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 8	99.61 7	99.77 74	99.38 278	99.37 123	99.58 135	99.62 51	99.41 21	99.87 48	99.92 18	98.81 49	100.00 1	99.97 2	99.93 32	99.94 17

test_fmvsm_n_1920		99.69 5	99.66 4	99.78 71	99.84 38	99.44 116	99.58 135	99.69 22	99.43 17	99.98 13	99.91 26	98.62 76	100.00 1	99.97 2	99.95 22	99.90 25

test_vis1_n_1920		98.63 219	98.40 227	99.31 199	99.86 25	97.94 304	99.67 76	99.62 51	99.43 17	99.99 2	99.91 26	87.29 441	100.00 1	99.92 24	99.92 38	99.98 2

fmvsm_s_conf0.5_n_11		99.32 78	99.16 91	99.80 64	99.83 47	99.70 60	99.57 143	99.56 89	99.45 11	99.99 2	99.93 10	94.18 307	99.99 4	99.96 13	99.98 4	99.73 127

fmvsm_s_conf0.5_n_10		99.41 59	99.24 77	99.92 1	99.83 47	99.84 20	99.53 180	99.56 89	99.45 11	99.99 2	99.92 18	94.92 257	99.99 4	99.97 2	99.97 9	99.95 11

fmvsm_l_conf0.5_n_9		99.58 16	99.47 24	99.92 1	99.85 31	99.82 28	99.47 238	99.63 46	99.45 11	99.98 13	99.89 45	97.02 147	99.99 4	99.98 1	99.96 17	99.95 11

NormalMVS		99.27 88	99.19 87	99.52 139	99.89 8	98.83 227	99.65 89	99.52 133	99.10 48	99.84 55	99.76 189	95.80 218	99.99 4	99.30 89	99.84 101	99.74 118

SymmetryMVS		99.15 114	99.02 126	99.52 139	99.72 111	98.83 227	99.65 89	99.34 315	99.10 48	99.84 55	99.76 189	95.80 218	99.99 4	99.30 89	98.72 262	99.73 127

fmvsm_s_conf0.5_n_5		99.37 68	99.21 83	99.86 34	99.80 63	99.68 64	99.42 266	99.61 60	99.37 24	99.97 25	99.86 78	94.96 252	99.99 4	99.97 2	99.93 32	99.92 23

fmvsm_l_conf0.5_n_3		99.61 10	99.51 18	99.92 1	99.84 38	99.82 28	99.54 171	99.66 32	99.46 7	99.98 13	99.89 45	97.27 132	99.99 4	99.97 2	99.95 22	99.95 11

fmvsm_l_conf0.5_n_a		99.71 2	99.67 1	99.85 43	99.86 25	99.61 85	99.56 151	99.63 46	99.48 3	99.98 13	99.83 106	98.75 60	99.99 4	99.97 2	99.96 17	99.94 17

fmvsm_l_conf0.5_n		99.71 2	99.67 1	99.85 43	99.84 38	99.63 82	99.56 151	99.63 46	99.47 4	99.98 13	99.82 119	98.75 60	99.99 4	99.97 2	99.97 9	99.94 17

test_fmvsmconf_n		99.70 4	99.64 5	99.87 21	99.80 63	99.66 71	99.48 228	99.64 42	99.45 11	99.92 29	99.92 18	98.62 76	99.99 4	99.96 13	99.99 1	99.96 7

patch_mono-2		99.26 91	99.62 6	98.16 364	99.81 57	94.59 447	99.52 182	99.64 42	99.33 28	99.73 96	99.90 36	99.00 24	99.99 4	99.69 34	99.98 4	99.89 29

h-mvs33		97.70 335	97.28 358	98.97 247	99.70 122	97.27 331	99.36 295	99.45 250	98.94 78	99.66 129	99.64 254	94.93 255	99.99 4	99.48 64	84.36 472	99.65 175

xiu_mvs_v1_base_debu		99.29 84	99.27 72	99.34 191	99.63 168	98.97 183	99.12 374	99.51 155	98.86 84	99.84 55	99.47 321	98.18 103	99.99 4	99.50 57	99.31 191	99.08 300

xiu_mvs_v1_base		99.29 84	99.27 72	99.34 191	99.63 168	98.97 183	99.12 374	99.51 155	98.86 84	99.84 55	99.47 321	98.18 103	99.99 4	99.50 57	99.31 191	99.08 300

xiu_mvs_v1_base_debi		99.29 84	99.27 72	99.34 191	99.63 168	98.97 183	99.12 374	99.51 155	98.86 84	99.84 55	99.47 321	98.18 103	99.99 4	99.50 57	99.31 191	99.08 300

EPNet		98.86 184	98.71 191	99.30 204	97.20 463	98.18 283	99.62 107	98.91 413	99.28 31	98.63 366	99.81 134	95.96 206	99.99 4	99.24 103	99.72 148	99.73 127
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
fmvsm_s_conf0.5_n_8		99.54 24	99.42 32	99.89 11	99.83 47	99.74 54	99.51 192	99.62 51	99.46 7	99.99 2	99.90 36	96.60 171	99.98 20	99.95 16	99.95 22	99.96 7

MM		99.40 64	99.28 68	99.74 80	99.67 137	99.31 135	99.52 182	98.87 420	99.55 1	99.74 94	99.80 152	96.47 179	99.98 20	99.97 2	99.97 9	99.94 17

test_cas_vis1_n_1920		99.16 110	99.01 133	99.61 109	99.81 57	98.86 221	99.65 89	99.64 42	99.39 22	99.97 25	99.94 6	93.20 336	99.98 20	99.55 50	99.91 45	99.99 1

test_vis1_n		97.92 293	97.44 334	99.34 191	99.53 219	98.08 291	99.74 48	99.49 192	99.15 38	100.00 1	99.94 6	79.51 482	99.98 20	99.88 26	99.76 140	99.97 4

xiu_mvs_v2_base		99.26 91	99.25 76	99.29 207	99.53 219	98.91 204	99.02 398	99.45 250	98.80 94	99.71 111	99.26 380	98.94 34	99.98 20	99.34 81	99.23 200	98.98 316

PS-MVSNAJ		99.32 78	99.32 53	99.30 204	99.57 203	98.94 197	98.97 412	99.46 239	98.92 81	99.71 111	99.24 382	99.01 20	99.98 20	99.35 76	99.66 159	98.97 318

QAPM		98.67 214	98.30 234	99.80 64	99.20 327	99.67 68	99.77 35	99.72 14	94.74 432	98.73 346	99.90 36	95.78 220	99.98 20	96.96 373	99.88 75	99.76 107

3Dnovator	97.25 9	99.24 96	99.05 111	99.81 60	99.12 349	99.66 71	99.84 12	99.74 13	99.09 55	98.92 317	99.90 36	95.94 209	99.98 20	98.95 146	99.92 38	99.79 92

OpenMVS	96.50 16	98.47 225	98.12 246	99.52 139	99.04 368	99.53 101	99.82 16	99.72 14	94.56 435	98.08 408	99.88 56	94.73 275	99.98 20	97.47 334	99.76 140	99.06 306

fmvsm_s_conf0.5_n_3		99.37 68	99.20 85	99.87 21	99.75 92	99.70 60	99.48 228	99.66 32	99.45 11	99.99 2	99.93 10	94.64 284	99.97 29	99.94 21	99.97 9	99.95 11

reproduce_model		99.63 9	99.54 13	99.90 8	99.78 70	99.88 10	99.56 151	99.55 99	99.15 38	99.90 33	99.90 36	99.00 24	99.97 29	99.11 122	99.91 45	99.86 42

test_fmvsmconf0.1_n		99.55 23	99.45 30	99.86 34	99.44 259	99.65 75	99.50 203	99.61 60	99.45 11	99.87 48	99.92 18	97.31 129	99.97 29	99.95 16	99.99 1	99.97 4

test_fmvs1_n		98.41 231	98.14 243	99.21 220	99.82 53	97.71 316	99.74 48	99.49 192	99.32 29	99.99 2	99.95 3	85.32 460	99.97 29	99.82 29	99.84 101	99.96 7

CANet_DTU		98.97 172	98.87 168	99.25 214	99.33 291	98.42 275	99.08 383	99.30 342	99.16 37	99.43 197	99.75 194	95.27 240	99.97 29	98.56 216	99.95 22	99.36 272

MGCNet		99.15 114	98.96 146	99.73 83	98.92 386	99.37 123	99.37 289	96.92 481	99.51 2	99.66 129	99.78 176	96.69 166	99.97 29	99.84 28	99.97 9	99.84 53

MTAPA		99.52 28	99.39 39	99.89 11	99.90 4	99.86 18	99.66 83	99.47 226	98.79 95	99.68 118	99.81 134	98.43 89	99.97 29	98.88 156	99.90 56	99.83 63

PGM-MVS		99.45 46	99.31 59	99.86 34	99.87 20	99.78 47	99.58 135	99.65 39	97.84 240	99.71 111	99.80 152	99.12 15	99.97 29	98.33 244	99.87 78	99.83 63

mPP-MVS		99.44 50	99.30 61	99.86 34	99.88 13	99.79 41	99.69 63	99.48 204	98.12 191	99.50 181	99.75 194	98.78 53	99.97 29	98.57 213	99.89 67	99.83 63

CP-MVS		99.45 46	99.32 53	99.85 43	99.83 47	99.75 51	99.69 63	99.52 133	98.07 202	99.53 176	99.63 260	98.93 38	99.97 29	98.74 184	99.91 45	99.83 63

SteuartSystems-ACMMP		99.54 24	99.42 32	99.87 21	99.82 53	99.81 33	99.59 125	99.51 155	98.62 112	99.79 75	99.83 106	99.28 6	99.97 29	98.48 223	99.90 56	99.84 53
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3Dnovator+	97.12 13	99.18 103	98.97 142	99.82 57	99.17 341	99.68 64	99.81 20	99.51 155	99.20 33	98.72 347	99.89 45	95.68 224	99.97 29	98.86 164	99.86 86	99.81 79

fmvsm_s_conf0.5_n_9		99.41 59	99.28 68	99.81 60	99.84 38	99.52 105	99.48 228	99.62 51	99.46 7	99.99 2	99.92 18	95.24 244	99.96 41	99.97 2	99.97 9	99.96 7

lecture		99.60 14	99.50 19	99.89 11	99.89 8	99.90 3	99.75 43	99.59 72	99.06 61	99.88 42	99.85 85	98.41 92	99.96 41	99.28 96	99.84 101	99.83 63

KinetiMVS		99.12 134	98.92 155	99.70 87	99.67 137	99.40 121	99.67 76	99.63 46	98.73 102	99.94 28	99.81 134	94.54 290	99.96 41	98.40 235	99.93 32	99.74 118

fmvsm_s_conf0.5_n_7		99.34 75	99.29 65	99.48 160	99.70 122	98.63 248	99.42 266	99.63 46	99.46 7	99.98 13	99.88 56	95.59 227	99.96 41	99.97 2	99.98 4	99.85 46

fmvsm_s_conf0.5_n_2		99.32 78	99.13 94	99.89 11	99.80 63	99.77 48	99.44 253	99.58 77	99.47 4	99.99 2	99.93 10	94.04 312	99.96 41	99.96 13	99.93 32	99.93 22

reproduce-ours		99.61 10	99.52 14	99.90 8	99.76 82	99.88 10	99.52 182	99.54 108	99.13 41	99.89 39	99.89 45	98.96 27	99.96 41	99.04 132	99.90 56	99.85 46

our_new_method		99.61 10	99.52 14	99.90 8	99.76 82	99.88 10	99.52 182	99.54 108	99.13 41	99.89 39	99.89 45	98.96 27	99.96 41	99.04 132	99.90 56	99.85 46

fmvsm_s_conf0.5_n_a		99.56 21	99.47 24	99.85 43	99.83 47	99.64 81	99.52 182	99.65 39	99.10 48	99.98 13	99.92 18	97.35 128	99.96 41	99.94 21	99.92 38	99.95 11

fmvsm_s_conf0.5_n		99.51 29	99.40 37	99.85 43	99.84 38	99.65 75	99.51 192	99.67 27	99.13 41	99.98 13	99.92 18	96.60 171	99.96 41	99.95 16	99.96 17	99.95 11

mvsany_test1		99.50 31	99.46 28	99.62 108	99.61 188	99.09 165	98.94 418	99.48 204	99.10 48	99.96 27	99.91 26	98.85 44	99.96 41	99.72 32	99.58 169	99.82 72

test_fmvs1		98.88 178	98.79 182	99.16 225	99.69 127	97.61 320	99.55 166	99.49 192	99.32 29	99.98 13	99.91 26	91.41 386	99.96 41	99.82 29	99.92 38	99.90 25

DVP-MVS++		99.59 15	99.50 19	99.88 15	99.51 228	99.88 10	99.87 8	99.51 155	98.99 69	99.88 42	99.81 134	99.27 7	99.96 41	98.85 166	99.80 125	99.81 79

MSC_two_6792asdad				99.87 21	99.51 228	99.76 49		99.33 323					99.96 41	98.87 159	99.84 101	99.89 29

No_MVS				99.87 21	99.51 228	99.76 49		99.33 323					99.96 41	98.87 159	99.84 101	99.89 29

ZD-MVS					99.71 117	99.79 41		99.61 60	96.84 347	99.56 167	99.54 294	98.58 78	99.96 41	96.93 376	99.75 142

SED-MVS		99.61 10	99.52 14	99.88 15	99.84 38	99.90 3	99.60 114	99.48 204	99.08 56	99.91 30	99.81 134	99.20 9	99.96 41	98.91 153	99.85 93	99.79 92

test_241102_TWO								99.48 204	99.08 56	99.88 42	99.81 134	98.94 34	99.96 41	98.91 153	99.84 101	99.88 35

ZNCC-MVS		99.47 40	99.33 51	99.87 21	99.87 20	99.81 33	99.64 96	99.67 27	98.08 201	99.55 173	99.64 254	98.91 39	99.96 41	98.72 187	99.90 56	99.82 72

DVP-MVS		99.57 20	99.47 24	99.88 15	99.85 31	99.89 6	99.57 143	99.37 301	99.10 48	99.81 68	99.80 152	98.94 34	99.96 41	98.93 150	99.86 86	99.81 79
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 69	99.81 68	99.80 152	99.09 16	99.96 41	98.85 166	99.90 56	99.88 35

test_0728_SECOND				99.91 6	99.84 38	99.89 6	99.57 143	99.51 155					99.96 41	98.93 150	99.86 86	99.88 35

SR-MVS		99.43 53	99.29 65	99.86 34	99.75 92	99.83 22	99.59 125	99.62 51	98.21 168	99.73 96	99.79 169	98.68 70	99.96 41	98.44 230	99.77 137	99.79 92

DPE-MVS		99.46 42	99.32 53	99.91 6	99.78 70	99.88 10	99.36 295	99.51 155	98.73 102	99.88 42	99.84 100	98.72 67	99.96 41	98.16 259	99.87 78	99.88 35
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
UA-Net		99.42 55	99.29 65	99.80 64	99.62 177	99.55 96	99.50 203	99.70 18	98.79 95	99.77 84	99.96 1	97.45 123	99.96 41	98.92 152	99.90 56	99.89 29

HFP-MVS		99.49 33	99.37 43	99.86 34	99.87 20	99.80 38	99.66 83	99.67 27	98.15 176	99.68 118	99.69 227	99.06 18	99.96 41	98.69 192	99.87 78	99.84 53

region2R		99.48 37	99.35 47	99.87 21	99.88 13	99.80 38	99.65 89	99.66 32	98.13 183	99.66 129	99.68 235	98.96 27	99.96 41	98.62 201	99.87 78	99.84 53

