HPM-MVS++		99.39 67	99.23 83	99.87 23	99.75 95	99.84 22	99.43 265	99.51 164	98.68 112	99.27 260	99.53 312	98.64 78	99.96 42	98.44 244	99.80 128	99.79 94

APDe-MVS		99.66 8	99.57 12	99.92 2	99.77 81	99.89 7	99.75 44	99.56 92	99.02 64	99.88 44	99.85 93	99.18 12	99.96 42	99.22 116	99.92 39	99.90 28
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
ACMMPR		99.49 34	99.36 47	99.86 35	99.87 22	99.79 44	99.66 86	99.67 28	98.15 187	99.67 134	99.69 238	98.95 33	99.96 42	98.69 206	99.87 81	99.84 56

MP-MVS		99.33 78	99.15 94	99.87 23	99.88 14	99.82 31	99.66 86	99.46 251	98.09 209	99.48 197	99.74 210	98.29 102	99.96 42	97.93 295	99.87 81	99.82 74
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CPTT-MVS		99.11 147	98.90 170	99.74 81	99.80 66	99.46 118	99.59 131	99.49 204	97.03 353	99.63 157	99.69 238	97.27 136	99.96 42	97.82 306	99.84 104	99.81 81

PVSNet_Blended_VisFu		99.36 73	99.28 70	99.61 111	99.86 27	99.07 177	99.47 244	99.93 2	97.66 281	99.71 120	99.86 86	97.73 122	99.96 42	99.47 67	99.82 120	99.79 94

UGNet		98.87 191	98.69 204	99.40 192	99.22 340	98.72 253	99.44 259	99.68 25	99.24 34	99.18 286	99.42 350	92.74 361	99.96 42	99.34 90	99.94 31	99.53 236
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
CSCG		99.32 79	99.32 55	99.32 209	99.85 33	98.29 292	99.71 59	99.66 34	98.11 204	99.41 218	99.80 161	98.37 99	99.96 42	98.99 151	99.96 18	99.72 140

ACMMP		99.45 47	99.32 55	99.82 58	99.89 9	99.67 71	99.62 111	99.69 23	98.12 202	99.63 157	99.84 108	98.73 69	99.96 42	98.55 233	99.83 116	99.81 81
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
RA-MVS			99.64 5		99.91 1	99.86 19	99.70 60	99.77 12	99.06 62	99.92 31	99.77 194	99.33 3	99.95 77	99.38 81	99.89 68

aaatest				99.87 23	99.88 14	99.81 36	99.69 65	99.87 6	99.34 29	99.90 36	99.83 117		99.95 77	98.83 185	99.89 68	99.83 66

MED-MVS		99.70 4	99.63 7	99.90 9	99.88 14	99.81 36	99.69 65	99.87 6	99.48 4	99.90 36	99.89 46	99.30 5	99.95 77	98.83 185	99.88 75	99.93 23

fmvsm_s_conf0.5_n_6		99.54 25	99.44 33	99.85 44	99.51 241	99.67 71	99.50 209	99.64 44	99.43 20	99.98 13	99.78 185	97.26 139	99.95 77	99.95 17	99.93 33	99.92 26

fmvsm_s_conf0.5_n_4		99.36 73	99.24 79	99.73 84	99.78 73	99.53 105	99.49 226	99.60 70	99.42 23	99.99 2	99.86 86	95.15 260	99.95 77	99.95 17	99.89 68	99.73 130

fmvsm_s_conf0.1_n_2		99.37 69	99.22 84	99.81 61	99.77 81	99.75 54	99.46 248	99.60 70	99.47 7	99.98 13	99.94 7	94.98 264	99.95 77	99.97 3	99.79 135	99.73 130

test_fmvsmconf0.01_n		99.22 100	99.03 121	99.79 69	98.42 468	99.48 115	99.55 172	99.51 164	99.39 25	99.78 88	99.93 11	94.80 279	99.95 77	99.93 24	99.95 23	99.94 18

SR-MVS-dyc-post		99.45 47	99.31 61	99.85 44	99.76 85	99.82 31	99.63 106	99.52 136	98.38 144	99.76 98	99.82 128	98.53 86	99.95 77	98.61 218	99.81 123	99.77 102

GST-MVS		99.40 65	99.24 79	99.85 44	99.86 27	99.79 44	99.60 120	99.67 28	97.97 239	99.63 157	99.68 246	98.52 87	99.95 77	98.38 251	99.86 89	99.81 81

CANet		99.25 96	99.14 95	99.59 115	99.41 280	99.16 160	99.35 310	99.57 87	98.82 92	99.51 192	99.61 281	96.46 186	99.95 77	99.59 46	99.98 5	99.65 186

MP-MVS-pluss		99.37 69	99.20 87	99.88 17	99.90 5	99.87 18	99.30 328	99.52 136	97.18 335	99.60 169	99.79 178	98.79 54	99.95 77	98.83 185	99.91 46	99.83 66
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MSP-MVS		99.42 56	99.27 74	99.88 17	99.89 9	99.80 41	99.67 79	99.50 189	98.70 109	99.77 92	99.49 328	98.21 105	99.95 77	98.46 242	99.77 140	99.88 37
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
testdata2													99.95 77	96.67 404

APD-MVS_3200maxsize		99.48 38	99.35 49	99.85 44	99.76 85	99.83 25	99.63 106	99.54 111	98.36 148	99.79 83	99.82 128	98.86 44	99.95 77	98.62 215	99.81 123	99.78 100

RPMNet		96.72 402	95.90 417	99.19 234	99.18 349	98.49 281	99.22 367	99.52 136	88.72 507	99.56 179	97.38 505	94.08 325	99.95 77	86.87 516	98.58 284	99.14 307

sss		99.17 110	99.05 116	99.53 137	99.62 186	98.97 192	99.36 304	99.62 54	97.83 256	99.67 134	99.65 260	97.37 131	99.95 77	99.19 120	99.19 208	99.68 165

test-260524					99.82 55	99.84 22		99.63 48		99.85 57		98.54 85	99.94 93	99.34 90	99.88 75

MVSMamba_PlusPlus		99.46 43	99.41 38	99.64 103	99.68 138	99.50 112	99.75 44	99.50 189	98.27 162	99.87 50	99.92 19	98.09 111	99.94 93	99.65 42	99.95 23	99.47 260

fmvsm_s_conf0.1_n_a		99.26 92	99.06 113	99.85 44	99.52 238	99.62 86	99.54 177	99.62 54	98.69 110	99.99 2	99.96 2	94.47 308	99.94 93	99.88 27	99.92 39	99.98 3

fmvsm_s_conf0.1_n		99.29 85	99.10 101	99.86 35	99.70 125	99.65 78	99.53 186	99.62 54	98.74 104	99.99 2	99.95 4	94.53 306	99.94 93	99.89 26	99.96 18	99.97 5

TSAR-MVS + MP.		99.58 17	99.50 21	99.81 61	99.91 1	99.66 74	99.63 106	99.39 297	98.91 85	99.78 88	99.85 93	99.36 2	99.94 93	98.84 182	99.88 75	99.82 74
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
RRT-MVS		98.91 186	98.75 196	99.39 197	99.46 265	98.61 266	99.76 39	99.50 189	98.06 218	99.81 74	99.88 59	93.91 333	99.94 93	99.11 134	99.27 198	99.61 203

XVS		99.53 28	99.42 34	99.87 23	99.85 33	99.83 25	99.69 65	99.68 25	98.98 74	99.37 230	99.74 210	98.81 51	99.94 93	98.79 193	99.86 89	99.84 56

X-MVStestdata		96.55 406	95.45 427	99.87 23	99.85 33	99.83 25	99.69 65	99.68 25	98.98 74	99.37 230	64.01 560	98.81 51	99.94 93	98.79 193	99.86 89	99.84 56

旧先验2							98.96 430		96.70 375	99.47 198			99.94 93	98.19 269

新几何1				99.75 78	99.75 95	99.59 92		99.54 111	96.76 371	99.29 253	99.64 266	98.43 93	99.94 93	96.92 394	99.66 162	99.72 140

testdata				99.54 129	99.75 95	98.95 202		99.51 164	97.07 347	99.43 210	99.70 227	98.87 43	99.94 93	97.76 315	99.64 165	99.72 140

HPM-MVS		99.42 56	99.28 70	99.83 57	99.90 5	99.72 59	99.81 20	99.54 111	97.59 287	99.68 128	99.63 272	98.91 40	99.94 93	98.58 224	99.91 46	99.84 56
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CHOSEN 1792x2688		99.19 102	99.10 101	99.45 177	99.89 9	98.52 276	99.39 289	99.94 1	98.73 105	99.11 295	99.89 46	95.50 243	99.94 93	99.50 58	99.97 10	99.89 31

APD-MVS		99.27 89	99.08 107	99.84 56	99.75 95	99.79 44	99.50 209	99.50 189	97.16 337	99.77 92	99.82 128	98.78 55	99.94 93	97.56 338	99.86 89	99.80 90
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
DELS-MVS		99.48 38	99.42 34	99.65 97	99.72 114	99.40 125	99.05 407	99.66 34	99.14 41	99.57 177	99.80 161	98.46 91	99.94 93	99.57 49	99.84 104	99.60 206
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
WTY-MVS		99.06 161	98.88 177	99.61 111	99.62 186	99.16 160	99.37 298	99.56 92	98.04 228	99.53 188	99.62 277	96.84 163	99.94 93	98.85 179	98.49 292	99.72 140

DeepC-MVS	98.35 2	99.30 83	99.19 89	99.64 103	99.82 55	99.23 152	99.62 111	99.55 102	98.94 81	99.63 157	99.95 4	95.82 228	99.94 93	99.37 83	99.97 10	99.73 130
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
LS3D		99.27 89	99.12 98	99.74 81	99.18 349	99.75 54	99.56 157	99.57 87	98.45 135	99.49 196	99.85 93	97.77 121	99.94 93	98.33 258	99.84 104	99.52 237

TestfortrainingZip a		99.70 4	99.63 7	99.92 2	99.88 14	99.90 3	99.69 65	99.79 11	99.48 4	99.93 30	99.89 46	98.78 55	99.93 111	99.32 94	99.88 75	99.93 23

aaEdge-Enhanced		99.56 22	99.46 30	99.86 35	99.80 66	99.81 36	99.37 298	99.70 19	99.18 36	99.83 68	99.83 117	98.74 68	99.93 111	98.83 185	99.89 68	99.83 66

GDP-MVS		99.08 156	98.89 174	99.64 103	99.53 232	99.34 131	99.64 100	99.48 216	98.32 154	99.77 92	99.66 258	95.14 261	99.93 111	98.97 157	99.50 180	99.64 193

SDMVSNet		99.11 147	98.90 170	99.75 78	99.81 60	99.59 92	99.81 20	99.65 41	98.78 101	99.64 154	99.88 59	94.56 301	99.93 111	99.67 38	98.26 308	99.72 140

FE-MVS		98.48 235	98.17 252	99.40 192	99.54 231	98.96 196	99.68 75	98.81 450	95.54 434	99.62 161	99.70 227	93.82 336	99.93 111	97.35 360	99.46 182	99.32 292

SF-MVS		99.38 68	99.24 79	99.79 69	99.79 71	99.68 67	99.57 149	99.54 111	97.82 261	99.71 120	99.80 161	98.95 33	99.93 111	98.19 269	99.84 104	99.74 120

dcpmvs_2		99.23 98	99.58 11	98.16 379	99.83 49	94.68 463	99.76 39	99.52 136	99.07 59	99.98 13	99.88 59	98.56 83	99.93 111	99.67 38	99.98 5	99.87 42

Anonymous20240529		98.09 273	97.68 314	99.34 203	99.66 154	98.44 286	99.40 285	99.43 282	93.67 464	99.22 272	99.89 46	90.23 421	99.93 111	99.26 114	98.33 300	99.66 179

ACMMP_NAP		99.47 41	99.34 51	99.88 17	99.87 22	99.86 19	99.47 244	99.48 216	98.05 221	99.76 98	99.86 86	98.82 50	99.93 111	98.82 192	99.91 46	99.84 56

balanced_ft_v1		99.02 170	98.98 149	99.15 241	99.39 288	98.12 303	99.79 32	99.51 164	98.20 180	99.66 139	99.87 75	94.84 275	99.93 111	99.69 35	99.84 104	99.41 277

EI-MVSNet-UG-set		99.58 17	99.57 12	99.64 103	99.78 73	99.14 166	99.60 120	99.45 262	99.01 66	99.90 36	99.83 117	98.98 27	99.93 111	99.59 46	99.95 23	99.86 44

无先验							98.99 423	99.51 164	96.89 363				99.93 111	97.53 341		99.72 140

VDDNet		97.55 363	97.02 388	99.16 237	99.49 255	98.12 303	99.38 294	99.30 359	95.35 436	99.68 128	99.90 37	82.62 493	99.93 111	99.31 96	98.13 321	99.42 274

ab-mvs		98.86 194	98.63 214	99.54 129	99.64 171	99.19 155	99.44 259	99.54 111	97.77 265	99.30 250	99.81 143	94.20 318	99.93 111	99.17 126	98.82 271	99.49 251

F-COLMAP		99.19 102	99.04 118	99.64 103	99.78 73	99.27 147	99.42 272	99.54 111	97.29 325	99.41 218	99.59 286	98.42 95	99.93 111	98.19 269	99.69 156	99.73 130

BP-MVS1		99.12 141	98.94 161	99.65 97	99.51 241	99.30 142	99.67 79	98.92 429	98.48 131	99.84 58	99.69 238	94.96 265	99.92 126	99.62 45	99.79 135	99.71 152

Anonymous202405211		98.30 254	97.98 276	99.26 225	99.57 216	98.16 298	99.41 277	98.55 481	96.03 428	99.19 282	99.74 210	91.87 386	99.92 126	99.16 129	98.29 307	99.70 156

EI-MVSNet-Vis-set		99.58 17	99.56 14	99.64 103	99.78 73	99.15 165	99.61 118	99.45 262	99.01 66	99.89 41	99.82 128	99.01 20	99.92 126	99.56 50	99.95 23	99.85 48

VDD-MVS		97.73 342	97.35 360	98.88 283	99.47 263	97.12 353	99.34 316	98.85 445	98.19 182	99.67 134	99.85 93	82.98 491	99.92 126	99.49 62	98.32 304	99.60 206

VNet		99.11 147	98.90 170	99.73 84	99.52 238	99.56 98	99.41 277	99.39 297	99.01 66	99.74 103	99.78 185	95.56 241	99.92 126	99.52 56	98.18 317	99.72 140

XVG-OURS-SEG-HR		98.69 223	98.62 219	98.89 278	99.71 120	97.74 325	99.12 390	99.54 111	98.44 138	99.42 213	99.71 223	94.20 318	99.92 126	98.54 234	98.90 264	99.00 328

mvsmamba		99.06 161	98.96 155	99.36 199	99.47 263	98.64 260	99.70 60	99.05 410	97.61 286	99.65 149	99.83 117	96.54 182	99.92 126	99.19 120	99.62 168	99.51 246

HPM-MVS_fast		99.51 30	99.40 39	99.85 44	99.91 1	99.79 44	99.76 39	99.56 92	97.72 272	99.76 98	99.75 204	99.13 14	99.92 126	99.07 141	99.92 39	99.85 48

