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	99.38 277	99.37 123	99.58 133	99.62 51	99.41 21	99.87 49	99.92 18	98.81 49	100.00 1	99.97 2	99.93 33	99.94 17

test_fmvsm_n_1920		99.69 5	99.66 4	99.78 71	99.84 38	99.44 116	99.58 133	99.69 22	99.43 17	99.98 13	99.91 26	98.62 76	100.00 1	99.97 2	99.95 23	99.90 25

test_vis1_n_1920		98.63 218	98.40 226	99.31 198	99.86 25	97.94 301	99.67 75	99.62 51	99.43 17	99.99 2	99.91 26	87.29 434	100.00 1	99.92 24	99.92 39	99.98 2

fmvsm_s_conf0.5_n_11		99.32 79	99.16 92	99.80 64	99.83 47	99.70 60	99.57 141	99.56 90	99.45 11	99.99 2	99.93 10	94.18 305	99.99 4	99.96 13	99.98 4	99.73 126

fmvsm_s_conf0.5_n_10		99.41 59	99.24 78	99.92 1	99.83 47	99.84 20	99.53 178	99.56 90	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	99.47 236	99.63 46	99.45 11	99.98 13	99.89 45	97.02 148	99.99 4	99.98 1	99.96 17	99.95 11

NormalMVS		99.27 89	99.19 88	99.52 139	99.89 8	98.83 225	99.65 88	99.52 133	99.10 48	99.84 56	99.76 188	95.80 218	99.99 4	99.30 89	99.84 102	99.74 117

SymmetryMVS		99.15 115	99.02 127	99.52 139	99.72 111	98.83 225	99.65 88	99.34 313	99.10 48	99.84 56	99.76 188	95.80 218	99.99 4	99.30 89	98.72 261	99.73 126

fmvsm_s_conf0.5_n_5		99.37 68	99.21 84	99.86 34	99.80 63	99.68 64	99.42 264	99.61 60	99.37 24	99.97 25	99.86 77	94.96 252	99.99 4	99.97 2	99.93 33	99.92 23

fmvsm_l_conf0.5_n_3		99.61 10	99.51 18	99.92 1	99.84 38	99.82 28	99.54 169	99.66 32	99.46 7	99.98 13	99.89 45	97.27 133	99.99 4	99.97 2	99.95 23	99.95 11

fmvsm_l_conf0.5_n_a		99.71 2	99.67 1	99.85 43	99.86 25	99.61 85	99.56 149	99.63 46	99.48 3	99.98 13	99.83 105	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	99.56 149	99.63 46	99.47 4	99.98 13	99.82 118	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	99.48 226	99.64 42	99.45 11	99.92 30	99.92 18	98.62 76	99.99 4	99.96 13	99.99 1	99.96 7

patch_mono-2		99.26 92	99.62 6	98.16 361	99.81 57	94.59 437	99.52 180	99.64 42	99.33 28	99.73 97	99.90 36	99.00 24	99.99 4	99.69 35	99.98 4	99.89 29

h-mvs33		97.70 333	97.28 356	98.97 246	99.70 122	97.27 329	99.36 294	99.45 248	98.94 78	99.66 129	99.64 253	94.93 255	99.99 4	99.48 64	84.36 470	99.65 174

xiu_mvs_v1_base_debu		99.29 85	99.27 73	99.34 190	99.63 168	98.97 183	99.12 372	99.51 155	98.86 84	99.84 56	99.47 320	98.18 104	99.99 4	99.50 57	99.31 191	99.08 299

xiu_mvs_v1_base		99.29 85	99.27 73	99.34 190	99.63 168	98.97 183	99.12 372	99.51 155	98.86 84	99.84 56	99.47 320	98.18 104	99.99 4	99.50 57	99.31 191	99.08 299

xiu_mvs_v1_base_debi		99.29 85	99.27 73	99.34 190	99.63 168	98.97 183	99.12 372	99.51 155	98.86 84	99.84 56	99.47 320	98.18 104	99.99 4	99.50 57	99.31 191	99.08 299

EPNet		98.86 183	98.71 190	99.30 203	97.20 460	98.18 281	99.62 106	98.91 406	99.28 31	98.63 364	99.81 133	95.96 206	99.99 4	99.24 102	99.72 148	99.73 126
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	99.51 190	99.62 51	99.46 7	99.99 2	99.90 36	96.60 172	99.98 20	99.95 16	99.95 23	99.96 7

MM		99.40 64	99.28 69	99.74 80	99.67 136	99.31 135	99.52 180	98.87 413	99.55 1	99.74 95	99.80 151	96.47 180	99.98 20	99.97 2	99.97 9	99.94 17

test_cas_vis1_n_1920		99.16 111	99.01 133	99.61 109	99.81 57	98.86 219	99.65 88	99.64 42	99.39 22	99.97 25	99.94 6	93.20 334	99.98 20	99.55 50	99.91 46	99.99 1

test_vis1_n		97.92 291	97.44 332	99.34 190	99.53 219	98.08 288	99.74 47	99.49 191	99.15 38	100.00 1	99.94 6	79.51 472	99.98 20	99.88 26	99.76 140	99.97 4

xiu_mvs_v2_base		99.26 92	99.25 77	99.29 206	99.53 219	98.91 204	99.02 396	99.45 248	98.80 94	99.71 111	99.26 379	98.94 34	99.98 20	99.34 81	99.23 200	98.98 313

PS-MVSNAJ		99.32 79	99.32 54	99.30 203	99.57 203	98.94 197	98.97 410	99.46 237	98.92 81	99.71 111	99.24 381	99.01 20	99.98 20	99.35 76	99.66 159	98.97 314

QAPM		98.67 213	98.30 233	99.80 64	99.20 326	99.67 68	99.77 34	99.72 14	94.74 430	98.73 344	99.90 36	95.78 220	99.98 20	96.96 363	99.88 76	99.76 107

3Dnovator	97.25 9	99.24 97	99.05 112	99.81 60	99.12 348	99.66 71	99.84 12	99.74 13	99.09 55	98.92 316	99.90 36	95.94 209	99.98 20	98.95 145	99.92 39	99.79 92

OpenMVS	96.50 16	98.47 224	98.12 245	99.52 139	99.04 367	99.53 101	99.82 16	99.72 14	94.56 433	98.08 401	99.88 56	94.73 273	99.98 20	97.47 329	99.76 140	99.06 305

fmvsm_s_conf0.5_n_3		99.37 68	99.20 86	99.87 21	99.75 92	99.70 60	99.48 226	99.66 32	99.45 11	99.99 2	99.93 10	94.64 282	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	99.56 149	99.55 100	99.15 38	99.90 34	99.90 36	99.00 24	99.97 29	99.11 121	99.91 46	99.86 42

test_fmvsmconf0.1_n		99.55 23	99.45 30	99.86 34	99.44 259	99.65 75	99.50 201	99.61 60	99.45 11	99.87 49	99.92 18	97.31 130	99.97 29	99.95 16	99.99 1	99.97 4

test_fmvs1_n		98.41 230	98.14 242	99.21 219	99.82 53	97.71 314	99.74 47	99.49 191	99.32 29	99.99 2	99.95 3	85.32 450	99.97 29	99.82 29	99.84 102	99.96 7

CANet_DTU		98.97 171	98.87 167	99.25 213	99.33 290	98.42 273	99.08 381	99.30 340	99.16 37	99.43 196	99.75 193	95.27 240	99.97 29	98.56 215	99.95 23	99.36 271

MGCNet		99.15 115	98.96 145	99.73 83	98.92 385	99.37 123	99.37 288	96.92 470	99.51 2	99.66 129	99.78 175	96.69 167	99.97 29	99.84 28	99.97 9	99.84 53

MTAPA		99.52 28	99.39 40	99.89 11	99.90 4	99.86 18	99.66 82	99.47 225	98.79 95	99.68 118	99.81 133	98.43 89	99.97 29	98.88 155	99.90 57	99.83 63

PGM-MVS		99.45 46	99.31 60	99.86 34	99.87 20	99.78 47	99.58 133	99.65 39	97.84 239	99.71 111	99.80 151	99.12 15	99.97 29	98.33 240	99.87 79	99.83 63

mPP-MVS		99.44 50	99.30 62	99.86 34	99.88 13	99.79 41	99.69 62	99.48 203	98.12 190	99.50 180	99.75 193	98.78 53	99.97 29	98.57 212	99.89 68	99.83 63

CP-MVS		99.45 46	99.32 54	99.85 43	99.83 47	99.75 51	99.69 62	99.52 133	98.07 201	99.53 175	99.63 259	98.93 38	99.97 29	98.74 183	99.91 46	99.83 63

SteuartSystems-ACMMP		99.54 24	99.42 32	99.87 21	99.82 53	99.81 33	99.59 123	99.51 155	98.62 112	99.79 76	99.83 105	99.28 6	99.97 29	98.48 222	99.90 57	99.84 53
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3Dnovator+	97.12 13	99.18 104	98.97 141	99.82 57	99.17 340	99.68 64	99.81 20	99.51 155	99.20 33	98.72 345	99.89 45	95.68 224	99.97 29	98.86 163	99.86 87	99.81 79

fmvsm_s_conf0.5_n_9		99.41 59	99.28 69	99.81 60	99.84 38	99.52 105	99.48 226	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 42	99.59 73	99.06 61	99.88 43	99.85 84	98.41 93	99.96 41	99.28 95	99.84 102	99.83 63

KinetiMVS		99.12 134	98.92 154	99.70 87	99.67 136	99.40 121	99.67 75	99.63 46	98.73 102	99.94 28	99.81 133	94.54 288	99.96 41	98.40 231	99.93 33	99.74 117

fmvsm_s_conf0.5_n_7		99.34 75	99.29 66	99.48 159	99.70 122	98.63 246	99.42 264	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 79	99.13 95	99.89 11	99.80 63	99.77 48	99.44 251	99.58 78	99.47 4	99.99 2	99.93 10	94.04 310	99.96 41	99.96 13	99.93 33	99.93 22

reproduce-ours		99.61 10	99.52 14	99.90 8	99.76 82	99.88 10	99.52 180	99.54 109	99.13 41	99.89 40	99.89 45	98.96 27	99.96 41	99.04 131	99.90 57	99.85 46

our_new_method		99.61 10	99.52 14	99.90 8	99.76 82	99.88 10	99.52 180	99.54 109	99.13 41	99.89 40	99.89 45	98.96 27	99.96 41	99.04 131	99.90 57	99.85 46

fmvsm_s_conf0.5_n_a		99.56 21	99.47 24	99.85 43	99.83 47	99.64 81	99.52 180	99.65 39	99.10 48	99.98 13	99.92 18	97.35 129	99.96 41	99.94 21	99.92 39	99.95 11

fmvsm_s_conf0.5_n		99.51 29	99.40 38	99.85 43	99.84 38	99.65 75	99.51 190	99.67 27	99.13 41	99.98 13	99.92 18	96.60 172	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	98.94 416	99.48 203	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		98.88 177	98.79 181	99.16 224	99.69 127	97.61 318	99.55 164	99.49 191	99.32 29	99.98 13	99.91 26	91.41 382	99.96 41	99.82 29	99.92 39	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 43	99.81 133	99.27 7	99.96 41	98.85 165	99.80 125	99.81 79

MSC_two_6792asdad				99.87 21	99.51 228	99.76 49		99.33 321					99.96 41	98.87 158	99.84 102	99.89 29

No_MVS				99.87 21	99.51 228	99.76 49		99.33 321					99.96 41	98.87 158	99.84 102	99.89 29

ZD-MVS					99.71 117	99.79 41		99.61 60	96.84 346	99.56 166	99.54 293	98.58 78	99.96 41	96.93 366	99.75 142

SED-MVS		99.61 10	99.52 14	99.88 15	99.84 38	99.90 3	99.60 113	99.48 203	99.08 56	99.91 31	99.81 133	99.20 9	99.96 41	98.91 152	99.85 94	99.79 92

test_241102_TWO								99.48 203	99.08 56	99.88 43	99.81 133	98.94 34	99.96 41	98.91 152	99.84 102	99.88 35

ZNCC-MVS		99.47 40	99.33 52	99.87 21	99.87 20	99.81 33	99.64 95	99.67 27	98.08 200	99.55 172	99.64 253	98.91 39	99.96 41	98.72 186	99.90 57	99.82 72

DVP-MVS		99.57 20	99.47 24	99.88 15	99.85 31	99.89 6	99.57 141	99.37 299	99.10 48	99.81 69	99.80 151	98.94 34	99.96 41	98.93 149	99.86 87	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 69	99.80 151	99.09 16	99.96 41	98.85 165	99.90 57	99.88 35

test_0728_SECOND				99.91 6	99.84 38	99.89 6	99.57 141	99.51 155					99.96 41	98.93 149	99.86 87	99.88 35

SR-MVS		99.43 53	99.29 66	99.86 34	99.75 92	99.83 22	99.59 123	99.62 51	98.21 168	99.73 97	99.79 168	98.68 70	99.96 41	98.44 228	99.77 137	99.79 92

DPE-MVS		99.46 42	99.32 54	99.91 6	99.78 70	99.88 10	99.36 294	99.51 155	98.73 102	99.88 43	99.84 99	98.72 67	99.96 41	98.16 255	99.87 79	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 66	99.80 64	99.62 177	99.55 96	99.50 201	99.70 18	98.79 95	99.77 85	99.96 1	97.45 124	99.96 41	98.92 151	99.90 57	99.89 29

HFP-MVS		99.49 33	99.37 44	99.86 34	99.87 20	99.80 38	99.66 82	99.67 27	98.15 175	99.68 118	99.69 226	99.06 18	99.96 41	98.69 191	99.87 79	99.84 53

region2R		99.48 37	99.35 48	99.87 21	99.88 13	99.80 38	99.65 88	99.66 32	98.13 183	99.66 129	99.68 234	98.96 27	99.96 41	98.62 200	99.87 79	99.84 53

HPM-MVS++		99.39 66	99.23 82	99.87 21	99.75 92	99.84 20	99.43 257	99.51 155	98.68 109	99.27 245	99.53 297	98.64 75	99.96 41	98.44 228	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 42	99.56 90	99.02 62	99.88 43	99.85 84	99.18 12	99.96 41	99.22 103	99.92 39	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 46	99.86 34	99.87 20	99.79 41	99.66 82	99.67 27	98.15 175	99.67 124	99.69 226	98.95 32	99.96 41	98.69 191	99.87 79	99.84 53