HPM-MVS++		99.39 66	99.23 81	99.87 21	99.75 92	99.84 20	99.43 259	99.51 155	98.68 109	99.27 246	99.53 298	98.64 75	99.96 41	98.44 230	99.80 125	99.79 92

APDe-MVS		99.66 6	99.57 10	99.92 1	99.77 78	99.89 6	99.75 43	99.56 89	99.02 62	99.88 42	99.85 85	99.18 12	99.96 41	99.22 104	99.92 38	99.90 25
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
ACMMPR		99.49 33	99.36 45	99.86 34	99.87 20	99.79 41	99.66 83	99.67 27	98.15 176	99.67 124	99.69 227	98.95 32	99.96 41	98.69 192	99.87 78	99.84 53

MP-MVS		99.33 77	99.15 92	99.87 21	99.88 13	99.82 28	99.66 83	99.46 239	98.09 197	99.48 185	99.74 199	98.29 98	99.96 41	97.93 281	99.87 78	99.82 72
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CPTT-MVS		99.11 140	98.90 160	99.74 80	99.80 63	99.46 114	99.59 125	99.49 192	97.03 334	99.63 147	99.69 227	97.27 132	99.96 41	97.82 292	99.84 101	99.81 79

PVSNet_Blended_VisFu		99.36 72	99.28 68	99.61 109	99.86 25	99.07 170	99.47 238	99.93 2	97.66 265	99.71 111	99.86 78	97.73 118	99.96 41	99.47 66	99.82 117	99.79 92

UGNet		98.87 181	98.69 193	99.40 181	99.22 324	98.72 240	99.44 253	99.68 24	99.24 32	99.18 271	99.42 332	92.74 346	99.96 41	99.34 81	99.94 30	99.53 224
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 78	99.32 53	99.32 197	99.85 31	98.29 278	99.71 58	99.66 32	98.11 193	99.41 205	99.80 152	98.37 95	99.96 41	98.99 138	99.96 17	99.72 137

ACMMP		99.45 46	99.32 53	99.82 57	99.89 8	99.67 68	99.62 107	99.69 22	98.12 191	99.63 147	99.84 100	98.73 66	99.96 41	98.55 219	99.83 113	99.81 79
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
MED-MVS test				99.87 21	99.88 13	99.81 33	99.69 63	99.87 6	99.34 26	99.90 33	99.83 106		99.95 76	98.83 172	99.89 67	99.83 63

MED-MVS		99.66 6	99.60 8	99.87 21	99.88 13	99.81 33	99.69 63	99.87 6	99.18 34	99.90 33	99.83 106	99.30 4	99.95 76	98.83 172	99.89 67	99.83 63

TestfortrainingZip a		99.73 1	99.67 1	99.92 1	99.88 13	99.91 2	99.69 63	99.87 6	99.34 26	99.90 33	99.83 106	99.30 4	99.95 76	99.32 84	99.89 67	99.90 25

fmvsm_s_conf0.5_n_6		99.54 24	99.44 31	99.85 43	99.51 228	99.67 68	99.50 203	99.64 42	99.43 17	99.98 13	99.78 176	97.26 135	99.95 76	99.95 16	99.93 32	99.92 23

fmvsm_s_conf0.5_n_4		99.36 72	99.24 77	99.73 83	99.78 70	99.53 101	99.49 220	99.60 67	99.42 20	99.99 2	99.86 78	95.15 247	99.95 76	99.95 16	99.89 67	99.73 127

fmvsm_s_conf0.1_n_2		99.37 68	99.22 82	99.81 60	99.77 78	99.75 51	99.46 242	99.60 67	99.47 4	99.98 13	99.94 6	94.98 251	99.95 76	99.97 2	99.79 132	99.73 127

test_fmvsmconf0.01_n		99.22 99	99.03 116	99.79 68	98.42 442	99.48 111	99.55 166	99.51 155	99.39 22	99.78 80	99.93 10	94.80 265	99.95 76	99.93 23	99.95 22	99.94 17

SR-MVS-dyc-post		99.45 46	99.31 59	99.85 43	99.76 82	99.82 28	99.63 102	99.52 133	98.38 137	99.76 90	99.82 119	98.53 82	99.95 76	98.61 204	99.81 120	99.77 100

GST-MVS		99.40 64	99.24 77	99.85 43	99.86 25	99.79 41	99.60 114	99.67 27	97.97 224	99.63 147	99.68 235	98.52 83	99.95 76	98.38 237	99.86 86	99.81 79

CANet		99.25 95	99.14 93	99.59 113	99.41 267	99.16 155	99.35 300	99.57 84	98.82 89	99.51 180	99.61 269	96.46 180	99.95 76	99.59 45	99.98 4	99.65 175

MP-MVS-pluss		99.37 68	99.20 85	99.88 15	99.90 4	99.87 17	99.30 314	99.52 133	97.18 316	99.60 159	99.79 169	98.79 52	99.95 76	98.83 172	99.91 45	99.83 63
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MSP-MVS		99.42 55	99.27 72	99.88 15	99.89 8	99.80 38	99.67 76	99.50 179	98.70 106	99.77 84	99.49 312	98.21 101	99.95 76	98.46 228	99.77 137	99.88 35
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 76	96.67 388

APD-MVS_3200maxsize		99.48 37	99.35 47	99.85 43	99.76 82	99.83 22	99.63 102	99.54 108	98.36 141	99.79 75	99.82 119	98.86 43	99.95 76	98.62 201	99.81 120	99.78 98

RPMNet		96.72 387	95.90 400	99.19 222	99.18 333	98.49 267	99.22 353	99.52 133	88.72 478	99.56 167	97.38 474	94.08 311	99.95 76	86.87 484	98.58 269	99.14 292

sss		99.17 108	99.05 111	99.53 133	99.62 177	98.97 183	99.36 295	99.62 51	97.83 241	99.67 124	99.65 248	97.37 127	99.95 76	99.19 108	99.19 203	99.68 160

MVSMamba_PlusPlus		99.46 42	99.41 36	99.64 101	99.68 134	99.50 108	99.75 43	99.50 179	98.27 152	99.87 48	99.92 18	98.09 107	99.94 92	99.65 41	99.95 22	99.47 248

fmvsm_s_conf0.1_n_a		99.26 91	99.06 109	99.85 43	99.52 225	99.62 83	99.54 171	99.62 51	98.69 107	99.99 2	99.96 1	94.47 294	99.94 92	99.88 26	99.92 38	99.98 2

fmvsm_s_conf0.1_n		99.29 84	99.10 98	99.86 34	99.70 122	99.65 75	99.53 180	99.62 51	98.74 101	99.99 2	99.95 3	94.53 292	99.94 92	99.89 25	99.96 17	99.97 4

TSAR-MVS + MP.		99.58 16	99.50 19	99.81 60	99.91 1	99.66 71	99.63 102	99.39 285	98.91 82	99.78 80	99.85 85	99.36 2	99.94 92	98.84 169	99.88 75	99.82 72
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
RRT-MVS		98.91 176	98.75 185	99.39 186	99.46 252	98.61 252	99.76 38	99.50 179	98.06 206	99.81 68	99.88 56	93.91 319	99.94 92	99.11 122	99.27 194	99.61 192

XVS		99.53 27	99.42 32	99.87 21	99.85 31	99.83 22	99.69 63	99.68 24	98.98 72	99.37 216	99.74 199	98.81 49	99.94 92	98.79 180	99.86 86	99.84 53

X-MVStestdata		96.55 390	95.45 409	99.87 21	99.85 31	99.83 22	99.69 63	99.68 24	98.98 72	99.37 216	64.01 500	98.81 49	99.94 92	98.79 180	99.86 86	99.84 53

旧先验2							98.96 413		96.70 356	99.47 186			99.94 92	98.19 255

新几何1				99.75 77	99.75 92	99.59 88		99.54 108	96.76 352	99.29 239	99.64 254	98.43 89	99.94 92	96.92 378	99.66 159	99.72 137

testdata				99.54 125	99.75 92	98.95 193		99.51 155	97.07 328	99.43 197	99.70 216	98.87 42	99.94 92	97.76 301	99.64 162	99.72 137

HPM-MVS		99.42 55	99.28 68	99.83 56	99.90 4	99.72 56	99.81 20	99.54 108	97.59 271	99.68 118	99.63 260	98.91 39	99.94 92	98.58 210	99.91 45	99.84 53
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CHOSEN 1792x2688		99.19 100	99.10 98	99.45 169	99.89 8	98.52 262	99.39 282	99.94 1	98.73 102	99.11 280	99.89 45	95.50 230	99.94 92	99.50 57	99.97 9	99.89 29

APD-MVS		99.27 88	99.08 104	99.84 55	99.75 92	99.79 41	99.50 203	99.50 179	97.16 318	99.77 84	99.82 119	98.78 53	99.94 92	97.56 323	99.86 86	99.80 88
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
DELS-MVS		99.48 37	99.42 32	99.65 95	99.72 111	99.40 121	99.05 390	99.66 32	99.14 40	99.57 166	99.80 152	98.46 87	99.94 92	99.57 48	99.84 101	99.60 195
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		99.06 153	98.88 167	99.61 109	99.62 177	99.16 155	99.37 289	99.56 89	98.04 216	99.53 176	99.62 265	96.84 158	99.94 92	98.85 166	98.49 277	99.72 137

DeepC-MVS	98.35 2	99.30 82	99.19 87	99.64 101	99.82 53	99.23 148	99.62 107	99.55 99	98.94 78	99.63 147	99.95 3	95.82 216	99.94 92	99.37 75	99.97 9	99.73 127
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 88	99.12 96	99.74 80	99.18 333	99.75 51	99.56 151	99.57 84	98.45 130	99.49 184	99.85 85	97.77 117	99.94 92	98.33 244	99.84 101	99.52 225

ME-MVS		99.56 21	99.46 28	99.86 34	99.80 63	99.81 33	99.37 289	99.70 18	99.18 34	99.83 63	99.83 106	98.74 65	99.93 109	98.83 172	99.89 67	99.83 63

GDP-MVS		99.08 148	98.89 164	99.64 101	99.53 219	99.34 127	99.64 96	99.48 204	98.32 147	99.77 84	99.66 246	95.14 248	99.93 109	98.97 144	99.50 176	99.64 182

SDMVSNet		99.11 140	98.90 160	99.75 77	99.81 57	99.59 88	99.81 20	99.65 39	98.78 98	99.64 144	99.88 56	94.56 287	99.93 109	99.67 37	98.26 292	99.72 137

FE-MVS		98.48 224	98.17 239	99.40 181	99.54 218	98.96 187	99.68 73	98.81 427	95.54 415	99.62 151	99.70 216	93.82 322	99.93 109	97.35 345	99.46 178	99.32 278

SF-MVS		99.38 67	99.24 77	99.79 68	99.79 68	99.68 64	99.57 143	99.54 108	97.82 246	99.71 111	99.80 152	98.95 32	99.93 109	98.19 255	99.84 101	99.74 118

dcpmvs_2		99.23 97	99.58 9	98.16 364	99.83 47	94.68 444	99.76 38	99.52 133	99.07 58	99.98 13	99.88 56	98.56 80	99.93 109	99.67 37	99.98 4	99.87 40

Anonymous20240529		98.09 261	97.68 301	99.34 191	99.66 149	98.44 272	99.40 278	99.43 270	93.67 443	99.22 258	99.89 45	90.23 406	99.93 109	99.26 102	98.33 284	99.66 169

ACMMP_NAP		99.47 40	99.34 49	99.88 15	99.87 20	99.86 18	99.47 238	99.48 204	98.05 209	99.76 90	99.86 78	98.82 48	99.93 109	98.82 179	99.91 45	99.84 53

balanced_ft_v1		99.02 161	98.98 140	99.15 229	99.39 275	98.12 289	99.79 31	99.51 155	98.20 170	99.66 129	99.87 69	94.84 262	99.93 109	99.69 34	99.84 101	99.41 263

EI-MVSNet-UG-set		99.58 16	99.57 10	99.64 101	99.78 70	99.14 160	99.60 114	99.45 250	99.01 64	99.90 33	99.83 106	98.98 26	99.93 109	99.59 45	99.95 22	99.86 42

无先验							98.99 406	99.51 155	96.89 344				99.93 109	97.53 326		99.72 137

VDDNet		97.55 350	97.02 372	99.16 225	99.49 242	98.12 289	99.38 287	99.30 342	95.35 417	99.68 118	99.90 36	82.62 473	99.93 109	99.31 86	98.13 304	99.42 260

ab-mvs		98.86 184	98.63 203	99.54 125	99.64 164	99.19 150	99.44 253	99.54 108	97.77 250	99.30 236	99.81 134	94.20 304	99.93 109	99.17 114	98.82 256	99.49 239

F-COLMAP		99.19 100	99.04 113	99.64 101	99.78 70	99.27 143	99.42 266	99.54 108	97.29 307	99.41 205	99.59 274	98.42 91	99.93 109	98.19 255	99.69 153	99.73 127

BP-MVS1		99.12 134	98.94 152	99.65 95	99.51 228	99.30 138	99.67 76	98.92 408	98.48 126	99.84 55	99.69 227	94.96 252	99.92 123	99.62 44	99.79 132	99.71 148

Anonymous202405211		98.30 242	97.98 263	99.26 213	99.57 203	98.16 284	99.41 270	98.55 453	96.03 409	99.19 267	99.74 199	91.87 371	99.92 123	99.16 117	98.29 291	99.70 151

EI-MVSNet-Vis-set		99.58 16	99.56 12	99.64 101	99.78 70	99.15 159	99.61 113	99.45 250	99.01 64	99.89 39	99.82 119	99.01 20	99.92 123	99.56 49	99.95 22	99.85 46

VDD-MVS		97.73 329	97.35 346	98.88 271	99.47 250	97.12 339	99.34 303	98.85 422	98.19 171	99.67 124	99.85 85	82.98 471	99.92 123	99.49 61	98.32 288	99.60 195

VNet		99.11 140	98.90 160	99.73 83	99.52 225	99.56 94	99.41 270	99.39 285	99.01 64	99.74 94	99.78 176	95.56 228	99.92 123	99.52 55	98.18 300	99.72 137

XVG-OURS-SEG-HR		98.69 212	98.62 208	98.89 266	99.71 117	97.74 311	99.12 374	99.54 108	98.44 133	99.42 200	99.71 212	94.20 304	99.92 123	98.54 220	98.90 250	99.00 312

mvsmamba		99.06 153	98.96 146	99.36 188	99.47 250	98.64 247	99.70 59	99.05 392	97.61 270	99.65 139	99.83 106	96.54 176	99.92 123	99.19 108	99.62 165	99.51 234

HPM-MVS_fast		99.51 29	99.40 37	99.85 43	99.91 1	99.79 41	99.76 38	99.56 89	97.72 256	99.76 90	99.75 194	99.13 14	99.92 123	99.07 129	99.92 38	99.85 46

HY-MVS	97.30 7	98.85 193	98.64 202	99.47 166	99.42 262	99.08 168	99.62 107	99.36 303	97.39 299	99.28 240	99.68 235	96.44 182	99.92 123	98.37 239	98.22 295	99.40 266