HY-MVS	97.30 7	98.85 203	98.64 213	99.47 173	99.42 275	99.08 175	99.62 111	99.36 319	97.39 317	99.28 254	99.68 246	96.44 188	99.92 126	98.37 253	98.22 311	99.40 280

DP-MVS		99.16 114	98.95 159	99.78 72	99.77 81	99.53 105	99.41 277	99.50 189	97.03 353	99.04 312	99.88 59	97.39 128	99.92 126	98.66 210	99.90 57	99.87 42

IB-MVS	95.67 18	96.22 412	95.44 428	98.57 327	99.21 341	96.70 389	98.65 474	97.74 502	96.71 374	97.27 454	98.54 465	86.03 470	99.92 126	98.47 240	86.30 507	99.10 311
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
DeepC-MVS_fast	98.69 1	99.49 34	99.39 41	99.77 75	99.63 176	99.59 92	99.36 304	99.46 251	99.07 59	99.79 83	99.82 128	98.85 45	99.92 126	98.68 208	99.87 81	99.82 74
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
LuminaMVS		99.23 98	99.10 101	99.61 111	99.35 299	99.31 139	99.46 248	99.13 398	98.61 116	99.86 54	99.89 46	96.41 191	99.91 138	99.67 38	99.51 178	99.63 198

BridgeMVS		99.46 43	99.39 41	99.67 92	99.55 224	99.58 97	99.74 49	99.51 164	98.42 139	99.87 50	99.84 108	98.05 114	99.91 138	99.58 48	99.94 31	99.52 237

9.14			99.10 101		99.72 114		99.40 285	99.51 164	97.53 298	99.64 154	99.78 185	98.84 47	99.91 138	97.63 329	99.82 120

SMA-MVS		99.44 51	99.30 63	99.85 44	99.73 110	99.83 25	99.56 157	99.47 238	97.45 307	99.78 88	99.82 128	99.18 12	99.91 138	98.79 193	99.89 68	99.81 81
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
TEST9					99.67 141	99.65 78	99.05 407	99.41 287	96.22 413	98.95 328	99.49 328	98.77 59	99.91 138

train_agg		99.02 170	98.77 194	99.77 75	99.67 141	99.65 78	99.05 407	99.41 287	96.28 407	98.95 328	99.49 328	98.76 60	99.91 138	97.63 329	99.72 151	99.75 115

test_8					99.67 141	99.61 89	99.03 412	99.41 287	96.28 407	98.93 331	99.48 336	98.76 60	99.91 138

agg_prior					99.67 141	99.62 86		99.40 294		98.87 343			99.91 138

原ACMM1				99.65 97	99.73 110	99.33 134		99.47 238	97.46 304	99.12 293	99.66 258	98.67 75	99.91 138	97.70 325	99.69 156	99.71 152

LFMVS		97.90 308	97.35 360	99.54 129	99.52 238	99.01 185	99.39 289	98.24 491	97.10 345	99.65 149	99.79 178	84.79 481	99.91 138	99.28 108	98.38 297	99.69 159

XVG-OURS		98.73 221	98.68 205	98.88 283	99.70 125	97.73 326	98.92 437	99.55 102	98.52 126	99.45 202	99.84 108	95.27 253	99.91 138	98.08 284	98.84 269	99.00 328

PLC	97.94 4	99.02 170	98.85 184	99.53 137	99.66 154	99.01 185	99.24 360	99.52 136	96.85 365	99.27 260	99.48 336	98.25 104	99.91 138	97.76 315	99.62 168	99.65 186
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PCF-MVS	97.08 14	97.66 356	97.06 387	99.47 173	99.61 197	99.09 172	98.04 513	99.25 377	91.24 495	98.51 395	99.70 227	94.55 303	99.91 138	92.76 480	99.85 96	99.42 274
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Elysia		98.88 188	98.65 211	99.58 119	99.58 210	99.34 131	99.65 92	99.52 136	98.26 165	99.83 68	99.87 75	93.37 344	99.90 151	97.81 308	99.91 46	99.49 251

StellarMVS		98.88 188	98.65 211	99.58 119	99.58 210	99.34 131	99.65 92	99.52 136	98.26 165	99.83 68	99.87 75	93.37 344	99.90 151	97.81 308	99.91 46	99.49 251

AstraMVS		99.09 154	99.03 121	99.25 226	99.66 154	98.13 301	99.57 149	98.24 491	98.82 92	99.91 33	99.88 59	95.81 229	99.90 151	99.72 33	99.67 161	99.74 120

mmtdpeth		96.95 397	96.71 397	97.67 427	99.33 305	94.90 457	99.89 2	99.28 365	98.15 187	99.72 110	98.57 463	86.56 466	99.90 151	99.82 30	89.02 500	98.20 461

UWE-MVS		97.58 362	97.29 372	98.48 341	99.09 373	96.25 410	99.01 420	96.61 520	97.86 249	99.19 282	99.01 430	88.72 438	99.90 151	97.38 358	98.69 278	99.28 296

test_vis1_rt		95.81 423	95.65 423	96.32 465	99.67 141	91.35 496	99.49 226	96.74 518	98.25 170	95.24 479	98.10 485	74.96 505	99.90 151	99.53 54	98.85 268	97.70 492

FA-MVS(test-final)		98.75 218	98.53 231	99.41 190	99.55 224	99.05 180	99.80 25	99.01 417	96.59 389	99.58 174	99.59 286	95.39 247	99.90 151	97.78 311	99.49 181	99.28 296

MCST-MVS		99.43 54	99.30 63	99.82 58	99.79 71	99.74 57	99.29 333	99.40 294	98.79 98	99.52 190	99.62 277	98.91 40	99.90 151	98.64 212	99.75 145	99.82 74

CDPH-MVS		99.13 131	98.91 168	99.80 65	99.75 95	99.71 61	99.15 383	99.41 287	96.60 387	99.60 169	99.55 302	98.83 49	99.90 151	97.48 347	99.83 116	99.78 100

NCCC		99.34 76	99.19 89	99.79 69	99.61 197	99.65 78	99.30 328	99.48 216	98.86 87	99.21 275	99.63 272	98.72 70	99.90 151	98.25 265	99.63 167	99.80 90

114514_t		98.93 184	98.67 206	99.72 87	99.85 33	99.53 105	99.62 111	99.59 75	92.65 481	99.71 120	99.78 185	98.06 113	99.90 151	98.84 182	99.91 46	99.74 120

1112_ss		98.98 179	98.77 194	99.59 115	99.68 138	99.02 183	99.25 355	99.48 216	97.23 331	99.13 291	99.58 290	96.93 156	99.90 151	98.87 172	98.78 274	99.84 56

PHI-MVS		99.30 83	99.17 92	99.70 88	99.56 220	99.52 109	99.58 141	99.80 10	97.12 341	99.62 161	99.73 216	98.58 81	99.90 151	98.61 218	99.91 46	99.68 165

AdaColmap		99.01 175	98.80 189	99.66 93	99.56 220	99.54 102	99.18 377	99.70 19	98.18 185	99.35 239	99.63 272	96.32 193	99.90 151	97.48 347	99.77 140	99.55 229

COLMAP_ROB	97.56 6	98.86 194	98.75 196	99.17 236	99.88 14	98.53 272	99.34 316	99.59 75	97.55 293	98.70 371	99.89 46	95.83 227	99.90 151	98.10 279	99.90 57	99.08 316
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
testing915		98.83 207	98.59 225	99.56 125	99.67 141	98.93 211	99.80 25	99.39 297	98.30 158	99.46 200	99.50 324	93.05 351	99.89 166	99.29 105	98.88 265	99.85 48

thisisatest0530		98.35 250	98.03 271	99.31 211	99.63 176	98.56 269	99.54 177	96.75 517	97.53 298	99.73 105	99.65 260	91.25 406	99.89 166	98.62 215	99.56 174	99.48 254

tttt0517		98.42 240	98.14 256	99.28 223	99.66 154	98.38 290	99.74 49	96.85 515	97.68 278	99.79 83	99.74 210	91.39 402	99.89 166	98.83 185	99.56 174	99.57 224

test12				99.75 78	99.64 171	99.61 89		99.29 363		99.21 275		98.38 98	99.89 166		99.74 148	99.74 120

Test_1112_low_res		98.89 187	98.66 209	99.57 123	99.69 131	98.95 202	99.03 412	99.47 238	96.98 355	99.15 289	99.23 402	96.77 168	99.89 166	98.83 185	98.78 274	99.86 44

CNLPA		99.14 127	98.99 146	99.59 115	99.58 210	99.41 124	99.16 379	99.44 271	98.45 135	99.19 282	99.49 328	98.08 112	99.89 166	97.73 319	99.75 145	99.48 254

diffmvs_AUTHOR		99.19 102	99.10 101	99.48 167	99.64 171	98.85 234	99.32 321	99.48 216	98.50 129	99.81 74	99.81 143	96.82 164	99.88 172	99.40 75	99.12 225	99.71 152

guyue		99.16 114	99.04 118	99.52 144	99.69 131	98.92 213	99.59 131	98.81 450	98.73 105	99.90 36	99.87 75	95.34 250	99.88 172	99.66 41	99.81 123	99.74 120

sd_testset		98.75 218	98.57 227	99.29 219	99.81 60	98.26 294	99.56 157	99.62 54	98.78 101	99.64 154	99.88 59	92.02 383	99.88 172	99.54 52	98.26 308	99.72 140

APD_test1		95.87 421	96.49 402	94.00 480	99.53 232	84.01 516	99.54 177	99.32 350	95.91 430	97.99 432	99.85 93	85.49 475	99.88 172	91.96 485	98.84 269	98.12 465

diffmvs		99.14 127	99.02 132	99.51 149	99.61 197	98.96 196	99.28 339	99.49 204	98.46 133	99.72 110	99.71 223	96.50 184	99.88 172	99.31 96	99.11 227	99.67 172
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
PVSNet_BlendedMVS		98.86 194	98.80 189	99.03 251	99.76 85	98.79 245	99.28 339	99.91 3	97.42 314	99.67 134	99.37 369	97.53 125	99.88 172	98.98 152	97.29 370	98.42 446

PVSNet_Blended		99.08 156	98.97 151	99.42 189	99.76 85	98.79 245	98.78 458	99.91 3	96.74 372	99.67 134	99.49 328	97.53 125	99.88 172	98.98 152	99.85 96	99.60 206

0.4-1-1-0.1		95.23 439	94.22 449	98.26 373	97.39 492	95.86 425	97.59 522	97.62 503	93.85 461	94.97 486	97.03 511	87.20 459	99.87 179	98.47 240	83.84 512	99.05 323

viewdifsd2359ckpt07		99.11 147	99.00 144	99.43 187	99.63 176	98.73 251	99.45 252	99.54 111	98.33 152	99.62 161	99.81 143	96.17 204	99.87 179	99.27 111	99.14 217	99.69 159

viewdifsd2359ckpt11		98.78 213	98.74 198	98.89 278	99.67 141	97.04 363	99.50 209	99.58 80	98.26 165	99.56 179	99.90 37	94.36 311	99.87 179	99.49 62	98.32 304	99.77 102

viewmsd2359difaftdt		98.78 213	98.74 198	98.90 274	99.67 141	97.04 363	99.50 209	99.58 80	98.26 165	99.56 179	99.90 37	94.36 311	99.87 179	99.49 62	98.32 304	99.77 102

MVS		97.28 385	96.55 400	99.48 167	98.78 428	98.95 202	99.27 344	99.39 297	83.53 518	98.08 427	99.54 307	96.97 154	99.87 179	94.23 456	99.16 210	99.63 198

MG-MVS		99.13 131	99.02 132	99.45 177	99.57 216	98.63 261	99.07 400	99.34 331	98.99 71	99.61 166	99.82 128	97.98 116	99.87 179	97.00 385	99.80 128	99.85 48

MSDG		98.98 179	98.80 189	99.53 137	99.76 85	99.19 155	98.75 463	99.55 102	97.25 328	99.47 198	99.77 194	97.82 119	99.87 179	96.93 392	99.90 57	99.54 231

0.3-1-1-0.015		94.79 447	93.69 461	98.10 385	96.99 504	95.46 439	97.02 527	97.61 505	93.53 466	94.03 494	96.54 516	85.60 474	99.86 186	98.43 247	83.45 517	98.99 331

0.4-1-1-0.2		94.94 446	93.92 455	97.99 394	96.84 505	95.13 452	96.64 529	97.62 503	93.45 470	94.92 487	96.56 515	87.14 461	99.86 186	98.43 247	83.69 516	98.98 332

ETV-MVS		99.26 92	99.21 85	99.40 192	99.46 265	99.30 142	99.56 157	99.52 136	98.52 126	99.44 207	99.27 397	98.41 96	99.86 186	99.10 137	99.59 171	99.04 324

thisisatest0515		98.14 268	97.79 297	99.19 234	99.50 253	98.50 280	98.61 477	96.82 516	96.95 359	99.54 186	99.43 348	91.66 395	99.86 186	98.08 284	99.51 178	99.22 304

thres600view7		97.86 314	97.51 334	98.92 268	99.72 114	97.95 316	99.59 131	98.74 462	97.94 241	99.27 260	98.62 459	91.75 389	99.86 186	93.73 464	98.19 316	98.96 336

lupinMVS		99.13 131	99.01 140	99.46 175	99.51 241	98.94 206	99.05 407	99.16 394	97.86 249	99.80 80	99.56 299	97.39 128	99.86 186	98.94 160	99.85 96	99.58 221

PVSNet	96.02 17	98.85 203	98.84 186	98.89 278	99.73 110	97.28 344	98.32 501	99.60 70	97.86 249	99.50 193	99.57 296	96.75 169	99.86 186	98.56 230	99.70 155	99.54 231

MAR-MVS		98.86 194	98.63 214	99.54 129	99.37 294	99.66 74	99.45 252	99.54 111	96.61 384	99.01 315	99.40 359	97.09 146	99.86 186	97.68 327	99.53 177	99.10 311
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
Casviewmamba		99.16 114	99.02 132	99.59 115	99.66 154	99.21 154	99.68 75	99.52 136	98.31 156	99.60 169	99.87 75	95.96 217	99.85 194	99.40 75	99.16 210	99.72 140

mamba_0408		99.08 156	98.96 155	99.44 183	99.62 186	98.88 225	99.25 355	99.47 238	98.05 221	99.37 230	99.81 143	96.85 158	99.85 194	98.98 152	99.25 201	99.60 206

SSM_0404		99.16 114	99.06 113	99.44 183	99.65 166	98.96 196	99.49 226	99.50 189	98.14 191	99.62 161	99.85 93	96.85 158	99.85 194	99.19 120	99.26 200	99.52 237

testing91		97.44 377	97.02 388	98.71 312	99.18 349	96.89 381	99.19 375	99.04 411	97.78 264	98.31 413	98.29 475	85.41 476	99.85 194	98.01 290	97.95 326	99.39 281

test2506		96.81 401	96.65 398	97.29 444	99.74 103	92.21 493	99.60 120	85.06 551	99.13 42	99.77 92	99.93 11	87.82 456	99.85 194	99.38 81	99.38 187	99.80 90