MP-MVS		99.33 77	99.15 93	99.87 21	99.88 13	99.82 28	99.66 82	99.46 237	98.09 196	99.48 184	99.74 198	98.29 99	99.96 41	97.93 277	99.87 79	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	98.90 159	99.74 80	99.80 63	99.46 114	99.59 123	99.49 191	97.03 333	99.63 146	99.69 226	97.27 133	99.96 41	97.82 288	99.84 102	99.81 79

PVSNet_Blended_VisFu		99.36 72	99.28 69	99.61 109	99.86 25	99.07 170	99.47 236	99.93 2	97.66 264	99.71 111	99.86 77	97.73 119	99.96 41	99.47 66	99.82 117	99.79 92

UGNet		98.87 180	98.69 192	99.40 180	99.22 323	98.72 238	99.44 251	99.68 24	99.24 32	99.18 270	99.42 331	92.74 344	99.96 41	99.34 81	99.94 31	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 79	99.32 54	99.32 196	99.85 31	98.29 276	99.71 57	99.66 32	98.11 192	99.41 204	99.80 151	98.37 96	99.96 41	98.99 137	99.96 17	99.72 136

ACMMP		99.45 46	99.32 54	99.82 57	99.89 8	99.67 68	99.62 106	99.69 22	98.12 190	99.63 146	99.84 99	98.73 66	99.96 41	98.55 218	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 62	99.87 6	99.34 26	99.90 34	99.83 105		99.95 76	98.83 171	99.89 68	99.83 63

MED-MVS		99.66 6	99.60 8	99.87 21	99.88 13	99.81 33	99.69 62	99.87 6	99.18 34	99.90 34	99.83 105	99.30 4	99.95 76	98.83 171	99.89 68	99.83 63

TestfortrainingZip a		99.73 1	99.67 1	99.92 1	99.88 13	99.91 2	99.69 62	99.87 6	99.34 26	99.90 34	99.83 105	99.30 4	99.95 76	99.32 84	99.89 68	99.90 25

fmvsm_s_conf0.5_n_6		99.54 24	99.44 31	99.85 43	99.51 228	99.67 68	99.50 201	99.64 42	99.43 17	99.98 13	99.78 175	97.26 136	99.95 76	99.95 16	99.93 33	99.92 23

fmvsm_s_conf0.5_n_4		99.36 72	99.24 78	99.73 83	99.78 70	99.53 101	99.49 218	99.60 67	99.42 20	99.99 2	99.86 77	95.15 247	99.95 76	99.95 16	99.89 68	99.73 126

fmvsm_s_conf0.1_n_2		99.37 68	99.22 83	99.81 60	99.77 78	99.75 51	99.46 240	99.60 67	99.47 4	99.98 13	99.94 6	94.98 251	99.95 76	99.97 2	99.79 132	99.73 126

test_fmvsmconf0.01_n		99.22 100	99.03 117	99.79 68	98.42 440	99.48 111	99.55 164	99.51 155	99.39 22	99.78 81	99.93 10	94.80 264	99.95 76	99.93 23	99.95 23	99.94 17

SR-MVS-dyc-post		99.45 46	99.31 60	99.85 43	99.76 82	99.82 28	99.63 101	99.52 133	98.38 137	99.76 91	99.82 118	98.53 82	99.95 76	98.61 203	99.81 120	99.77 100

GST-MVS		99.40 64	99.24 78	99.85 43	99.86 25	99.79 41	99.60 113	99.67 27	97.97 223	99.63 146	99.68 234	98.52 83	99.95 76	98.38 233	99.86 87	99.81 79

CANet		99.25 96	99.14 94	99.59 113	99.41 267	99.16 155	99.35 299	99.57 85	98.82 89	99.51 179	99.61 268	96.46 181	99.95 76	99.59 45	99.98 4	99.65 174

MP-MVS-pluss		99.37 68	99.20 86	99.88 15	99.90 4	99.87 17	99.30 313	99.52 133	97.18 315	99.60 158	99.79 168	98.79 52	99.95 76	98.83 171	99.91 46	99.83 63
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MSP-MVS		99.42 55	99.27 73	99.88 15	99.89 8	99.80 38	99.67 75	99.50 178	98.70 106	99.77 85	99.49 311	98.21 102	99.95 76	98.46 226	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	96.67 378

APD-MVS_3200maxsize		99.48 37	99.35 48	99.85 43	99.76 82	99.83 22	99.63 101	99.54 109	98.36 141	99.79 76	99.82 118	98.86 43	99.95 76	98.62 200	99.81 120	99.78 98

RPMNet		96.72 385	95.90 398	99.19 221	99.18 332	98.49 265	99.22 352	99.52 133	88.72 468	99.56 166	97.38 466	94.08 309	99.95 76	86.87 474	98.58 268	99.14 291

sss		99.17 109	99.05 112	99.53 133	99.62 177	98.97 183	99.36 294	99.62 51	97.83 240	99.67 124	99.65 247	97.37 128	99.95 76	99.19 107	99.19 203	99.68 159

MVSMamba_PlusPlus		99.46 42	99.41 37	99.64 101	99.68 133	99.50 108	99.75 42	99.50 178	98.27 152	99.87 49	99.92 18	98.09 108	99.94 92	99.65 41	99.95 23	99.47 248

fmvsm_s_conf0.1_n_a		99.26 92	99.06 110	99.85 43	99.52 225	99.62 83	99.54 169	99.62 51	98.69 107	99.99 2	99.96 1	94.47 292	99.94 92	99.88 26	99.92 39	99.98 2

fmvsm_s_conf0.1_n		99.29 85	99.10 99	99.86 34	99.70 122	99.65 75	99.53 178	99.62 51	98.74 101	99.99 2	99.95 3	94.53 290	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	99.63 101	99.39 283	98.91 82	99.78 81	99.85 84	99.36 2	99.94 92	98.84 168	99.88 76	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		98.91 175	98.75 184	99.39 185	99.46 252	98.61 250	99.76 37	99.50 178	98.06 205	99.81 69	99.88 56	93.91 317	99.94 92	99.11 121	99.27 194	99.61 191

mamv4		99.33 77	99.42 32	99.07 232	99.67 136	97.73 309	99.42 264	99.60 67	98.15 175	99.94 28	99.91 26	98.42 91	99.94 92	99.72 32	99.96 17	99.54 218

XVS		99.53 27	99.42 32	99.87 21	99.85 31	99.83 22	99.69 62	99.68 24	98.98 72	99.37 215	99.74 198	98.81 49	99.94 92	98.79 179	99.86 87	99.84 53

X-MVStestdata		96.55 388	95.45 407	99.87 21	99.85 31	99.83 22	99.69 62	99.68 24	98.98 72	99.37 215	64.01 489	98.81 49	99.94 92	98.79 179	99.86 87	99.84 53

旧先验2							98.96 411		96.70 354	99.47 185			99.94 92	98.19 251

新几何1				99.75 77	99.75 92	99.59 88		99.54 109	96.76 350	99.29 238	99.64 253	98.43 89	99.94 92	96.92 368	99.66 159	99.72 136

testdata				99.54 125	99.75 92	98.95 193		99.51 155	97.07 327	99.43 196	99.70 215	98.87 42	99.94 92	97.76 297	99.64 162	99.72 136

HPM-MVS		99.42 55	99.28 69	99.83 56	99.90 4	99.72 56	99.81 20	99.54 109	97.59 270	99.68 118	99.63 259	98.91 39	99.94 92	98.58 209	99.91 46	99.84 53
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CHOSEN 1792x2688		99.19 101	99.10 99	99.45 168	99.89 8	98.52 260	99.39 281	99.94 1	98.73 102	99.11 279	99.89 45	95.50 230	99.94 92	99.50 57	99.97 9	99.89 29

APD-MVS		99.27 89	99.08 105	99.84 55	99.75 92	99.79 41	99.50 201	99.50 178	97.16 317	99.77 85	99.82 118	98.78 53	99.94 92	97.56 318	99.86 87	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	99.05 388	99.66 32	99.14 40	99.57 165	99.80 151	98.46 87	99.94 92	99.57 48	99.84 102	99.60 194
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	98.88 166	99.61 109	99.62 177	99.16 155	99.37 288	99.56 90	98.04 215	99.53 175	99.62 264	96.84 159	99.94 92	98.85 165	98.49 276	99.72 136

DeepC-MVS	98.35 2	99.30 83	99.19 88	99.64 101	99.82 53	99.23 148	99.62 106	99.55 100	98.94 78	99.63 146	99.95 3	95.82 216	99.94 92	99.37 75	99.97 9	99.73 126
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 97	99.74 80	99.18 332	99.75 51	99.56 149	99.57 85	98.45 130	99.49 183	99.85 84	97.77 118	99.94 92	98.33 240	99.84 102	99.52 225

ME-MVS		99.56 21	99.46 28	99.86 34	99.80 63	99.81 33	99.37 288	99.70 18	99.18 34	99.83 64	99.83 105	98.74 65	99.93 110	98.83 171	99.89 68	99.83 63

GDP-MVS		99.08 148	98.89 163	99.64 101	99.53 219	99.34 127	99.64 95	99.48 203	98.32 147	99.77 85	99.66 245	95.14 248	99.93 110	98.97 143	99.50 176	99.64 181

SDMVSNet		99.11 140	98.90 159	99.75 77	99.81 57	99.59 88	99.81 20	99.65 39	98.78 98	99.64 143	99.88 56	94.56 285	99.93 110	99.67 37	98.26 291	99.72 136

FE-MVS		98.48 223	98.17 238	99.40 180	99.54 218	98.96 187	99.68 72	98.81 420	95.54 413	99.62 150	99.70 215	93.82 320	99.93 110	97.35 339	99.46 178	99.32 277

SF-MVS		99.38 67	99.24 78	99.79 68	99.79 68	99.68 64	99.57 141	99.54 109	97.82 245	99.71 111	99.80 151	98.95 32	99.93 110	98.19 251	99.84 102	99.74 117

dcpmvs_2		99.23 98	99.58 9	98.16 361	99.83 47	94.68 434	99.76 37	99.52 133	99.07 58	99.98 13	99.88 56	98.56 80	99.93 110	99.67 37	99.98 4	99.87 40

Anonymous20240529		98.09 260	97.68 299	99.34 190	99.66 149	98.44 270	99.40 277	99.43 268	93.67 440	99.22 257	99.89 45	90.23 400	99.93 110	99.26 101	98.33 283	99.66 168

ACMMP_NAP		99.47 40	99.34 50	99.88 15	99.87 20	99.86 18	99.47 236	99.48 203	98.05 208	99.76 91	99.86 77	98.82 48	99.93 110	98.82 178	99.91 46	99.84 53

EI-MVSNet-UG-set		99.58 16	99.57 10	99.64 101	99.78 70	99.14 160	99.60 113	99.45 248	99.01 64	99.90 34	99.83 105	98.98 26	99.93 110	99.59 45	99.95 23	99.86 42

无先验							98.99 404	99.51 155	96.89 343				99.93 110	97.53 321		99.72 136

VDDNet		97.55 348	97.02 370	99.16 224	99.49 242	98.12 287	99.38 286	99.30 340	95.35 415	99.68 118	99.90 36	82.62 463	99.93 110	99.31 86	98.13 303	99.42 260

ab-mvs		98.86 183	98.63 202	99.54 125	99.64 164	99.19 150	99.44 251	99.54 109	97.77 249	99.30 235	99.81 133	94.20 302	99.93 110	99.17 113	98.82 255	99.49 239

F-COLMAP		99.19 101	99.04 114	99.64 101	99.78 70	99.27 143	99.42 264	99.54 109	97.29 306	99.41 204	99.59 273	98.42 91	99.93 110	98.19 251	99.69 153	99.73 126

BP-MVS1		99.12 134	98.94 151	99.65 95	99.51 228	99.30 138	99.67 75	98.92 401	98.48 126	99.84 56	99.69 226	94.96 252	99.92 123	99.62 44	99.79 132	99.71 147

Anonymous202405211		98.30 241	97.98 262	99.26 212	99.57 203	98.16 282	99.41 269	98.55 445	96.03 407	99.19 266	99.74 198	91.87 369	99.92 123	99.16 116	98.29 290	99.70 150

EI-MVSNet-Vis-set		99.58 16	99.56 12	99.64 101	99.78 70	99.15 159	99.61 112	99.45 248	99.01 64	99.89 40	99.82 118	99.01 20	99.92 123	99.56 49	99.95 23	99.85 46

VDD-MVS		97.73 327	97.35 344	98.88 269	99.47 250	97.12 337	99.34 302	98.85 415	98.19 170	99.67 124	99.85 84	82.98 461	99.92 123	99.49 61	98.32 287	99.60 194

VNet		99.11 140	98.90 159	99.73 83	99.52 225	99.56 94	99.41 269	99.39 283	99.01 64	99.74 95	99.78 175	95.56 228	99.92 123	99.52 55	98.18 299	99.72 136

XVG-OURS-SEG-HR		98.69 211	98.62 207	98.89 265	99.71 117	97.74 308	99.12 372	99.54 109	98.44 133	99.42 199	99.71 211	94.20 302	99.92 123	98.54 219	98.90 249	99.00 310

mvsmamba		99.06 153	98.96 145	99.36 187	99.47 250	98.64 245	99.70 58	99.05 385	97.61 269	99.65 138	99.83 105	96.54 177	99.92 123	99.19 107	99.62 165	99.51 234

HPM-MVS_fast		99.51 29	99.40 38	99.85 43	99.91 1	99.79 41	99.76 37	99.56 90	97.72 255	99.76 91	99.75 193	99.13 14	99.92 123	99.07 128	99.92 39	99.85 46

HY-MVS	97.30 7	98.85 192	98.64 201	99.47 165	99.42 262	99.08 168	99.62 106	99.36 301	97.39 298	99.28 239	99.68 234	96.44 183	99.92 123	98.37 235	98.22 294	99.40 265

DP-MVS		99.16 111	98.95 149	99.78 71	99.77 78	99.53 101	99.41 269	99.50 178	97.03 333	99.04 296	99.88 56	97.39 125	99.92 123	98.66 195	99.90 57	99.87 40