DP-MVS		99.16 110	98.95 150	99.78 71	99.77 78	99.53 101	99.41 270	99.50 179	97.03 334	99.04 297	99.88 56	97.39 124	99.92 123	98.66 196	99.90 56	99.87 40

IB-MVS	95.67 18	96.22 396	95.44 410	98.57 313	99.21 325	96.70 373	98.65 450	97.74 472	96.71 355	97.27 434	98.54 439	86.03 453	99.92 123	98.47 226	86.30 470	99.10 295
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 33	99.39 39	99.77 74	99.63 168	99.59 88	99.36 295	99.46 239	99.07 58	99.79 75	99.82 119	98.85 44	99.92 123	98.68 194	99.87 78	99.82 72
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 97	99.10 98	99.61 109	99.35 285	99.31 135	99.46 242	99.13 380	98.61 113	99.86 52	99.89 45	96.41 185	99.91 135	99.67 37	99.51 174	99.63 187

balanced_conf03		99.46 42	99.39 39	99.67 90	99.55 211	99.58 93	99.74 48	99.51 155	98.42 134	99.87 48	99.84 100	98.05 110	99.91 135	99.58 47	99.94 30	99.52 225

9.14			99.10 98		99.72 111		99.40 278	99.51 155	97.53 281	99.64 144	99.78 176	98.84 46	99.91 135	97.63 314	99.82 117

SMA-MVS		99.44 50	99.30 61	99.85 43	99.73 107	99.83 22	99.56 151	99.47 226	97.45 290	99.78 80	99.82 119	99.18 12	99.91 135	98.79 180	99.89 67	99.81 79
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					99.67 137	99.65 75	99.05 390	99.41 275	96.22 394	98.95 313	99.49 312	98.77 56	99.91 135

train_agg		99.02 161	98.77 183	99.77 74	99.67 137	99.65 75	99.05 390	99.41 275	96.28 388	98.95 313	99.49 312	98.76 57	99.91 135	97.63 314	99.72 148	99.75 113

test_8					99.67 137	99.61 85	99.03 395	99.41 275	96.28 388	98.93 316	99.48 318	98.76 57	99.91 135

agg_prior					99.67 137	99.62 83		99.40 282		98.87 326			99.91 135

原ACMM1				99.65 95	99.73 107	99.33 130		99.47 226	97.46 287	99.12 278	99.66 246	98.67 72	99.91 135	97.70 311	99.69 153	99.71 148

LFMVS		97.90 296	97.35 346	99.54 125	99.52 225	99.01 177	99.39 282	98.24 461	97.10 326	99.65 139	99.79 169	84.79 463	99.91 135	99.28 96	98.38 281	99.69 154

XVG-OURS		98.73 210	98.68 194	98.88 271	99.70 122	97.73 312	98.92 420	99.55 99	98.52 122	99.45 189	99.84 100	95.27 240	99.91 135	98.08 270	98.84 254	99.00 312

PLC	97.94 4	99.02 161	98.85 174	99.53 133	99.66 149	99.01 177	99.24 346	99.52 133	96.85 346	99.27 246	99.48 318	98.25 100	99.91 135	97.76 301	99.62 165	99.65 175
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PCF-MVS	97.08 14	97.66 343	97.06 371	99.47 166	99.61 188	99.09 165	98.04 481	99.25 360	91.24 469	98.51 377	99.70 216	94.55 289	99.91 135	92.76 459	99.85 93	99.42 260
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Elysia		98.88 178	98.65 200	99.58 116	99.58 198	99.34 127	99.65 89	99.52 133	98.26 155	99.83 63	99.87 69	93.37 330	99.90 148	97.81 294	99.91 45	99.49 239

StellarMVS		98.88 178	98.65 200	99.58 116	99.58 198	99.34 127	99.65 89	99.52 133	98.26 155	99.83 63	99.87 69	93.37 330	99.90 148	97.81 294	99.91 45	99.49 239

AstraMVS		99.09 146	99.03 116	99.25 214	99.66 149	98.13 287	99.57 143	98.24 461	98.82 89	99.91 30	99.88 56	95.81 217	99.90 148	99.72 32	99.67 158	99.74 118

mmtdpeth		96.95 382	96.71 381	97.67 412	99.33 291	94.90 439	99.89 2	99.28 348	98.15 176	99.72 101	98.57 438	86.56 449	99.90 148	99.82 29	89.02 465	98.20 444

UWE-MVS		97.58 349	97.29 357	98.48 326	99.09 357	96.25 393	99.01 403	96.61 487	97.86 234	99.19 267	99.01 409	88.72 422	99.90 148	97.38 343	98.69 263	99.28 281

test_vis1_rt		95.81 406	95.65 405	96.32 447	99.67 137	91.35 475	99.49 220	96.74 485	98.25 160	95.24 458	98.10 458	74.96 483	99.90 148	99.53 53	98.85 253	97.70 468

FA-MVS(test-final)		98.75 207	98.53 219	99.41 180	99.55 211	99.05 173	99.80 25	99.01 397	96.59 370	99.58 163	99.59 274	95.39 234	99.90 148	97.78 297	99.49 177	99.28 281

MCST-MVS		99.43 53	99.30 61	99.82 57	99.79 68	99.74 54	99.29 319	99.40 282	98.79 95	99.52 178	99.62 265	98.91 39	99.90 148	98.64 198	99.75 142	99.82 72

CDPH-MVS		99.13 126	98.91 158	99.80 64	99.75 92	99.71 58	99.15 367	99.41 275	96.60 368	99.60 159	99.55 289	98.83 47	99.90 148	97.48 332	99.83 113	99.78 98

NCCC		99.34 75	99.19 87	99.79 68	99.61 188	99.65 75	99.30 314	99.48 204	98.86 84	99.21 261	99.63 260	98.72 67	99.90 148	98.25 251	99.63 164	99.80 88

114514_t		98.93 174	98.67 195	99.72 86	99.85 31	99.53 101	99.62 107	99.59 72	92.65 458	99.71 111	99.78 176	98.06 109	99.90 148	98.84 169	99.91 45	99.74 118

1112_ss		98.98 170	98.77 183	99.59 113	99.68 134	99.02 175	99.25 341	99.48 204	97.23 313	99.13 276	99.58 278	96.93 152	99.90 148	98.87 159	98.78 259	99.84 53

PHI-MVS		99.30 82	99.17 90	99.70 87	99.56 207	99.52 105	99.58 135	99.80 11	97.12 322	99.62 151	99.73 205	98.58 78	99.90 148	98.61 204	99.91 45	99.68 160

AdaColmap		99.01 166	98.80 179	99.66 91	99.56 207	99.54 98	99.18 362	99.70 18	98.18 174	99.35 225	99.63 260	96.32 187	99.90 148	97.48 332	99.77 137	99.55 217

COLMAP_ROB	97.56 6	98.86 184	98.75 185	99.17 224	99.88 13	98.53 258	99.34 303	99.59 72	97.55 277	98.70 354	99.89 45	95.83 215	99.90 148	98.10 265	99.90 56	99.08 300
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
thisisatest0530		98.35 238	98.03 258	99.31 199	99.63 168	98.56 255	99.54 171	96.75 484	97.53 281	99.73 96	99.65 248	91.25 391	99.89 163	98.62 201	99.56 170	99.48 242

tttt0517		98.42 229	98.14 243	99.28 211	99.66 149	98.38 276	99.74 48	96.85 482	97.68 262	99.79 75	99.74 199	91.39 387	99.89 163	98.83 172	99.56 170	99.57 213

test12				99.75 77	99.64 164	99.61 85		99.29 346		99.21 261		98.38 94	99.89 163		99.74 145	99.74 118

Test_1112_low_res		98.89 177	98.66 198	99.57 120	99.69 127	98.95 193	99.03 395	99.47 226	96.98 336	99.15 274	99.23 383	96.77 163	99.89 163	98.83 172	98.78 259	99.86 42

CNLPA		99.14 122	98.99 137	99.59 113	99.58 198	99.41 120	99.16 364	99.44 259	98.45 130	99.19 267	99.49 312	98.08 108	99.89 163	97.73 305	99.75 142	99.48 242

diffmvs_AUTHOR		99.19 100	99.10 98	99.48 160	99.64 164	98.85 222	99.32 308	99.48 204	98.50 124	99.81 68	99.81 134	96.82 159	99.88 168	99.40 71	99.12 216	99.71 148

guyue		99.16 110	99.04 113	99.52 139	99.69 127	98.92 203	99.59 125	98.81 427	98.73 102	99.90 33	99.87 69	95.34 237	99.88 168	99.66 40	99.81 120	99.74 118

sd_testset		98.75 207	98.57 215	99.29 207	99.81 57	98.26 280	99.56 151	99.62 51	98.78 98	99.64 144	99.88 56	92.02 368	99.88 168	99.54 51	98.26 292	99.72 137

APD_test1		95.87 404	96.49 386	94.00 456	99.53 219	84.01 485	99.54 171	99.32 333	95.91 411	97.99 413	99.85 85	85.49 458	99.88 168	91.96 462	98.84 254	98.12 448

diffmvs		99.14 122	99.02 126	99.51 144	99.61 188	98.96 187	99.28 325	99.49 192	98.46 128	99.72 101	99.71 212	96.50 178	99.88 168	99.31 86	99.11 218	99.67 164
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		98.86 184	98.80 179	99.03 239	99.76 82	98.79 233	99.28 325	99.91 3	97.42 296	99.67 124	99.37 350	97.53 121	99.88 168	98.98 139	97.29 352	98.42 429

PVSNet_Blended		99.08 148	98.97 142	99.42 179	99.76 82	98.79 233	98.78 437	99.91 3	96.74 353	99.67 124	99.49 312	97.53 121	99.88 168	98.98 139	99.85 93	99.60 195

0.4-1-1-0.1		95.23 422	94.22 430	98.26 358	97.39 457	95.86 408	97.59 486	97.62 473	93.85 441	94.97 465	97.03 478	87.20 442	99.87 175	98.47 226	83.84 473	99.05 307

viewdifsd2359ckpt07		99.11 140	99.00 136	99.43 177	99.63 168	98.73 238	99.45 246	99.54 108	98.33 145	99.62 151	99.81 134	96.17 197	99.87 175	99.27 99	99.14 208	99.69 154

viewdifsd2359ckpt11		98.78 202	98.74 187	98.89 266	99.67 137	97.04 349	99.50 203	99.58 77	98.26 155	99.56 167	99.90 36	94.36 297	99.87 175	99.49 61	98.32 288	99.77 100

viewmsd2359difaftdt		98.78 202	98.74 187	98.90 262	99.67 137	97.04 349	99.50 203	99.58 77	98.26 155	99.56 167	99.90 36	94.36 297	99.87 175	99.49 61	98.32 288	99.77 100

MVS		97.28 371	96.55 384	99.48 160	98.78 407	98.95 193	99.27 330	99.39 285	83.53 487	98.08 408	99.54 294	96.97 150	99.87 175	94.23 439	99.16 204	99.63 187

MG-MVS		99.13 126	99.02 126	99.45 169	99.57 203	98.63 248	99.07 384	99.34 315	98.99 69	99.61 156	99.82 119	97.98 112	99.87 175	97.00 369	99.80 125	99.85 46

MSDG		98.98 170	98.80 179	99.53 133	99.76 82	99.19 150	98.75 440	99.55 99	97.25 310	99.47 186	99.77 185	97.82 115	99.87 175	96.93 376	99.90 56	99.54 219

0.3-1-1-0.015		94.79 430	93.69 441	98.10 370	96.99 468	95.46 422	97.02 488	97.61 475	93.53 445	94.03 471	96.54 482	85.60 457	99.86 182	98.43 233	83.45 477	98.99 315

0.4-1-1-0.2		94.94 429	93.92 436	97.99 379	96.84 469	95.13 434	96.64 490	97.62 473	93.45 449	94.92 466	96.56 481	87.14 444	99.86 182	98.43 233	83.69 476	98.98 316

ETV-MVS		99.26 91	99.21 83	99.40 181	99.46 252	99.30 138	99.56 151	99.52 133	98.52 122	99.44 194	99.27 378	98.41 92	99.86 182	99.10 125	99.59 168	99.04 308

thisisatest0515		98.14 256	97.79 284	99.19 222	99.50 240	98.50 266	98.61 452	96.82 483	96.95 340	99.54 174	99.43 330	91.66 380	99.86 182	98.08 270	99.51 174	99.22 289

thres600view7		97.86 302	97.51 320	98.92 256	99.72 111	97.95 302	99.59 125	98.74 437	97.94 226	99.27 246	98.62 435	91.75 374	99.86 182	93.73 445	98.19 299	98.96 320

lupinMVS		99.13 126	99.01 133	99.46 168	99.51 228	98.94 197	99.05 390	99.16 376	97.86 234	99.80 73	99.56 286	97.39 124	99.86 182	98.94 147	99.85 93	99.58 210

PVSNet	96.02 17	98.85 193	98.84 176	98.89 266	99.73 107	97.28 330	98.32 473	99.60 67	97.86 234	99.50 181	99.57 283	96.75 164	99.86 182	98.56 216	99.70 152	99.54 219

MAR-MVS		98.86 184	98.63 203	99.54 125	99.37 281	99.66 71	99.45 246	99.54 108	96.61 365	99.01 300	99.40 340	97.09 142	99.86 182	97.68 313	99.53 173	99.10 295
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
mamba_0408		99.08 148	98.96 146	99.44 174	99.62 177	98.88 214	99.25 341	99.47 226	98.05 209	99.37 216	99.81 134	96.85 154	99.85 190	98.98 139	99.25 197	99.60 195

SSM_0404		99.16 110	99.06 109	99.44 174	99.65 159	98.96 187	99.49 220	99.50 179	98.14 180	99.62 151	99.85 85	96.85 154	99.85 190	99.19 108	99.26 196	99.52 225

testing91		97.44 363	97.02 372	98.71 300	99.18 333	96.89 367	99.19 360	99.04 393	97.78 249	98.31 394	98.29 449	85.41 459	99.85 190	98.01 276	97.95 309	99.39 267

test2506		96.81 386	96.65 382	97.29 427	99.74 100	92.21 472	99.60 114	85.06 504	99.13 41	99.77 84	99.93 10	87.82 439	99.85 190	99.38 74	99.38 183	99.80 88

AllTest		98.87 181	98.72 189	99.31 199	99.86 25	98.48 269	99.56 151	99.61 60	97.85 237	99.36 222	99.85 85	95.95 207	99.85 190	96.66 389	99.83 113	99.59 206

TestCases				99.31 199	99.86 25	98.48 269		99.61 60	97.85 237	99.36 222	99.85 85	95.95 207	99.85 190	96.66 389	99.83 113	99.59 206

jason		99.13 126	99.03 116	99.45 169	99.46 252	98.87 218	99.12 374	99.26 357	98.03 218	99.79 75	99.65 248	97.02 147	99.85 190	99.02 136	99.90 56	99.65 175
jason: jason.
CNVR-MVS		99.42 55	99.30 61	99.78 71	99.62 177	99.71 58	99.26 339	99.52 133	98.82 89	99.39 212	99.71 212	98.96 27	99.85 190	98.59 209	99.80 125	99.77 100

PAPM_NR		99.04 158	98.84 176	99.66 91	99.74 100	99.44 116	99.39 282	99.38 293	97.70 260	99.28 240	99.28 375	98.34 96	99.85 190	96.96 373	99.45 179	99.69 154

E5new		99.14 122	99.02 126	99.50 149	99.69 127	98.91 204	99.60 114	99.53 124	98.13 183	99.72 101	99.91 26	96.26 194	99.84 199	99.30 89	99.10 225	99.76 107