AllTest		98.87 191	98.72 200	99.31 211	99.86 27	98.48 283	99.56 157	99.61 63	97.85 252	99.36 236	99.85 93	95.95 219	99.85 194	96.66 405	99.83 116	99.59 217

TestCases				99.31 211	99.86 27	98.48 283		99.61 63	97.85 252	99.36 236	99.85 93	95.95 219	99.85 194	96.66 405	99.83 116	99.59 217

jason		99.13 131	99.03 121	99.45 177	99.46 265	98.87 229	99.12 390	99.26 374	98.03 230	99.79 83	99.65 260	97.02 151	99.85 194	99.02 149	99.90 57	99.65 186
jason: jason.
CNVR-MVS		99.42 56	99.30 63	99.78 72	99.62 186	99.71 61	99.26 353	99.52 136	98.82 92	99.39 225	99.71 223	98.96 28	99.85 194	98.59 223	99.80 128	99.77 102

PAPM_NR		99.04 166	98.84 186	99.66 93	99.74 103	99.44 120	99.39 289	99.38 307	97.70 276	99.28 254	99.28 394	98.34 100	99.85 194	96.96 389	99.45 183	99.69 159

E5new		99.14 127	99.02 132	99.50 155	99.69 131	98.91 214	99.60 120	99.53 127	98.13 194	99.72 110	99.91 27	96.26 201	99.84 204	99.30 99	99.10 236	99.76 109

E6new		99.15 119	99.03 121	99.50 155	99.66 154	98.90 219	99.60 120	99.53 127	98.13 194	99.72 110	99.91 27	96.31 195	99.84 204	99.30 99	99.10 236	99.76 109

E6		99.15 119	99.03 121	99.50 155	99.66 154	98.90 219	99.60 120	99.53 127	98.13 194	99.72 110	99.91 27	96.31 195	99.84 204	99.30 99	99.10 236	99.76 109

E5		99.14 127	99.02 132	99.50 155	99.69 131	98.91 214	99.60 120	99.53 127	98.13 194	99.72 110	99.91 27	96.26 201	99.84 204	99.30 99	99.10 236	99.76 109

E4		99.13 131	99.01 140	99.49 162	99.68 138	98.90 219	99.52 188	99.52 136	98.13 194	99.71 120	99.90 37	96.32 193	99.84 204	99.21 118	99.11 227	99.75 115

E3new		99.18 105	99.08 107	99.48 167	99.63 176	98.94 206	99.46 248	99.50 189	98.06 218	99.72 110	99.84 108	97.27 136	99.84 204	99.10 137	99.13 220	99.67 172

E2		99.15 119	99.03 121	99.49 162	99.65 166	98.93 211	99.49 226	99.52 136	98.14 191	99.72 110	99.88 59	96.57 181	99.84 204	99.17 126	99.13 220	99.72 140

E3		99.15 119	99.03 121	99.49 162	99.62 186	98.91 214	99.49 226	99.52 136	98.13 194	99.72 110	99.88 59	96.61 176	99.84 204	99.17 126	99.13 220	99.72 140

viewcassd2359sk11		99.18 105	99.08 107	99.49 162	99.65 166	98.95 202	99.48 234	99.51 164	98.10 208	99.72 110	99.87 75	97.13 142	99.84 204	99.13 131	99.14 217	99.69 159

testing99		97.36 380	96.94 391	98.63 319	99.18 349	96.70 389	99.30 328	98.93 426	97.71 273	98.23 418	98.26 477	84.92 480	99.84 204	98.04 289	97.85 334	99.35 287

testing222		97.16 390	96.50 401	99.16 237	99.16 359	98.47 285	99.27 344	98.66 475	97.71 273	98.23 418	98.15 481	82.28 496	99.84 204	97.36 359	97.66 340	99.18 306

test1111		98.04 285	98.11 260	97.83 415	99.74 103	93.82 476	99.58 141	95.40 527	99.12 47	99.65 149	99.93 11	90.73 414	99.84 204	99.43 72	99.38 187	99.82 74

ECVR-MVS		98.04 285	98.05 269	98.00 393	99.74 103	94.37 471	99.59 131	94.98 528	99.13 42	99.66 139	99.93 11	90.67 415	99.84 204	99.40 75	99.38 187	99.80 90

test_yl		98.86 194	98.63 214	99.54 129	99.49 255	99.18 157	99.50 209	99.07 407	98.22 176	99.61 166	99.51 321	95.37 248	99.84 204	98.60 221	98.33 300	99.59 217

DCV-MVSNet		98.86 194	98.63 214	99.54 129	99.49 255	99.18 157	99.50 209	99.07 407	98.22 176	99.61 166	99.51 321	95.37 248	99.84 204	98.60 221	98.33 300	99.59 217

Fast-Effi-MVS+		98.70 222	98.43 236	99.51 149	99.51 241	99.28 145	99.52 188	99.47 238	96.11 423	99.01 315	99.34 379	96.20 203	99.84 204	97.88 298	98.82 271	99.39 281

TSAR-MVS + GP.		99.36 73	99.36 47	99.36 199	99.67 141	98.61 266	99.07 400	99.33 339	99.00 69	99.82 72	99.81 143	99.06 18	99.84 204	99.09 139	99.42 185	99.65 186

tpmrst		98.33 251	98.48 234	97.90 403	99.16 359	94.78 459	99.31 326	99.11 400	97.27 326	99.45 202	99.59 286	95.33 251	99.84 204	98.48 237	98.61 281	99.09 315

Vis-MVSNet		99.12 141	98.97 151	99.56 125	99.78 73	99.10 171	99.68 75	99.66 34	98.49 130	99.86 54	99.87 75	94.77 284	99.84 204	99.19 120	99.41 186	99.74 120
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
PAPR		98.63 230	98.34 242	99.51 149	99.40 285	99.03 182	98.80 455	99.36 319	96.33 404	99.00 319	99.12 417	98.46 91	99.84 204	95.23 442	99.37 194	99.66 179

PatchMatch-RL		98.84 206	98.62 219	99.52 144	99.71 120	99.28 145	99.06 404	99.77 12	97.74 271	99.50 193	99.53 312	95.41 246	99.84 204	97.17 377	99.64 165	99.44 270

EPP-MVSNet		99.13 131	98.99 146	99.53 137	99.65 166	99.06 178	99.81 20	99.33 339	97.43 311	99.60 169	99.88 59	97.14 141	99.84 204	99.13 131	98.94 255	99.69 159

SSM_0407		99.13 131	99.03 121	99.43 187	99.62 186	98.88 225	99.51 198	99.50 189	98.14 191	99.37 230	99.85 93	96.85 158	99.83 226	99.19 120	99.25 201	99.60 206

testing3-2		97.84 319	97.70 312	98.24 374	99.53 232	95.37 444	99.55 172	98.67 474	98.46 133	99.27 260	99.34 379	86.58 465	99.83 226	99.32 94	98.63 280	99.52 237

testing11		97.50 369	97.10 384	98.71 312	99.20 343	96.91 379	99.29 333	98.82 448	97.89 246	98.21 421	98.40 470	85.63 473	99.83 226	98.45 243	98.04 324	99.37 285

thres100view900		97.76 334	97.45 343	98.69 314	99.72 114	97.86 322	99.59 131	98.74 462	97.93 242	99.26 265	98.62 459	91.75 389	99.83 226	93.22 472	98.18 317	98.37 452

tfpn200view9		97.72 344	97.38 356	98.72 309	99.69 131	97.96 313	99.50 209	98.73 468	97.83 256	99.17 287	98.45 468	91.67 393	99.83 226	93.22 472	98.18 317	98.37 452

test_prior				99.68 91	99.67 141	99.48 115		99.56 92					99.83 226			99.74 120

1314		98.68 224	98.54 230	99.11 244	98.89 410	98.65 258	99.27 344	99.49 204	96.89 363	97.99 432	99.56 299	97.72 123	99.83 226	97.74 318	99.27 198	98.84 342

thres400		97.77 333	97.38 356	98.92 268	99.69 131	97.96 313	99.50 209	98.73 468	97.83 256	99.17 287	98.45 468	91.67 393	99.83 226	93.22 472	98.18 317	98.96 336

casdiffmvs		99.13 131	98.98 149	99.56 125	99.65 166	99.16 160	99.56 157	99.50 189	98.33 152	99.41 218	99.86 86	95.92 222	99.83 226	99.45 71	99.16 210	99.70 156
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
SPE-MVS-test		99.49 34	99.48 24	99.54 129	99.78 73	99.30 142	99.89 2	99.58 80	98.56 122	99.73 105	99.69 238	98.55 84	99.82 235	99.69 35	99.85 96	99.48 254

MVS_Test		99.10 153	98.97 151	99.48 167	99.49 255	99.14 166	99.67 79	99.34 331	97.31 323	99.58 174	99.76 199	97.65 124	99.82 235	98.87 172	99.07 244	99.46 265

dp		97.75 338	97.80 296	97.59 433	99.10 370	93.71 479	99.32 321	98.88 440	96.48 396	99.08 303	99.55 302	92.67 367	99.82 235	96.52 409	98.58 284	99.24 302

RPSCF		98.22 258	98.62 219	96.99 451	99.82 55	91.58 495	99.72 55	99.44 271	96.61 384	99.66 139	99.89 46	95.92 222	99.82 235	97.46 350	99.10 236	99.57 224

PMMVS		98.80 211	98.62 219	99.34 203	99.27 323	98.70 254	98.76 462	99.31 354	97.34 320	99.21 275	99.07 419	97.20 140	99.82 235	98.56 230	98.87 266	99.52 237

onestephybrid01		99.17 110	99.06 113	99.49 162	99.60 204	98.98 188	99.38 294	99.50 189	98.52 126	99.81 74	99.87 75	96.27 198	99.81 240	99.47 67	99.10 236	99.67 172

UBG		97.85 315	97.48 337	98.95 262	99.25 332	97.64 332	99.24 360	98.74 462	97.90 245	98.64 381	98.20 479	88.65 442	99.81 240	98.27 263	98.40 294	99.42 274

EIA-MVS		99.18 105	99.09 106	99.45 177	99.49 255	99.18 157	99.67 79	99.53 127	97.66 281	99.40 223	99.44 346	98.10 110	99.81 240	98.94 160	99.62 168	99.35 287

Effi-MVS+		98.81 208	98.59 225	99.48 167	99.46 265	99.12 170	98.08 512	99.50 189	97.50 302	99.38 227	99.41 354	96.37 192	99.81 240	99.11 134	98.54 289	99.51 246

thres200		97.61 360	97.28 373	98.62 320	99.64 171	98.03 307	99.26 353	98.74 462	97.68 278	99.09 301	98.32 474	91.66 395	99.81 240	92.88 477	98.22 311	98.03 473

tpmvs		97.98 296	98.02 273	97.84 412	99.04 388	94.73 460	99.31 326	99.20 388	96.10 427	98.76 361	99.42 350	94.94 267	99.81 240	96.97 388	98.45 293	98.97 334

casdiffmvs_mvg		99.15 119	99.02 132	99.55 128	99.66 154	99.09 172	99.64 100	99.56 92	98.26 165	99.45 202	99.87 75	96.03 214	99.81 240	99.54 52	99.15 216	99.73 130
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
DeepPCF-MVS	98.18 3	98.81 208	99.37 45	97.12 448	99.60 204	91.75 494	98.61 477	99.44 271	99.35 28	99.83 68	99.85 93	98.70 72	99.81 240	99.02 149	99.91 46	99.81 81

hybridnocas07		99.13 131	99.03 121	99.46 175	99.63 176	98.90 219	99.38 294	99.52 136	98.41 141	99.82 72	99.84 108	96.09 209	99.80 248	99.40 75	99.16 210	99.68 165

viewmacassd2359aftdt		99.08 156	98.94 161	99.50 155	99.66 154	98.96 196	99.51 198	99.54 111	98.27 162	99.42 213	99.89 46	95.88 226	99.80 248	99.20 119	99.11 227	99.76 109

viewmanbaseed2359cas		99.18 105	99.07 112	99.50 155	99.62 186	99.01 185	99.50 209	99.52 136	98.25 170	99.68 128	99.82 128	96.93 156	99.80 248	99.15 130	99.11 227	99.70 156

IMVS_0403		98.86 194	98.89 174	98.78 304	99.55 224	96.93 374	99.58 141	99.44 271	98.05 221	99.68 128	99.80 161	96.81 165	99.80 248	98.15 275	98.92 258	99.60 206

DPM-MVS		98.95 183	98.71 202	99.66 93	99.63 176	99.55 100	98.64 475	99.10 401	97.93 242	99.42 213	99.55 302	98.67 75	99.80 248	95.80 426	99.68 159	99.61 203

DP-MVS Recon		99.12 141	98.95 159	99.65 97	99.74 103	99.70 63	99.27 344	99.57 87	96.40 403	99.42 213	99.68 246	98.75 63	99.80 248	97.98 292	99.72 151	99.44 270

MVS_111021_LR		99.41 60	99.33 53	99.65 97	99.77 81	99.51 111	98.94 435	99.85 8	98.82 92	99.65 149	99.74 210	98.51 88	99.80 248	98.83 185	99.89 68	99.64 193

dtuplus		99.03 168	98.92 164	99.36 199	99.60 204	98.62 263	99.35 310	99.51 164	97.99 236	99.38 227	99.88 59	96.04 212	99.79 255	99.37 83	99.17 209	99.68 165

hybrid		99.11 147	99.01 140	99.41 190	99.64 171	98.76 249	99.35 310	99.52 136	98.31 156	99.80 80	99.84 108	96.16 205	99.79 255	99.40 75	99.06 245	99.68 165

viewmambaseed2359dif		99.01 175	98.90 170	99.32 209	99.58 210	98.51 278	99.33 318	99.54 111	97.85 252	99.44 207	99.85 93	96.01 215	99.79 255	99.41 73	99.13 220	99.67 172

CS-MVS		99.50 32	99.48 24	99.54 129	99.76 85	99.42 122	99.90 1	99.55 102	98.56 122	99.78 88	99.70 227	98.65 77	99.79 255	99.65 42	99.78 137	99.41 277

Fast-Effi-MVS+-dtu		98.77 217	98.83 188	98.60 321	99.41 280	96.99 369	99.52 188	99.49 204	98.11 204	99.24 267	99.34 379	96.96 155	99.79 255	97.95 294	99.45 183	99.02 327

baseline1		98.31 252	97.95 280	99.38 198	99.50 253	98.74 250	99.59 131	98.93 426	98.41 141	99.14 290	99.60 284	94.59 299	99.79 255	98.48 237	93.29 461	99.61 203

baseline		99.15 119	99.02 132	99.53 137	99.66 154	99.14 166	99.72 55	99.48 216	98.35 149	99.42 213	99.84 108	96.07 210	99.79 255	99.51 57	99.14 217	99.67 172

PVSNet_0	94.43 19	96.09 418	95.47 426	97.94 399	99.31 313	94.34 473	97.81 518	99.70 19	97.12 341	97.46 448	98.75 456	89.71 428	99.79 255	97.69 326	81.69 523	99.68 165

viewmamba		99.20 101	99.12 98	99.44 183	99.61 197	98.87 229	99.42 272	99.52 136	98.42 139	99.84 58	99.84 108	96.85 158	99.78 263	99.46 69	99.11 227	99.67 172

hybridcas		99.13 131	99.00 144	99.51 149	99.70 125	99.04 181	99.65 92	99.52 136	98.20 180	99.75 102	99.88 59	95.78 232	99.78 263	99.41 73	99.16 210	99.71 152