IB-MVS	95.67 18	96.22 394	95.44 408	98.57 311	99.21 324	96.70 371	98.65 446	97.74 464	96.71 353	97.27 427	98.54 437	86.03 444	99.92 123	98.47 225	86.30 468	99.10 294
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 40	99.77 74	99.63 168	99.59 88	99.36 294	99.46 237	99.07 58	99.79 76	99.82 118	98.85 44	99.92 123	98.68 193	99.87 79	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 98	99.10 99	99.61 109	99.35 284	99.31 135	99.46 240	99.13 373	98.61 113	99.86 53	99.89 45	96.41 186	99.91 135	99.67 37	99.51 174	99.63 186

balanced_conf03		99.46 42	99.39 40	99.67 90	99.55 211	99.58 93	99.74 47	99.51 155	98.42 134	99.87 49	99.84 99	98.05 111	99.91 135	99.58 47	99.94 31	99.52 225

9.14			99.10 99		99.72 111		99.40 277	99.51 155	97.53 280	99.64 143	99.78 175	98.84 46	99.91 135	97.63 309	99.82 117

SMA-MVS		99.44 50	99.30 62	99.85 43	99.73 107	99.83 22	99.56 149	99.47 225	97.45 289	99.78 81	99.82 118	99.18 12	99.91 135	98.79 179	99.89 68	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					99.67 136	99.65 75	99.05 388	99.41 273	96.22 392	98.95 312	99.49 311	98.77 56	99.91 135

train_agg		99.02 161	98.77 182	99.77 74	99.67 136	99.65 75	99.05 388	99.41 273	96.28 386	98.95 312	99.49 311	98.76 57	99.91 135	97.63 309	99.72 148	99.75 112

test_8					99.67 136	99.61 85	99.03 393	99.41 273	96.28 386	98.93 315	99.48 317	98.76 57	99.91 135

agg_prior					99.67 136	99.62 83		99.40 280		98.87 325			99.91 135

原ACMM1				99.65 95	99.73 107	99.33 130		99.47 225	97.46 286	99.12 277	99.66 245	98.67 72	99.91 135	97.70 306	99.69 153	99.71 147

LFMVS		97.90 294	97.35 344	99.54 125	99.52 225	99.01 177	99.39 281	98.24 453	97.10 325	99.65 138	99.79 168	84.79 453	99.91 135	99.28 95	98.38 280	99.69 153

XVG-OURS		98.73 209	98.68 193	98.88 269	99.70 122	97.73 309	98.92 418	99.55 100	98.52 122	99.45 188	99.84 99	95.27 240	99.91 135	98.08 266	98.84 253	99.00 310

PLC	97.94 4	99.02 161	98.85 173	99.53 133	99.66 149	99.01 177	99.24 345	99.52 133	96.85 345	99.27 245	99.48 317	98.25 101	99.91 135	97.76 297	99.62 165	99.65 174
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PCF-MVS	97.08 14	97.66 341	97.06 369	99.47 165	99.61 188	99.09 165	98.04 473	99.25 353	91.24 459	98.51 375	99.70 215	94.55 287	99.91 135	92.76 449	99.85 94	99.42 260
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Elysia		98.88 177	98.65 199	99.58 116	99.58 198	99.34 127	99.65 88	99.52 133	98.26 155	99.83 64	99.87 69	93.37 328	99.90 148	97.81 290	99.91 46	99.49 239

StellarMVS		98.88 177	98.65 199	99.58 116	99.58 198	99.34 127	99.65 88	99.52 133	98.26 155	99.83 64	99.87 69	93.37 328	99.90 148	97.81 290	99.91 46	99.49 239

AstraMVS		99.09 146	99.03 117	99.25 213	99.66 149	98.13 285	99.57 141	98.24 453	98.82 89	99.91 31	99.88 56	95.81 217	99.90 148	99.72 32	99.67 158	99.74 117

mmtdpeth		96.95 380	96.71 379	97.67 402	99.33 290	94.90 429	99.89 2	99.28 346	98.15 175	99.72 102	98.57 436	86.56 440	99.90 148	99.82 29	89.02 463	98.20 433

UWE-MVS		97.58 347	97.29 355	98.48 324	99.09 356	96.25 391	99.01 401	96.61 476	97.86 233	99.19 266	99.01 407	88.72 415	99.90 148	97.38 337	98.69 262	99.28 280

test_vis1_rt		95.81 404	95.65 403	96.32 437	99.67 136	91.35 465	99.49 218	96.74 474	98.25 160	95.24 451	98.10 455	74.96 473	99.90 148	99.53 53	98.85 252	97.70 457

FA-MVS(test-final)		98.75 206	98.53 218	99.41 179	99.55 211	99.05 173	99.80 25	99.01 390	96.59 368	99.58 162	99.59 273	95.39 234	99.90 148	97.78 293	99.49 177	99.28 280

MCST-MVS		99.43 53	99.30 62	99.82 57	99.79 68	99.74 54	99.29 318	99.40 280	98.79 95	99.52 177	99.62 264	98.91 39	99.90 148	98.64 197	99.75 142	99.82 72

CDPH-MVS		99.13 126	98.91 157	99.80 64	99.75 92	99.71 58	99.15 366	99.41 273	96.60 366	99.60 158	99.55 288	98.83 47	99.90 148	97.48 327	99.83 113	99.78 98

NCCC		99.34 75	99.19 88	99.79 68	99.61 188	99.65 75	99.30 313	99.48 203	98.86 84	99.21 260	99.63 259	98.72 67	99.90 148	98.25 247	99.63 164	99.80 88

114514_t		98.93 173	98.67 194	99.72 86	99.85 31	99.53 101	99.62 106	99.59 73	92.65 453	99.71 111	99.78 175	98.06 110	99.90 148	98.84 168	99.91 46	99.74 117

1112_ss		98.98 169	98.77 182	99.59 113	99.68 133	99.02 175	99.25 340	99.48 203	97.23 312	99.13 275	99.58 277	96.93 153	99.90 148	98.87 158	98.78 258	99.84 53

PHI-MVS		99.30 83	99.17 91	99.70 87	99.56 207	99.52 105	99.58 133	99.80 11	97.12 321	99.62 150	99.73 204	98.58 78	99.90 148	98.61 203	99.91 46	99.68 159

AdaColmap		99.01 165	98.80 178	99.66 91	99.56 207	99.54 98	99.18 361	99.70 18	98.18 173	99.35 224	99.63 259	96.32 188	99.90 148	97.48 327	99.77 137	99.55 216

COLMAP_ROB	97.56 6	98.86 183	98.75 184	99.17 223	99.88 13	98.53 256	99.34 302	99.59 73	97.55 276	98.70 352	99.89 45	95.83 215	99.90 148	98.10 261	99.90 57	99.08 299
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
thisisatest0530		98.35 237	98.03 257	99.31 198	99.63 168	98.56 253	99.54 169	96.75 473	97.53 280	99.73 97	99.65 247	91.25 387	99.89 163	98.62 200	99.56 170	99.48 242

tttt0517		98.42 228	98.14 242	99.28 210	99.66 149	98.38 274	99.74 47	96.85 471	97.68 261	99.79 76	99.74 198	91.39 383	99.89 163	98.83 171	99.56 170	99.57 212

test12				99.75 77	99.64 164	99.61 85		99.29 344		99.21 260		98.38 95	99.89 163		99.74 145	99.74 117

Test_1112_low_res		98.89 176	98.66 197	99.57 120	99.69 127	98.95 193	99.03 393	99.47 225	96.98 335	99.15 273	99.23 382	96.77 164	99.89 163	98.83 171	98.78 258	99.86 42

CNLPA		99.14 123	98.99 137	99.59 113	99.58 198	99.41 120	99.16 363	99.44 257	98.45 130	99.19 266	99.49 311	98.08 109	99.89 163	97.73 301	99.75 142	99.48 242

diffmvs_AUTHOR		99.19 101	99.10 99	99.48 159	99.64 164	98.85 220	99.32 307	99.48 203	98.50 124	99.81 69	99.81 133	96.82 160	99.88 168	99.40 71	99.12 216	99.71 147

guyue		99.16 111	99.04 114	99.52 139	99.69 127	98.92 203	99.59 123	98.81 420	98.73 102	99.90 34	99.87 69	95.34 237	99.88 168	99.66 40	99.81 120	99.74 117

sd_testset		98.75 206	98.57 214	99.29 206	99.81 57	98.26 278	99.56 149	99.62 51	98.78 98	99.64 143	99.88 56	92.02 366	99.88 168	99.54 51	98.26 291	99.72 136

APD_test1		95.87 402	96.49 384	94.00 445	99.53 219	84.01 474	99.54 169	99.32 331	95.91 409	97.99 406	99.85 84	85.49 448	99.88 168	91.96 452	98.84 253	98.12 437

diffmvs		99.14 123	99.02 127	99.51 144	99.61 188	98.96 187	99.28 324	99.49 191	98.46 128	99.72 102	99.71 211	96.50 179	99.88 168	99.31 86	99.11 218	99.67 163
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		98.86 183	98.80 178	99.03 238	99.76 82	98.79 231	99.28 324	99.91 3	97.42 295	99.67 124	99.37 349	97.53 122	99.88 168	98.98 138	97.29 351	98.42 418

PVSNet_Blended		99.08 148	98.97 141	99.42 178	99.76 82	98.79 231	98.78 433	99.91 3	96.74 351	99.67 124	99.49 311	97.53 122	99.88 168	98.98 138	99.85 94	99.60 194

viewdifsd2359ckpt07		99.11 140	99.00 136	99.43 176	99.63 168	98.73 236	99.45 244	99.54 109	98.33 145	99.62 150	99.81 133	96.17 197	99.87 175	99.27 98	99.14 208	99.69 153

viewdifsd2359ckpt11		98.78 201	98.74 186	98.89 265	99.67 136	97.04 347	99.50 201	99.58 78	98.26 155	99.56 166	99.90 36	94.36 295	99.87 175	99.49 61	98.32 287	99.77 100

viewmsd2359difaftdt		98.78 201	98.74 186	98.90 261	99.67 136	97.04 347	99.50 201	99.58 78	98.26 155	99.56 166	99.90 36	94.36 295	99.87 175	99.49 61	98.32 287	99.77 100

MVS		97.28 369	96.55 382	99.48 159	98.78 406	98.95 193	99.27 329	99.39 283	83.53 476	98.08 401	99.54 293	96.97 151	99.87 175	94.23 429	99.16 204	99.63 186

MG-MVS		99.13 126	99.02 127	99.45 168	99.57 203	98.63 246	99.07 382	99.34 313	98.99 69	99.61 155	99.82 118	97.98 113	99.87 175	97.00 359	99.80 125	99.85 46

MSDG		98.98 169	98.80 178	99.53 133	99.76 82	99.19 150	98.75 436	99.55 100	97.25 309	99.47 185	99.77 184	97.82 116	99.87 175	96.93 366	99.90 57	99.54 218

ETV-MVS		99.26 92	99.21 84	99.40 180	99.46 252	99.30 138	99.56 149	99.52 133	98.52 122	99.44 193	99.27 377	98.41 93	99.86 181	99.10 124	99.59 168	99.04 306

thisisatest0515		98.14 255	97.79 282	99.19 221	99.50 240	98.50 264	98.61 448	96.82 472	96.95 339	99.54 173	99.43 329	91.66 378	99.86 181	98.08 266	99.51 174	99.22 288

thres600view7		97.86 300	97.51 318	98.92 255	99.72 111	97.95 299	99.59 123	98.74 430	97.94 225	99.27 245	98.62 433	91.75 372	99.86 181	93.73 435	98.19 298	98.96 316

lupinMVS		99.13 126	99.01 133	99.46 167	99.51 228	98.94 197	99.05 388	99.16 369	97.86 233	99.80 74	99.56 285	97.39 125	99.86 181	98.94 146	99.85 94	99.58 209

PVSNet	96.02 17	98.85 192	98.84 175	98.89 265	99.73 107	97.28 328	98.32 465	99.60 67	97.86 233	99.50 180	99.57 282	96.75 165	99.86 181	98.56 215	99.70 152	99.54 218

MAR-MVS		98.86 183	98.63 202	99.54 125	99.37 280	99.66 71	99.45 244	99.54 109	96.61 363	99.01 299	99.40 339	97.09 143	99.86 181	97.68 308	99.53 173	99.10 294
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	98.96 145	99.44 173	99.62 177	98.88 212	99.25 340	99.47 225	98.05 208	99.37 215	99.81 133	96.85 155	99.85 187	98.98 138	99.25 197	99.60 194

SSM_0404		99.16 111	99.06 110	99.44 173	99.65 159	98.96 187	99.49 218	99.50 178	98.14 180	99.62 150	99.85 84	96.85 155	99.85 187	99.19 107	99.26 196	99.52 225

testing91		97.44 361	97.02 370	98.71 298	99.18 332	96.89 365	99.19 359	99.04 386	97.78 248	98.31 387	98.29 447	85.41 449	99.85 187	98.01 272	97.95 308	99.39 266

test2506		96.81 384	96.65 380	97.29 417	99.74 100	92.21 462	99.60 113	85.06 493	99.13 41	99.77 85	99.93 10	87.82 432	99.85 187	99.38 74	99.38 183	99.80 88

AllTest		98.87 180	98.72 188	99.31 198	99.86 25	98.48 267	99.56 149	99.61 60	97.85 236	99.36 221	99.85 84	95.95 207	99.85 187	96.66 379	99.83 113	99.59 205

TestCases				99.31 198	99.86 25	98.48 267		99.61 60	97.85 236	99.36 221	99.85 84	95.95 207	99.85 187	96.66 379	99.83 113	99.59 205

jason		99.13 126	99.03 117	99.45 168	99.46 252	98.87 216	99.12 372	99.26 351	98.03 217	99.79 76	99.65 247	97.02 148	99.85 187	99.02 135	99.90 57	99.65 174
jason: jason.
CNVR-MVS		99.42 55	99.30 62	99.78 71	99.62 177	99.71 58	99.26 338	99.52 133	98.82 89	99.39 211	99.71 211	98.96 27	99.85 187	98.59 208	99.80 125	99.77 100

PAPM_NR		99.04 158	98.84 175	99.66 91	99.74 100	99.44 116	99.39 281	99.38 291	97.70 259	99.28 239	99.28 374	98.34 97	99.85 187	96.96 363	99.45 179	99.69 153