E6new		99.15 114	99.03 116	99.50 149	99.66 149	98.90 209	99.60 114	99.53 124	98.13 183	99.72 101	99.91 26	96.31 189	99.84 199	99.30 89	99.10 225	99.76 107

E6		99.15 114	99.03 116	99.50 149	99.66 149	98.90 209	99.60 114	99.53 124	98.13 183	99.72 101	99.91 26	96.31 189	99.84 199	99.30 89	99.10 225	99.76 107

E5		99.14 122	99.02 126	99.50 149	99.69 127	98.91 204	99.60 114	99.53 124	98.13 183	99.72 101	99.91 26	96.26 194	99.84 199	99.30 89	99.10 225	99.76 107

E4		99.13 126	99.01 133	99.49 156	99.68 134	98.90 209	99.52 182	99.52 133	98.13 183	99.71 111	99.90 36	96.32 187	99.84 199	99.21 106	99.11 218	99.75 113

E3new		99.18 103	99.08 104	99.48 160	99.63 168	98.94 197	99.46 242	99.50 179	98.06 206	99.72 101	99.84 100	97.27 132	99.84 199	99.10 125	99.13 211	99.67 164

E2		99.15 114	99.03 116	99.49 156	99.65 159	98.93 202	99.49 220	99.52 133	98.14 180	99.72 101	99.88 56	96.57 175	99.84 199	99.17 114	99.13 211	99.72 137

E3		99.15 114	99.03 116	99.49 156	99.62 177	98.91 204	99.49 220	99.52 133	98.13 183	99.72 101	99.88 56	96.61 170	99.84 199	99.17 114	99.13 211	99.72 137

viewcassd2359sk11		99.18 103	99.08 104	99.49 156	99.65 159	98.95 193	99.48 228	99.51 155	98.10 196	99.72 101	99.87 69	97.13 138	99.84 199	99.13 119	99.14 208	99.69 154

testing99		97.36 366	96.94 375	98.63 306	99.18 333	96.70 373	99.30 314	98.93 405	97.71 257	98.23 399	98.26 451	84.92 462	99.84 199	98.04 275	97.85 316	99.35 273

testing222		97.16 376	96.50 385	99.16 225	99.16 343	98.47 271	99.27 330	98.66 449	97.71 257	98.23 399	98.15 454	82.28 476	99.84 199	97.36 344	97.66 322	99.18 291

test1111		98.04 273	98.11 247	97.83 400	99.74 100	93.82 456	99.58 135	95.40 491	99.12 46	99.65 139	99.93 10	90.73 399	99.84 199	99.43 69	99.38 183	99.82 72

ECVR-MVS		98.04 273	98.05 256	98.00 378	99.74 100	94.37 451	99.59 125	94.98 492	99.13 41	99.66 129	99.93 10	90.67 400	99.84 199	99.40 71	99.38 183	99.80 88

test_yl		98.86 184	98.63 203	99.54 125	99.49 242	99.18 152	99.50 203	99.07 389	98.22 166	99.61 156	99.51 306	95.37 235	99.84 199	98.60 207	98.33 284	99.59 206

DCV-MVSNet		98.86 184	98.63 203	99.54 125	99.49 242	99.18 152	99.50 203	99.07 389	98.22 166	99.61 156	99.51 306	95.37 235	99.84 199	98.60 207	98.33 284	99.59 206

Fast-Effi-MVS+		98.70 211	98.43 224	99.51 144	99.51 228	99.28 141	99.52 182	99.47 226	96.11 404	99.01 300	99.34 360	96.20 196	99.84 199	97.88 284	98.82 256	99.39 267

TSAR-MVS + GP.		99.36 72	99.36 45	99.36 188	99.67 137	98.61 252	99.07 384	99.33 323	99.00 67	99.82 67	99.81 134	99.06 18	99.84 199	99.09 127	99.42 181	99.65 175

tpmrst		98.33 239	98.48 222	97.90 388	99.16 343	94.78 440	99.31 312	99.11 382	97.27 308	99.45 189	99.59 274	95.33 238	99.84 199	98.48 223	98.61 266	99.09 299

Vis-MVSNet		99.12 134	98.97 142	99.56 122	99.78 70	99.10 164	99.68 73	99.66 32	98.49 125	99.86 52	99.87 69	94.77 270	99.84 199	99.19 108	99.41 182	99.74 118
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
PAPR		98.63 219	98.34 230	99.51 144	99.40 272	99.03 174	98.80 435	99.36 303	96.33 385	99.00 304	99.12 397	98.46 87	99.84 199	95.23 425	99.37 190	99.66 169

PatchMatch-RL		98.84 196	98.62 208	99.52 139	99.71 117	99.28 141	99.06 388	99.77 12	97.74 255	99.50 181	99.53 298	95.41 233	99.84 199	97.17 362	99.64 162	99.44 258

EPP-MVSNet		99.13 126	98.99 137	99.53 133	99.65 159	99.06 171	99.81 20	99.33 323	97.43 294	99.60 159	99.88 56	97.14 137	99.84 199	99.13 119	98.94 241	99.69 154

SSM_0407		99.13 126	99.03 116	99.43 177	99.62 177	98.88 214	99.51 192	99.50 179	98.14 180	99.37 216	99.85 85	96.85 154	99.83 221	99.19 108	99.25 197	99.60 195

testing3-2		97.84 307	97.70 299	98.24 359	99.53 219	95.37 427	99.55 166	98.67 448	98.46 128	99.27 246	99.34 360	86.58 448	99.83 221	99.32 84	98.63 265	99.52 225

testing11		97.50 356	97.10 369	98.71 300	99.20 327	96.91 365	99.29 319	98.82 425	97.89 231	98.21 402	98.40 444	85.63 456	99.83 221	98.45 229	98.04 307	99.37 271

thres100view900		97.76 321	97.45 329	98.69 302	99.72 111	97.86 308	99.59 125	98.74 437	97.93 227	99.26 251	98.62 435	91.75 374	99.83 221	93.22 451	98.18 300	98.37 435

tfpn200view9		97.72 331	97.38 342	98.72 297	99.69 127	97.96 299	99.50 203	98.73 443	97.83 241	99.17 272	98.45 442	91.67 378	99.83 221	93.22 451	98.18 300	98.37 435

test_prior				99.68 89	99.67 137	99.48 111		99.56 89					99.83 221			99.74 118

1314		98.68 213	98.54 218	99.11 232	98.89 390	98.65 245	99.27 330	99.49 192	96.89 344	97.99 413	99.56 286	97.72 119	99.83 221	97.74 304	99.27 194	98.84 326

thres400		97.77 320	97.38 342	98.92 256	99.69 127	97.96 299	99.50 203	98.73 443	97.83 241	99.17 272	98.45 442	91.67 378	99.83 221	93.22 451	98.18 300	98.96 320

casdiffmvs		99.13 126	98.98 140	99.56 122	99.65 159	99.16 155	99.56 151	99.50 179	98.33 145	99.41 205	99.86 78	95.92 210	99.83 221	99.45 68	99.16 204	99.70 151
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 33	99.48 22	99.54 125	99.78 70	99.30 138	99.89 2	99.58 77	98.56 118	99.73 96	99.69 227	98.55 81	99.82 230	99.69 34	99.85 93	99.48 242

MVS_Test		99.10 145	98.97 142	99.48 160	99.49 242	99.14 160	99.67 76	99.34 315	97.31 305	99.58 163	99.76 189	97.65 120	99.82 230	98.87 159	99.07 232	99.46 253

dp		97.75 325	97.80 283	97.59 418	99.10 354	93.71 459	99.32 308	98.88 418	96.48 377	99.08 288	99.55 289	92.67 352	99.82 230	96.52 393	98.58 269	99.24 287

RPSCF		98.22 246	98.62 208	96.99 434	99.82 53	91.58 474	99.72 54	99.44 259	96.61 365	99.66 129	99.89 45	95.92 210	99.82 230	97.46 335	99.10 225	99.57 213

PMMVS		98.80 200	98.62 208	99.34 191	99.27 309	98.70 241	98.76 439	99.31 337	97.34 302	99.21 261	99.07 399	97.20 136	99.82 230	98.56 216	98.87 251	99.52 225

UBG		97.85 303	97.48 323	98.95 250	99.25 316	97.64 318	99.24 346	98.74 437	97.90 230	98.64 364	98.20 453	88.65 426	99.81 235	98.27 249	98.40 279	99.42 260

EIA-MVS		99.18 103	99.09 103	99.45 169	99.49 242	99.18 152	99.67 76	99.53 124	97.66 265	99.40 210	99.44 328	98.10 106	99.81 235	98.94 147	99.62 165	99.35 273

Effi-MVS+		98.81 197	98.59 214	99.48 160	99.46 252	99.12 163	98.08 480	99.50 179	97.50 285	99.38 214	99.41 336	96.37 186	99.81 235	99.11 122	98.54 274	99.51 234

thres200		97.61 347	97.28 358	98.62 307	99.64 164	98.03 293	99.26 339	98.74 437	97.68 262	99.09 286	98.32 448	91.66 380	99.81 235	92.88 456	98.22 295	98.03 454

tpmvs		97.98 284	98.02 260	97.84 397	99.04 368	94.73 441	99.31 312	99.20 371	96.10 408	98.76 344	99.42 332	94.94 254	99.81 235	96.97 372	98.45 278	98.97 318

casdiffmvs_mvg		99.15 114	99.02 126	99.55 124	99.66 149	99.09 165	99.64 96	99.56 89	98.26 155	99.45 189	99.87 69	96.03 203	99.81 235	99.54 51	99.15 207	99.73 127
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	98.81 197	99.37 43	97.12 431	99.60 194	91.75 473	98.61 452	99.44 259	99.35 25	99.83 63	99.85 85	98.70 69	99.81 235	99.02 136	99.91 45	99.81 79

viewmacassd2359aftdt		99.08 148	98.94 152	99.50 149	99.66 149	98.96 187	99.51 192	99.54 108	98.27 152	99.42 200	99.89 45	95.88 214	99.80 242	99.20 107	99.11 218	99.76 107

viewmanbaseed2359cas		99.18 103	99.07 108	99.50 149	99.62 177	99.01 177	99.50 203	99.52 133	98.25 160	99.68 118	99.82 119	96.93 152	99.80 242	99.15 118	99.11 218	99.70 151

IMVS_0403		98.86 184	98.89 164	98.78 292	99.55 211	96.93 360	99.58 135	99.44 259	98.05 209	99.68 118	99.80 152	96.81 160	99.80 242	98.15 261	98.92 244	99.60 195

DPM-MVS		98.95 173	98.71 191	99.66 91	99.63 168	99.55 96	98.64 451	99.10 383	97.93 227	99.42 200	99.55 289	98.67 72	99.80 242	95.80 410	99.68 156	99.61 192

DP-MVS Recon		99.12 134	98.95 150	99.65 95	99.74 100	99.70 60	99.27 330	99.57 84	96.40 384	99.42 200	99.68 235	98.75 60	99.80 242	97.98 278	99.72 148	99.44 258

MVS_111021_LR		99.41 59	99.33 51	99.65 95	99.77 78	99.51 107	98.94 418	99.85 9	98.82 89	99.65 139	99.74 199	98.51 84	99.80 242	98.83 172	99.89 67	99.64 182

viewmambaseed2359dif		99.01 166	98.90 160	99.32 197	99.58 198	98.51 264	99.33 305	99.54 108	97.85 237	99.44 194	99.85 85	96.01 204	99.79 248	99.41 70	99.13 211	99.67 164

CS-MVS		99.50 31	99.48 22	99.54 125	99.76 82	99.42 118	99.90 1	99.55 99	98.56 118	99.78 80	99.70 216	98.65 74	99.79 248	99.65 41	99.78 134	99.41 263

Fast-Effi-MVS+-dtu		98.77 206	98.83 178	98.60 308	99.41 267	96.99 355	99.52 182	99.49 192	98.11 193	99.24 253	99.34 360	96.96 151	99.79 248	97.95 280	99.45 179	99.02 311

baseline1		98.31 240	97.95 267	99.38 187	99.50 240	98.74 237	99.59 125	98.93 405	98.41 135	99.14 275	99.60 272	94.59 285	99.79 248	98.48 223	93.29 437	99.61 192

baseline		99.15 114	99.02 126	99.53 133	99.66 149	99.14 160	99.72 54	99.48 204	98.35 142	99.42 200	99.84 100	96.07 200	99.79 248	99.51 56	99.14 208	99.67 164

PVSNet_0	94.43 19	96.09 401	95.47 408	97.94 384	99.31 299	94.34 453	97.81 482	99.70 18	97.12 322	97.46 428	98.75 432	89.71 412	99.79 248	97.69 312	81.69 480	99.68 160

API-MVS		99.04 158	99.03 116	99.06 235	99.40 272	99.31 135	99.55 166	99.56 89	98.54 120	99.33 230	99.39 344	98.76 57	99.78 254	96.98 371	99.78 134	98.07 451

OMC-MVS		99.08 148	99.04 113	99.20 221	99.67 137	98.22 282	99.28 325	99.52 133	98.07 202	99.66 129	99.81 134	97.79 116	99.78 254	97.79 296	99.81 120	99.60 195

GeoE		98.85 193	98.62 208	99.53 133	99.61 188	99.08 168	99.80 25	99.51 155	97.10 326	99.31 232	99.78 176	95.23 245	99.77 256	98.21 253	99.03 235	99.75 113

alignmvs		98.81 197	98.56 217	99.58 116	99.43 260	99.42 118	99.51 192	98.96 403	98.61 113	99.35 225	98.92 422	94.78 267	99.77 256	99.35 76	98.11 305	99.54 219

tpm cat1		97.39 365	97.36 344	97.50 421	99.17 341	93.73 458	99.43 259	99.31 337	91.27 468	98.71 348	99.08 398	94.31 302	99.77 256	96.41 398	98.50 276	99.00 312

CostFormer		97.72 331	97.73 296	97.71 410	99.15 347	94.02 455	99.54 171	99.02 396	94.67 433	99.04 297	99.35 356	92.35 364	99.77 256	98.50 222	97.94 310	99.34 276

MGCFI-Net		99.01 166	98.85 174	99.50 149	99.42 262	99.26 144	99.82 16	99.48 204	98.60 115	99.28 240	98.81 427	97.04 146	99.76 260	99.29 95	97.87 314	99.47 248

test_241102_ONE					99.84 38	99.90 3		99.48 204	99.07 58	99.91 30	99.74 199	99.20 9	99.76 260

MDTV_nov1_ep13			98.32 232		99.11 351	94.44 449	99.27 330	98.74 437	97.51 284	99.40 210	99.62 265	94.78 267	99.76 260	97.59 317	98.81 258

viewdifsd2359ckpt09		99.01 166	98.87 168	99.40 181	99.62 177	98.79 233	99.44 253	99.51 155	97.76 251	99.35 225	99.69 227	96.42 184	99.75 263	98.97 144	99.11 218	99.66 169

sasdasda		99.02 161	98.86 171	99.51 144	99.42 262	99.32 131	99.80 25	99.48 204	98.63 110	99.31 232	98.81 427	97.09 142	99.75 263	99.27 99	97.90 311	99.47 248

canonicalmvs		99.02 161	98.86 171	99.51 144	99.42 262	99.32 131	99.80 25	99.48 204	98.63 110	99.31 232	98.81 427	97.09 142	99.75 263	99.27 99	97.90 311	99.47 248