TestfortrainingZip				99.69 90	99.58 210	99.62 86	99.69 65	99.38 307	98.98 74	99.84 58	99.75 204	98.84 47	99.78 263		99.21 205	99.66 179

API-MVS		99.04 166	99.03 121	99.06 247	99.40 285	99.31 139	99.55 172	99.56 92	98.54 124	99.33 244	99.39 363	98.76 60	99.78 263	96.98 387	99.78 137	98.07 469

OMC-MVS		99.08 156	99.04 118	99.20 233	99.67 141	98.22 296	99.28 339	99.52 136	98.07 214	99.66 139	99.81 143	97.79 120	99.78 263	97.79 310	99.81 123	99.60 206

GeoE		98.85 203	98.62 219	99.53 137	99.61 197	99.08 175	99.80 25	99.51 164	97.10 345	99.31 246	99.78 185	95.23 258	99.77 268	98.21 267	99.03 249	99.75 115

alignmvs		98.81 208	98.56 229	99.58 119	99.43 273	99.42 122	99.51 198	98.96 424	98.61 116	99.35 239	98.92 444	94.78 281	99.77 268	99.35 85	98.11 322	99.54 231

tpm cat1		97.39 379	97.36 358	97.50 436	99.17 357	93.73 478	99.43 265	99.31 354	91.27 494	98.71 365	99.08 418	94.31 316	99.77 268	96.41 414	98.50 291	99.00 328

CostFormer		97.72 344	97.73 309	97.71 425	99.15 363	94.02 475	99.54 177	99.02 415	94.67 453	99.04 312	99.35 375	92.35 379	99.77 268	98.50 236	97.94 327	99.34 290

PRO-TEST		99.17 110	99.08 107	99.45 177	99.37 294	99.14 166	99.62 111	99.50 189	98.59 120	99.69 127	99.58 290	96.72 171	99.76 272	99.06 143	99.58 172	99.44 270

MGCFI-Net		99.01 175	98.85 184	99.50 155	99.42 275	99.26 148	99.82 16	99.48 216	98.60 118	99.28 254	98.81 451	97.04 150	99.76 272	99.29 105	97.87 332	99.47 260

test_241102_ONE					99.84 40	99.90 3		99.48 216	99.07 59	99.91 33	99.74 210	99.20 9	99.76 272

MDTV_nov1_ep13			98.32 244		99.11 367	94.44 469	99.27 344	98.74 462	97.51 301	99.40 223	99.62 277	94.78 281	99.76 272	97.59 332	98.81 273

viewdifsd2359ckpt09		99.01 175	98.87 178	99.40 192	99.62 186	98.79 245	99.44 259	99.51 164	97.76 267	99.35 239	99.69 238	96.42 190	99.75 276	98.97 157	99.11 227	99.66 179

sasdasda		99.02 170	98.86 181	99.51 149	99.42 275	99.32 135	99.80 25	99.48 216	98.63 113	99.31 246	98.81 451	97.09 146	99.75 276	99.27 111	97.90 328	99.47 260

canonicalmvs		99.02 170	98.86 181	99.51 149	99.42 275	99.32 135	99.80 25	99.48 216	98.63 113	99.31 246	98.81 451	97.09 146	99.75 276	99.27 111	97.90 328	99.47 260

Effi-MVS+-dtu		98.78 213	98.89 174	98.47 346	99.33 305	96.91 379	99.57 149	99.30 359	98.47 132	99.41 218	98.99 434	96.78 167	99.74 279	98.73 200	99.38 187	98.74 358

patchmatchnet-post											98.70 457	94.79 280	99.74 279

SCA		98.19 262	98.16 253	98.27 372	99.30 314	95.55 434	99.07 400	98.97 422	97.57 290	99.43 210	99.57 296	92.72 362	99.74 279	97.58 333	99.20 207	99.52 237

BH-untuned		98.42 240	98.36 240	98.59 322	99.49 255	96.70 389	99.27 344	99.13 398	97.24 330	98.80 356	99.38 366	95.75 234	99.74 279	97.07 382	99.16 210	99.33 291

BH-RMVSNet		98.41 242	98.08 265	99.40 192	99.41 280	98.83 239	99.30 328	98.77 457	97.70 276	98.94 330	99.65 260	92.91 357	99.74 279	96.52 409	99.55 176	99.64 193

MVS_111021_HR		99.41 60	99.32 55	99.66 93	99.72 114	99.47 117	98.95 433	99.85 8	98.82 92	99.54 186	99.73 216	98.51 88	99.74 279	98.91 166	99.88 75	99.77 102

test_post											65.99 558	94.65 297	99.73 285

XVG-ACMP-BASELINE		97.83 322	97.71 311	98.20 376	99.11 367	96.33 406	99.41 277	99.52 136	98.06 218	99.05 311	99.50 324	89.64 430	99.73 285	97.73 319	97.38 367	98.53 432

HyFIR lowres test		99.11 147	98.92 164	99.65 97	99.90 5	99.37 127	99.02 415	99.91 3	97.67 280	99.59 173	99.75 204	95.90 224	99.73 285	99.53 54	99.02 251	99.86 44

DeepMVS_CX				93.34 486	99.29 318	82.27 520		99.22 383	85.15 516	96.33 471	99.05 423	90.97 412	99.73 285	93.57 467	97.77 337	98.01 475

nomal-1		97.78 332	97.52 331	98.54 337	99.27 323	96.47 401	99.32 321	98.56 478	97.43 311	98.92 332	98.91 445	88.14 451	99.72 289	98.75 196	98.39 295	99.44 270

Patchmatch-test		97.93 302	97.65 317	98.77 305	99.18 349	97.07 358	99.03 412	99.14 397	96.16 418	98.74 362	99.57 296	94.56 301	99.72 289	93.36 470	99.11 227	99.52 237

LPG-MVS_test		98.22 258	98.13 258	98.49 339	99.33 305	97.05 360	99.58 141	99.55 102	97.46 304	99.24 267	99.83 117	92.58 369	99.72 289	98.09 280	97.51 353	98.68 376

LGP-MVS_train				98.49 339	99.33 305	97.05 360		99.55 102	97.46 304	99.24 267	99.83 117	92.58 369	99.72 289	98.09 280	97.51 353	98.68 376

BH-w/o		98.00 294	97.89 289	98.32 364	99.35 299	96.20 412	99.01 420	98.90 436	96.42 401	98.38 405	99.00 432	95.26 255	99.72 289	96.06 419	98.61 281	99.03 325

ACMP	97.20 11	98.06 279	97.94 282	98.45 349	99.37 294	97.01 367	99.44 259	99.49 204	97.54 297	98.45 401	99.79 178	91.95 385	99.72 289	97.91 296	97.49 358	98.62 406
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LTVRE_ROB	97.16 12	98.02 289	97.90 285	98.40 357	99.23 336	96.80 387	99.70 60	99.60 70	97.12 341	98.18 423	99.70 227	91.73 391	99.72 289	98.39 250	97.45 360	98.68 376
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
dtuonly		98.37 248	98.26 249	98.69 314	99.07 379	96.81 386	98.51 489	98.75 458	97.77 265	99.57 177	99.68 246	96.12 207	99.71 296	95.76 427	99.11 227	99.57 224

viewdifsd2359ckpt13		99.06 161	98.93 163	99.45 177	99.63 176	98.96 196	99.50 209	99.51 164	97.83 256	99.28 254	99.80 161	96.68 174	99.71 296	99.05 144	99.12 225	99.68 165

test_post1							99.23 363				65.14 559	94.18 321	99.71 296	97.58 333

ADS-MVSNet		98.20 261	98.08 265	98.56 331	99.33 305	96.48 400	99.23 363	99.15 395	96.24 411	99.10 298	99.67 253	94.11 323	99.71 296	96.81 397	99.05 246	99.48 254

JIA-IIPM		97.50 369	97.02 388	98.93 266	98.73 437	97.80 324	99.30 328	98.97 422	91.73 491	98.91 334	94.86 524	95.10 262	99.71 296	97.58 333	97.98 325	99.28 296

EPMVS		97.82 325	97.65 317	98.35 361	98.88 412	95.98 416	99.49 226	94.71 533	97.57 290	99.26 265	99.48 336	92.46 376	99.71 296	97.87 300	99.08 243	99.35 287

TDRefinement		95.42 433	94.57 444	97.97 396	89.83 551	96.11 415	99.48 234	98.75 458	96.74 372	96.68 468	99.88 59	88.65 442	99.71 296	98.37 253	82.74 520	98.09 467

ACMM	97.58 5	98.37 248	98.34 242	98.48 341	99.41 280	97.10 354	99.56 157	99.45 262	98.53 125	99.04 312	99.85 93	93.00 353	99.71 296	98.74 198	97.45 360	98.64 397
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
casdiffseed414692147		98.97 181	98.78 193	99.53 137	99.66 154	99.16 160	99.61 118	99.52 136	98.01 234	99.21 275	99.88 59	94.82 276	99.70 304	99.29 105	99.04 248	99.74 120

tt0805		97.97 299	97.77 302	98.57 327	99.59 208	96.61 396	99.45 252	99.08 404	98.21 178	98.88 340	99.80 161	88.66 441	99.70 304	98.58 224	97.72 338	99.39 281

CHOSEN 280x420		99.12 141	99.13 96	99.08 245	99.66 154	97.89 319	98.43 495	99.71 17	98.88 86	99.62 161	99.76 199	96.63 175	99.70 304	99.46 69	99.99 1	99.66 179

EC-MVSNet		99.44 51	99.39 41	99.58 119	99.56 220	99.49 113	99.88 4	99.58 80	98.38 144	99.73 105	99.69 238	98.20 106	99.70 304	99.64 44	99.82 120	99.54 231

PatchmatchNet		98.31 252	98.36 240	98.19 377	99.16 359	95.32 445	99.27 344	98.92 429	97.37 318	99.37 230	99.58 290	94.90 272	99.70 304	97.43 355	99.21 205	99.54 231
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
ACMH	97.28 8	98.10 272	97.99 275	98.44 352	99.41 280	96.96 373	99.60 120	99.56 92	98.09 209	98.15 425	99.91 27	90.87 413	99.70 304	98.88 169	97.45 360	98.67 384
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ETVMVS		97.50 369	96.90 392	99.29 219	99.23 336	98.78 248	99.32 321	98.90 436	97.52 300	98.56 391	98.09 486	84.72 482	99.69 310	97.86 301	97.88 331	99.39 281

HQP_MVS		98.27 257	98.22 251	98.44 352	99.29 318	96.97 371	99.39 289	99.47 238	98.97 78	99.11 295	99.61 281	92.71 364	99.69 310	97.78 311	97.63 341	98.67 384

plane_prior5								99.47 238					99.69 310	97.78 311	97.63 341	98.67 384

D2MVS		98.41 242	98.50 233	98.15 382	99.26 328	96.62 395	99.40 285	99.61 63	97.71 273	98.98 322	99.36 372	96.04 212	99.67 313	98.70 203	97.41 365	98.15 464

IS-MVSNet		99.05 165	98.87 178	99.57 123	99.73 110	99.32 135	99.75 44	99.20 388	98.02 233	99.56 179	99.86 86	96.54 182	99.67 313	98.09 280	99.13 220	99.73 130

CLD-MVS		98.16 266	98.10 261	98.33 362	99.29 318	96.82 385	98.75 463	99.44 271	97.83 256	99.13 291	99.55 302	92.92 355	99.67 313	98.32 260	97.69 339	98.48 438
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
test_fmvs2		97.25 387	97.30 370	97.09 449	99.43 273	93.31 485	99.73 53	98.87 442	98.83 91	99.28 254	99.80 161	84.45 483	99.66 316	97.88 298	97.45 360	98.30 454

AUN-MVS		96.88 399	96.31 406	98.59 322	99.48 262	97.04 363	99.27 344	99.22 383	97.44 310	98.51 395	99.41 354	91.97 384	99.66 316	97.71 322	83.83 513	99.07 321

UniMVSNet_ETH3D		97.32 384	96.81 394	98.87 287	99.40 285	97.46 338	99.51 198	99.53 127	95.86 431	98.54 393	99.77 194	82.44 494	99.66 316	98.68 208	97.52 352	99.50 250

OPM-MVS		98.19 262	98.10 261	98.45 349	98.88 412	97.07 358	99.28 339	99.38 307	98.57 121	99.22 272	99.81 143	92.12 381	99.66 316	98.08 284	97.54 350	98.61 415
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
ACMH+	97.24 10	97.92 305	97.78 300	98.32 364	99.46 265	96.68 393	99.56 157	99.54 111	98.41 141	97.79 443	99.87 75	90.18 424	99.66 316	98.05 288	97.18 375	98.62 406

IMVS_0407		98.86 194	98.91 168	98.72 309	99.55 224	96.93 374	99.50 209	99.44 271	98.05 221	99.66 139	99.80 161	97.13 142	99.65 321	98.15 275	98.92 258	99.60 206

hse-mvs2		97.50 369	97.14 381	98.59 322	99.49 255	97.05 360	99.28 339	99.22 383	98.94 81	99.66 139	99.42 350	94.93 268	99.65 321	99.48 65	83.80 514	99.08 316

VPA-MVSNet		98.29 255	97.95 280	99.30 216	99.16 359	99.54 102	99.50 209	99.58 80	98.27 162	99.35 239	99.37 369	92.53 371	99.65 321	99.35 85	94.46 439	98.72 360

TR-MVS		97.76 334	97.41 354	98.82 296	99.06 382	97.87 320	98.87 444	98.56 478	96.63 383	98.68 373	99.22 403	92.49 372	99.65 321	95.40 438	97.79 336	98.95 338

reproduce_monomvs		97.89 309	97.87 290	97.96 398	99.51 241	95.45 440	99.60 120	99.25 377	99.17 37	98.85 350	99.49 328	89.29 433	99.64 325	99.35 85	96.31 393	98.78 346

gm-plane-assit					98.54 462	92.96 487			94.65 454		99.15 411		99.64 325	97.56 338

HQP4-MVS										98.66 374			99.64 325			98.64 397

HQP-MVS		98.02 289	97.90 285	98.37 360	99.19 346	96.83 383	98.98 426	99.39 297	98.24 172	98.66 374	99.40 359	92.47 373	99.64 325	97.19 374	97.58 346	98.64 397

PAPM		97.59 361	97.09 385	99.07 246	99.06 382	98.26 294	98.30 502	99.10 401	94.88 448	98.08 427	99.34 379	96.27 198	99.64 325	89.87 497	98.92 258	99.31 294

TAPA-MVS	97.07 15	97.74 340	97.34 363	98.94 264	99.70 125	97.53 335	99.25 355	99.51 164	91.90 490	99.30 250	99.63 272	98.78 55	99.64 325	88.09 506	99.87 81	99.65 186
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
XXY-MVS		98.38 246	98.09 264	99.24 229	99.26 328	99.32 135	99.56 157	99.55 102	97.45 307	98.71 365	99.83 117	93.23 347	99.63 331	98.88 169	96.32 392	98.76 352