E6new		99.15 115	99.03 117	99.50 149	99.66 149	98.90 208	99.60 113	99.53 125	98.13 183	99.72 102	99.91 26	96.31 190	99.84 196	99.30 89	99.10 225	99.76 107

E6		99.15 115	99.03 117	99.50 149	99.66 149	98.90 208	99.60 113	99.53 125	98.13 183	99.72 102	99.91 26	96.31 190	99.84 196	99.30 89	99.10 225	99.76 107

E5		99.14 123	99.02 127	99.50 149	99.69 127	98.91 204	99.60 113	99.53 125	98.13 183	99.72 102	99.91 26	96.26 195	99.84 196	99.30 89	99.10 225	99.76 107

E4		99.13 126	99.01 133	99.49 155	99.68 133	98.90 208	99.52 180	99.52 133	98.13 183	99.71 111	99.90 36	96.32 188	99.84 196	99.21 105	99.11 218	99.75 112

E3new		99.18 104	99.08 105	99.48 159	99.63 168	98.94 197	99.46 240	99.50 178	98.06 205	99.72 102	99.84 99	97.27 133	99.84 196	99.10 124	99.13 211	99.67 163

E2		99.15 115	99.03 117	99.49 155	99.65 159	98.93 202	99.49 218	99.52 133	98.14 180	99.72 102	99.88 56	96.57 176	99.84 196	99.17 113	99.13 211	99.72 136

E3		99.15 115	99.03 117	99.49 155	99.62 177	98.91 204	99.49 218	99.52 133	98.13 183	99.72 102	99.88 56	96.61 171	99.84 196	99.17 113	99.13 211	99.72 136

viewcassd2359sk11		99.18 104	99.08 105	99.49 155	99.65 159	98.95 193	99.48 226	99.51 155	98.10 195	99.72 102	99.87 69	97.13 139	99.84 196	99.13 118	99.14 208	99.69 153

testing99		97.36 364	96.94 373	98.63 304	99.18 332	96.70 371	99.30 313	98.93 398	97.71 256	98.23 392	98.26 448	84.92 452	99.84 196	98.04 271	97.85 315	99.35 272

testing222		97.16 374	96.50 383	99.16 224	99.16 342	98.47 269	99.27 329	98.66 441	97.71 256	98.23 392	98.15 451	82.28 466	99.84 196	97.36 338	97.66 321	99.18 290

test1111		98.04 271	98.11 246	97.83 392	99.74 100	93.82 446	99.58 133	95.40 480	99.12 46	99.65 138	99.93 10	90.73 393	99.84 196	99.43 69	99.38 183	99.82 72

ECVR-MVS		98.04 271	98.05 255	98.00 374	99.74 100	94.37 441	99.59 123	94.98 481	99.13 41	99.66 129	99.93 10	90.67 394	99.84 196	99.40 71	99.38 183	99.80 88

test_yl		98.86 183	98.63 202	99.54 125	99.49 242	99.18 152	99.50 201	99.07 382	98.22 166	99.61 155	99.51 305	95.37 235	99.84 196	98.60 206	98.33 283	99.59 205

DCV-MVSNet		98.86 183	98.63 202	99.54 125	99.49 242	99.18 152	99.50 201	99.07 382	98.22 166	99.61 155	99.51 305	95.37 235	99.84 196	98.60 206	98.33 283	99.59 205

Fast-Effi-MVS+		98.70 210	98.43 223	99.51 144	99.51 228	99.28 141	99.52 180	99.47 225	96.11 402	99.01 299	99.34 359	96.20 196	99.84 196	97.88 280	98.82 255	99.39 266

TSAR-MVS + GP.		99.36 72	99.36 46	99.36 187	99.67 136	98.61 250	99.07 382	99.33 321	99.00 67	99.82 68	99.81 133	99.06 18	99.84 196	99.09 126	99.42 181	99.65 174

tpmrst		98.33 238	98.48 221	97.90 383	99.16 342	94.78 430	99.31 311	99.11 375	97.27 307	99.45 188	99.59 273	95.33 238	99.84 196	98.48 222	98.61 265	99.09 298

Vis-MVSNet		99.12 134	98.97 141	99.56 122	99.78 70	99.10 164	99.68 72	99.66 32	98.49 125	99.86 53	99.87 69	94.77 269	99.84 196	99.19 107	99.41 182	99.74 117
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
PAPR		98.63 218	98.34 229	99.51 144	99.40 272	99.03 174	98.80 431	99.36 301	96.33 383	99.00 303	99.12 396	98.46 87	99.84 196	95.23 415	99.37 190	99.66 168

PatchMatch-RL		98.84 195	98.62 207	99.52 139	99.71 117	99.28 141	99.06 386	99.77 12	97.74 254	99.50 180	99.53 297	95.41 233	99.84 196	97.17 352	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	99.33 321	97.43 293	99.60 158	99.88 56	97.14 138	99.84 196	99.13 118	98.94 240	99.69 153

SSM_0407		99.13 126	99.03 117	99.43 176	99.62 177	98.88 212	99.51 190	99.50 178	98.14 180	99.37 215	99.85 84	96.85 155	99.83 217	99.19 107	99.25 197	99.60 194

testing3-2		97.84 305	97.70 297	98.24 356	99.53 219	95.37 418	99.55 164	98.67 440	98.46 128	99.27 245	99.34 359	86.58 439	99.83 217	99.32 84	98.63 264	99.52 225

testing11		97.50 354	97.10 367	98.71 298	99.20 326	96.91 363	99.29 318	98.82 418	97.89 230	98.21 395	98.40 442	85.63 447	99.83 217	98.45 227	98.04 306	99.37 270

thres100view900		97.76 319	97.45 327	98.69 300	99.72 111	97.86 305	99.59 123	98.74 430	97.93 226	99.26 250	98.62 433	91.75 372	99.83 217	93.22 441	98.18 299	98.37 424

tfpn200view9		97.72 329	97.38 340	98.72 295	99.69 127	97.96 296	99.50 201	98.73 436	97.83 240	99.17 271	98.45 440	91.67 376	99.83 217	93.22 441	98.18 299	98.37 424

test_prior				99.68 89	99.67 136	99.48 111		99.56 90					99.83 217			99.74 117

1314		98.68 212	98.54 217	99.11 230	98.89 389	98.65 243	99.27 329	99.49 191	96.89 343	97.99 406	99.56 285	97.72 120	99.83 217	97.74 300	99.27 194	98.84 322

thres400		97.77 318	97.38 340	98.92 255	99.69 127	97.96 296	99.50 201	98.73 436	97.83 240	99.17 271	98.45 440	91.67 376	99.83 217	93.22 441	98.18 299	98.96 316

casdiffmvs		99.13 126	98.98 140	99.56 122	99.65 159	99.16 155	99.56 149	99.50 178	98.33 145	99.41 204	99.86 77	95.92 210	99.83 217	99.45 68	99.16 204	99.70 150
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 78	98.56 118	99.73 97	99.69 226	98.55 81	99.82 226	99.69 35	99.85 94	99.48 242

MVS_Test		99.10 145	98.97 141	99.48 159	99.49 242	99.14 160	99.67 75	99.34 313	97.31 304	99.58 162	99.76 188	97.65 121	99.82 226	98.87 158	99.07 231	99.46 253

dp		97.75 323	97.80 281	97.59 408	99.10 353	93.71 449	99.32 307	98.88 411	96.48 375	99.08 287	99.55 288	92.67 350	99.82 226	96.52 383	98.58 268	99.24 286

RPSCF		98.22 245	98.62 207	96.99 424	99.82 53	91.58 464	99.72 53	99.44 257	96.61 363	99.66 129	99.89 45	95.92 210	99.82 226	97.46 330	99.10 225	99.57 212

PMMVS		98.80 199	98.62 207	99.34 190	99.27 308	98.70 239	98.76 435	99.31 335	97.34 301	99.21 260	99.07 398	97.20 137	99.82 226	98.56 215	98.87 250	99.52 225

UBG		97.85 301	97.48 321	98.95 249	99.25 315	97.64 316	99.24 345	98.74 430	97.90 229	98.64 362	98.20 450	88.65 419	99.81 231	98.27 245	98.40 278	99.42 260

EIA-MVS		99.18 104	99.09 104	99.45 168	99.49 242	99.18 152	99.67 75	99.53 125	97.66 264	99.40 209	99.44 327	98.10 107	99.81 231	98.94 146	99.62 165	99.35 272

Effi-MVS+		98.81 196	98.59 213	99.48 159	99.46 252	99.12 163	98.08 472	99.50 178	97.50 284	99.38 213	99.41 335	96.37 187	99.81 231	99.11 121	98.54 273	99.51 234

thres200		97.61 345	97.28 356	98.62 305	99.64 164	98.03 290	99.26 338	98.74 430	97.68 261	99.09 285	98.32 446	91.66 378	99.81 231	92.88 446	98.22 294	98.03 443

tpmvs		97.98 282	98.02 259	97.84 390	99.04 367	94.73 431	99.31 311	99.20 364	96.10 406	98.76 342	99.42 331	94.94 254	99.81 231	96.97 362	98.45 277	98.97 314

casdiffmvs_mvg		99.15 115	99.02 127	99.55 124	99.66 149	99.09 165	99.64 95	99.56 90	98.26 155	99.45 188	99.87 69	96.03 203	99.81 231	99.54 51	99.15 207	99.73 126
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	98.81 196	99.37 44	97.12 421	99.60 194	91.75 463	98.61 448	99.44 257	99.35 25	99.83 64	99.85 84	98.70 69	99.81 231	99.02 135	99.91 46	99.81 79

viewmacassd2359aftdt		99.08 148	98.94 151	99.50 149	99.66 149	98.96 187	99.51 190	99.54 109	98.27 152	99.42 199	99.89 45	95.88 214	99.80 238	99.20 106	99.11 218	99.76 107

viewmanbaseed2359cas		99.18 104	99.07 109	99.50 149	99.62 177	99.01 177	99.50 201	99.52 133	98.25 160	99.68 118	99.82 118	96.93 153	99.80 238	99.15 117	99.11 218	99.70 150

IMVS_0403		98.86 183	98.89 163	98.78 290	99.55 211	96.93 358	99.58 133	99.44 257	98.05 208	99.68 118	99.80 151	96.81 161	99.80 238	98.15 257	98.92 243	99.60 194

DPM-MVS		98.95 172	98.71 190	99.66 91	99.63 168	99.55 96	98.64 447	99.10 376	97.93 226	99.42 199	99.55 288	98.67 72	99.80 238	95.80 400	99.68 156	99.61 191

DP-MVS Recon		99.12 134	98.95 149	99.65 95	99.74 100	99.70 60	99.27 329	99.57 85	96.40 382	99.42 199	99.68 234	98.75 60	99.80 238	97.98 274	99.72 148	99.44 258

MVS_111021_LR		99.41 59	99.33 52	99.65 95	99.77 78	99.51 107	98.94 416	99.85 9	98.82 89	99.65 138	99.74 198	98.51 84	99.80 238	98.83 171	99.89 68	99.64 181

viewmambaseed2359dif		99.01 165	98.90 159	99.32 196	99.58 198	98.51 262	99.33 304	99.54 109	97.85 236	99.44 193	99.85 84	96.01 204	99.79 244	99.41 70	99.13 211	99.67 163

CS-MVS		99.50 31	99.48 22	99.54 125	99.76 82	99.42 118	99.90 1	99.55 100	98.56 118	99.78 81	99.70 215	98.65 74	99.79 244	99.65 41	99.78 134	99.41 263

Fast-Effi-MVS+-dtu		98.77 205	98.83 177	98.60 306	99.41 267	96.99 353	99.52 180	99.49 191	98.11 192	99.24 252	99.34 359	96.96 152	99.79 244	97.95 276	99.45 179	99.02 309

baseline1		98.31 239	97.95 266	99.38 186	99.50 240	98.74 235	99.59 123	98.93 398	98.41 135	99.14 274	99.60 271	94.59 283	99.79 244	98.48 222	93.29 435	99.61 191

baseline		99.15 115	99.02 127	99.53 133	99.66 149	99.14 160	99.72 53	99.48 203	98.35 142	99.42 199	99.84 99	96.07 200	99.79 244	99.51 56	99.14 208	99.67 163

PVSNet_0	94.43 19	96.09 399	95.47 406	97.94 379	99.31 298	94.34 443	97.81 474	99.70 18	97.12 321	97.46 421	98.75 430	89.71 405	99.79 244	97.69 307	81.69 475	99.68 159

API-MVS		99.04 158	99.03 117	99.06 234	99.40 272	99.31 135	99.55 164	99.56 90	98.54 120	99.33 229	99.39 343	98.76 57	99.78 250	96.98 361	99.78 134	98.07 440

OMC-MVS		99.08 148	99.04 114	99.20 220	99.67 136	98.22 280	99.28 324	99.52 133	98.07 201	99.66 129	99.81 133	97.79 117	99.78 250	97.79 292	99.81 120	99.60 194

GeoE		98.85 192	98.62 207	99.53 133	99.61 188	99.08 168	99.80 25	99.51 155	97.10 325	99.31 231	99.78 175	95.23 245	99.77 252	98.21 249	99.03 234	99.75 112

alignmvs		98.81 196	98.56 216	99.58 116	99.43 260	99.42 118	99.51 190	98.96 396	98.61 113	99.35 224	98.92 420	94.78 266	99.77 252	99.35 76	98.11 304	99.54 218

tpm cat1		97.39 363	97.36 342	97.50 411	99.17 340	93.73 448	99.43 257	99.31 335	91.27 458	98.71 346	99.08 397	94.31 300	99.77 252	96.41 388	98.50 275	99.00 310

CostFormer		97.72 329	97.73 294	97.71 400	99.15 346	94.02 445	99.54 169	99.02 389	94.67 431	99.04 296	99.35 355	92.35 362	99.77 252	98.50 221	97.94 309	99.34 275

MGCFI-Net		99.01 165	98.85 173	99.50 149	99.42 262	99.26 144	99.82 16	99.48 203	98.60 115	99.28 239	98.81 425	97.04 147	99.76 256	99.29 94	97.87 313	99.47 248

test_241102_ONE					99.84 38	99.90 3		99.48 203	99.07 58	99.91 31	99.74 198	99.20 9	99.76 256