Effi-MVS+-dtu		98.78 202	98.89 164	98.47 331	99.33 291	96.91 365	99.57 143	99.30 342	98.47 127	99.41 205	98.99 412	96.78 162	99.74 266	98.73 186	99.38 183	98.74 342

patchmatchnet-post											98.70 433	94.79 266	99.74 266

SCA		98.19 250	98.16 240	98.27 357	99.30 300	95.55 417	99.07 384	98.97 401	97.57 274	99.43 197	99.57 283	92.72 347	99.74 266	97.58 318	99.20 202	99.52 225

BH-untuned		98.42 229	98.36 228	98.59 309	99.49 242	96.70 373	99.27 330	99.13 380	97.24 312	98.80 339	99.38 347	95.75 221	99.74 266	97.07 367	99.16 204	99.33 277

BH-RMVSNet		98.41 231	98.08 252	99.40 181	99.41 267	98.83 227	99.30 314	98.77 433	97.70 260	98.94 315	99.65 248	92.91 342	99.74 266	96.52 393	99.55 172	99.64 182

MVS_111021_HR		99.41 59	99.32 53	99.66 91	99.72 111	99.47 113	98.95 416	99.85 9	98.82 89	99.54 174	99.73 205	98.51 84	99.74 266	98.91 153	99.88 75	99.77 100

test_post											65.99 498	94.65 283	99.73 272

XVG-ACMP-BASELINE		97.83 310	97.71 298	98.20 361	99.11 351	96.33 389	99.41 270	99.52 133	98.06 206	99.05 296	99.50 309	89.64 414	99.73 272	97.73 305	97.38 349	98.53 415

HyFIR lowres test		99.11 140	98.92 155	99.65 95	99.90 4	99.37 123	99.02 398	99.91 3	97.67 264	99.59 162	99.75 194	95.90 212	99.73 272	99.53 53	99.02 237	99.86 42

DeepMVS_CX				93.34 459	99.29 304	82.27 488		99.22 366	85.15 485	96.33 450	99.05 402	90.97 397	99.73 272	93.57 447	97.77 319	98.01 455

Patchmatch-test		97.93 290	97.65 304	98.77 293	99.18 333	97.07 344	99.03 395	99.14 379	96.16 399	98.74 345	99.57 283	94.56 287	99.72 276	93.36 449	99.11 218	99.52 225

LPG-MVS_test		98.22 246	98.13 245	98.49 324	99.33 291	97.05 346	99.58 135	99.55 99	97.46 287	99.24 253	99.83 106	92.58 354	99.72 276	98.09 266	97.51 335	98.68 360

LGP-MVS_train				98.49 324	99.33 291	97.05 346		99.55 99	97.46 287	99.24 253	99.83 106	92.58 354	99.72 276	98.09 266	97.51 335	98.68 360

BH-w/o		98.00 282	97.89 276	98.32 349	99.35 285	96.20 395	99.01 403	98.90 415	96.42 382	98.38 387	99.00 410	95.26 242	99.72 276	96.06 403	98.61 266	99.03 309

ACMP	97.20 11	98.06 267	97.94 269	98.45 334	99.37 281	97.01 353	99.44 253	99.49 192	97.54 280	98.45 383	99.79 169	91.95 370	99.72 276	97.91 282	97.49 340	98.62 390
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LTVRE_ROB	97.16 12	98.02 277	97.90 272	98.40 342	99.23 320	96.80 371	99.70 59	99.60 67	97.12 322	98.18 404	99.70 216	91.73 376	99.72 276	98.39 236	97.45 342	98.68 360
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
viewdifsd2359ckpt13		99.06 153	98.93 154	99.45 169	99.63 168	98.96 187	99.50 203	99.51 155	97.83 241	99.28 240	99.80 152	96.68 168	99.71 282	99.05 131	99.12 216	99.68 160

test_post1							99.23 349				65.14 499	94.18 307	99.71 282	97.58 318

ADS-MVSNet		98.20 249	98.08 252	98.56 317	99.33 291	96.48 384	99.23 349	99.15 377	96.24 392	99.10 283	99.67 241	94.11 309	99.71 282	96.81 381	99.05 233	99.48 242

JIA-IIPM		97.50 356	97.02 372	98.93 254	98.73 416	97.80 310	99.30 314	98.97 401	91.73 467	98.91 318	94.86 487	95.10 249	99.71 282	97.58 318	97.98 308	99.28 281

EPMVS		97.82 313	97.65 304	98.35 346	98.88 391	95.98 399	99.49 220	94.71 494	97.57 274	99.26 251	99.48 318	92.46 361	99.71 282	97.87 286	99.08 231	99.35 273

TDRefinement		95.42 416	94.57 425	97.97 381	89.83 497	96.11 398	99.48 228	98.75 434	96.74 353	96.68 447	99.88 56	88.65 426	99.71 282	98.37 239	82.74 478	98.09 450

ACMM	97.58 5	98.37 237	98.34 230	98.48 326	99.41 267	97.10 340	99.56 151	99.45 250	98.53 121	99.04 297	99.85 85	93.00 338	99.71 282	98.74 184	97.45 342	98.64 381
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tt0805		97.97 287	97.77 289	98.57 313	99.59 196	96.61 380	99.45 246	99.08 386	98.21 168	98.88 323	99.80 152	88.66 425	99.70 289	98.58 210	97.72 320	99.39 267

CHOSEN 280x420		99.12 134	99.13 94	99.08 233	99.66 149	97.89 305	98.43 467	99.71 16	98.88 83	99.62 151	99.76 189	96.63 169	99.70 289	99.46 67	99.99 1	99.66 169

EC-MVSNet		99.44 50	99.39 39	99.58 116	99.56 207	99.49 109	99.88 4	99.58 77	98.38 137	99.73 96	99.69 227	98.20 102	99.70 289	99.64 43	99.82 117	99.54 219

PatchmatchNet		98.31 240	98.36 228	98.19 362	99.16 343	95.32 428	99.27 330	98.92 408	97.37 300	99.37 216	99.58 278	94.90 259	99.70 289	97.43 340	99.21 201	99.54 219
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
ACMH	97.28 8	98.10 260	97.99 262	98.44 337	99.41 267	96.96 359	99.60 114	99.56 89	98.09 197	98.15 406	99.91 26	90.87 398	99.70 289	98.88 156	97.45 342	98.67 368
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ETVMVS		97.50 356	96.90 376	99.29 207	99.23 320	98.78 236	99.32 308	98.90 415	97.52 283	98.56 374	98.09 459	84.72 464	99.69 294	97.86 287	97.88 313	99.39 267

HQP_MVS		98.27 245	98.22 238	98.44 337	99.29 304	96.97 357	99.39 282	99.47 226	98.97 75	99.11 280	99.61 269	92.71 349	99.69 294	97.78 297	97.63 323	98.67 368

plane_prior5								99.47 226					99.69 294	97.78 297	97.63 323	98.67 368

D2MVS		98.41 231	98.50 221	98.15 367	99.26 312	96.62 379	99.40 278	99.61 60	97.71 257	98.98 307	99.36 353	96.04 202	99.67 297	98.70 189	97.41 347	98.15 447

IS-MVSNet		99.05 157	98.87 168	99.57 120	99.73 107	99.32 131	99.75 43	99.20 371	98.02 221	99.56 167	99.86 78	96.54 176	99.67 297	98.09 266	99.13 211	99.73 127

CLD-MVS		98.16 254	98.10 248	98.33 347	99.29 304	96.82 370	98.75 440	99.44 259	97.83 241	99.13 276	99.55 289	92.92 340	99.67 297	98.32 246	97.69 321	98.48 421
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
test_fmvs2		97.25 373	97.30 355	97.09 432	99.43 260	93.31 465	99.73 52	98.87 420	98.83 88	99.28 240	99.80 152	84.45 465	99.66 300	97.88 284	97.45 342	98.30 437

AUN-MVS		96.88 384	96.31 390	98.59 309	99.48 249	97.04 349	99.27 330	99.22 366	97.44 293	98.51 377	99.41 336	91.97 369	99.66 300	97.71 308	83.83 474	99.07 305

UniMVSNet_ETH3D		97.32 370	96.81 378	98.87 275	99.40 272	97.46 324	99.51 192	99.53 124	95.86 412	98.54 376	99.77 185	82.44 474	99.66 300	98.68 194	97.52 334	99.50 238

OPM-MVS		98.19 250	98.10 248	98.45 334	98.88 391	97.07 344	99.28 325	99.38 293	98.57 117	99.22 258	99.81 134	92.12 366	99.66 300	98.08 270	97.54 332	98.61 399
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
ACMH+	97.24 10	97.92 293	97.78 287	98.32 349	99.46 252	96.68 377	99.56 151	99.54 108	98.41 135	97.79 424	99.87 69	90.18 408	99.66 300	98.05 274	97.18 357	98.62 390

IMVS_0407		98.86 184	98.91 158	98.72 297	99.55 211	96.93 360	99.50 203	99.44 259	98.05 209	99.66 129	99.80 152	97.13 138	99.65 305	98.15 261	98.92 244	99.60 195

hse-mvs2		97.50 356	97.14 366	98.59 309	99.49 242	97.05 346	99.28 325	99.22 366	98.94 78	99.66 129	99.42 332	94.93 255	99.65 305	99.48 64	83.80 475	99.08 300

VPA-MVSNet		98.29 243	97.95 267	99.30 204	99.16 343	99.54 98	99.50 203	99.58 77	98.27 152	99.35 225	99.37 350	92.53 356	99.65 305	99.35 76	94.46 418	98.72 344

TR-MVS		97.76 321	97.41 340	98.82 284	99.06 363	97.87 306	98.87 426	98.56 452	96.63 364	98.68 356	99.22 384	92.49 357	99.65 305	95.40 421	97.79 318	98.95 322

reproduce_monomvs		97.89 297	97.87 277	97.96 383	99.51 228	95.45 423	99.60 114	99.25 360	99.17 36	98.85 333	99.49 312	89.29 417	99.64 309	99.35 76	96.31 375	98.78 330

gm-plane-assit					98.54 438	92.96 467			94.65 434		99.15 392		99.64 309	97.56 323

HQP4-MVS										98.66 357			99.64 309			98.64 381

HQP-MVS		98.02 277	97.90 272	98.37 345	99.19 330	96.83 368	98.98 409	99.39 285	98.24 162	98.66 357	99.40 340	92.47 358	99.64 309	97.19 359	97.58 328	98.64 381

PAPM		97.59 348	97.09 370	99.07 234	99.06 363	98.26 280	98.30 474	99.10 383	94.88 428	98.08 408	99.34 360	96.27 192	99.64 309	89.87 470	98.92 244	99.31 279

TAPA-MVS	97.07 15	97.74 327	97.34 349	98.94 252	99.70 122	97.53 321	99.25 341	99.51 155	91.90 466	99.30 236	99.63 260	98.78 53	99.64 309	88.09 477	99.87 78	99.65 175
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
XXY-MVS		98.38 235	98.09 251	99.24 217	99.26 312	99.32 131	99.56 151	99.55 99	97.45 290	98.71 348	99.83 106	93.23 333	99.63 315	98.88 156	96.32 374	98.76 336

ITE_SJBPF				98.08 371	99.29 304	96.37 387		98.92 408	98.34 143	98.83 334	99.75 194	91.09 395	99.62 316	95.82 408	97.40 348	98.25 441

LF4IMVS		97.52 353	97.46 328	97.70 411	98.98 379	95.55 417	99.29 319	98.82 425	98.07 202	98.66 357	99.64 254	89.97 409	99.61 317	97.01 368	96.68 364	97.94 462

tpm		97.67 342	97.55 313	98.03 373	99.02 370	95.01 436	99.43 259	98.54 454	96.44 380	99.12 278	99.34 360	91.83 373	99.60 318	97.75 303	96.46 370	99.48 242

tpm2		97.44 363	97.34 349	97.74 409	99.15 347	94.36 452	99.45 246	98.94 404	93.45 449	98.90 320	99.44 328	91.35 388	99.59 319	97.31 346	98.07 306	99.29 280

SSM_04072		99.06 153	98.96 146	99.35 190	99.62 177	98.88 214	99.25 341	99.47 226	98.05 209	99.37 216	99.81 134	96.85 154	99.58 320	98.98 139	99.25 197	99.60 195

SD_0403		97.55 350	97.53 317	97.62 414	99.61 188	93.64 462	99.72 54	99.44 259	98.03 218	98.62 369	99.39 344	96.06 201	99.57 321	87.88 479	99.01 238	99.66 169

baseline2		97.87 300	97.55 313	98.82 284	99.18 333	98.02 294	99.41 270	96.58 488	96.97 337	96.51 448	99.17 389	93.43 328	99.57 321	97.71 308	99.03 235	98.86 324

MS-PatchMatch		97.24 375	97.32 353	96.99 434	98.45 441	93.51 464	98.82 433	99.32 333	97.41 297	98.13 407	99.30 371	88.99 419	99.56 323	95.68 414	99.80 125	97.90 465

TinyColmap		97.12 378	96.89 377	97.83 400	99.07 361	95.52 420	98.57 455	98.74 437	97.58 273	97.81 423	99.79 169	88.16 433	99.56 323	95.10 426	97.21 355	98.39 433

USDC		97.34 368	97.20 363	97.75 407	99.07 361	95.20 430	98.51 462	99.04 393	97.99 222	98.31 394	99.86 78	89.02 418	99.55 325	95.67 415	97.36 350	98.49 420

MSLP-MVS++		99.46 42	99.47 24	99.44 174	99.60 194	99.16 155	99.41 270	99.71 16	98.98 72	99.45 189	99.78 176	99.19 11	99.54 326	99.28 96	99.84 101	99.63 187

UWE-MVS-28		97.36 366	97.24 362	97.75 407	98.84 400	94.44 449	99.24 346	97.58 477	97.98 223	99.00 304	99.00 410	91.35 388	99.53 327	93.75 444	98.39 280	99.27 285

TAMVS		99.12 134	99.08 104	99.24 217	99.46 252	98.55 256	99.51 192	99.46 239	98.09 197	99.45 189	99.82 119	98.34 96	99.51 328	98.70 189	98.93 242	99.67 164

EPNet_dtu		98.03 275	97.96 265	98.23 360	98.27 444	95.54 419	99.23 349	98.75 434	99.02 62	97.82 422	99.71 212	96.11 199	99.48 329	93.04 454	99.65 161	99.69 154
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
mvs5depth		96.66 388	96.22 392	97.97 381	97.00 467	96.28 391	98.66 449	99.03 395	96.61 365	96.93 445	99.79 169	87.20 442	99.47 330	96.65 391	94.13 425	98.16 446

EG-PatchMatch MVS		95.97 403	95.69 404	96.81 441	97.78 451	92.79 468	99.16 364	98.93 405	96.16 399	94.08 470	99.22 384	82.72 472	99.47 330	95.67 415	97.50 337	98.17 445

myMVS_eth3d28		97.69 336	97.34 349	98.73 295	99.27 309	97.52 322	99.33 305	98.78 432	98.03 218	98.82 336	98.49 440	86.64 447	99.46 332	98.44 230	98.24 294	99.23 288