FBQ-MVS		97.45 376	97.07 386	98.59 322	99.27 323	96.84 382	99.35 310	98.81 450	97.55 293	98.89 339	98.61 461	85.29 478	99.62 332	97.67 328	98.21 315	99.32 292

ITE_SJBPF				98.08 386	99.29 318	96.37 404		98.92 429	98.34 150	98.83 351	99.75 204	91.09 410	99.62 332	95.82 424	97.40 366	98.25 458

LF4IMVS		97.52 366	97.46 342	97.70 426	98.98 399	95.55 434	99.29 333	98.82 448	98.07 214	98.66 374	99.64 266	89.97 425	99.61 334	97.01 384	96.68 382	97.94 482

tpm		97.67 355	97.55 326	98.03 388	99.02 390	95.01 454	99.43 265	98.54 482	96.44 399	99.12 293	99.34 379	91.83 388	99.60 335	97.75 317	96.46 388	99.48 254

tpm2		97.44 377	97.34 363	97.74 424	99.15 363	94.36 472	99.45 252	98.94 425	93.45 470	98.90 336	99.44 346	91.35 403	99.59 336	97.31 361	98.07 323	99.29 295

SSM_04072		99.06 161	98.96 155	99.35 202	99.62 186	98.88 225	99.25 355	99.47 238	98.05 221	99.37 230	99.81 143	96.85 158	99.58 337	98.98 152	99.25 201	99.60 206

SD_0403		97.55 363	97.53 330	97.62 429	99.61 197	93.64 482	99.72 55	99.44 271	98.03 230	98.62 386	99.39 363	96.06 211	99.57 338	87.88 508	99.01 252	99.66 179

baseline2		97.87 312	97.55 326	98.82 296	99.18 349	98.02 308	99.41 277	96.58 521	96.97 356	96.51 469	99.17 408	93.43 342	99.57 338	97.71 322	99.03 249	98.86 340

MS-PatchMatch		97.24 389	97.32 368	96.99 451	98.45 467	93.51 484	98.82 453	99.32 350	97.41 315	98.13 426	99.30 390	88.99 435	99.56 340	95.68 431	99.80 128	97.90 486

TinyColmap		97.12 392	96.89 393	97.83 415	99.07 379	95.52 437	98.57 481	98.74 462	97.58 289	97.81 442	99.79 178	88.16 449	99.56 340	95.10 443	97.21 373	98.39 450

USDC		97.34 382	97.20 378	97.75 422	99.07 379	95.20 447	98.51 489	99.04 411	97.99 236	98.31 413	99.86 86	89.02 434	99.55 342	95.67 432	97.36 368	98.49 437

MSLP-MVS++		99.46 43	99.47 26	99.44 183	99.60 204	99.16 160	99.41 277	99.71 17	98.98 74	99.45 202	99.78 185	99.19 11	99.54 343	99.28 108	99.84 104	99.63 198

UWE-MVS-28		97.36 380	97.24 377	97.75 422	98.84 421	94.44 469	99.24 360	97.58 507	97.98 238	99.00 319	99.00 432	91.35 403	99.53 344	93.75 463	98.39 295	99.27 300

TAMVS		99.12 141	99.08 107	99.24 229	99.46 265	98.55 270	99.51 198	99.46 251	98.09 209	99.45 202	99.82 128	98.34 100	99.51 345	98.70 203	98.93 256	99.67 172

MASt3R-SfM		94.79 447	95.11 431	93.81 483	97.96 478	85.14 514	98.52 487	98.99 419	95.33 437	97.53 447	99.13 413	79.99 502	99.48 346	93.66 465	94.90 433	96.80 510

EPNet_dtu		98.03 287	97.96 278	98.23 375	98.27 471	95.54 436	99.23 363	98.75 458	99.02 64	97.82 441	99.71 223	96.11 208	99.48 346	93.04 475	99.65 164	99.69 159
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
mvs5depth		96.66 403	96.22 409	97.97 396	97.00 503	96.28 408	98.66 473	99.03 414	96.61 384	96.93 465	99.79 178	87.20 459	99.47 348	96.65 407	94.13 448	98.16 463

EG-PatchMatch MVS		95.97 420	95.69 422	96.81 458	97.78 485	92.79 488	99.16 379	98.93 426	96.16 418	94.08 493	99.22 403	82.72 492	99.47 348	95.67 432	97.50 355	98.17 462

myMVS_eth3d28		97.69 349	97.34 363	98.73 307	99.27 323	97.52 336	99.33 318	98.78 456	98.03 230	98.82 353	98.49 466	86.64 464	99.46 350	98.44 244	98.24 310	99.23 303

MVP-Stereo		97.81 327	97.75 307	97.99 394	97.53 490	96.60 397	98.96 430	98.85 445	97.22 332	97.23 455	99.36 372	95.28 252	99.46 350	95.51 434	99.78 137	97.92 484
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
CVMVSNet		98.57 232	98.67 206	98.30 366	99.35 299	95.59 433	99.50 209	99.55 102	98.60 118	99.39 225	99.83 117	94.48 307	99.45 352	98.75 196	98.56 287	99.85 48

test-LLR		98.06 279	97.90 285	98.55 333	98.79 425	97.10 354	98.67 470	97.75 500	97.34 320	98.61 387	98.85 448	94.45 309	99.45 352	97.25 368	99.38 187	99.10 311

TESTMET0.1,1		97.55 363	97.27 376	98.40 357	98.93 404	96.53 398	98.67 470	97.61 505	96.96 357	98.64 381	99.28 394	88.63 444	99.45 352	97.30 364	99.38 187	99.21 305

test-mter		97.49 374	97.13 383	98.55 333	98.79 425	97.10 354	98.67 470	97.75 500	96.65 379	98.61 387	98.85 448	88.23 448	99.45 352	97.25 368	99.38 187	99.10 311

mvs_anonymous		99.03 168	98.99 146	99.16 237	99.38 291	98.52 276	99.51 198	99.38 307	97.79 262	99.38 227	99.81 143	97.30 134	99.45 352	99.35 85	98.99 253	99.51 246

tfpnnormal		97.84 319	97.47 340	98.98 257	99.20 343	99.22 153	99.64 100	99.61 63	96.32 405	98.27 417	99.70 227	93.35 346	99.44 357	95.69 430	95.40 420	98.27 456

v7n		97.87 312	97.52 331	98.92 268	98.76 435	98.58 268	99.84 12	99.46 251	96.20 414	98.91 334	99.70 227	94.89 273	99.44 357	96.03 420	93.89 454	98.75 354

jajsoiax		98.43 239	98.28 247	98.88 283	98.60 457	98.43 287	99.82 16	99.53 127	98.19 182	98.63 383	99.80 161	93.22 349	99.44 357	99.22 116	97.50 355	98.77 350

mvs_tets		98.40 245	98.23 250	98.91 272	98.67 448	98.51 278	99.66 86	99.53 127	98.19 182	98.65 380	99.81 143	92.75 359	99.44 357	99.31 96	97.48 359	98.77 350

ArgMatch-SfM		96.18 415	95.78 421	97.38 441	99.08 376	94.64 465	99.20 372	99.33 339	98.01 234	98.54 393	99.54 307	83.13 490	99.43 361	93.86 461	91.29 482	98.08 468

sc_t1		95.75 424	95.05 433	97.87 405	98.83 422	94.61 466	99.21 369	99.45 262	87.45 509	97.97 434	99.85 93	81.19 499	99.43 361	98.27 263	93.20 464	99.57 224

Vis-MVSNet (Re-imp)		98.87 191	98.72 200	99.31 211	99.71 120	98.88 225	99.80 25	99.44 271	97.91 244	99.36 236	99.78 185	95.49 244	99.43 361	97.91 296	99.11 227	99.62 201

OPU-MVS				99.64 103	99.56 220	99.72 59	99.60 120				99.70 227	99.27 7	99.42 364	98.24 266	99.80 128	99.79 94

Anonymous20231211		97.88 310	97.54 329	98.90 274	99.71 120	98.53 272	99.48 234	99.57 87	94.16 458	98.81 354	99.68 246	93.23 347	99.42 364	98.84 182	94.42 442	98.76 352

ArgMatch-Sym		96.59 405	96.31 406	97.42 438	98.89 410	94.84 458	99.16 379	99.39 297	98.11 204	98.35 410	99.53 312	84.38 484	99.40 366	94.16 458	94.85 435	98.03 473

ttmdpeth		97.80 329	97.63 321	98.29 367	98.77 433	97.38 341	99.64 100	99.36 319	98.78 101	96.30 472	99.58 290	92.34 380	99.39 367	98.36 255	95.58 415	98.10 466

VPNet		97.84 319	97.44 348	99.01 253	99.21 341	98.94 206	99.48 234	99.57 87	98.38 144	99.28 254	99.73 216	88.89 436	99.39 367	99.19 120	93.27 462	98.71 362

nrg030		98.64 229	98.42 237	99.28 223	99.05 386	99.69 66	99.81 20	99.46 251	98.04 228	99.01 315	99.82 128	96.69 172	99.38 369	99.34 90	94.59 438	98.78 346

GA-MVS		97.85 315	97.47 340	99.00 255	99.38 291	97.99 310	98.57 481	99.15 395	97.04 352	98.90 336	99.30 390	89.83 427	99.38 369	96.70 402	98.33 300	99.62 201

UniMVSNet (Re)		98.29 255	98.00 274	99.13 243	99.00 393	99.36 130	99.49 226	99.51 164	97.95 240	98.97 324	99.13 413	96.30 197	99.38 369	98.36 255	93.34 460	98.66 393

FIs		98.78 213	98.63 214	99.23 231	99.18 349	99.54 102	99.83 15	99.59 75	98.28 160	98.79 358	99.81 143	96.75 169	99.37 372	99.08 140	96.38 390	98.78 346

PS-MVSNAJss		98.92 185	98.92 164	98.90 274	98.78 428	98.53 272	99.78 34	99.54 111	98.07 214	99.00 319	99.76 199	99.01 20	99.37 372	99.13 131	97.23 372	98.81 343

CDS-MVSNet		99.09 154	99.03 121	99.25 226	99.42 275	98.73 251	99.45 252	99.46 251	98.11 204	99.46 200	99.77 194	98.01 115	99.37 372	98.70 203	98.92 258	99.66 179
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MVS-HIRNet		95.75 424	95.16 430	97.51 435	99.30 314	93.69 480	98.88 442	95.78 524	85.09 517	98.78 359	92.65 534	91.29 405	99.37 372	94.85 448	99.85 96	99.46 265

v1192		97.81 327	97.44 348	98.91 272	98.88 412	98.68 255	99.51 198	99.34 331	96.18 416	99.20 279	99.34 379	94.03 327	99.36 376	95.32 440	95.18 424	98.69 371

EI-MVSNet		98.67 225	98.67 206	98.68 316	99.35 299	97.97 311	99.50 209	99.38 307	96.93 362	99.20 279	99.83 117	97.87 117	99.36 376	98.38 251	97.56 348	98.71 362

MVSTER		98.49 234	98.32 244	99.00 255	99.35 299	99.02 183	99.54 177	99.38 307	97.41 315	99.20 279	99.73 216	93.86 335	99.36 376	98.87 172	97.56 348	98.62 406

gg-mvs-nofinetune		96.17 416	95.32 429	98.73 307	98.79 425	98.14 300	99.38 294	94.09 535	91.07 497	98.07 430	91.04 539	89.62 431	99.35 379	96.75 399	99.09 242	98.68 376

pm-mvs1		97.68 352	97.28 373	98.88 283	99.06 382	98.62 263	99.50 209	99.45 262	96.32 405	97.87 439	99.79 178	92.47 373	99.35 379	97.54 340	93.54 458	98.67 384

OurMVSNet-221017-0		97.88 310	97.77 302	98.19 377	98.71 442	96.53 398	99.88 4	99.00 418	97.79 262	98.78 359	99.94 7	91.68 392	99.35 379	97.21 370	96.99 379	98.69 371

EGC-MVSNET		82.80 495	77.86 503	97.62 429	97.91 479	96.12 414	99.33 318	99.28 365	8.40 561	25.05 563	99.27 397	84.11 485	99.33 382	89.20 500	98.22 311	97.42 500

pmmvs6		96.53 407	96.09 413	97.82 417	98.69 446	95.47 438	99.37 298	99.47 238	93.46 469	97.41 449	99.78 185	87.06 463	99.33 382	96.92 394	92.70 474	98.65 395

V42		98.06 279	97.79 297	98.86 290	98.98 399	98.84 236	99.69 65	99.34 331	96.53 391	99.30 250	99.37 369	94.67 294	99.32 384	97.57 337	94.66 436	98.42 446

lessismore_v0				97.79 419	98.69 446	95.44 442		94.75 531		95.71 478	99.87 75	88.69 440	99.32 384	95.89 423	94.93 431	98.62 406

OpenMVS_ROB	92.34 20	94.38 453	93.70 460	96.41 464	97.38 493	93.17 486	99.06 404	98.75 458	86.58 512	94.84 488	98.26 477	81.53 497	99.32 384	89.01 502	97.87 332	96.76 511

v8		97.95 301	97.63 321	98.93 266	98.95 403	98.81 244	99.80 25	99.41 287	96.03 428	99.10 298	99.42 350	94.92 270	99.30 387	96.94 391	94.08 451	98.66 393

v1921920		97.80 329	97.45 343	98.84 294	98.80 424	98.53 272	99.52 188	99.34 331	96.15 420	99.24 267	99.47 339	93.98 329	99.29 388	95.40 438	95.13 426	98.69 371

anonymousdsp		98.44 238	98.28 247	98.94 264	98.50 464	98.96 196	99.77 36	99.50 189	97.07 347	98.87 343	99.77 194	94.76 285	99.28 389	98.66 210	97.60 344	98.57 428

MVSFormer		99.17 110	99.12 98	99.29 219	99.51 241	98.94 206	99.88 4	99.46 251	97.55 293	99.80 80	99.65 260	97.39 128	99.28 389	99.03 147	99.85 96	99.65 186

test_djsdf		98.67 225	98.57 227	98.98 257	98.70 443	98.91 214	99.88 4	99.46 251	97.55 293	99.22 272	99.88 59	95.73 235	99.28 389	99.03 147	97.62 343	98.75 354

VortexMVS		98.67 225	98.66 209	98.68 316	99.62 186	97.96 313	99.59 131	99.41 287	98.13 194	99.31 246	99.70 227	95.48 245	99.27 392	99.40 75	97.32 369	98.79 344

SSC-MVS3.2		97.34 382	97.15 380	97.93 400	99.02 390	95.76 428	99.48 234	99.58 80	97.62 285	99.09 301	99.53 312	87.95 452	99.27 392	96.42 412	95.66 413	98.75 354

cascas		97.69 349	97.43 352	98.48 341	98.60 457	97.30 343	98.18 507	99.39 297	92.96 477	98.41 403	98.78 455	93.77 338	99.27 392	98.16 273	98.61 281	98.86 340