MDTV_nov1_ep13			98.32 231		99.11 350	94.44 439	99.27 329	98.74 430	97.51 283	99.40 209	99.62 264	94.78 266	99.76 256	97.59 312	98.81 257

viewdifsd2359ckpt09		99.01 165	98.87 167	99.40 180	99.62 177	98.79 231	99.44 251	99.51 155	97.76 250	99.35 224	99.69 226	96.42 185	99.75 259	98.97 143	99.11 218	99.66 168

sasdasda		99.02 161	98.86 170	99.51 144	99.42 262	99.32 131	99.80 25	99.48 203	98.63 110	99.31 231	98.81 425	97.09 143	99.75 259	99.27 98	97.90 310	99.47 248

canonicalmvs		99.02 161	98.86 170	99.51 144	99.42 262	99.32 131	99.80 25	99.48 203	98.63 110	99.31 231	98.81 425	97.09 143	99.75 259	99.27 98	97.90 310	99.47 248

Effi-MVS+-dtu		98.78 201	98.89 163	98.47 329	99.33 290	96.91 363	99.57 141	99.30 340	98.47 127	99.41 204	98.99 410	96.78 163	99.74 262	98.73 185	99.38 183	98.74 338

patchmatchnet-post											98.70 431	94.79 265	99.74 262

SCA		98.19 249	98.16 239	98.27 355	99.30 299	95.55 409	99.07 382	98.97 394	97.57 273	99.43 196	99.57 282	92.72 345	99.74 262	97.58 313	99.20 202	99.52 225

BH-untuned		98.42 228	98.36 227	98.59 307	99.49 242	96.70 371	99.27 329	99.13 373	97.24 311	98.80 337	99.38 346	95.75 221	99.74 262	97.07 357	99.16 204	99.33 276

BH-RMVSNet		98.41 230	98.08 251	99.40 180	99.41 267	98.83 225	99.30 313	98.77 426	97.70 259	98.94 314	99.65 247	92.91 340	99.74 262	96.52 383	99.55 172	99.64 181

MVS_111021_HR		99.41 59	99.32 54	99.66 91	99.72 111	99.47 113	98.95 414	99.85 9	98.82 89	99.54 173	99.73 204	98.51 84	99.74 262	98.91 152	99.88 76	99.77 100

test_post											65.99 487	94.65 281	99.73 268

XVG-ACMP-BASELINE		97.83 308	97.71 296	98.20 358	99.11 350	96.33 387	99.41 269	99.52 133	98.06 205	99.05 295	99.50 308	89.64 407	99.73 268	97.73 301	97.38 348	98.53 405

HyFIR lowres test		99.11 140	98.92 154	99.65 95	99.90 4	99.37 123	99.02 396	99.91 3	97.67 263	99.59 161	99.75 193	95.90 212	99.73 268	99.53 53	99.02 236	99.86 42

DeepMVS_CX				93.34 448	99.29 303	82.27 477		99.22 359	85.15 474	96.33 443	99.05 401	90.97 391	99.73 268	93.57 437	97.77 318	98.01 444

Patchmatch-test		97.93 288	97.65 302	98.77 291	99.18 332	97.07 342	99.03 393	99.14 372	96.16 397	98.74 343	99.57 282	94.56 285	99.72 272	93.36 439	99.11 218	99.52 225

LPG-MVS_test		98.22 245	98.13 244	98.49 322	99.33 290	97.05 344	99.58 133	99.55 100	97.46 286	99.24 252	99.83 105	92.58 352	99.72 272	98.09 262	97.51 334	98.68 356

LGP-MVS_train				98.49 322	99.33 290	97.05 344		99.55 100	97.46 286	99.24 252	99.83 105	92.58 352	99.72 272	98.09 262	97.51 334	98.68 356

BH-w/o		98.00 280	97.89 275	98.32 347	99.35 284	96.20 393	99.01 401	98.90 408	96.42 380	98.38 382	99.00 408	95.26 242	99.72 272	96.06 393	98.61 265	99.03 307

ACMP	97.20 11	98.06 265	97.94 268	98.45 332	99.37 280	97.01 351	99.44 251	99.49 191	97.54 279	98.45 379	99.79 168	91.95 368	99.72 272	97.91 278	97.49 339	98.62 386
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LTVRE_ROB	97.16 12	98.02 275	97.90 271	98.40 340	99.23 319	96.80 369	99.70 58	99.60 67	97.12 321	98.18 397	99.70 215	91.73 374	99.72 272	98.39 232	97.45 341	98.68 356
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	98.93 153	99.45 168	99.63 168	98.96 187	99.50 201	99.51 155	97.83 240	99.28 239	99.80 151	96.68 169	99.71 278	99.05 130	99.12 216	99.68 159

test_post1							99.23 348				65.14 488	94.18 305	99.71 278	97.58 313

ADS-MVSNet		98.20 248	98.08 251	98.56 315	99.33 290	96.48 382	99.23 348	99.15 370	96.24 390	99.10 282	99.67 240	94.11 307	99.71 278	96.81 371	99.05 232	99.48 242

JIA-IIPM		97.50 354	97.02 370	98.93 253	98.73 415	97.80 307	99.30 313	98.97 394	91.73 457	98.91 317	94.86 476	95.10 249	99.71 278	97.58 313	97.98 307	99.28 280

EPMVS		97.82 311	97.65 302	98.35 344	98.88 390	95.98 397	99.49 218	94.71 483	97.57 273	99.26 250	99.48 317	92.46 359	99.71 278	97.87 282	99.08 230	99.35 272

TDRefinement		95.42 410	94.57 418	97.97 376	89.83 486	96.11 396	99.48 226	98.75 427	96.74 351	96.68 440	99.88 56	88.65 419	99.71 278	98.37 235	82.74 473	98.09 439

ACMM	97.58 5	98.37 236	98.34 229	98.48 324	99.41 267	97.10 338	99.56 149	99.45 248	98.53 121	99.04 296	99.85 84	93.00 336	99.71 278	98.74 183	97.45 341	98.64 377
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tt0805		97.97 285	97.77 287	98.57 311	99.59 196	96.61 378	99.45 244	99.08 379	98.21 168	98.88 322	99.80 151	88.66 418	99.70 285	98.58 209	97.72 319	99.39 266

CHOSEN 280x420		99.12 134	99.13 95	99.08 231	99.66 149	97.89 302	98.43 459	99.71 16	98.88 83	99.62 150	99.76 188	96.63 170	99.70 285	99.46 67	99.99 1	99.66 168

EC-MVSNet		99.44 50	99.39 40	99.58 116	99.56 207	99.49 109	99.88 4	99.58 78	98.38 137	99.73 97	99.69 226	98.20 103	99.70 285	99.64 43	99.82 117	99.54 218

PatchmatchNet		98.31 239	98.36 227	98.19 359	99.16 342	95.32 419	99.27 329	98.92 401	97.37 299	99.37 215	99.58 277	94.90 259	99.70 285	97.43 334	99.21 201	99.54 218
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
ACMH	97.28 8	98.10 259	97.99 261	98.44 335	99.41 267	96.96 357	99.60 113	99.56 90	98.09 196	98.15 399	99.91 26	90.87 392	99.70 285	98.88 155	97.45 341	98.67 364
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ETVMVS		97.50 354	96.90 374	99.29 206	99.23 319	98.78 234	99.32 307	98.90 408	97.52 282	98.56 372	98.09 456	84.72 454	99.69 290	97.86 283	97.88 312	99.39 266

HQP_MVS		98.27 244	98.22 237	98.44 335	99.29 303	96.97 355	99.39 281	99.47 225	98.97 75	99.11 279	99.61 268	92.71 347	99.69 290	97.78 293	97.63 322	98.67 364

plane_prior5								99.47 225					99.69 290	97.78 293	97.63 322	98.67 364

D2MVS		98.41 230	98.50 220	98.15 364	99.26 311	96.62 377	99.40 277	99.61 60	97.71 256	98.98 306	99.36 352	96.04 202	99.67 293	98.70 188	97.41 346	98.15 436

IS-MVSNet		99.05 157	98.87 167	99.57 120	99.73 107	99.32 131	99.75 42	99.20 364	98.02 220	99.56 166	99.86 77	96.54 177	99.67 293	98.09 262	99.13 211	99.73 126

CLD-MVS		98.16 253	98.10 247	98.33 345	99.29 303	96.82 368	98.75 436	99.44 257	97.83 240	99.13 275	99.55 288	92.92 338	99.67 293	98.32 242	97.69 320	98.48 410
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
test_fmvs2		97.25 371	97.30 353	97.09 422	99.43 260	93.31 455	99.73 51	98.87 413	98.83 88	99.28 239	99.80 151	84.45 455	99.66 296	97.88 280	97.45 341	98.30 426

AUN-MVS		96.88 382	96.31 388	98.59 307	99.48 249	97.04 347	99.27 329	99.22 359	97.44 292	98.51 375	99.41 335	91.97 367	99.66 296	97.71 304	83.83 471	99.07 304

UniMVSNet_ETH3D		97.32 368	96.81 376	98.87 273	99.40 272	97.46 322	99.51 190	99.53 125	95.86 410	98.54 374	99.77 184	82.44 464	99.66 296	98.68 193	97.52 333	99.50 238

OPM-MVS		98.19 249	98.10 247	98.45 332	98.88 390	97.07 342	99.28 324	99.38 291	98.57 117	99.22 257	99.81 133	92.12 364	99.66 296	98.08 266	97.54 331	98.61 395
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
ACMH+	97.24 10	97.92 291	97.78 285	98.32 347	99.46 252	96.68 375	99.56 149	99.54 109	98.41 135	97.79 417	99.87 69	90.18 401	99.66 296	98.05 270	97.18 356	98.62 386

IMVS_0407		98.86 183	98.91 157	98.72 295	99.55 211	96.93 358	99.50 201	99.44 257	98.05 208	99.66 129	99.80 151	97.13 139	99.65 301	98.15 257	98.92 243	99.60 194

hse-mvs2		97.50 354	97.14 364	98.59 307	99.49 242	97.05 344	99.28 324	99.22 359	98.94 78	99.66 129	99.42 331	94.93 255	99.65 301	99.48 64	83.80 472	99.08 299

VPA-MVSNet		98.29 242	97.95 266	99.30 203	99.16 342	99.54 98	99.50 201	99.58 78	98.27 152	99.35 224	99.37 349	92.53 354	99.65 301	99.35 76	94.46 416	98.72 340

TR-MVS		97.76 319	97.41 338	98.82 282	99.06 362	97.87 303	98.87 424	98.56 444	96.63 362	98.68 354	99.22 383	92.49 355	99.65 301	95.40 411	97.79 317	98.95 318

reproduce_monomvs		97.89 295	97.87 276	97.96 378	99.51 228	95.45 414	99.60 113	99.25 353	99.17 36	98.85 331	99.49 311	89.29 410	99.64 305	99.35 76	96.31 373	98.78 326

gm-plane-assit					98.54 436	92.96 457			94.65 432		99.15 391		99.64 305	97.56 318

HQP4-MVS										98.66 355			99.64 305			98.64 377

HQP-MVS		98.02 275	97.90 271	98.37 343	99.19 329	96.83 366	98.98 407	99.39 283	98.24 162	98.66 355	99.40 339	92.47 356	99.64 305	97.19 349	97.58 327	98.64 377

PAPM		97.59 346	97.09 368	99.07 232	99.06 362	98.26 278	98.30 466	99.10 376	94.88 426	98.08 401	99.34 359	96.27 193	99.64 305	89.87 460	98.92 243	99.31 278

TAPA-MVS	97.07 15	97.74 325	97.34 347	98.94 251	99.70 122	97.53 319	99.25 340	99.51 155	91.90 456	99.30 235	99.63 259	98.78 53	99.64 305	88.09 467	99.87 79	99.65 174
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
XXY-MVS		98.38 234	98.09 250	99.24 216	99.26 311	99.32 131	99.56 149	99.55 100	97.45 289	98.71 346	99.83 105	93.23 331	99.63 311	98.88 155	96.32 372	98.76 332

ITE_SJBPF				98.08 367	99.29 303	96.37 385		98.92 401	98.34 143	98.83 332	99.75 193	91.09 389	99.62 312	95.82 398	97.40 347	98.25 430

LF4IMVS		97.52 351	97.46 326	97.70 401	98.98 378	95.55 409	99.29 318	98.82 418	98.07 201	98.66 355	99.64 253	89.97 402	99.61 313	97.01 358	96.68 362	97.94 451

tpm		97.67 340	97.55 311	98.03 369	99.02 369	95.01 426	99.43 257	98.54 446	96.44 378	99.12 277	99.34 359	91.83 371	99.60 314	97.75 299	96.46 368	99.48 242

tpm2		97.44 361	97.34 347	97.74 399	99.15 346	94.36 442	99.45 244	98.94 397	93.45 445	98.90 319	99.44 327	91.35 384	99.59 315	97.31 340	98.07 305	99.29 279

SSM_04072		99.06 153	98.96 145	99.35 189	99.62 177	98.88 212	99.25 340	99.47 225	98.05 208	99.37 215	99.81 133	96.85 155	99.58 316	98.98 138	99.25 197	99.60 194

SD_0403		97.55 348	97.53 315	97.62 404	99.61 188	93.64 452	99.72 53	99.44 257	98.03 217	98.62 367	99.39 343	96.06 201	99.57 317	87.88 469	99.01 237	99.66 168

baseline2		97.87 298	97.55 311	98.82 282	99.18 332	98.02 291	99.41 269	96.58 477	96.97 336	96.51 441	99.17 388	93.43 326	99.57 317	97.71 304	99.03 234	98.86 320

MS-PatchMatch		97.24 373	97.32 351	96.99 424	98.45 439	93.51 454	98.82 429	99.32 331	97.41 296	98.13 400	99.30 370	88.99 412	99.56 319	95.68 404	99.80 125	97.90 454

TinyColmap		97.12 376	96.89 375	97.83 392	99.07 360	95.52 412	98.57 451	98.74 430	97.58 272	97.81 416	99.79 168	88.16 426	99.56 319	95.10 416	97.21 354	98.39 422

USDC		97.34 366	97.20 361	97.75 397	99.07 360	95.20 421	98.51 456	99.04 386	97.99 221	98.31 387	99.86 77	89.02 411	99.55 321	95.67 405	97.36 349	98.49 409