MVP-Stereo		97.81 315	97.75 294	97.99 379	97.53 455	96.60 381	98.96 413	98.85 422	97.22 314	97.23 435	99.36 353	95.28 239	99.46 332	95.51 417	99.78 134	97.92 464
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
CVMVSNet		98.57 221	98.67 195	98.30 351	99.35 285	95.59 416	99.50 203	99.55 99	98.60 115	99.39 212	99.83 106	94.48 293	99.45 334	98.75 183	98.56 272	99.85 46

test-LLR		98.06 267	97.90 272	98.55 319	98.79 404	97.10 340	98.67 446	97.75 470	97.34 302	98.61 370	98.85 424	94.45 295	99.45 334	97.25 353	99.38 183	99.10 295

TESTMET0.1,1		97.55 350	97.27 361	98.40 342	98.93 384	96.53 382	98.67 446	97.61 475	96.96 338	98.64 364	99.28 375	88.63 428	99.45 334	97.30 349	99.38 183	99.21 290

test-mter		97.49 361	97.13 368	98.55 319	98.79 404	97.10 340	98.67 446	97.75 470	96.65 360	98.61 370	98.85 424	88.23 432	99.45 334	97.25 353	99.38 183	99.10 295

mvs_anonymous		99.03 160	98.99 137	99.16 225	99.38 278	98.52 262	99.51 192	99.38 293	97.79 247	99.38 214	99.81 134	97.30 130	99.45 334	99.35 76	98.99 239	99.51 234

tfpnnormal		97.84 307	97.47 326	98.98 245	99.20 327	99.22 149	99.64 96	99.61 60	96.32 386	98.27 398	99.70 216	93.35 332	99.44 339	95.69 413	95.40 401	98.27 439

v7n		97.87 300	97.52 318	98.92 256	98.76 414	98.58 254	99.84 12	99.46 239	96.20 395	98.91 318	99.70 216	94.89 260	99.44 339	96.03 404	93.89 430	98.75 338

jajsoiax		98.43 228	98.28 235	98.88 271	98.60 433	98.43 273	99.82 16	99.53 124	98.19 171	98.63 366	99.80 152	93.22 335	99.44 339	99.22 104	97.50 337	98.77 334

mvs_tets		98.40 234	98.23 237	98.91 260	98.67 426	98.51 264	99.66 83	99.53 124	98.19 171	98.65 363	99.81 134	92.75 344	99.44 339	99.31 86	97.48 341	98.77 334

sc_t1		95.75 407	95.05 414	97.87 390	98.83 401	94.61 446	99.21 355	99.45 250	87.45 479	97.97 415	99.85 85	81.19 479	99.43 343	98.27 249	93.20 439	99.57 213

Vis-MVSNet (Re-imp)		98.87 181	98.72 189	99.31 199	99.71 117	98.88 214	99.80 25	99.44 259	97.91 229	99.36 222	99.78 176	95.49 231	99.43 343	97.91 282	99.11 218	99.62 190

OPU-MVS				99.64 101	99.56 207	99.72 56	99.60 114				99.70 216	99.27 7	99.42 345	98.24 252	99.80 125	99.79 92

Anonymous20231211		97.88 298	97.54 316	98.90 262	99.71 117	98.53 258	99.48 228	99.57 84	94.16 438	98.81 337	99.68 235	93.23 333	99.42 345	98.84 169	94.42 420	98.76 336

ttmdpeth		97.80 317	97.63 308	98.29 352	98.77 412	97.38 327	99.64 96	99.36 303	98.78 98	96.30 451	99.58 278	92.34 365	99.39 347	98.36 241	95.58 396	98.10 449

VPNet		97.84 307	97.44 334	99.01 241	99.21 325	98.94 197	99.48 228	99.57 84	98.38 137	99.28 240	99.73 205	88.89 420	99.39 347	99.19 108	93.27 438	98.71 346

nrg030		98.64 218	98.42 225	99.28 211	99.05 366	99.69 63	99.81 20	99.46 239	98.04 216	99.01 300	99.82 119	96.69 166	99.38 349	99.34 81	94.59 417	98.78 330

GA-MVS		97.85 303	97.47 326	99.00 243	99.38 278	97.99 296	98.57 455	99.15 377	97.04 333	98.90 320	99.30 371	89.83 411	99.38 349	96.70 386	98.33 284	99.62 190

UniMVSNet (Re)		98.29 243	98.00 261	99.13 231	99.00 373	99.36 126	99.49 220	99.51 155	97.95 225	98.97 309	99.13 394	96.30 191	99.38 349	98.36 241	93.34 436	98.66 377

FIs		98.78 202	98.63 203	99.23 219	99.18 333	99.54 98	99.83 15	99.59 72	98.28 150	98.79 341	99.81 134	96.75 164	99.37 352	99.08 128	96.38 372	98.78 330

PS-MVSNAJss		98.92 175	98.92 155	98.90 262	98.78 407	98.53 258	99.78 33	99.54 108	98.07 202	99.00 304	99.76 189	99.01 20	99.37 352	99.13 119	97.23 354	98.81 327

CDS-MVSNet		99.09 146	99.03 116	99.25 214	99.42 262	98.73 238	99.45 246	99.46 239	98.11 193	99.46 188	99.77 185	98.01 111	99.37 352	98.70 189	98.92 244	99.66 169
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MVS-HIRNet		95.75 407	95.16 412	97.51 420	99.30 300	93.69 460	98.88 424	95.78 489	85.09 486	98.78 342	92.65 489	91.29 390	99.37 352	94.85 431	99.85 93	99.46 253

v1192		97.81 315	97.44 334	98.91 260	98.88 391	98.68 242	99.51 192	99.34 315	96.18 397	99.20 264	99.34 360	94.03 313	99.36 356	95.32 423	95.18 405	98.69 355

EI-MVSNet		98.67 214	98.67 195	98.68 303	99.35 285	97.97 297	99.50 203	99.38 293	96.93 343	99.20 264	99.83 106	97.87 113	99.36 356	98.38 237	97.56 330	98.71 346

MVSTER		98.49 223	98.32 232	99.00 243	99.35 285	99.02 175	99.54 171	99.38 293	97.41 297	99.20 264	99.73 205	93.86 321	99.36 356	98.87 159	97.56 330	98.62 390

gg-mvs-nofinetune		96.17 399	95.32 411	98.73 295	98.79 404	98.14 286	99.38 287	94.09 495	91.07 471	98.07 411	91.04 493	89.62 415	99.35 359	96.75 383	99.09 230	98.68 360

pm-mvs1		97.68 339	97.28 358	98.88 271	99.06 363	98.62 250	99.50 203	99.45 250	96.32 386	97.87 420	99.79 169	92.47 358	99.35 359	97.54 325	93.54 434	98.67 368

OurMVSNet-221017-0		97.88 298	97.77 289	98.19 362	98.71 420	96.53 382	99.88 4	99.00 398	97.79 247	98.78 342	99.94 6	91.68 377	99.35 359	97.21 355	96.99 361	98.69 355

EGC-MVSNET		82.80 457	77.86 463	97.62 414	97.91 448	96.12 397	99.33 305	99.28 348	8.40 501	25.05 502	99.27 378	84.11 466	99.33 362	89.20 472	98.22 295	97.42 475

pmmvs6		96.53 391	96.09 396	97.82 402	98.69 424	95.47 421	99.37 289	99.47 226	93.46 448	97.41 429	99.78 176	87.06 446	99.33 362	96.92 378	92.70 446	98.65 379

V42		98.06 267	97.79 284	98.86 278	98.98 379	98.84 224	99.69 63	99.34 315	96.53 372	99.30 236	99.37 350	94.67 280	99.32 364	97.57 322	94.66 415	98.42 429

lessismore_v0				97.79 404	98.69 424	95.44 425		94.75 493		95.71 457	99.87 69	88.69 424	99.32 364	95.89 407	94.93 412	98.62 390

OpenMVS_ROB	92.34 20	94.38 435	93.70 440	96.41 446	97.38 458	93.17 466	99.06 388	98.75 434	86.58 482	94.84 467	98.26 451	81.53 477	99.32 364	89.01 473	97.87 314	96.76 478

v8		97.95 289	97.63 308	98.93 254	98.95 383	98.81 232	99.80 25	99.41 275	96.03 409	99.10 283	99.42 332	94.92 257	99.30 367	96.94 375	94.08 427	98.66 377

v1921920		97.80 317	97.45 329	98.84 282	98.80 403	98.53 258	99.52 182	99.34 315	96.15 401	99.24 253	99.47 321	93.98 315	99.29 368	95.40 421	95.13 407	98.69 355

anonymousdsp		98.44 227	98.28 235	98.94 252	98.50 439	98.96 187	99.77 35	99.50 179	97.07 328	98.87 326	99.77 185	94.76 271	99.28 369	98.66 196	97.60 326	98.57 411

MVSFormer		99.17 108	99.12 96	99.29 207	99.51 228	98.94 197	99.88 4	99.46 239	97.55 277	99.80 73	99.65 248	97.39 124	99.28 369	99.03 134	99.85 93	99.65 175

test_djsdf		98.67 214	98.57 215	98.98 245	98.70 421	98.91 204	99.88 4	99.46 239	97.55 277	99.22 258	99.88 56	95.73 222	99.28 369	99.03 134	97.62 325	98.75 338

VortexMVS		98.67 214	98.66 198	98.68 303	99.62 177	97.96 299	99.59 125	99.41 275	98.13 183	99.31 232	99.70 216	95.48 232	99.27 372	99.40 71	97.32 351	98.79 328

SSC-MVS3.2		97.34 368	97.15 365	97.93 385	99.02 370	95.76 411	99.48 228	99.58 77	97.62 269	99.09 286	99.53 298	87.95 435	99.27 372	96.42 396	95.66 394	98.75 338

cascas		97.69 336	97.43 338	98.48 326	98.60 433	97.30 329	98.18 478	99.39 285	92.96 454	98.41 385	98.78 431	93.77 324	99.27 372	98.16 259	98.61 266	98.86 324

v144192		97.92 293	97.60 311	98.87 275	98.83 401	98.65 245	99.55 166	99.34 315	96.20 395	99.32 231	99.40 340	94.36 297	99.26 375	96.37 400	95.03 409	98.70 351

dmvs_re		98.08 265	98.16 240	97.85 394	99.55 211	94.67 445	99.70 59	98.92 408	98.15 176	99.06 294	99.35 356	93.67 327	99.25 376	97.77 300	97.25 353	99.64 182

v2v482		98.06 267	97.77 289	98.92 256	98.90 389	98.82 230	99.57 143	99.36 303	96.65 360	99.19 267	99.35 356	94.20 304	99.25 376	97.72 307	94.97 410	98.69 355

v1240		97.69 336	97.32 353	98.79 290	98.85 398	98.43 273	99.48 228	99.36 303	96.11 404	99.27 246	99.36 353	93.76 325	99.24 378	94.46 435	95.23 404	98.70 351

usedtu_dtu_shiyan1		98.09 261	97.82 281	98.89 266	98.70 421	98.90 209	98.57 455	99.47 226	96.78 350	98.87 326	99.05 402	94.75 272	99.23 379	97.45 337	96.74 362	98.53 415

FE-MVSNET3		98.09 261	97.82 281	98.89 266	98.70 421	98.90 209	98.57 455	99.47 226	96.78 350	98.87 326	99.05 402	94.75 272	99.23 379	97.45 337	96.74 362	98.53 415

WBMVS		97.74 327	97.50 321	98.46 332	99.24 318	97.43 325	99.21 355	99.42 272	97.45 290	98.96 311	99.41 336	88.83 421	99.23 379	98.94 147	96.02 380	98.71 346

v1144		97.98 284	97.69 300	98.85 281	98.87 394	98.66 244	99.54 171	99.35 310	96.27 390	99.23 257	99.35 356	94.67 280	99.23 379	96.73 384	95.16 406	98.68 360

v10		97.85 303	97.52 318	98.86 278	98.99 376	98.67 243	99.75 43	99.41 275	95.70 413	98.98 307	99.41 336	94.75 272	99.23 379	96.01 406	94.63 416	98.67 368

WR-MVS_H		98.13 257	97.87 277	98.90 262	99.02 370	98.84 224	99.70 59	99.59 72	97.27 308	98.40 386	99.19 388	95.53 229	99.23 379	98.34 243	93.78 432	98.61 399

miper_enhance_ethall		98.16 254	98.08 252	98.41 340	98.96 382	97.72 313	98.45 466	99.32 333	96.95 340	98.97 309	99.17 389	97.06 145	99.22 385	97.86 287	95.99 383	98.29 438

GG-mvs-BLEND				98.45 334	98.55 437	98.16 284	99.43 259	93.68 496		97.23 435	98.46 441	89.30 416	99.22 385	95.43 420	98.22 295	97.98 460

FC-MVSNet-test		98.75 207	98.62 208	99.15 229	99.08 360	99.45 115	99.86 11	99.60 67	98.23 165	98.70 354	99.82 119	96.80 161	99.22 385	99.07 129	96.38 372	98.79 328

UniMVSNet_NR-MVSNet		98.22 246	97.97 264	98.96 248	98.92 386	98.98 180	99.48 228	99.53 124	97.76 251	98.71 348	99.46 325	96.43 183	99.22 385	98.57 213	92.87 444	98.69 355

DU-MVS		98.08 265	97.79 284	98.96 248	98.87 394	98.98 180	99.41 270	99.45 250	97.87 233	98.71 348	99.50 309	94.82 263	99.22 385	98.57 213	92.87 444	98.68 360

cl____		98.01 280	97.84 280	98.55 319	99.25 316	97.97 297	98.71 444	99.34 315	96.47 379	98.59 373	99.54 294	95.65 225	99.21 390	97.21 355	95.77 389	98.46 426

WR-MVS		98.06 267	97.73 296	99.06 235	98.86 397	99.25 146	99.19 360	99.35 310	97.30 306	98.66 357	99.43 330	93.94 316	99.21 390	98.58 210	94.28 422	98.71 346

test_0402		96.64 389	96.24 391	97.85 394	98.85 398	96.43 386	99.44 253	99.26 357	93.52 446	96.98 443	99.52 302	88.52 429	99.20 392	92.58 461	97.50 337	97.93 463

icg_test_0407_2		98.79 201	98.86 171	98.57 313	99.55 211	96.93 360	99.07 384	99.44 259	98.05 209	99.66 129	99.80 152	97.13 138	99.18 393	98.15 261	98.92 244	99.60 195

SixPastTwentyTwo		97.50 356	97.33 352	98.03 373	98.65 427	96.23 394	99.77 35	98.68 446	97.14 319	97.90 418	99.93 10	90.45 401	99.18 393	97.00 369	96.43 371	98.67 368

cl22		97.85 303	97.64 307	98.48 326	99.09 357	97.87 306	98.60 454	99.33 323	97.11 325	98.87 326	99.22 384	92.38 363	99.17 395	98.21 253	95.99 383	98.42 429

tt0320		95.71 409	95.07 413	97.62 414	99.05 366	95.02 435	99.25 341	99.52 133	86.81 480	97.97 415	99.72 209	83.58 469	99.15 396	96.38 399	93.35 435	98.68 360

WB-MVSnew		97.65 344	97.65 304	97.63 413	98.78 407	97.62 319	99.13 371	98.33 458	97.36 301	99.07 289	98.94 418	95.64 226	99.15 396	92.95 455	98.68 264	96.12 485