LoFTR		93.25 462	92.33 469	95.99 469	97.91 479	90.83 497	99.06 404	98.56 478	92.19 483	90.24 514	98.18 480	72.97 509	99.26 395	89.37 499	92.52 477	97.89 487

v144192		97.92 305	97.60 324	98.87 287	98.83 422	98.65 258	99.55 172	99.34 331	96.20 414	99.32 245	99.40 359	94.36 311	99.26 395	96.37 416	95.03 428	98.70 367

dmvs_re		98.08 277	98.16 253	97.85 409	99.55 224	94.67 464	99.70 60	98.92 429	98.15 187	99.06 309	99.35 375	93.67 341	99.25 397	97.77 314	97.25 371	99.64 193

v2v482		98.06 279	97.77 302	98.92 268	98.90 409	98.82 242	99.57 149	99.36 319	96.65 379	99.19 282	99.35 375	94.20 318	99.25 397	97.72 321	94.97 429	98.69 371

v1240		97.69 349	97.32 368	98.79 302	98.85 419	98.43 287	99.48 234	99.36 319	96.11 423	99.27 260	99.36 372	93.76 339	99.24 399	94.46 452	95.23 423	98.70 367

MatchFormer		91.94 470	90.72 476	95.58 473	97.82 484	89.79 505	98.92 437	98.87 442	88.24 508	88.03 519	97.92 493	70.39 517	99.23 400	85.21 521	91.12 485	97.72 488

usedtu_dtu_shiyan1		98.09 273	97.82 294	98.89 278	98.70 443	98.90 219	98.57 481	99.47 238	96.78 369	98.87 343	99.05 423	94.75 286	99.23 400	97.45 352	96.74 380	98.53 432

FE-MVSNET3		98.09 273	97.82 294	98.89 278	98.70 443	98.90 219	98.57 481	99.47 238	96.78 369	98.87 343	99.05 423	94.75 286	99.23 400	97.45 352	96.74 380	98.53 432

WBMVS		97.74 340	97.50 335	98.46 347	99.24 334	97.43 339	99.21 369	99.42 284	97.45 307	98.96 326	99.41 354	88.83 437	99.23 400	98.94 160	96.02 399	98.71 362

v1144		97.98 296	97.69 313	98.85 293	98.87 415	98.66 257	99.54 177	99.35 326	96.27 409	99.23 271	99.35 375	94.67 294	99.23 400	96.73 400	95.16 425	98.68 376

v10		97.85 315	97.52 331	98.86 290	98.99 396	98.67 256	99.75 44	99.41 287	95.70 432	98.98 322	99.41 354	94.75 286	99.23 400	96.01 422	94.63 437	98.67 384

WR-MVS_H		98.13 269	97.87 290	98.90 274	99.02 390	98.84 236	99.70 60	99.59 75	97.27 326	98.40 404	99.19 407	95.53 242	99.23 400	98.34 257	93.78 456	98.61 415

miper_enhance_ethall		98.16 266	98.08 265	98.41 355	98.96 402	97.72 327	98.45 494	99.32 350	96.95 359	98.97 324	99.17 408	97.06 149	99.22 407	97.86 301	95.99 402	98.29 455

GG-mvs-BLEND				98.45 349	98.55 461	98.16 298	99.43 265	93.68 536		97.23 455	98.46 467	89.30 432	99.22 407	95.43 437	98.22 311	97.98 480

FC-MVSNet-test		98.75 218	98.62 219	99.15 241	99.08 376	99.45 119	99.86 11	99.60 70	98.23 175	98.70 371	99.82 128	96.80 166	99.22 407	99.07 141	96.38 390	98.79 344

UniMVSNet_NR-MVSNet		98.22 258	97.97 277	98.96 260	98.92 406	98.98 188	99.48 234	99.53 127	97.76 267	98.71 365	99.46 343	96.43 189	99.22 407	98.57 227	92.87 472	98.69 371

DU-MVS		98.08 277	97.79 297	98.96 260	98.87 415	98.98 188	99.41 277	99.45 262	97.87 248	98.71 365	99.50 324	94.82 276	99.22 407	98.57 227	92.87 472	98.68 376

cl____		98.01 292	97.84 293	98.55 333	99.25 332	97.97 311	98.71 468	99.34 331	96.47 398	98.59 390	99.54 307	95.65 238	99.21 412	97.21 370	95.77 408	98.46 443

WR-MVS		98.06 279	97.73 309	99.06 247	98.86 418	99.25 150	99.19 375	99.35 326	97.30 324	98.66 374	99.43 348	93.94 330	99.21 412	98.58 224	94.28 445	98.71 362

DenseAffine		94.28 455	93.53 462	96.52 463	98.72 439	92.31 491	98.78 458	99.02 415	93.14 474	94.45 489	99.01 430	74.73 508	99.20 414	90.98 492	92.94 469	98.04 472

test_0402		96.64 404	96.24 408	97.85 409	98.85 419	96.43 403	99.44 259	99.26 374	93.52 467	96.98 463	99.52 317	88.52 445	99.20 414	92.58 483	97.50 355	97.93 483

icg_test_0407_2		98.79 212	98.86 181	98.57 327	99.55 224	96.93 374	99.07 400	99.44 271	98.05 221	99.66 139	99.80 161	97.13 142	99.18 416	98.15 275	98.92 258	99.60 206

SixPastTwentyTwo		97.50 369	97.33 366	98.03 388	98.65 450	96.23 411	99.77 36	98.68 471	97.14 338	97.90 437	99.93 11	90.45 416	99.18 416	97.00 385	96.43 389	98.67 384

cl22		97.85 315	97.64 320	98.48 341	99.09 373	97.87 320	98.60 480	99.33 339	97.11 344	98.87 343	99.22 403	92.38 378	99.17 418	98.21 267	95.99 402	98.42 446

tt0320		95.71 426	95.07 432	97.62 429	99.05 386	95.02 453	99.25 355	99.52 136	86.81 510	97.97 434	99.72 220	83.58 488	99.15 419	96.38 415	93.35 459	98.68 376

WB-MVSnew		97.65 357	97.65 317	97.63 428	98.78 428	97.62 333	99.13 387	98.33 487	97.36 319	99.07 304	98.94 440	95.64 239	99.15 419	92.95 476	98.68 279	96.12 520

IterMVS-SCA-FT		97.82 325	97.75 307	98.06 387	99.57 216	96.36 405	99.02 415	99.49 204	97.18 335	98.71 365	99.72 220	92.72 362	99.14 421	97.44 354	95.86 407	98.67 384

pmmvs5		97.52 366	97.30 370	98.16 379	98.57 460	96.73 388	99.27 344	98.90 436	96.14 421	98.37 406	99.53 312	91.54 398	99.14 421	97.51 344	95.87 406	98.63 404

v148		97.79 331	97.55 326	98.50 338	98.74 436	97.72 327	99.54 177	99.33 339	96.26 410	98.90 336	99.51 321	94.68 293	99.14 421	97.83 305	93.15 466	98.63 404

PatchmatchNet3													99.13 424
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
IMVS_0404		98.53 233	98.52 232	98.55 333	99.55 224	96.93 374	99.20 372	99.44 271	98.05 221	98.96 326	99.80 161	94.66 296	99.13 424	98.15 275	98.92 258	99.60 206

miper_ehance_all_eth		98.18 264	98.10 261	98.41 355	99.23 336	97.72 327	98.72 467	99.31 354	96.60 387	98.88 340	99.29 392	97.29 135	99.13 424	97.60 331	95.99 402	98.38 451

NR-MVSNet		97.97 299	97.61 323	99.02 252	98.87 415	99.26 148	99.47 244	99.42 284	97.63 283	97.08 461	99.50 324	95.07 263	99.13 424	97.86 301	93.59 457	98.68 376

IterMVS		97.83 322	97.77 302	98.02 390	99.58 210	96.27 409	99.02 415	99.48 216	97.22 332	98.71 365	99.70 227	92.75 359	99.13 424	97.46 350	96.00 401	98.67 384
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CMPMVS	69.68 23	94.13 456	94.90 435	91.84 492	97.24 497	80.01 531	98.52 487	99.48 216	89.01 504	91.99 507	99.67 253	85.67 472	99.13 424	95.44 436	97.03 378	96.39 517
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
eth_miper_zixun_eth		98.05 284	97.96 278	98.33 362	99.26 328	97.38 341	98.56 485	99.31 354	96.65 379	98.88 340	99.52 317	96.58 179	99.12 430	97.39 357	95.53 418	98.47 440

blended_shiyan8		95.56 427	94.79 436	97.87 405	96.60 507	95.90 422	98.85 446	99.27 372	92.19 483	98.47 399	97.94 492	91.43 400	99.11 431	97.26 367	81.09 526	98.60 418

pmmvs4		98.13 269	97.90 285	98.81 299	98.61 455	98.87 229	98.99 423	99.21 387	96.44 399	99.06 309	99.58 290	95.90 224	99.11 431	97.18 376	96.11 398	98.46 443

TransMVSNet (Re)		97.15 391	96.58 399	98.86 290	99.12 365	98.85 234	99.49 226	98.91 434	95.48 435	97.16 459	99.80 161	93.38 343	99.11 431	94.16 458	91.73 480	98.62 406

ambc				93.06 489	92.68 542	82.36 519	98.47 493	98.73 468		95.09 484	97.41 504	55.55 534	99.10 434	96.42 412	91.32 481	97.71 489

Baseline_NR-MVSNet		97.76 334	97.45 343	98.68 316	99.09 373	98.29 292	99.41 277	98.85 445	95.65 433	98.63 383	99.67 253	94.82 276	99.10 434	98.07 287	92.89 471	98.64 397

RoMa-SfM		94.36 454	93.86 456	95.88 471	98.61 455	90.62 499	98.85 446	99.04 411	91.63 492	94.14 491	99.49 328	77.16 504	99.09 436	92.66 481	93.13 467	97.91 485

usedtu_blend_shiyan5		95.04 441	94.10 450	97.86 408	96.45 509	95.92 420	99.29 333	99.22 383	86.17 515	98.36 407	97.68 497	91.20 407	99.07 437	97.53 341	80.97 527	98.60 418

blend_shiyan4		95.25 438	94.39 447	97.84 412	96.70 506	95.92 420	98.84 450	99.28 365	92.21 482	98.16 424	97.84 494	87.10 462	99.07 437	97.53 341	81.87 522	98.54 430

test_vis3_rt		87.04 487	85.81 492	90.73 500	93.99 535	81.96 521	99.76 39	90.23 545	92.81 479	81.35 536	91.56 536	40.06 554	99.07 437	94.27 455	88.23 503	91.15 531

CP-MVSNet		98.09 273	97.78 300	99.01 253	98.97 401	99.24 151	99.67 79	99.46 251	97.25 328	98.48 398	99.64 266	93.79 337	99.06 440	98.63 214	94.10 450	98.74 358

PS-CasMVS		97.93 302	97.59 325	98.95 262	98.99 396	99.06 178	99.68 75	99.52 136	97.13 339	98.31 413	99.68 246	92.44 377	99.05 441	98.51 235	94.08 451	98.75 354

K. test v3		97.10 393	96.79 395	98.01 391	98.72 439	96.33 406	99.87 8	97.05 512	97.59 287	96.16 474	99.80 161	88.71 439	99.04 442	96.69 403	96.55 387	98.65 395

new_pmnet		96.38 411	96.03 414	97.41 439	98.13 477	95.16 450	99.05 407	99.20 388	93.94 459	97.39 452	98.79 454	91.61 397	99.04 442	90.43 495	95.77 408	98.05 471

wanda-best-256-512		95.43 431	94.66 439	97.77 420	96.45 509	95.68 429	98.48 491	99.28 365	92.18 485	98.36 407	97.68 497	91.20 407	99.03 444	97.31 361	80.97 527	98.60 418

FE-blended-shiyan7		95.43 431	94.66 439	97.77 420	96.45 509	95.68 429	98.48 491	99.28 365	92.18 485	98.36 407	97.68 497	91.20 407	99.03 444	97.31 361	80.97 527	98.60 418

DIV-MVS_self_test		98.01 292	97.85 292	98.48 341	99.24 334	97.95 316	98.71 468	99.35 326	96.50 392	98.60 389	99.54 307	95.72 236	99.03 444	97.21 370	95.77 408	98.46 443

IterMVS-LS		98.46 237	98.42 237	98.58 326	99.59 208	98.00 309	99.37 298	99.43 282	96.94 361	99.07 304	99.59 286	97.87 117	99.03 444	98.32 260	95.62 414	98.71 362
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
blended_shiyan6		95.54 428	94.78 437	97.84 412	96.60 507	95.89 423	98.85 446	99.28 365	92.17 487	98.43 402	97.95 489	91.44 399	99.02 448	97.30 364	80.97 527	98.60 418

our_test_3		97.65 357	97.68 314	97.55 434	98.62 453	94.97 455	98.84 450	99.30 359	96.83 368	98.19 422	99.34 379	97.01 153	99.02 448	95.00 446	96.01 400	98.64 397

Patchmtry		97.75 338	97.40 355	98.81 299	99.10 370	98.87 229	99.11 396	99.33 339	94.83 450	98.81 354	99.38 366	94.33 314	99.02 448	96.10 418	95.57 416	98.53 432

ELoFTR		89.95 479	88.65 485	93.85 481	95.93 515	85.85 511	98.64 475	98.31 488	90.34 498	85.03 524	97.76 495	60.28 533	99.01 451	87.27 513	84.26 511	96.71 514

N_pmnet		94.95 445	95.83 419	92.31 491	98.47 465	79.33 534	99.12 390	92.81 541	93.87 460	97.68 444	99.13 413	93.87 334	99.01 451	91.38 490	96.19 396	98.59 424

gbinet_0.2-2-1-0.02		95.40 434	94.58 443	97.85 409	96.11 514	95.97 417	98.56 485	99.26 374	92.12 489	98.47 399	97.49 503	90.23 421	99.00 453	97.71 322	81.25 524	98.58 426

CR-MVSNet		98.17 265	97.93 283	98.87 287	99.18 349	98.49 281	99.22 367	99.33 339	96.96 357	99.56 179	99.38 366	94.33 314	99.00 453	94.83 449	98.58 284	99.14 307

c3_l		98.12 271	98.04 270	98.38 359	99.30 314	97.69 331	98.81 454	99.33 339	96.67 377	98.83 351	99.34 379	97.11 145	98.99 455	97.58 333	95.34 421	98.48 438

test0.0.03 1		97.71 347	97.42 353	98.56 331	98.41 469	97.82 323	98.78 458	98.63 476	97.34 320	98.05 431	98.98 436	94.45 309	98.98 456	95.04 445	97.15 376	98.89 339

PatchT		97.03 396	96.44 403	98.79 302	98.99 396	98.34 291	99.16 379	99.07 407	92.13 488	99.52 190	97.31 509	94.54 304	98.98 456	88.54 504	98.73 276	99.03 325

GBi-Net		97.68 352	97.48 337	98.29 367	99.51 241	97.26 347	99.43 265	99.48 216	96.49 393	99.07 304	99.32 387	90.26 418	98.98 456	97.10 378	96.65 383	98.62 406

test1		97.68 352	97.48 337	98.29 367	99.51 241	97.26 347	99.43 265	99.48 216	96.49 393	99.07 304	99.32 387	90.26 418	98.98 456	97.10 378	96.65 383	98.62 406

FMVSNet3		98.03 287	97.76 306	98.84 294	99.39 288	98.98 188	99.40 285	99.38 307	96.67 377	99.07 304	99.28 394	92.93 354	98.98 456	97.10 378	96.65 383	98.56 429