MSLP-MVS++		99.46 42	99.47 24	99.44 173	99.60 194	99.16 155	99.41 269	99.71 16	98.98 72	99.45 188	99.78 175	99.19 11	99.54 322	99.28 95	99.84 102	99.63 186

UWE-MVS-28		97.36 364	97.24 360	97.75 397	98.84 399	94.44 439	99.24 345	97.58 466	97.98 222	99.00 303	99.00 408	91.35 384	99.53 323	93.75 434	98.39 279	99.27 284

TAMVS		99.12 134	99.08 105	99.24 216	99.46 252	98.55 254	99.51 190	99.46 237	98.09 196	99.45 188	99.82 118	98.34 97	99.51 324	98.70 188	98.93 241	99.67 163

EPNet_dtu		98.03 273	97.96 264	98.23 357	98.27 442	95.54 411	99.23 348	98.75 427	99.02 62	97.82 415	99.71 211	96.11 199	99.48 325	93.04 444	99.65 161	99.69 153
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
mvs5depth		96.66 386	96.22 390	97.97 376	97.00 464	96.28 389	98.66 445	99.03 388	96.61 363	96.93 438	99.79 168	87.20 435	99.47 326	96.65 381	94.13 423	98.16 435

EG-PatchMatch MVS		95.97 401	95.69 402	96.81 431	97.78 449	92.79 458	99.16 363	98.93 398	96.16 397	94.08 460	99.22 383	82.72 462	99.47 326	95.67 405	97.50 336	98.17 434

myMVS_eth3d28		97.69 334	97.34 347	98.73 293	99.27 308	97.52 320	99.33 304	98.78 425	98.03 217	98.82 334	98.49 438	86.64 438	99.46 328	98.44 228	98.24 293	99.23 287

MVP-Stereo		97.81 313	97.75 292	97.99 375	97.53 453	96.60 379	98.96 411	98.85 415	97.22 313	97.23 428	99.36 352	95.28 239	99.46 328	95.51 407	99.78 134	97.92 453
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
CVMVSNet		98.57 220	98.67 194	98.30 349	99.35 284	95.59 408	99.50 201	99.55 100	98.60 115	99.39 211	99.83 105	94.48 291	99.45 330	98.75 182	98.56 271	99.85 46

test-LLR		98.06 265	97.90 271	98.55 317	98.79 403	97.10 338	98.67 442	97.75 462	97.34 301	98.61 368	98.85 422	94.45 293	99.45 330	97.25 343	99.38 183	99.10 294

TESTMET0.1,1		97.55 348	97.27 359	98.40 340	98.93 383	96.53 380	98.67 442	97.61 465	96.96 337	98.64 362	99.28 374	88.63 421	99.45 330	97.30 341	99.38 183	99.21 289

test-mter		97.49 359	97.13 366	98.55 317	98.79 403	97.10 338	98.67 442	97.75 462	96.65 358	98.61 368	98.85 422	88.23 425	99.45 330	97.25 343	99.38 183	99.10 294

mvs_anonymous		99.03 160	98.99 137	99.16 224	99.38 277	98.52 260	99.51 190	99.38 291	97.79 246	99.38 213	99.81 133	97.30 131	99.45 330	99.35 76	98.99 238	99.51 234

tfpnnormal		97.84 305	97.47 324	98.98 244	99.20 326	99.22 149	99.64 95	99.61 60	96.32 384	98.27 391	99.70 215	93.35 330	99.44 335	95.69 403	95.40 399	98.27 428

v7n		97.87 298	97.52 316	98.92 255	98.76 413	98.58 252	99.84 12	99.46 237	96.20 393	98.91 317	99.70 215	94.89 260	99.44 335	96.03 394	93.89 428	98.75 334

jajsoiax		98.43 227	98.28 234	98.88 269	98.60 431	98.43 271	99.82 16	99.53 125	98.19 170	98.63 364	99.80 151	93.22 333	99.44 335	99.22 103	97.50 336	98.77 330

mvs_tets		98.40 233	98.23 236	98.91 259	98.67 424	98.51 262	99.66 82	99.53 125	98.19 170	98.65 361	99.81 133	92.75 342	99.44 335	99.31 86	97.48 340	98.77 330

sc_t1		95.75 405	95.05 412	97.87 385	98.83 400	94.61 436	99.21 354	99.45 248	87.45 469	97.97 408	99.85 84	81.19 469	99.43 339	98.27 245	93.20 437	99.57 212

Vis-MVSNet (Re-imp)		98.87 180	98.72 188	99.31 198	99.71 117	98.88 212	99.80 25	99.44 257	97.91 228	99.36 221	99.78 175	95.49 231	99.43 339	97.91 278	99.11 218	99.62 189

OPU-MVS				99.64 101	99.56 207	99.72 56	99.60 113				99.70 215	99.27 7	99.42 341	98.24 248	99.80 125	99.79 92

Anonymous20231211		97.88 296	97.54 314	98.90 261	99.71 117	98.53 256	99.48 226	99.57 85	94.16 436	98.81 335	99.68 234	93.23 331	99.42 341	98.84 168	94.42 418	98.76 332

ttmdpeth		97.80 315	97.63 306	98.29 350	98.77 411	97.38 325	99.64 95	99.36 301	98.78 98	96.30 444	99.58 277	92.34 363	99.39 343	98.36 237	95.58 394	98.10 438

VPNet		97.84 305	97.44 332	99.01 240	99.21 324	98.94 197	99.48 226	99.57 85	98.38 137	99.28 239	99.73 204	88.89 413	99.39 343	99.19 107	93.27 436	98.71 342

nrg030		98.64 217	98.42 224	99.28 210	99.05 365	99.69 63	99.81 20	99.46 237	98.04 215	99.01 299	99.82 118	96.69 167	99.38 345	99.34 81	94.59 415	98.78 326

GA-MVS		97.85 301	97.47 324	99.00 242	99.38 277	97.99 293	98.57 451	99.15 370	97.04 332	98.90 319	99.30 370	89.83 404	99.38 345	96.70 376	98.33 283	99.62 189

UniMVSNet (Re)		98.29 242	98.00 260	99.13 229	99.00 372	99.36 126	99.49 218	99.51 155	97.95 224	98.97 308	99.13 393	96.30 192	99.38 345	98.36 237	93.34 434	98.66 373

FIs		98.78 201	98.63 202	99.23 218	99.18 332	99.54 98	99.83 15	99.59 73	98.28 150	98.79 339	99.81 133	96.75 165	99.37 348	99.08 127	96.38 370	98.78 326

PS-MVSNAJss		98.92 174	98.92 154	98.90 261	98.78 406	98.53 256	99.78 32	99.54 109	98.07 201	99.00 303	99.76 188	99.01 20	99.37 348	99.13 118	97.23 353	98.81 323

CDS-MVSNet		99.09 146	99.03 117	99.25 213	99.42 262	98.73 236	99.45 244	99.46 237	98.11 192	99.46 187	99.77 184	98.01 112	99.37 348	98.70 188	98.92 243	99.66 168
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MVS-HIRNet		95.75 405	95.16 410	97.51 410	99.30 299	93.69 450	98.88 422	95.78 478	85.09 475	98.78 340	92.65 478	91.29 386	99.37 348	94.85 421	99.85 94	99.46 253

v1192		97.81 313	97.44 332	98.91 259	98.88 390	98.68 240	99.51 190	99.34 313	96.18 395	99.20 263	99.34 359	94.03 311	99.36 352	95.32 413	95.18 403	98.69 351

EI-MVSNet		98.67 213	98.67 194	98.68 301	99.35 284	97.97 294	99.50 201	99.38 291	96.93 342	99.20 263	99.83 105	97.87 114	99.36 352	98.38 233	97.56 329	98.71 342

MVSTER		98.49 222	98.32 231	99.00 242	99.35 284	99.02 175	99.54 169	99.38 291	97.41 296	99.20 263	99.73 204	93.86 319	99.36 352	98.87 158	97.56 329	98.62 386

gg-mvs-nofinetune		96.17 397	95.32 409	98.73 293	98.79 403	98.14 284	99.38 286	94.09 484	91.07 461	98.07 404	91.04 482	89.62 408	99.35 355	96.75 373	99.09 229	98.68 356

pm-mvs1		97.68 337	97.28 356	98.88 269	99.06 362	98.62 248	99.50 201	99.45 248	96.32 384	97.87 413	99.79 168	92.47 356	99.35 355	97.54 320	93.54 432	98.67 364

OurMVSNet-221017-0		97.88 296	97.77 287	98.19 359	98.71 419	96.53 380	99.88 4	99.00 391	97.79 246	98.78 340	99.94 6	91.68 375	99.35 355	97.21 345	96.99 360	98.69 351

EGC-MVSNET		82.80 446	77.86 452	97.62 404	97.91 446	96.12 395	99.33 304	99.28 346	8.40 490	25.05 491	99.27 377	84.11 456	99.33 358	89.20 462	98.22 294	97.42 464

pmmvs6		96.53 389	96.09 394	97.82 394	98.69 422	95.47 413	99.37 288	99.47 225	93.46 444	97.41 422	99.78 175	87.06 437	99.33 358	96.92 368	92.70 444	98.65 375

V42		98.06 265	97.79 282	98.86 276	98.98 378	98.84 222	99.69 62	99.34 313	96.53 370	99.30 235	99.37 349	94.67 278	99.32 360	97.57 317	94.66 413	98.42 418

lessismore_v0				97.79 396	98.69 422	95.44 416		94.75 482		95.71 450	99.87 69	88.69 417	99.32 360	95.89 397	94.93 410	98.62 386

OpenMVS_ROB	92.34 20	94.38 425	93.70 431	96.41 436	97.38 455	93.17 456	99.06 386	98.75 427	86.58 472	94.84 457	98.26 448	81.53 467	99.32 360	89.01 463	97.87 313	96.76 467

v8		97.95 287	97.63 306	98.93 253	98.95 382	98.81 230	99.80 25	99.41 273	96.03 407	99.10 282	99.42 331	94.92 257	99.30 363	96.94 365	94.08 425	98.66 373

v1921920		97.80 315	97.45 327	98.84 280	98.80 402	98.53 256	99.52 180	99.34 313	96.15 399	99.24 252	99.47 320	93.98 313	99.29 364	95.40 411	95.13 405	98.69 351

anonymousdsp		98.44 226	98.28 234	98.94 251	98.50 437	98.96 187	99.77 34	99.50 178	97.07 327	98.87 325	99.77 184	94.76 270	99.28 365	98.66 195	97.60 325	98.57 402

MVSFormer		99.17 109	99.12 97	99.29 206	99.51 228	98.94 197	99.88 4	99.46 237	97.55 276	99.80 74	99.65 247	97.39 125	99.28 365	99.03 133	99.85 94	99.65 174

test_djsdf		98.67 213	98.57 214	98.98 244	98.70 420	98.91 204	99.88 4	99.46 237	97.55 276	99.22 257	99.88 56	95.73 222	99.28 365	99.03 133	97.62 324	98.75 334

VortexMVS		98.67 213	98.66 197	98.68 301	99.62 177	97.96 296	99.59 123	99.41 273	98.13 183	99.31 231	99.70 215	95.48 232	99.27 368	99.40 71	97.32 350	98.79 324

SSC-MVS3.2		97.34 366	97.15 363	97.93 380	99.02 369	95.76 405	99.48 226	99.58 78	97.62 268	99.09 285	99.53 297	87.95 428	99.27 368	96.42 386	95.66 392	98.75 334

cascas		97.69 334	97.43 336	98.48 324	98.60 431	97.30 327	98.18 470	99.39 283	92.96 449	98.41 380	98.78 429	93.77 322	99.27 368	98.16 255	98.61 265	98.86 320

v144192		97.92 291	97.60 309	98.87 273	98.83 400	98.65 243	99.55 164	99.34 313	96.20 393	99.32 230	99.40 339	94.36 295	99.26 371	96.37 390	95.03 407	98.70 347

dmvs_re		98.08 263	98.16 239	97.85 388	99.55 211	94.67 435	99.70 58	98.92 401	98.15 175	99.06 293	99.35 355	93.67 325	99.25 372	97.77 296	97.25 352	99.64 181

v2v482		98.06 265	97.77 287	98.92 255	98.90 388	98.82 228	99.57 141	99.36 301	96.65 358	99.19 266	99.35 355	94.20 302	99.25 372	97.72 303	94.97 408	98.69 351

v1240		97.69 334	97.32 351	98.79 288	98.85 397	98.43 271	99.48 226	99.36 301	96.11 402	99.27 245	99.36 352	93.76 323	99.24 374	94.46 425	95.23 402	98.70 347

FE-MVSNET3		98.09 260	97.82 280	98.89 265	98.70 420	98.90 208	98.57 451	99.47 225	96.78 349	98.87 325	99.05 401	94.75 271	99.23 375	97.45 332	96.74 361	98.53 405

WBMVS		97.74 325	97.50 319	98.46 330	99.24 317	97.43 323	99.21 354	99.42 270	97.45 289	98.96 310	99.41 335	88.83 414	99.23 375	98.94 146	96.02 378	98.71 342

v1144		97.98 282	97.69 298	98.85 279	98.87 393	98.66 242	99.54 169	99.35 308	96.27 388	99.23 256	99.35 355	94.67 278	99.23 375	96.73 374	95.16 404	98.68 356

v10		97.85 301	97.52 316	98.86 276	98.99 375	98.67 241	99.75 42	99.41 273	95.70 411	98.98 306	99.41 335	94.75 271	99.23 375	96.01 396	94.63 414	98.67 364

WR-MVS_H		98.13 256	97.87 276	98.90 261	99.02 369	98.84 222	99.70 58	99.59 73	97.27 307	98.40 381	99.19 387	95.53 229	99.23 375	98.34 239	93.78 430	98.61 395

miper_enhance_ethall		98.16 253	98.08 251	98.41 338	98.96 381	97.72 311	98.45 458	99.32 331	96.95 339	98.97 308	99.17 388	97.06 146	99.22 380	97.86 283	95.99 381	98.29 427

GG-mvs-BLEND				98.45 332	98.55 435	98.16 282	99.43 257	93.68 485		97.23 428	98.46 439	89.30 409	99.22 380	95.43 410	98.22 294	97.98 449

FC-MVSNet-test		98.75 206	98.62 207	99.15 228	99.08 359	99.45 115	99.86 11	99.60 67	98.23 165	98.70 352	99.82 118	96.80 162	99.22 380	99.07 128	96.38 370	98.79 324