IterMVS-SCA-FT		97.82 313	97.75 294	98.06 372	99.57 203	96.36 388	99.02 398	99.49 192	97.18 316	98.71 348	99.72 209	92.72 347	99.14 398	97.44 339	95.86 388	98.67 368

pmmvs5		97.52 353	97.30 355	98.16 364	98.57 436	96.73 372	99.27 330	98.90 415	96.14 402	98.37 388	99.53 298	91.54 383	99.14 398	97.51 329	95.87 387	98.63 388

v148		97.79 319	97.55 313	98.50 323	98.74 415	97.72 313	99.54 171	99.33 323	96.26 391	98.90 320	99.51 306	94.68 279	99.14 398	97.83 291	93.15 441	98.63 388

IMVS_0404		98.53 222	98.52 220	98.55 319	99.55 211	96.93 360	99.20 358	99.44 259	98.05 209	98.96 311	99.80 152	94.66 282	99.13 401	98.15 261	98.92 244	99.60 195

miper_ehance_all_eth		98.18 252	98.10 248	98.41 340	99.23 320	97.72 313	98.72 443	99.31 337	96.60 368	98.88 323	99.29 373	97.29 131	99.13 401	97.60 316	95.99 383	98.38 434

NR-MVSNet		97.97 287	97.61 310	99.02 240	98.87 394	99.26 144	99.47 238	99.42 272	97.63 267	97.08 441	99.50 309	95.07 250	99.13 401	97.86 287	93.59 433	98.68 360

IterMVS		97.83 310	97.77 289	98.02 375	99.58 198	96.27 392	99.02 398	99.48 204	97.22 314	98.71 348	99.70 216	92.75 344	99.13 401	97.46 335	96.00 382	98.67 368
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CMPMVS	69.68 23	94.13 436	94.90 416	91.84 464	97.24 462	80.01 494	98.52 461	99.48 204	89.01 476	91.99 481	99.67 241	85.67 455	99.13 401	95.44 419	97.03 360	96.39 482
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
eth_miper_zixun_eth		98.05 272	97.96 265	98.33 347	99.26 312	97.38 327	98.56 459	99.31 337	96.65 360	98.88 323	99.52 302	96.58 173	99.12 406	97.39 342	95.53 399	98.47 423

blended_shiyan8		95.56 410	94.79 417	97.87 390	96.60 471	95.90 405	98.85 427	99.27 355	92.19 460	98.47 381	97.94 463	91.43 385	99.11 407	97.26 352	81.09 483	98.60 402

pmmvs4		98.13 257	97.90 272	98.81 287	98.61 432	98.87 218	98.99 406	99.21 370	96.44 380	99.06 294	99.58 278	95.90 212	99.11 407	97.18 361	96.11 379	98.46 426

TransMVSNet (Re)		97.15 377	96.58 383	98.86 278	99.12 349	98.85 222	99.49 220	98.91 413	95.48 416	97.16 439	99.80 152	93.38 329	99.11 407	94.16 441	91.73 451	98.62 390

ambc				93.06 462	92.68 493	82.36 487	98.47 465	98.73 443		95.09 463	97.41 473	55.55 493	99.10 410	96.42 396	91.32 452	97.71 466

Baseline_NR-MVSNet		97.76 321	97.45 329	98.68 303	99.09 357	98.29 278	99.41 270	98.85 422	95.65 414	98.63 366	99.67 241	94.82 263	99.10 410	98.07 273	92.89 443	98.64 381

usedtu_blend_shiyan5		95.04 424	94.10 431	97.86 393	96.45 473	95.92 403	99.29 319	99.22 366	86.17 484	98.36 389	97.68 466	91.20 392	99.07 412	97.53 326	80.97 484	98.60 402

blend_shiyan4		95.25 421	94.39 428	97.84 397	96.70 470	95.92 403	98.84 430	99.28 348	92.21 459	98.16 405	97.84 464	87.10 445	99.07 412	97.53 326	81.87 479	98.54 413

test_vis3_rt		87.04 453	85.81 456	90.73 468	93.99 491	81.96 489	99.76 38	90.23 503	92.81 456	81.35 491	91.56 491	40.06 499	99.07 412	94.27 438	88.23 467	91.15 491

CP-MVSNet		98.09 261	97.78 287	99.01 241	98.97 381	99.24 147	99.67 76	99.46 239	97.25 310	98.48 380	99.64 254	93.79 323	99.06 415	98.63 200	94.10 426	98.74 342

PS-CasMVS		97.93 290	97.59 312	98.95 250	98.99 376	99.06 171	99.68 73	99.52 133	97.13 320	98.31 394	99.68 235	92.44 362	99.05 416	98.51 221	94.08 427	98.75 338

K. test v3		97.10 379	96.79 379	98.01 376	98.72 418	96.33 389	99.87 8	97.05 480	97.59 271	96.16 453	99.80 152	88.71 423	99.04 417	96.69 387	96.55 369	98.65 379

new_pmnet		96.38 395	96.03 397	97.41 423	98.13 447	95.16 433	99.05 390	99.20 371	93.94 439	97.39 432	98.79 430	91.61 382	99.04 417	90.43 468	95.77 389	98.05 453

wanda-best-256-512		95.43 414	94.66 420	97.77 405	96.45 473	95.68 412	98.48 463	99.28 348	92.18 461	98.36 389	97.68 466	91.20 392	99.03 419	97.31 346	80.97 484	98.60 402

FE-blended-shiyan7		95.43 414	94.66 420	97.77 405	96.45 473	95.68 412	98.48 463	99.28 348	92.18 461	98.36 389	97.68 466	91.20 392	99.03 419	97.31 346	80.97 484	98.60 402

DIV-MVS_self_test		98.01 280	97.85 279	98.48 326	99.24 318	97.95 302	98.71 444	99.35 310	96.50 373	98.60 372	99.54 294	95.72 223	99.03 419	97.21 355	95.77 389	98.46 426

IterMVS-LS		98.46 226	98.42 225	98.58 312	99.59 196	98.00 295	99.37 289	99.43 270	96.94 342	99.07 289	99.59 274	97.87 113	99.03 419	98.32 246	95.62 395	98.71 346
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
blended_shiyan6		95.54 411	94.78 418	97.84 397	96.60 471	95.89 406	98.85 427	99.28 348	92.17 463	98.43 384	97.95 462	91.44 384	99.02 423	97.30 349	80.97 484	98.60 402

our_test_3		97.65 344	97.68 301	97.55 419	98.62 430	94.97 437	98.84 430	99.30 342	96.83 349	98.19 403	99.34 360	97.01 149	99.02 423	95.00 429	96.01 381	98.64 381

Patchmtry		97.75 325	97.40 341	98.81 287	99.10 354	98.87 218	99.11 380	99.33 323	94.83 430	98.81 337	99.38 347	94.33 300	99.02 423	96.10 402	95.57 397	98.53 415

N_pmnet		94.95 428	95.83 402	92.31 463	98.47 440	79.33 495	99.12 374	92.81 501	93.87 440	97.68 425	99.13 394	93.87 320	99.01 426	91.38 465	96.19 377	98.59 408

gbinet_0.2-2-1-0.02		95.40 417	94.58 424	97.85 394	96.11 478	95.97 400	98.56 459	99.26 357	92.12 465	98.47 381	97.49 472	90.23 406	99.00 427	97.71 308	81.25 481	98.58 409

CR-MVSNet		98.17 253	97.93 270	98.87 275	99.18 333	98.49 267	99.22 353	99.33 323	96.96 338	99.56 167	99.38 347	94.33 300	99.00 427	94.83 432	98.58 269	99.14 292

c3_l		98.12 259	98.04 257	98.38 344	99.30 300	97.69 317	98.81 434	99.33 323	96.67 358	98.83 334	99.34 360	97.11 141	98.99 429	97.58 318	95.34 402	98.48 421

test0.0.03 1		97.71 334	97.42 339	98.56 317	98.41 443	97.82 309	98.78 437	98.63 450	97.34 302	98.05 412	98.98 414	94.45 295	98.98 430	95.04 428	97.15 358	98.89 323

PatchT		97.03 381	96.44 387	98.79 290	98.99 376	98.34 277	99.16 364	99.07 389	92.13 464	99.52 178	97.31 477	94.54 290	98.98 430	88.54 475	98.73 261	99.03 309

GBi-Net		97.68 339	97.48 323	98.29 352	99.51 228	97.26 333	99.43 259	99.48 204	96.49 374	99.07 289	99.32 368	90.26 403	98.98 430	97.10 363	96.65 365	98.62 390

test1		97.68 339	97.48 323	98.29 352	99.51 228	97.26 333	99.43 259	99.48 204	96.49 374	99.07 289	99.32 368	90.26 403	98.98 430	97.10 363	96.65 365	98.62 390

FMVSNet3		98.03 275	97.76 293	98.84 282	99.39 275	98.98 180	99.40 278	99.38 293	96.67 358	99.07 289	99.28 375	92.93 339	98.98 430	97.10 363	96.65 365	98.56 412

FMVSNet2		97.72 331	97.36 344	98.80 289	99.51 228	98.84 224	99.45 246	99.42 272	96.49 374	98.86 332	99.29 373	90.26 403	98.98 430	96.44 395	96.56 368	98.58 409

FMVSNet1		96.84 385	96.36 389	98.29 352	99.32 298	97.26 333	99.43 259	99.48 204	95.11 421	98.55 375	99.32 368	83.95 467	98.98 430	95.81 409	96.26 376	98.62 390

ppachtmachnet_test		97.49 361	97.45 329	97.61 417	98.62 430	95.24 429	98.80 435	99.46 239	96.11 404	98.22 401	99.62 265	96.45 181	98.97 437	93.77 443	95.97 386	98.61 399

TranMVSNet+NR-MVSNet		97.93 290	97.66 303	98.76 294	98.78 407	98.62 250	99.65 89	99.49 192	97.76 251	98.49 379	99.60 272	94.23 303	98.97 437	98.00 277	92.90 442	98.70 351

MVStest1		96.08 402	95.48 407	97.89 389	98.93 384	96.70 373	99.56 151	99.35 310	92.69 457	91.81 482	99.46 325	89.90 410	98.96 439	95.00 429	92.61 447	98.00 458

tt0320-xc		95.31 420	94.59 423	97.45 422	98.92 386	94.73 441	99.20 358	99.31 337	86.74 481	97.23 435	99.72 209	81.14 480	98.95 440	97.08 366	91.98 450	98.67 368

test_method		91.10 448	91.36 449	90.31 469	95.85 479	73.72 502	94.89 491	99.25 360	68.39 493	95.82 456	99.02 408	80.50 481	98.95 440	93.64 446	94.89 414	98.25 441

ADS-MVSNet2		98.02 277	98.07 255	97.87 390	99.33 291	95.19 431	99.23 349	99.08 386	96.24 392	99.10 283	99.67 241	94.11 309	98.93 442	96.81 381	99.05 233	99.48 242

ET-MVSNet_ETH3D		96.49 392	95.64 406	99.05 237	99.53 219	98.82 230	98.84 430	97.51 478	97.63 267	84.77 487	99.21 387	92.09 367	98.91 443	98.98 139	92.21 449	99.41 263

miper_lstm_enhance		98.00 282	97.91 271	98.28 356	99.34 290	97.43 325	98.88 424	99.36 303	96.48 377	98.80 339	99.55 289	95.98 205	98.91 443	97.27 351	95.50 400	98.51 419

MonoMVSNet		98.38 235	98.47 223	98.12 369	98.59 435	96.19 396	99.72 54	98.79 431	97.89 231	99.44 194	99.52 302	96.13 198	98.90 445	98.64 198	97.54 332	99.28 281

PEN-MVS		97.76 321	97.44 334	98.72 297	98.77 412	98.54 257	99.78 33	99.51 155	97.06 330	98.29 397	99.64 254	92.63 353	98.89 446	98.09 266	93.16 440	98.72 344

testing3		97.28 371	96.76 380	98.82 284	99.37 281	98.07 292	99.45 246	99.36 303	97.56 276	97.89 419	98.95 417	83.70 468	98.82 447	96.03 404	98.56 272	99.58 210

testgi		97.65 344	97.50 321	98.13 368	99.36 284	96.45 385	99.42 266	99.48 204	97.76 251	97.87 420	99.45 327	91.09 395	98.81 448	94.53 434	98.52 275	99.13 294

testf1		90.42 451	90.68 451	89.65 472	97.78 451	73.97 500	99.13 371	98.81 427	89.62 473	91.80 483	98.93 419	62.23 491	98.80 449	86.61 485	91.17 453	96.19 483

APD_test2		90.42 451	90.68 451	89.65 472	97.78 451	73.97 500	99.13 371	98.81 427	89.62 473	91.80 483	98.93 419	62.23 491	98.80 449	86.61 485	91.17 453	96.19 483

MIMVSNet		97.73 329	97.45 329	98.57 313	99.45 258	97.50 323	99.02 398	98.98 400	96.11 404	99.41 205	99.14 393	90.28 402	98.74 451	95.74 411	98.93 242	99.47 248

LCM-MVSNet-Re		97.83 310	98.15 242	96.87 440	99.30 300	92.25 471	99.59 125	98.26 459	97.43 294	96.20 452	99.13 394	96.27 192	98.73 452	98.17 258	98.99 239	99.64 182

Syy-MVS		97.09 380	97.14 366	96.95 437	99.00 373	92.73 469	99.29 319	99.39 285	97.06 330	97.41 429	98.15 454	93.92 318	98.68 453	91.71 463	98.34 282	99.45 256

myMVS_eth3d		96.89 383	96.37 388	98.43 339	99.00 373	97.16 337	99.29 319	99.39 285	97.06 330	97.41 429	98.15 454	83.46 470	98.68 453	95.27 424	98.34 282	99.45 256

DTE-MVSNet		97.51 355	97.19 364	98.46 332	98.63 429	98.13 287	99.84 12	99.48 204	96.68 357	97.97 415	99.67 241	92.92 340	98.56 455	96.88 380	92.60 448	98.70 351

PC_three_1452									98.18 174	99.84 55	99.70 216	99.31 3	98.52 456	98.30 248	99.80 125	99.81 79

mvsany_test3		93.77 439	93.45 442	94.74 454	95.78 480	88.01 480	99.64 96	98.25 460	98.28 150	94.31 468	97.97 461	68.89 487	98.51 457	97.50 330	90.37 458	97.71 466

UnsupCasMVSNet_bld		93.53 440	92.51 446	96.58 445	97.38 458	93.82 456	98.24 475	99.48 204	91.10 470	93.10 476	96.66 480	74.89 484	98.37 458	94.03 442	87.71 468	97.56 472

Anonymous20240521		96.20 398	95.89 401	97.13 430	97.72 454	94.96 438	99.79 31	99.29 346	93.01 453	97.20 438	99.03 406	89.69 413	98.36 459	91.16 466	96.13 378	98.07 451

test_f		91.90 446	91.26 450	93.84 457	95.52 484	85.92 482	99.69 63	98.53 455	95.31 418	93.87 472	96.37 484	55.33 494	98.27 460	95.70 412	90.98 456	97.32 476

MDA-MVSNet_test_wron		95.45 413	94.60 422	98.01 376	98.16 446	97.21 336	99.11 380	99.24 363	93.49 447	80.73 493	98.98 414	93.02 337	98.18 461	94.22 440	94.45 419	98.64 381

UnsupCasMVSNet_eth		96.44 393	96.12 394	97.40 424	98.65 427	95.65 414	99.36 295	99.51 155	97.13 320	96.04 455	98.99 412	88.40 430	98.17 462	96.71 385	90.27 459	98.40 432