FMVSNet2		97.72 344	97.36 358	98.80 301	99.51 241	98.84 236	99.45 252	99.42 284	96.49 393	98.86 349	99.29 392	90.26 418	98.98 456	96.44 411	96.56 386	98.58 426

FMVSNet1		96.84 400	96.36 405	98.29 367	99.32 312	97.26 347	99.43 265	99.48 216	95.11 441	98.55 392	99.32 387	83.95 486	98.98 456	95.81 425	96.26 394	98.62 406

ppachtmachnet_test		97.49 374	97.45 343	97.61 432	98.62 453	95.24 446	98.80 455	99.46 251	96.11 423	98.22 420	99.62 277	96.45 187	98.97 463	93.77 462	95.97 405	98.61 415

TranMVSNet+NR-MVSNet		97.93 302	97.66 316	98.76 306	98.78 428	98.62 263	99.65 92	99.49 204	97.76 267	98.49 397	99.60 284	94.23 317	98.97 463	98.00 291	92.90 470	98.70 367

MVStest1		96.08 419	95.48 425	97.89 404	98.93 404	96.70 389	99.56 157	99.35 326	92.69 480	91.81 508	99.46 343	89.90 426	98.96 465	95.00 446	92.61 475	98.00 478

tt0320-xc		95.31 437	94.59 442	97.45 437	98.92 406	94.73 460	99.20 372	99.31 354	86.74 511	97.23 455	99.72 220	81.14 500	98.95 466	97.08 381	91.98 479	98.67 384

test_method		91.10 473	91.36 474	90.31 502	95.85 517	73.72 543	94.89 531	99.25 377	68.39 533	95.82 477	99.02 429	80.50 501	98.95 466	93.64 466	94.89 434	98.25 458

ADS-MVSNet2		98.02 289	98.07 268	97.87 405	99.33 305	95.19 448	99.23 363	99.08 404	96.24 411	99.10 298	99.67 253	94.11 323	98.93 468	96.81 397	99.05 246	99.48 254

ET-MVSNet_ETH3D		96.49 408	95.64 424	99.05 249	99.53 232	98.82 242	98.84 450	97.51 508	97.63 283	84.77 525	99.21 406	92.09 382	98.91 469	98.98 152	92.21 478	99.41 277

miper_lstm_enhance		98.00 294	97.91 284	98.28 371	99.34 304	97.43 339	98.88 442	99.36 319	96.48 396	98.80 356	99.55 302	95.98 216	98.91 469	97.27 366	95.50 419	98.51 436

MonoMVSNet		98.38 246	98.47 235	98.12 384	98.59 459	96.19 413	99.72 55	98.79 455	97.89 246	99.44 207	99.52 317	96.13 206	98.90 471	98.64 212	97.54 350	99.28 296

PEN-MVS		97.76 334	97.44 348	98.72 309	98.77 433	98.54 271	99.78 34	99.51 164	97.06 349	98.29 416	99.64 266	92.63 368	98.89 472	98.09 280	93.16 465	98.72 360

testing3		97.28 385	96.76 396	98.82 296	99.37 294	98.07 306	99.45 252	99.36 319	97.56 292	97.89 438	98.95 439	83.70 487	98.82 473	96.03 420	98.56 287	99.58 221

testgi		97.65 357	97.50 335	98.13 383	99.36 298	96.45 402	99.42 272	99.48 216	97.76 267	97.87 439	99.45 345	91.09 410	98.81 474	94.53 451	98.52 290	99.13 310

testf1		90.42 477	90.68 478	89.65 509	97.78 485	73.97 541	99.13 387	98.81 450	89.62 501	91.80 509	98.93 441	62.23 530	98.80 475	86.61 517	91.17 483	96.19 518

APD_test2		90.42 477	90.68 478	89.65 509	97.78 485	73.97 541	99.13 387	98.81 450	89.62 501	91.80 509	98.93 441	62.23 530	98.80 475	86.61 517	91.17 483	96.19 518

dtuonlycased		97.04 395	97.33 366	96.16 467	99.08 376	90.59 500	98.79 457	99.38 307	97.19 334	96.91 466	99.49 328	90.22 423	98.75 477	97.04 383	97.89 330	99.14 307

MIMVSNet		97.73 342	97.45 343	98.57 327	99.45 271	97.50 337	99.02 415	98.98 421	96.11 423	99.41 218	99.14 412	90.28 417	98.74 478	95.74 428	98.93 256	99.47 260

LCM-MVSNet-Re		97.83 322	98.15 255	96.87 457	99.30 314	92.25 492	99.59 131	98.26 489	97.43 311	96.20 473	99.13 413	96.27 198	98.73 479	98.17 272	98.99 253	99.64 193

Syy-MVS		97.09 394	97.14 381	96.95 454	99.00 393	92.73 489	99.29 333	99.39 297	97.06 349	97.41 449	98.15 481	93.92 332	98.68 480	91.71 487	98.34 298	99.45 268

myMVS_eth3d		96.89 398	96.37 404	98.43 354	99.00 393	97.16 351	99.29 333	99.39 297	97.06 349	97.41 449	98.15 481	83.46 489	98.68 480	95.27 441	98.34 298	99.45 268

DTE-MVSNet		97.51 368	97.19 379	98.46 347	98.63 452	98.13 301	99.84 12	99.48 216	96.68 376	97.97 434	99.67 253	92.92 355	98.56 482	96.88 396	92.60 476	98.70 367

PC_three_1452									98.18 185	99.84 58	99.70 227	99.31 4	98.52 483	98.30 262	99.80 128	99.81 81

mvsany_test3		93.77 459	93.45 463	94.74 477	95.78 518	88.01 507	99.64 100	98.25 490	98.28 160	94.31 490	97.97 488	68.89 522	98.51 484	97.50 345	90.37 490	97.71 489

UnsupCasMVSNet_bld		93.53 460	92.51 467	96.58 462	97.38 493	93.82 476	98.24 503	99.48 216	91.10 496	93.10 499	96.66 514	74.89 507	98.37 485	94.03 460	87.71 505	97.56 497

Anonymous20240521		96.20 414	95.89 418	97.13 447	97.72 489	94.96 456	99.79 32	99.29 363	93.01 475	97.20 458	99.03 427	89.69 429	98.36 486	91.16 491	96.13 397	98.07 469

test_f		91.90 471	91.26 475	93.84 482	95.52 523	85.92 510	99.69 65	98.53 483	95.31 438	93.87 495	96.37 518	55.33 535	98.27 487	95.70 429	90.98 488	97.32 501

MDA-MVSNet_test_wron		95.45 430	94.60 441	98.01 391	98.16 476	97.21 350	99.11 396	99.24 380	93.49 468	80.73 538	98.98 436	93.02 352	98.18 488	94.22 457	94.45 441	98.64 397

UnsupCasMVSNet_eth		96.44 409	96.12 411	97.40 440	98.65 450	95.65 431	99.36 304	99.51 164	97.13 339	96.04 476	98.99 434	88.40 446	98.17 489	96.71 401	90.27 492	98.40 449

KD-MVS_2432*1600		94.62 449	93.72 458	97.31 442	97.19 499	95.82 426	98.34 498	99.20 388	95.00 446	97.57 445	98.35 472	87.95 452	98.10 490	92.87 478	77.00 538	98.01 475

miper_refine_blended		94.62 449	93.72 458	97.31 442	97.19 499	95.82 426	98.34 498	99.20 388	95.00 446	97.57 445	98.35 472	87.95 452	98.10 490	92.87 478	77.00 538	98.01 475

YYNet1		95.36 435	94.51 445	97.92 401	97.89 481	97.10 354	99.10 398	99.23 381	93.26 472	80.77 537	99.04 426	92.81 358	98.02 492	94.30 453	94.18 447	98.64 397

EU-MVSNet		97.98 296	98.03 271	97.81 418	98.72 439	96.65 394	99.66 86	99.66 34	98.09 209	98.35 410	99.82 128	95.25 256	98.01 493	97.41 356	95.30 422	98.78 346

Gipuma		90.99 474	90.15 480	93.51 485	98.73 437	90.12 503	93.98 536	99.45 262	79.32 521	92.28 504	94.91 523	69.61 520	97.98 494	87.42 511	95.67 412	92.45 528
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
pmmvs-eth3d		95.34 436	94.73 438	97.15 445	95.53 522	95.94 419	99.35 310	99.10 401	95.13 439	93.55 497	97.54 502	88.15 450	97.91 495	94.58 450	89.69 498	97.61 494

PM-MVS		92.96 465	92.23 470	95.14 476	95.61 520	89.98 504	99.37 298	98.21 493	94.80 451	95.04 485	97.69 496	65.06 526	97.90 496	94.30 453	89.98 494	97.54 498

MDA-MVSNet-bldmvs		94.96 444	93.98 453	97.92 401	98.24 472	97.27 345	99.15 383	99.33 339	93.80 463	80.09 539	99.03 427	88.31 447	97.86 497	93.49 468	94.36 443	98.62 406

Patchmatch-RL test		95.84 422	95.81 420	95.95 470	95.61 520	90.57 501	98.24 503	98.39 485	95.10 443	95.20 481	98.67 458	94.78 281	97.77 498	96.28 417	90.02 493	99.51 246

Anonymous20231206		96.22 412	96.03 414	96.79 459	97.31 496	94.14 474	99.63 106	99.08 404	96.17 417	97.04 462	99.06 421	93.94 330	97.76 499	86.96 515	95.06 427	98.47 440

DKM		93.17 463	92.50 468	95.21 475	98.53 463	90.26 502	98.74 466	98.90 436	93.00 476	92.61 502	99.06 421	70.06 519	97.74 500	91.92 486	89.65 499	97.62 493

SD-MVS		99.41 60	99.52 16	99.05 249	99.74 103	99.68 67	99.46 248	99.52 136	99.11 48	99.88 44	99.91 27	99.43 1	97.70 501	98.72 201	99.93 33	99.77 102
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
DSMNet-mixed		97.25 387	97.35 360	96.95 454	97.84 483	93.61 483	99.57 149	96.63 519	96.13 422	98.87 343	98.61 461	94.59 299	97.70 501	95.08 444	98.86 267	99.55 229

FE-MVSNET2		95.10 440	94.44 446	97.08 450	95.08 526	95.97 417	99.51 198	99.37 317	95.02 445	94.10 492	97.57 500	86.18 469	97.66 503	93.28 471	89.86 495	97.61 494

dongtai		93.26 461	92.93 466	94.25 478	99.39 288	85.68 512	97.68 520	93.27 537	92.87 478	96.85 467	99.39 363	82.33 495	97.48 504	76.78 531	97.80 335	99.58 221

pmmvs3		94.09 457	93.25 465	96.60 461	94.76 530	94.49 468	98.92 437	98.18 495	89.66 500	96.48 470	98.06 487	86.28 468	97.33 505	89.68 498	87.20 506	97.97 481

RoMa-HiRes		92.56 467	92.07 471	94.02 479	97.77 488	87.59 508	98.87 444	98.46 484	89.82 499	92.47 503	99.41 354	71.58 515	97.29 506	90.47 494	89.79 497	97.17 504

KD-MVS_self_test		95.00 443	94.34 448	96.96 453	97.07 502	95.39 443	99.56 157	99.44 271	95.11 441	97.13 460	97.32 508	91.86 387	97.27 507	90.35 496	81.23 525	98.23 460

FMVSNet5		96.43 410	96.19 410	97.15 445	99.11 367	95.89 423	99.32 321	99.52 136	94.47 457	98.34 412	99.07 419	87.54 457	97.07 508	92.61 482	95.72 411	98.47 440

usedtu_dtu_shiyan2		91.34 472	89.96 482	95.47 474	93.61 538	90.81 498	99.15 383	98.68 471	86.37 513	95.19 482	98.27 476	72.64 511	97.05 509	85.40 520	80.32 533	98.54 430

new-patchmatchnet		94.48 452	94.08 452	95.67 472	95.08 526	92.41 490	99.18 377	99.28 365	94.55 456	93.49 498	97.37 506	87.86 455	97.01 510	91.57 488	88.36 502	97.61 494

LCM-MVSNet		86.80 490	85.22 496	91.53 494	87.81 554	80.96 527	98.23 505	98.99 419	71.05 530	90.13 515	96.51 517	48.45 547	96.88 511	90.51 493	85.30 509	96.76 511

ALIKED-LG		88.17 486	87.32 489	90.75 499	98.67 448	81.68 523	98.16 508	94.72 532	78.63 522	86.08 523	97.07 510	70.16 518	96.62 512	71.97 540	90.37 490	93.95 525

CL-MVSNet_self_test		94.49 451	93.97 454	96.08 468	96.16 513	93.67 481	98.33 500	99.38 307	95.13 439	97.33 453	98.15 481	92.69 366	96.57 513	88.67 503	79.87 535	97.99 479

MIMVSNet1		95.51 429	95.04 434	96.92 456	97.38 493	95.60 432	99.52 188	99.50 189	93.65 465	96.97 464	99.17 408	85.28 479	96.56 514	88.36 505	95.55 417	98.60 418

FE-MVSNET		94.07 458	93.36 464	96.22 466	94.05 534	94.71 462	99.56 157	98.36 486	93.15 473	93.76 496	97.55 501	86.47 467	96.49 515	87.48 510	89.83 496	97.48 499

ALIKED-MNN		86.97 488	85.90 491	90.16 504	99.06 382	79.59 533	97.93 515	94.82 530	72.37 528	84.41 526	95.46 521	68.55 523	96.43 516	72.40 538	88.11 504	94.47 524

test20.03		96.12 417	95.96 416	96.63 460	97.44 491	95.45 440	99.51 198	99.38 307	96.55 390	96.16 474	99.25 400	93.76 339	96.17 517	87.35 512	94.22 446	98.27 456

tmp_tt		82.80 495	81.52 500	86.66 513	66.61 562	68.44 546	92.79 544	97.92 497	68.96 532	80.04 540	99.85 93	85.77 471	96.15 518	97.86 301	43.89 554	95.39 522

DKM-HiRes		92.13 468	91.58 473	93.78 484	98.24 472	88.09 506	98.61 477	98.68 471	91.39 493	90.36 512	98.90 447	67.97 524	96.01 519	91.39 489	88.65 501	97.24 502

test_fmvs3		92.10 469	91.77 472	93.08 488	96.19 512	86.25 509	99.82 16	98.62 477	96.65 379	95.19 482	96.90 512	55.05 536	95.93 520	96.63 408	90.92 489	97.06 507

ALIKED-NN		88.27 485	87.61 488	90.24 503	98.46 466	79.97 532	97.04 526	94.61 534	75.25 523	86.99 520	96.90 512	72.78 510	95.78 521	75.45 535	91.01 487	94.97 523

PMatch-SfM		88.28 484	86.92 490	92.38 490	95.93 515	84.56 515	97.84 517	96.01 523	88.80 506	84.11 527	97.95 489	49.73 542	95.66 522	89.15 501	82.72 521	96.91 508

kuosan		90.92 475	90.11 481	93.34 486	98.78 428	85.59 513	98.15 510	93.16 539	89.37 503	92.07 506	98.38 471	81.48 498	95.19 523	62.54 544	97.04 377	99.25 301

dmvs_testset		95.02 442	96.12 411	91.72 493	99.10 370	80.43 530	99.58 141	97.87 499	97.47 303	95.22 480	98.82 450	93.99 328	95.18 524	88.09 506	94.91 432	99.56 228