UniMVSNet_NR-MVSNet		98.22 245	97.97 263	98.96 247	98.92 385	98.98 180	99.48 226	99.53 125	97.76 250	98.71 346	99.46 324	96.43 184	99.22 380	98.57 212	92.87 442	98.69 351

DU-MVS		98.08 263	97.79 282	98.96 247	98.87 393	98.98 180	99.41 269	99.45 248	97.87 232	98.71 346	99.50 308	94.82 262	99.22 380	98.57 212	92.87 442	98.68 356

cl____		98.01 278	97.84 279	98.55 317	99.25 315	97.97 294	98.71 440	99.34 313	96.47 377	98.59 371	99.54 293	95.65 225	99.21 385	97.21 345	95.77 387	98.46 415

WR-MVS		98.06 265	97.73 294	99.06 234	98.86 396	99.25 146	99.19 359	99.35 308	97.30 305	98.66 355	99.43 329	93.94 314	99.21 385	98.58 209	94.28 420	98.71 342

test_0402		96.64 387	96.24 389	97.85 388	98.85 397	96.43 384	99.44 251	99.26 351	93.52 442	96.98 436	99.52 301	88.52 422	99.20 387	92.58 451	97.50 336	97.93 452

icg_test_0407_2		98.79 200	98.86 170	98.57 311	99.55 211	96.93 358	99.07 382	99.44 257	98.05 208	99.66 129	99.80 151	97.13 139	99.18 388	98.15 257	98.92 243	99.60 194

SixPastTwentyTwo		97.50 354	97.33 350	98.03 369	98.65 425	96.23 392	99.77 34	98.68 439	97.14 318	97.90 411	99.93 10	90.45 395	99.18 388	97.00 359	96.43 369	98.67 364

cl22		97.85 301	97.64 305	98.48 324	99.09 356	97.87 303	98.60 450	99.33 321	97.11 324	98.87 325	99.22 383	92.38 361	99.17 390	98.21 249	95.99 381	98.42 418

tt0320		95.71 407	95.07 411	97.62 404	99.05 365	95.02 425	99.25 340	99.52 133	86.81 470	97.97 408	99.72 208	83.58 459	99.15 391	96.38 389	93.35 433	98.68 356

WB-MVSnew		97.65 342	97.65 302	97.63 403	98.78 406	97.62 317	99.13 369	98.33 450	97.36 300	99.07 288	98.94 416	95.64 226	99.15 391	92.95 445	98.68 263	96.12 474

IterMVS-SCA-FT		97.82 311	97.75 292	98.06 368	99.57 203	96.36 386	99.02 396	99.49 191	97.18 315	98.71 346	99.72 208	92.72 345	99.14 393	97.44 333	95.86 386	98.67 364

pmmvs5		97.52 351	97.30 353	98.16 361	98.57 434	96.73 370	99.27 329	98.90 408	96.14 400	98.37 383	99.53 297	91.54 381	99.14 393	97.51 324	95.87 385	98.63 384

v148		97.79 317	97.55 311	98.50 321	98.74 414	97.72 311	99.54 169	99.33 321	96.26 389	98.90 319	99.51 305	94.68 277	99.14 393	97.83 287	93.15 439	98.63 384

IMVS_0404		98.53 221	98.52 219	98.55 317	99.55 211	96.93 358	99.20 357	99.44 257	98.05 208	98.96 310	99.80 151	94.66 280	99.13 396	98.15 257	98.92 243	99.60 194

miper_ehance_all_eth		98.18 251	98.10 247	98.41 338	99.23 319	97.72 311	98.72 439	99.31 335	96.60 366	98.88 322	99.29 372	97.29 132	99.13 396	97.60 311	95.99 381	98.38 423

NR-MVSNet		97.97 285	97.61 308	99.02 239	98.87 393	99.26 144	99.47 236	99.42 270	97.63 266	97.08 434	99.50 308	95.07 250	99.13 396	97.86 283	93.59 431	98.68 356

IterMVS		97.83 308	97.77 287	98.02 371	99.58 198	96.27 390	99.02 396	99.48 203	97.22 313	98.71 346	99.70 215	92.75 342	99.13 396	97.46 330	96.00 380	98.67 364
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CMPMVS	69.68 23	94.13 426	94.90 414	91.84 453	97.24 459	80.01 483	98.52 455	99.48 203	89.01 466	91.99 470	99.67 240	85.67 446	99.13 396	95.44 409	97.03 359	96.39 471
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
eth_miper_zixun_eth		98.05 270	97.96 264	98.33 345	99.26 311	97.38 325	98.56 454	99.31 335	96.65 358	98.88 322	99.52 301	96.58 174	99.12 401	97.39 336	95.53 397	98.47 412

pmmvs4		98.13 256	97.90 271	98.81 285	98.61 430	98.87 216	98.99 404	99.21 363	96.44 378	99.06 293	99.58 277	95.90 212	99.11 402	97.18 351	96.11 377	98.46 415

TransMVSNet (Re)		97.15 375	96.58 381	98.86 276	99.12 348	98.85 220	99.49 218	98.91 406	95.48 414	97.16 432	99.80 151	93.38 327	99.11 402	94.16 431	91.73 449	98.62 386

ambc				93.06 451	92.68 482	82.36 476	98.47 457	98.73 436		95.09 455	97.41 465	55.55 482	99.10 404	96.42 386	91.32 450	97.71 455

Baseline_NR-MVSNet		97.76 319	97.45 327	98.68 301	99.09 356	98.29 276	99.41 269	98.85 415	95.65 412	98.63 364	99.67 240	94.82 262	99.10 404	98.07 269	92.89 441	98.64 377

usedtu_blend_shiyan5		95.04 416	94.10 423	97.86 387	96.45 466	95.92 400	99.29 318	99.22 359	86.17 473	98.36 384	97.68 461	91.20 388	99.07 406	97.53 321	80.97 477	98.60 398

blend_shiyan4		95.25 414	94.39 421	97.84 390	96.70 465	95.92 400	98.84 426	99.28 346	92.21 454	98.16 398	97.84 459	87.10 436	99.07 406	97.53 321	81.87 474	98.54 404

test_vis3_rt		87.04 442	85.81 445	90.73 457	93.99 481	81.96 478	99.76 37	90.23 492	92.81 451	81.35 480	91.56 480	40.06 488	99.07 406	94.27 428	88.23 465	91.15 480

CP-MVSNet		98.09 260	97.78 285	99.01 240	98.97 380	99.24 147	99.67 75	99.46 237	97.25 309	98.48 378	99.64 253	93.79 321	99.06 409	98.63 199	94.10 424	98.74 338

PS-CasMVS		97.93 288	97.59 310	98.95 249	98.99 375	99.06 171	99.68 72	99.52 133	97.13 319	98.31 387	99.68 234	92.44 360	99.05 410	98.51 220	94.08 425	98.75 334

K. test v3		97.10 377	96.79 377	98.01 372	98.72 417	96.33 387	99.87 8	97.05 469	97.59 270	96.16 446	99.80 151	88.71 416	99.04 411	96.69 377	96.55 367	98.65 375

new_pmnet		96.38 393	96.03 395	97.41 413	98.13 445	95.16 424	99.05 388	99.20 364	93.94 437	97.39 425	98.79 428	91.61 380	99.04 411	90.43 458	95.77 387	98.05 442

DIV-MVS_self_test		98.01 278	97.85 278	98.48 324	99.24 317	97.95 299	98.71 440	99.35 308	96.50 371	98.60 370	99.54 293	95.72 223	99.03 413	97.21 345	95.77 387	98.46 415

IterMVS-LS		98.46 225	98.42 224	98.58 310	99.59 196	98.00 292	99.37 288	99.43 268	96.94 341	99.07 288	99.59 273	97.87 114	99.03 413	98.32 242	95.62 393	98.71 342
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
our_test_3		97.65 342	97.68 299	97.55 409	98.62 428	94.97 427	98.84 426	99.30 340	96.83 348	98.19 396	99.34 359	97.01 150	99.02 415	95.00 419	96.01 379	98.64 377

Patchmtry		97.75 323	97.40 339	98.81 285	99.10 353	98.87 216	99.11 378	99.33 321	94.83 428	98.81 335	99.38 346	94.33 298	99.02 415	96.10 392	95.57 395	98.53 405

N_pmnet		94.95 420	95.83 400	92.31 452	98.47 438	79.33 484	99.12 372	92.81 490	93.87 438	97.68 418	99.13 393	93.87 318	99.01 417	91.38 455	96.19 375	98.59 400

CR-MVSNet		98.17 252	97.93 269	98.87 273	99.18 332	98.49 265	99.22 352	99.33 321	96.96 337	99.56 166	99.38 346	94.33 298	99.00 418	94.83 422	98.58 268	99.14 291

c3_l		98.12 258	98.04 256	98.38 342	99.30 299	97.69 315	98.81 430	99.33 321	96.67 356	98.83 332	99.34 359	97.11 142	98.99 419	97.58 313	95.34 400	98.48 410

test0.0.03 1		97.71 332	97.42 337	98.56 315	98.41 441	97.82 306	98.78 433	98.63 442	97.34 301	98.05 405	98.98 412	94.45 293	98.98 420	95.04 418	97.15 357	98.89 319

PatchT		97.03 379	96.44 385	98.79 288	98.99 375	98.34 275	99.16 363	99.07 382	92.13 455	99.52 177	97.31 469	94.54 288	98.98 420	88.54 465	98.73 260	99.03 307

GBi-Net		97.68 337	97.48 321	98.29 350	99.51 228	97.26 331	99.43 257	99.48 203	96.49 372	99.07 288	99.32 367	90.26 397	98.98 420	97.10 353	96.65 363	98.62 386

test1		97.68 337	97.48 321	98.29 350	99.51 228	97.26 331	99.43 257	99.48 203	96.49 372	99.07 288	99.32 367	90.26 397	98.98 420	97.10 353	96.65 363	98.62 386

FMVSNet3		98.03 273	97.76 291	98.84 280	99.39 275	98.98 180	99.40 277	99.38 291	96.67 356	99.07 288	99.28 374	92.93 337	98.98 420	97.10 353	96.65 363	98.56 403

FMVSNet2		97.72 329	97.36 342	98.80 287	99.51 228	98.84 222	99.45 244	99.42 270	96.49 372	98.86 330	99.29 372	90.26 397	98.98 420	96.44 385	96.56 366	98.58 401

FMVSNet1		96.84 383	96.36 387	98.29 350	99.32 297	97.26 331	99.43 257	99.48 203	95.11 419	98.55 373	99.32 367	83.95 457	98.98 420	95.81 399	96.26 374	98.62 386

ppachtmachnet_test		97.49 359	97.45 327	97.61 407	98.62 428	95.24 420	98.80 431	99.46 237	96.11 402	98.22 394	99.62 264	96.45 182	98.97 427	93.77 433	95.97 384	98.61 395

TranMVSNet+NR-MVSNet		97.93 288	97.66 301	98.76 292	98.78 406	98.62 248	99.65 88	99.49 191	97.76 250	98.49 377	99.60 271	94.23 301	98.97 427	98.00 273	92.90 440	98.70 347

MVStest1		96.08 400	95.48 405	97.89 384	98.93 383	96.70 371	99.56 149	99.35 308	92.69 452	91.81 471	99.46 324	89.90 403	98.96 429	95.00 419	92.61 445	98.00 447

tt0320-xc		95.31 413	94.59 417	97.45 412	98.92 385	94.73 431	99.20 357	99.31 335	86.74 471	97.23 428	99.72 208	81.14 470	98.95 430	97.08 356	91.98 448	98.67 364

test_method		91.10 437	91.36 439	90.31 458	95.85 469	73.72 491	94.89 480	99.25 353	68.39 482	95.82 449	99.02 406	80.50 471	98.95 430	93.64 436	94.89 412	98.25 430

ADS-MVSNet2		98.02 275	98.07 254	97.87 385	99.33 290	95.19 422	99.23 348	99.08 379	96.24 390	99.10 282	99.67 240	94.11 307	98.93 432	96.81 371	99.05 232	99.48 242

ET-MVSNet_ETH3D		96.49 390	95.64 404	99.05 236	99.53 219	98.82 228	98.84 426	97.51 467	97.63 266	84.77 476	99.21 386	92.09 365	98.91 433	98.98 138	92.21 447	99.41 263

miper_lstm_enhance		98.00 280	97.91 270	98.28 354	99.34 289	97.43 323	98.88 422	99.36 301	96.48 375	98.80 337	99.55 288	95.98 205	98.91 433	97.27 342	95.50 398	98.51 408

MonoMVSNet		98.38 234	98.47 222	98.12 366	98.59 433	96.19 394	99.72 53	98.79 424	97.89 230	99.44 193	99.52 301	96.13 198	98.90 435	98.64 197	97.54 331	99.28 280

PEN-MVS		97.76 319	97.44 332	98.72 295	98.77 411	98.54 255	99.78 32	99.51 155	97.06 329	98.29 390	99.64 253	92.63 351	98.89 436	98.09 262	93.16 438	98.72 340

testing3		97.28 369	96.76 378	98.82 282	99.37 280	98.07 289	99.45 244	99.36 301	97.56 275	97.89 412	98.95 415	83.70 458	98.82 437	96.03 394	98.56 271	99.58 209

testgi		97.65 342	97.50 319	98.13 365	99.36 283	96.45 383	99.42 264	99.48 203	97.76 250	97.87 413	99.45 326	91.09 389	98.81 438	94.53 424	98.52 274	99.13 293

testf1		90.42 440	90.68 441	89.65 461	97.78 449	73.97 489	99.13 369	98.81 420	89.62 463	91.80 472	98.93 417	62.23 480	98.80 439	86.61 475	91.17 451	96.19 472

APD_test2		90.42 440	90.68 441	89.65 461	97.78 449	73.97 489	99.13 369	98.81 420	89.62 463	91.80 472	98.93 417	62.23 480	98.80 439	86.61 475	91.17 451	96.19 472

MIMVSNet		97.73 327	97.45 327	98.57 311	99.45 258	97.50 321	99.02 396	98.98 393	96.11 402	99.41 204	99.14 392	90.28 396	98.74 441	95.74 401	98.93 241	99.47 248