KD-MVS_2432*1600		94.62 431	93.72 438	97.31 425	97.19 464	95.82 409	98.34 470	99.20 371	95.00 426	97.57 426	98.35 446	87.95 435	98.10 463	92.87 457	77.00 491	98.01 455

miper_refine_blended		94.62 431	93.72 438	97.31 425	97.19 464	95.82 409	98.34 470	99.20 371	95.00 426	97.57 426	98.35 446	87.95 435	98.10 463	92.87 457	77.00 491	98.01 455

YYNet1		95.36 418	94.51 426	97.92 386	97.89 449	97.10 340	99.10 382	99.23 364	93.26 451	80.77 492	99.04 405	92.81 343	98.02 465	94.30 436	94.18 424	98.64 381

EU-MVSNet		97.98 284	98.03 258	97.81 403	98.72 418	96.65 378	99.66 83	99.66 32	98.09 197	98.35 392	99.82 119	95.25 243	98.01 466	97.41 341	95.30 403	98.78 330

Gipuma		90.99 449	90.15 453	93.51 458	98.73 416	90.12 478	93.98 492	99.45 250	79.32 489	92.28 479	94.91 486	69.61 486	97.98 467	87.42 481	95.67 393	92.45 489
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
pmmvs-eth3d		95.34 419	94.73 419	97.15 428	95.53 483	95.94 402	99.35 300	99.10 383	95.13 419	93.55 474	97.54 471	88.15 434	97.91 468	94.58 433	89.69 464	97.61 469

PM-MVS		92.96 443	92.23 447	95.14 453	95.61 481	89.98 479	99.37 289	98.21 463	94.80 431	95.04 464	97.69 465	65.06 488	97.90 469	94.30 436	89.98 461	97.54 473

MDA-MVSNet-bldmvs		94.96 427	93.98 434	97.92 386	98.24 445	97.27 331	99.15 367	99.33 323	93.80 442	80.09 494	99.03 406	88.31 431	97.86 470	93.49 448	94.36 421	98.62 390

Patchmatch-RL test		95.84 405	95.81 403	95.95 450	95.61 481	90.57 477	98.24 475	98.39 456	95.10 423	95.20 460	98.67 434	94.78 267	97.77 471	96.28 401	90.02 460	99.51 234

Anonymous20231206		96.22 396	96.03 397	96.79 442	97.31 461	94.14 454	99.63 102	99.08 386	96.17 398	97.04 442	99.06 401	93.94 316	97.76 472	86.96 483	95.06 408	98.47 423

SD-MVS		99.41 59	99.52 14	99.05 237	99.74 100	99.68 64	99.46 242	99.52 133	99.11 47	99.88 42	99.91 26	99.43 1	97.70 473	98.72 187	99.93 32	99.77 100
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		97.25 373	97.35 346	96.95 437	97.84 450	93.61 463	99.57 143	96.63 486	96.13 403	98.87 326	98.61 437	94.59 285	97.70 473	95.08 427	98.86 252	99.55 217

FE-MVSNET2		95.10 423	94.44 427	97.08 433	95.08 486	95.97 400	99.51 192	99.37 301	95.02 425	94.10 469	97.57 469	86.18 452	97.66 475	93.28 450	89.86 462	97.61 469

dongtai		93.26 441	92.93 445	94.25 455	99.39 275	85.68 483	97.68 484	93.27 497	92.87 455	96.85 446	99.39 344	82.33 475	97.48 476	76.78 491	97.80 317	99.58 210

pmmvs3		94.09 437	93.25 444	96.60 444	94.76 489	94.49 448	98.92 420	98.18 465	89.66 472	96.48 449	98.06 460	86.28 451	97.33 477	89.68 471	87.20 469	97.97 461

KD-MVS_self_test		95.00 426	94.34 429	96.96 436	97.07 466	95.39 426	99.56 151	99.44 259	95.11 421	97.13 440	97.32 476	91.86 372	97.27 478	90.35 469	81.23 482	98.23 443

FMVSNet5		96.43 394	96.19 393	97.15 428	99.11 351	95.89 406	99.32 308	99.52 133	94.47 437	98.34 393	99.07 399	87.54 440	97.07 479	92.61 460	95.72 392	98.47 423

usedtu_dtu_shiyan2		91.34 447	89.96 455	95.47 452	93.61 492	90.81 476	99.15 367	98.68 446	86.37 483	95.19 461	98.27 450	72.64 485	97.05 480	85.40 488	80.32 488	98.54 413

new-patchmatchnet		94.48 434	94.08 433	95.67 451	95.08 486	92.41 470	99.18 362	99.28 348	94.55 436	93.49 475	97.37 475	87.86 438	97.01 481	91.57 464	88.36 466	97.61 469

LCM-MVSNet		86.80 455	85.22 459	91.53 466	87.81 498	80.96 492	98.23 477	98.99 399	71.05 491	90.13 486	96.51 483	48.45 498	96.88 482	90.51 467	85.30 471	96.76 478

CL-MVSNet_self_test		94.49 433	93.97 435	96.08 449	96.16 477	93.67 461	98.33 472	99.38 293	95.13 419	97.33 433	98.15 454	92.69 351	96.57 483	88.67 474	79.87 489	97.99 459

MIMVSNet1		95.51 412	95.04 415	96.92 439	97.38 458	95.60 415	99.52 182	99.50 179	93.65 444	96.97 444	99.17 389	85.28 461	96.56 484	88.36 476	95.55 398	98.60 402

FE-MVSNET		94.07 438	93.36 443	96.22 448	94.05 490	94.71 443	99.56 151	98.36 457	93.15 452	93.76 473	97.55 470	86.47 450	96.49 485	87.48 480	89.83 463	97.48 474

test20.03		96.12 400	95.96 399	96.63 443	97.44 456	95.45 423	99.51 192	99.38 293	96.55 371	96.16 453	99.25 381	93.76 325	96.17 486	87.35 482	94.22 423	98.27 439

tmp_tt		82.80 457	81.52 460	86.66 474	66.61 504	68.44 503	92.79 494	97.92 467	68.96 492	80.04 495	99.85 85	85.77 454	96.15 487	97.86 287	43.89 497	95.39 487

test_fmvs3		92.10 445	91.77 448	93.08 461	96.19 476	86.25 481	99.82 16	98.62 451	96.65 360	95.19 461	96.90 479	55.05 495	95.93 488	96.63 392	90.92 457	97.06 477

kuosan		90.92 450	90.11 454	93.34 459	98.78 407	85.59 484	98.15 479	93.16 499	89.37 475	92.07 480	98.38 445	81.48 478	95.19 489	62.54 498	97.04 359	99.25 286

dmvs_testset		95.02 425	96.12 394	91.72 465	99.10 354	80.43 493	99.58 135	97.87 469	97.47 286	95.22 459	98.82 426	93.99 314	95.18 490	88.09 477	94.91 413	99.56 216

PMMVS2		86.87 454	85.37 458	91.35 467	90.21 496	83.80 486	98.89 423	97.45 479	83.13 488	91.67 485	95.03 485	48.49 497	94.70 491	85.86 487	77.62 490	95.54 486

PMVS	70.75 22	75.98 463	74.97 464	79.01 480	70.98 503	55.18 505	93.37 493	98.21 463	65.08 497	61.78 498	93.83 488	21.74 504	92.53 492	78.59 490	91.12 455	89.34 493
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
FPMVS		84.93 456	85.65 457	82.75 478	86.77 499	63.39 504	98.35 469	98.92 408	74.11 490	83.39 489	98.98 414	50.85 496	92.40 493	84.54 489	94.97 410	92.46 488

WB-MVS		93.10 442	94.10 431	90.12 470	95.51 485	81.88 490	99.73 52	99.27 355	95.05 424	93.09 477	98.91 423	94.70 278	91.89 494	76.62 492	94.02 429	96.58 480

SSC-MVS		92.73 444	93.73 437	89.72 471	95.02 488	81.38 491	99.76 38	99.23 364	94.87 429	92.80 478	98.93 419	94.71 277	91.37 495	74.49 494	93.80 431	96.42 481

MVE	76.82 21	76.91 462	74.31 466	84.70 475	85.38 501	76.05 499	96.88 489	93.17 498	67.39 494	71.28 496	89.01 495	21.66 505	87.69 496	71.74 495	72.29 493	90.35 492
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		80.61 459	79.88 461	82.81 477	90.75 495	76.38 498	97.69 483	95.76 490	66.44 495	83.52 488	92.25 490	62.54 490	87.16 497	68.53 496	61.40 494	84.89 495

EMVS		80.02 460	79.22 462	82.43 479	91.19 494	76.40 497	97.55 487	92.49 502	66.36 496	83.01 490	91.27 492	64.63 489	85.79 498	65.82 497	60.65 495	85.08 494

ANet_high		77.30 461	74.86 465	84.62 476	75.88 502	77.61 496	97.63 485	93.15 500	88.81 477	64.27 497	89.29 494	36.51 500	83.93 499	75.89 493	52.31 496	92.33 490

wuyk23d		40.18 464	41.29 469	36.84 481	86.18 500	49.12 506	79.73 495	22.81 506	27.64 498	25.46 501	28.45 501	21.98 503	48.89 500	55.80 499	23.56 500	12.51 498

test123		39.01 466	42.50 468	28.53 482	39.17 505	20.91 507	98.75 440	19.17 507	19.83 500	38.57 499	66.67 497	33.16 501	15.42 501	37.50 501	29.66 499	49.26 496

testmvs		39.17 465	43.78 467	25.37 483	36.04 506	16.84 508	98.36 468	26.56 505	20.06 499	38.51 500	67.32 496	29.64 502	15.30 502	37.59 500	39.90 498	43.98 497

mmdepth		0.02 471	0.03 474	0.00 484	0.00 507	0.00 509	0.00 496	0.00 508	0.00 502	0.00 503	0.27 503	0.00 506	0.00 503	0.00 502	0.00 501	0.00 499

monomultidepth		0.02 471	0.03 474	0.00 484	0.00 507	0.00 509	0.00 496	0.00 508	0.00 502	0.00 503	0.27 503	0.00 506	0.00 503	0.00 502	0.00 501	0.00 499

test_blank		0.13 470	0.17 473	0.00 484	0.00 507	0.00 509	0.00 496	0.00 508	0.00 502	0.00 503	1.57 502	0.00 506	0.00 503	0.00 502	0.00 501	0.00 499

uanet_test		0.02 471	0.03 474	0.00 484	0.00 507	0.00 509	0.00 496	0.00 508	0.00 502	0.00 503	0.27 503	0.00 506	0.00 503	0.00 502	0.00 501	0.00 499

DCPMVS		0.02 471	0.03 474	0.00 484	0.00 507	0.00 509	0.00 496	0.00 508	0.00 502	0.00 503	0.27 503	0.00 506	0.00 503	0.00 502	0.00 501	0.00 499

cdsmvs_eth3d_5k		24.64 467	32.85 470	0.00 484	0.00 507	0.00 509	0.00 496	99.51 155	0.00 502	0.00 503	99.56 286	96.58 173	0.00 503	0.00 502	0.00 501	0.00 499

pcd_1.5k_mvsjas		8.27 469	11.03 472	0.00 484	0.00 507	0.00 509	0.00 496	0.00 508	0.00 502	0.00 503	0.27 503	99.01 20	0.00 503	0.00 502	0.00 501	0.00 499

sosnet-low-res		0.02 471	0.03 474	0.00 484	0.00 507	0.00 509	0.00 496	0.00 508	0.00 502	0.00 503	0.27 503	0.00 506	0.00 503	0.00 502	0.00 501	0.00 499

sosnet		0.02 471	0.03 474	0.00 484	0.00 507	0.00 509	0.00 496	0.00 508	0.00 502	0.00 503	0.27 503	0.00 506	0.00 503	0.00 502	0.00 501	0.00 499

uncertanet		0.02 471	0.03 474	0.00 484	0.00 507	0.00 509	0.00 496	0.00 508	0.00 502	0.00 503	0.27 503	0.00 506	0.00 503	0.00 502	0.00 501	0.00 499

Regformer		0.02 471	0.03 474	0.00 484	0.00 507	0.00 509	0.00 496	0.00 508	0.00 502	0.00 503	0.27 503	0.00 506	0.00 503	0.00 502	0.00 501	0.00 499

ab-mvs-re		8.30 468	11.06 471	0.00 484	0.00 507	0.00 509	0.00 496	0.00 508	0.00 502	0.00 503	99.58 278	0.00 506	0.00 503	0.00 502	0.00 501	0.00 499

uanet		0.02 471	0.03 474	0.00 484	0.00 507	0.00 509	0.00 496	0.00 508	0.00 502	0.00 503	0.27 503	0.00 506	0.00 503	0.00 502	0.00 501	0.00 499

TestfortrainingZip							99.69 63

WAC-MVS						97.16 337								95.47 418

FOURS1					99.91 1	99.93 1	99.87 8	99.56 89	99.10 48	99.81 68

test_one_0601					99.81 57	99.88 10		99.49 192	98.97 75	99.65 139	99.81 134	99.09 16

eth-test2					0.00 507

eth-test					0.00 507

RE-MVS-def			99.34 49		99.76 82	99.82 28	99.63 102	99.52 133	98.38 137	99.76 90	99.82 119	98.75 60		98.61 204	99.81 120	99.77 100

IU-MVS					99.84 38	99.88 10		99.32 333	98.30 149	99.84 55				98.86 164	99.85 93	99.89 29

save fliter					99.76 82	99.59 88	99.14 370	99.40 282	99.00 67

test0726					99.85 31	99.89 6	99.62 107	99.50 179	99.10 48	99.86 52	99.82 119	98.94 34

GSMVS																99.52 225

test_part2					99.81 57	99.83 22				99.77 84

sam_mvs1												94.86 261				99.52 225

sam_mvs												94.72 276

MTGPA								99.47 226

MTMP							99.54 171	98.88 418

test9_res														97.49 331	99.72 148	99.75 113

agg_prior2														97.21 355	99.73 147	99.75 113

test_prior4						99.56 94	98.99 406

test_prior2							98.96 413		98.34 143	99.01 300	99.52 302	98.68 70		97.96 279	99.74 145

新几何2							99.01 403

旧先验1					99.74 100	99.59 88		99.54 108			99.69 227	98.47 86			99.68 156	99.73 127

原ACMM2							98.95 416

test222					99.75 92	99.49 109	98.91 422	99.49 192	96.42 382	99.34 229	99.65 248	98.28 99			99.69 153	99.72 137

segment_acmp												98.96 27

testdata1							98.85 427		98.32 147

plane_prior7					99.29 304	97.03 352

plane_prior6					99.27 309	96.98 356						92.71 349

plane_prior4											99.61 269

plane_prior3						97.00 354			98.69 107	99.11 280

plane_prior2							99.39 282		98.97 75

plane_prior1					99.26 312

plane_prior						96.97 357	99.21 355		98.45 130						97.60 326

n2								0.00 508

nn								0.00 508

door-mid								98.05 466

test11								99.35 310

door								97.92 467

HQP5-MVS						96.83 368

HQP-NCC					99.19 330		98.98 409		98.24 162	98.66 357

ACMP_Plane					99.19 330		98.98 409		98.24 162	98.66 357

BP-MVS														97.19 359

HQP3-MVS								99.39 285							97.58 328

HQP2-MVS												92.47 358

NP-MVS					99.23 320	96.92 364					99.40 340

MDTV_nov1_ep13_2view						95.18 432	99.35 300		96.84 347	99.58 163		95.19 246		97.82 292		99.46 253

ACMMP++_ref															97.19 356

ACMMP++															97.43 346

Test By Simon												98.75 60

High-resolution multi-view benchmark