SP-LightGlue		89.28 480	88.68 483	91.06 496	98.21 475	80.90 528	98.19 506	96.96 513	72.38 527	89.60 517	94.43 526	72.44 512	95.06 525	82.91 524	93.03 468	97.22 503

SP-SuperGlue		89.23 481	88.68 483	90.88 498	98.23 474	80.60 529	98.16 508	97.30 510	73.08 526	89.64 516	94.62 525	71.80 514	94.91 526	82.11 526	93.22 463	97.14 506

SP-DiffGlue		90.78 476	90.71 477	90.98 497	95.45 525	81.30 526	97.92 516	97.30 510	75.18 524	92.09 505	95.93 519	74.93 506	94.89 527	93.46 469	94.12 449	96.74 513

SP-MNN		88.33 483	87.78 487	89.95 507	98.28 470	77.92 536	98.01 514	95.69 526	70.61 531	86.18 522	94.36 528	71.09 516	94.76 528	81.51 527	94.32 444	97.17 504

PMMVS2		86.87 489	85.37 495	91.35 495	90.21 548	83.80 518	98.89 441	97.45 509	83.13 520	91.67 511	95.03 522	48.49 546	94.70 529	85.86 519	77.62 537	95.54 521

GLUNet-SfM		78.99 500	76.32 505	86.99 512	89.16 553	73.30 544	93.36 540	90.45 544	66.38 536	74.95 545	93.30 533	52.29 538	94.61 530	75.35 536	51.65 551	93.07 526

PMatch-Up-SfM		86.75 491	85.43 494	90.73 500	94.97 529	81.39 524	97.55 523	94.92 529	86.33 514	83.10 531	97.95 489	46.03 548	93.97 531	87.59 509	80.39 532	96.83 509

SP-NN		88.62 482	88.17 486	89.96 506	97.89 481	78.51 535	97.19 525	96.09 522	71.28 529	88.29 518	94.00 530	71.98 513	93.65 532	82.37 525	94.46 439	97.71 489

PDCNetPlus		84.77 493	83.24 497	89.36 511	94.33 533	83.93 517	98.13 511	76.80 556	83.26 519	86.31 521	97.33 507	62.90 528	92.65 533	87.20 514	62.90 544	91.50 530

PMVS	70.75 22	75.98 504	74.97 508	79.01 521	70.98 561	55.18 560	93.37 539	98.21 493	65.08 538	61.78 551	93.83 531	21.74 564	92.53 534	78.59 530	91.12 485	89.34 536
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
FPMVS		84.93 492	85.65 493	82.75 518	86.77 555	63.39 548	98.35 497	98.92 429	74.11 525	83.39 530	98.98 436	50.85 539	92.40 535	84.54 522	94.97 429	92.46 527

WB-MVS		93.10 464	94.10 450	90.12 505	95.51 524	81.88 522	99.73 53	99.27 372	95.05 444	93.09 500	98.91 445	94.70 292	91.89 536	76.62 532	94.02 453	96.58 515

XFeat-MNN		82.40 497	82.10 499	83.31 516	93.04 540	68.49 545	95.39 530	90.86 543	60.29 540	81.56 535	94.09 529	66.79 525	91.70 537	76.62 532	80.26 534	89.74 534

SSC-MVS		92.73 466	93.73 457	89.72 508	95.02 528	81.38 525	99.76 39	99.23 381	94.87 449	92.80 501	98.93 441	94.71 291	91.37 538	74.49 537	93.80 455	96.42 516

XFeat-NN		82.84 494	83.12 498	82.00 520	94.35 532	67.14 547	93.32 541	89.27 547	62.21 539	84.06 528	93.50 532	69.15 521	89.40 539	78.92 529	83.33 518	89.46 535

SIFT-NN-NCMNet		75.53 506	75.57 507	75.42 524	93.93 536	61.35 550	94.41 532	86.44 550	58.51 543	76.23 542	90.44 543	50.56 540	89.34 540	46.60 547	83.04 519	75.58 544

SIFT-MNN		75.73 505	75.71 506	75.77 523	95.65 519	60.92 551	94.36 533	87.62 548	58.67 542	75.90 543	90.94 540	49.64 544	89.04 541	44.85 551	83.80 514	77.35 540

SIFT-NN		76.99 502	77.37 504	75.84 522	97.10 501	62.39 549	94.15 535	87.21 549	59.41 541	79.90 541	90.73 541	54.60 537	88.56 542	47.22 546	86.03 508	76.57 542

SIFT-NCM-Cal		71.65 509	70.76 515	74.34 526	94.61 531	60.18 554	94.16 534	81.72 553	57.21 547	55.36 555	89.56 549	42.48 549	88.45 543	41.31 557	80.41 531	74.39 546

SIFT-NN-UMatch		71.65 509	70.86 514	74.00 527	90.69 547	60.53 552	93.59 537	81.89 552	58.42 544	60.99 552	89.71 548	50.18 541	87.89 544	45.77 549	66.55 543	73.57 548

MVE	76.82 21	76.91 503	74.31 510	84.70 514	85.38 558	76.05 540	96.88 528	93.17 538	67.39 534	71.28 546	89.01 552	21.66 565	87.69 545	71.74 541	72.29 542	90.35 533
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
SIFT-NN-CMatch		72.61 507	71.92 513	74.68 525	92.79 541	60.24 553	93.28 542	81.57 554	58.24 545	75.18 544	90.26 545	49.66 543	87.35 546	46.02 548	60.26 547	76.45 543

SIFT-UMatch		68.14 514	66.40 519	73.38 529	92.20 544	59.42 556	92.84 543	76.01 558	56.87 548	58.37 553	90.35 544	41.97 552	87.16 547	42.64 553	46.35 553	73.55 549

E-PMN		80.61 498	79.88 501	82.81 517	90.75 546	76.38 539	97.69 519	95.76 525	66.44 535	83.52 529	92.25 535	62.54 529	87.16 547	68.53 542	61.40 545	84.89 539

SIFT-ConvMatch		69.43 513	68.09 517	73.45 528	93.86 537	60.02 555	92.57 545	77.69 555	57.58 546	62.69 549	90.53 542	42.14 551	86.65 549	43.98 552	51.72 550	73.67 547

EMVS		80.02 499	79.22 502	82.43 519	91.19 545	76.40 538	97.55 523	92.49 542	66.36 537	83.01 532	91.27 537	64.63 527	85.79 550	65.82 543	60.65 546	85.08 538

ANet_high		77.30 501	74.86 509	84.62 515	75.88 560	77.61 537	97.63 521	93.15 540	88.81 505	64.27 548	89.29 550	36.51 558	83.93 551	75.89 534	52.31 549	92.33 529

SIFT-NN-PointCN		70.32 512	69.71 516	72.13 530	90.01 549	58.29 558	93.45 538	76.20 557	56.66 550	70.25 547	89.20 551	48.94 545	83.41 552	45.45 550	57.26 548	74.70 545

SIFT-CM-Cal		66.94 515	65.48 520	71.33 531	93.05 539	58.77 557	91.46 548	70.45 560	56.64 551	61.97 550	89.98 546	40.72 553	83.32 553	42.57 554	42.47 555	71.90 550

SIFT-UM-Cal		64.60 517	62.65 521	70.42 532	92.22 543	58.07 559	92.29 546	66.92 561	56.70 549	50.16 557	89.97 547	37.90 555	82.95 554	42.33 555	35.40 558	70.24 552

SIFT-PCN-Cal		61.29 519	60.21 523	64.54 535	89.88 550	50.56 562	91.21 549	65.73 563	53.15 553	48.59 558	87.20 553	36.60 557	76.52 555	37.37 560	32.17 559	66.54 553

SIFT-PointCN		62.71 518	61.56 522	66.18 534	89.53 552	50.88 561	91.81 547	72.35 559	53.65 552	50.49 556	86.32 554	33.30 559	76.23 556	35.91 561	40.66 556	71.43 551

VLMVS_CLIP		71.76 508	73.17 512	67.54 533	63.66 564	40.57 567	82.57 551	89.67 546	44.24 555	82.97 533	95.88 520	37.85 556	71.58 557	83.87 523	77.80 536	90.48 532

SIFT-NCMNet		55.02 520	53.54 524	59.46 537	86.55 556	47.35 564	87.85 550	46.22 565	51.77 554	44.11 559	83.50 555	27.88 562	68.75 558	32.81 562	21.14 562	62.27 554

VLMVS		64.83 516	67.01 518	58.30 538	65.95 563	42.53 566	76.90 554	66.20 562	29.52 557	82.93 534	94.37 527	42.34 550	55.19 559	72.39 539	72.45 541	77.18 541

MVS_clip		71.06 511	74.26 511	61.45 536	84.42 559	45.51 565	79.78 552	56.58 564	40.80 556	90.25 513	98.55 464	61.46 532	49.70 560	80.63 528	75.89 540	89.13 537

wuyk23d		40.18 521	41.29 527	36.84 539	86.18 557	49.12 563	79.73 553	22.81 567	27.64 558	25.46 562	28.45 561	21.98 563	48.89 561	55.80 545	23.56 561	12.51 558

test123		39.01 523	42.50 526	28.53 540	39.17 566	20.91 568	98.75 463	19.17 568	19.83 560	38.57 560	66.67 557	33.16 560	15.42 562	37.50 559	29.66 560	49.26 556

testmvs		39.17 522	43.78 525	25.37 541	36.04 567	16.84 569	98.36 496	26.56 566	20.06 559	38.51 561	67.32 556	29.64 561	15.30 563	37.59 558	39.90 557	43.98 557

MVS_baseline		35.35 524	39.65 528	22.45 542	47.29 565	11.23 570	38.03 555	9.90 569	5.09 562	58.24 554	91.18 538	16.48 566	0.13 564	42.28 556	48.39 552	55.99 555

Meshroom													0.00 565
: In preparation.
AliceVision / Meshro													0.00 565
: In preparation.
AliceVision_Meshroom													0.00 565
: In preparation.
mmdepth		0.02 529	0.03 533	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	0.27 563	0.00 567	0.00 565	0.00 563	0.00 563	0.00 559

monomultidepth		0.02 529	0.03 533	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	0.27 563	0.00 567	0.00 565	0.00 563	0.00 563	0.00 559

test_blank		0.13 528	0.17 532	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	1.57 562	0.00 567	0.00 565	0.00 563	0.00 563	0.00 559

uanet_test		0.02 529	0.03 533	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	0.27 563	0.00 567	0.00 565	0.00 563	0.00 563	0.00 559

DCPMVS		0.02 529	0.03 533	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	0.27 563	0.00 567	0.00 565	0.00 563	0.00 563	0.00 559

cdsmvs_eth3d_5k		24.64 525	32.85 529	0.00 543	0.00 568	0.00 571	0.00 556	99.51 164	0.00 563	0.00 564	99.56 299	96.58 179	0.00 565	0.00 563	0.00 563	0.00 559

pcd_1.5k_mvsjas		8.27 527	11.03 531	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	0.27 563	99.01 20	0.00 565	0.00 563	0.00 563	0.00 559

sosnet-low-res		0.02 529	0.03 533	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	0.27 563	0.00 567	0.00 565	0.00 563	0.00 563	0.00 559

sosnet		0.02 529	0.03 533	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	0.27 563	0.00 567	0.00 565	0.00 563	0.00 563	0.00 559

uncertanet		0.02 529	0.03 533	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	0.27 563	0.00 567	0.00 565	0.00 563	0.00 563	0.00 559

Regformer		0.02 529	0.03 533	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	0.27 563	0.00 567	0.00 565	0.00 563	0.00 563	0.00 559

ab-mvs-re		8.30 526	11.06 530	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	99.58 290	0.00 567	0.00 565	0.00 563	0.00 563	0.00 559

uanet		0.02 529	0.03 533	0.00 543	0.00 568	0.00 571	0.00 556	0.00 570	0.00 563	0.00 564	0.27 563	0.00 567	0.00 565	0.00 563	0.00 563	0.00 559

PatchmatchNet2					0.00 568	95.16 450	98.77 461	99.17 393	93.82 462
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet1														91.97 484	96.20 395	98.59 424
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
WAC-MVS						97.16 351								95.47 435

FOURS1					99.91 1	99.93 1	99.87 8	99.56 92	99.10 49	99.81 74

test_one_0601					99.81 60	99.88 11		99.49 204	98.97 78	99.65 149	99.81 143	99.09 16

eth-test2					0.00 568

eth-test					0.00 568

RE-MVS-def			99.34 51		99.76 85	99.82 31	99.63 106	99.52 136	98.38 144	99.76 98	99.82 128	98.75 63		98.61 218	99.81 123	99.77 102

IU-MVS					99.84 40	99.88 11		99.32 350	98.30 158	99.84 58				98.86 177	99.85 96	99.89 31

save fliter					99.76 85	99.59 92	99.14 386	99.40 294	99.00 69

test0726					99.85 33	99.89 7	99.62 111	99.50 189	99.10 49	99.86 54	99.82 128	98.94 35

GSMVS																99.52 237

test_part2					99.81 60	99.83 25				99.77 92

sam_mvs1												94.86 274				99.52 237

sam_mvs												94.72 290

MTGPA								99.47 238

MTMP							99.54 177	98.88 440

test9_res														97.49 346	99.72 151	99.75 115

agg_prior2														97.21 370	99.73 150	99.75 115

test_prior4						99.56 98	98.99 423

test_prior2							98.96 430		98.34 150	99.01 315	99.52 317	98.68 73		97.96 293	99.74 148

新几何2							99.01 420

旧先验1					99.74 103	99.59 92		99.54 111			99.69 238	98.47 90			99.68 159	99.73 130

原ACMM2							98.95 433

test222					99.75 95	99.49 113	98.91 440	99.49 204	96.42 401	99.34 243	99.65 260	98.28 103			99.69 156	99.72 140

segment_acmp												98.96 28

testdata1							98.85 446		98.32 154

plane_prior7					99.29 318	97.03 366

plane_prior6					99.27 323	96.98 370						92.71 364

plane_prior4											99.61 281

plane_prior3						97.00 368			98.69 110	99.11 295

plane_prior2							99.39 289		98.97 78

plane_prior1					99.26 328

plane_prior						96.97 371	99.21 369		98.45 135						97.60 344

n2								0.00 570

nn								0.00 570

door-mid								98.05 496

test11								99.35 326

door								97.92 497

HQP5-MVS						96.83 383

HQP-NCC					99.19 346		98.98 426		98.24 172	98.66 374

ACMP_Plane					99.19 346		98.98 426		98.24 172	98.66 374

BP-MVS														97.19 374

HQP3-MVS								99.39 297							97.58 346

HQP2-MVS												92.47 373

NP-MVS					99.23 336	96.92 378					99.40 359

MDTV_nov1_ep13_2view						95.18 449	99.35 310		96.84 366	99.58 174		95.19 259		97.82 306		99.46 265

ACMMP++_ref															97.19 374

ACMMP++															97.43 364

Test By Simon												98.75 63

High-resolution multi-view benchmark