LCM-MVSNet-Re		97.83 308	98.15 241	96.87 430	99.30 299	92.25 461	99.59 123	98.26 451	97.43 293	96.20 445	99.13 393	96.27 193	98.73 442	98.17 254	98.99 238	99.64 181

Syy-MVS		97.09 378	97.14 364	96.95 427	99.00 372	92.73 459	99.29 318	99.39 283	97.06 329	97.41 422	98.15 451	93.92 316	98.68 443	91.71 453	98.34 281	99.45 256

myMVS_eth3d		96.89 381	96.37 386	98.43 337	99.00 372	97.16 335	99.29 318	99.39 283	97.06 329	97.41 422	98.15 451	83.46 460	98.68 443	95.27 414	98.34 281	99.45 256

DTE-MVSNet		97.51 353	97.19 362	98.46 330	98.63 427	98.13 285	99.84 12	99.48 203	96.68 355	97.97 408	99.67 240	92.92 338	98.56 445	96.88 370	92.60 446	98.70 347

PC_three_1452									98.18 173	99.84 56	99.70 215	99.31 3	98.52 446	98.30 244	99.80 125	99.81 79

mvsany_test3		93.77 429	93.45 432	94.74 443	95.78 470	88.01 469	99.64 95	98.25 452	98.28 150	94.31 458	97.97 458	68.89 476	98.51 447	97.50 325	90.37 456	97.71 455

UnsupCasMVSNet_bld		93.53 430	92.51 436	96.58 435	97.38 455	93.82 446	98.24 467	99.48 203	91.10 460	93.10 465	96.66 471	74.89 474	98.37 448	94.03 432	87.71 466	97.56 461

Anonymous20240521		96.20 396	95.89 399	97.13 420	97.72 452	94.96 428	99.79 31	99.29 344	93.01 448	97.20 431	99.03 404	89.69 406	98.36 449	91.16 456	96.13 376	98.07 440

test_f		91.90 436	91.26 440	93.84 446	95.52 474	85.92 471	99.69 62	98.53 447	95.31 416	93.87 461	96.37 473	55.33 483	98.27 450	95.70 402	90.98 454	97.32 465

MDA-MVSNet_test_wron		95.45 409	94.60 416	98.01 372	98.16 444	97.21 334	99.11 378	99.24 356	93.49 443	80.73 482	98.98 412	93.02 335	98.18 451	94.22 430	94.45 417	98.64 377

UnsupCasMVSNet_eth		96.44 391	96.12 392	97.40 414	98.65 425	95.65 406	99.36 294	99.51 155	97.13 319	96.04 448	98.99 410	88.40 423	98.17 452	96.71 375	90.27 457	98.40 421

KD-MVS_2432*1600		94.62 421	93.72 429	97.31 415	97.19 461	95.82 403	98.34 462	99.20 364	95.00 424	97.57 419	98.35 444	87.95 428	98.10 453	92.87 447	77.00 480	98.01 444

miper_refine_blended		94.62 421	93.72 429	97.31 415	97.19 461	95.82 403	98.34 462	99.20 364	95.00 424	97.57 419	98.35 444	87.95 428	98.10 453	92.87 447	77.00 480	98.01 444

YYNet1		95.36 411	94.51 419	97.92 381	97.89 447	97.10 338	99.10 380	99.23 357	93.26 446	80.77 481	99.04 403	92.81 341	98.02 455	94.30 426	94.18 422	98.64 377

EU-MVSNet		97.98 282	98.03 257	97.81 395	98.72 417	96.65 376	99.66 82	99.66 32	98.09 196	98.35 385	99.82 118	95.25 243	98.01 456	97.41 335	95.30 401	98.78 326

Gipuma		90.99 438	90.15 443	93.51 447	98.73 415	90.12 467	93.98 481	99.45 248	79.32 478	92.28 468	94.91 475	69.61 475	97.98 457	87.42 471	95.67 391	92.45 478
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
pmmvs-eth3d		95.34 412	94.73 415	97.15 418	95.53 473	95.94 399	99.35 299	99.10 376	95.13 417	93.55 463	97.54 464	88.15 427	97.91 458	94.58 423	89.69 462	97.61 458

PM-MVS		92.96 433	92.23 437	95.14 442	95.61 471	89.98 468	99.37 288	98.21 455	94.80 429	95.04 456	97.69 460	65.06 477	97.90 459	94.30 426	89.98 459	97.54 462

MDA-MVSNet-bldmvs		94.96 419	93.98 426	97.92 381	98.24 443	97.27 329	99.15 366	99.33 321	93.80 439	80.09 483	99.03 404	88.31 424	97.86 460	93.49 438	94.36 419	98.62 386

Patchmatch-RL test		95.84 403	95.81 401	95.95 440	95.61 471	90.57 466	98.24 467	98.39 448	95.10 421	95.20 453	98.67 432	94.78 266	97.77 461	96.28 391	90.02 458	99.51 234

Anonymous20231206		96.22 394	96.03 395	96.79 432	97.31 458	94.14 444	99.63 101	99.08 379	96.17 396	97.04 435	99.06 400	93.94 314	97.76 462	86.96 473	95.06 406	98.47 412

SD-MVS		99.41 59	99.52 14	99.05 236	99.74 100	99.68 64	99.46 240	99.52 133	99.11 47	99.88 43	99.91 26	99.43 1	97.70 463	98.72 186	99.93 33	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		97.25 371	97.35 344	96.95 427	97.84 448	93.61 453	99.57 141	96.63 475	96.13 401	98.87 325	98.61 435	94.59 283	97.70 463	95.08 417	98.86 251	99.55 216

FE-MVSNET2		95.10 415	94.44 420	97.08 423	95.08 476	95.97 398	99.51 190	99.37 299	95.02 423	94.10 459	97.57 462	86.18 443	97.66 465	93.28 440	89.86 460	97.61 458

dongtai		93.26 431	92.93 435	94.25 444	99.39 275	85.68 472	97.68 476	93.27 486	92.87 450	96.85 439	99.39 343	82.33 465	97.48 466	76.78 480	97.80 316	99.58 209

pmmvs3		94.09 427	93.25 434	96.60 434	94.76 479	94.49 438	98.92 418	98.18 457	89.66 462	96.48 442	98.06 457	86.28 442	97.33 467	89.68 461	87.20 467	97.97 450

KD-MVS_self_test		95.00 418	94.34 422	96.96 426	97.07 463	95.39 417	99.56 149	99.44 257	95.11 419	97.13 433	97.32 468	91.86 370	97.27 468	90.35 459	81.23 476	98.23 432

FMVSNet5		96.43 392	96.19 391	97.15 418	99.11 350	95.89 402	99.32 307	99.52 133	94.47 435	98.34 386	99.07 398	87.54 433	97.07 469	92.61 450	95.72 390	98.47 412

new-patchmatchnet		94.48 424	94.08 425	95.67 441	95.08 476	92.41 460	99.18 361	99.28 346	94.55 434	93.49 464	97.37 467	87.86 431	97.01 470	91.57 454	88.36 464	97.61 458

LCM-MVSNet		86.80 444	85.22 448	91.53 455	87.81 487	80.96 481	98.23 469	98.99 392	71.05 480	90.13 475	96.51 472	48.45 487	96.88 471	90.51 457	85.30 469	96.76 467

CL-MVSNet_self_test		94.49 423	93.97 427	96.08 439	96.16 468	93.67 451	98.33 464	99.38 291	95.13 417	97.33 426	98.15 451	92.69 349	96.57 472	88.67 464	79.87 478	97.99 448

MIMVSNet1		95.51 408	95.04 413	96.92 429	97.38 455	95.60 407	99.52 180	99.50 178	93.65 441	96.97 437	99.17 388	85.28 451	96.56 473	88.36 466	95.55 396	98.60 398

FE-MVSNET		94.07 428	93.36 433	96.22 438	94.05 480	94.71 433	99.56 149	98.36 449	93.15 447	93.76 462	97.55 463	86.47 441	96.49 474	87.48 470	89.83 461	97.48 463

test20.03		96.12 398	95.96 397	96.63 433	97.44 454	95.45 414	99.51 190	99.38 291	96.55 369	96.16 446	99.25 380	93.76 323	96.17 475	87.35 472	94.22 421	98.27 428

tmp_tt		82.80 446	81.52 449	86.66 463	66.61 493	68.44 492	92.79 483	97.92 459	68.96 481	80.04 484	99.85 84	85.77 445	96.15 476	97.86 283	43.89 486	95.39 476

test_fmvs3		92.10 435	91.77 438	93.08 450	96.19 467	86.25 470	99.82 16	98.62 443	96.65 358	95.19 454	96.90 470	55.05 484	95.93 477	96.63 382	90.92 455	97.06 466

kuosan		90.92 439	90.11 444	93.34 448	98.78 406	85.59 473	98.15 471	93.16 488	89.37 465	92.07 469	98.38 443	81.48 468	95.19 478	62.54 487	97.04 358	99.25 285

dmvs_testset		95.02 417	96.12 392	91.72 454	99.10 353	80.43 482	99.58 133	97.87 461	97.47 285	95.22 452	98.82 424	93.99 312	95.18 479	88.09 467	94.91 411	99.56 215

PMMVS2		86.87 443	85.37 447	91.35 456	90.21 485	83.80 475	98.89 421	97.45 468	83.13 477	91.67 474	95.03 474	48.49 486	94.70 480	85.86 477	77.62 479	95.54 475

PMVS	70.75 22	75.98 452	74.97 453	79.01 469	70.98 492	55.18 494	93.37 482	98.21 455	65.08 486	61.78 487	93.83 477	21.74 493	92.53 481	78.59 479	91.12 453	89.34 482
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
FPMVS		84.93 445	85.65 446	82.75 467	86.77 488	63.39 493	98.35 461	98.92 401	74.11 479	83.39 478	98.98 412	50.85 485	92.40 482	84.54 478	94.97 408	92.46 477

WB-MVS		93.10 432	94.10 423	90.12 459	95.51 475	81.88 479	99.73 51	99.27 350	95.05 422	93.09 466	98.91 421	94.70 276	91.89 483	76.62 481	94.02 427	96.58 469

SSC-MVS		92.73 434	93.73 428	89.72 460	95.02 478	81.38 480	99.76 37	99.23 357	94.87 427	92.80 467	98.93 417	94.71 275	91.37 484	74.49 483	93.80 429	96.42 470

MVE	76.82 21	76.91 451	74.31 455	84.70 464	85.38 490	76.05 488	96.88 479	93.17 487	67.39 483	71.28 485	89.01 484	21.66 494	87.69 485	71.74 484	72.29 482	90.35 481
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		80.61 448	79.88 450	82.81 466	90.75 484	76.38 487	97.69 475	95.76 479	66.44 484	83.52 477	92.25 479	62.54 479	87.16 486	68.53 485	61.40 483	84.89 484

EMVS		80.02 449	79.22 451	82.43 468	91.19 483	76.40 486	97.55 478	92.49 491	66.36 485	83.01 479	91.27 481	64.63 478	85.79 487	65.82 486	60.65 484	85.08 483

ANet_high		77.30 450	74.86 454	84.62 465	75.88 491	77.61 485	97.63 477	93.15 489	88.81 467	64.27 486	89.29 483	36.51 489	83.93 488	75.89 482	52.31 485	92.33 479

wuyk23d		40.18 453	41.29 458	36.84 470	86.18 489	49.12 495	79.73 484	22.81 495	27.64 487	25.46 490	28.45 490	21.98 492	48.89 489	55.80 488	23.56 489	12.51 487

test123		39.01 455	42.50 457	28.53 471	39.17 494	20.91 496	98.75 436	19.17 496	19.83 489	38.57 488	66.67 486	33.16 490	15.42 490	37.50 490	29.66 488	49.26 485

testmvs		39.17 454	43.78 456	25.37 472	36.04 495	16.84 497	98.36 460	26.56 494	20.06 488	38.51 489	67.32 485	29.64 491	15.30 491	37.59 489	39.90 487	43.98 486

mmdepth		0.02 460	0.03 463	0.00 473	0.00 496	0.00 498	0.00 485	0.00 497	0.00 491	0.00 492	0.27 492	0.00 495	0.00 492	0.00 491	0.00 490	0.00 488

monomultidepth		0.02 460	0.03 463	0.00 473	0.00 496	0.00 498	0.00 485	0.00 497	0.00 491	0.00 492	0.27 492	0.00 495	0.00 492	0.00 491	0.00 490	0.00 488

test_blank		0.13 459	0.17 462	0.00 473	0.00 496	0.00 498	0.00 485	0.00 497	0.00 491	0.00 492	1.57 491	0.00 495	0.00 492	0.00 491	0.00 490	0.00 488

uanet_test		0.02 460	0.03 463	0.00 473	0.00 496	0.00 498	0.00 485	0.00 497	0.00 491	0.00 492	0.27 492	0.00 495	0.00 492	0.00 491	0.00 490	0.00 488

DCPMVS		0.02 460	0.03 463	0.00 473	0.00 496	0.00 498	0.00 485	0.00 497	0.00 491	0.00 492	0.27 492	0.00 495	0.00 492	0.00 491	0.00 490	0.00 488

cdsmvs_eth3d_5k		24.64 456	32.85 459	0.00 473	0.00 496	0.00 498	0.00 485	99.51 155	0.00 491	0.00 492	99.56 285	96.58 174	0.00 492	0.00 491	0.00 490	0.00 488

pcd_1.5k_mvsjas		8.27 458	11.03 461	0.00 473	0.00 496	0.00 498	0.00 485	0.00 497	0.00 491	0.00 492	0.27 492	99.01 20	0.00 492	0.00 491	0.00 490	0.00 488

sosnet-low-res		0.02 460	0.03 463	0.00 473	0.00 496	0.00 498	0.00 485	0.00 497	0.00 491	0.00 492	0.27 492	0.00 495	0.00 492	0.00 491	0.00 490	0.00 488

sosnet		0.02 460	0.03 463	0.00 473	0.00 496	0.00 498	0.00 485	0.00 497	0.00 491	0.00 492	0.27 492	0.00 495	0.00 492	0.00 491	0.00 490	0.00 488

uncertanet		0.02 460	0.03 463	0.00 473	0.00 496	0.00 498	0.00 485	0.00 497	0.00 491	0.00 492	0.27 492	0.00 495	0.00 492	0.00 491	0.00 490	0.00 488

Regformer		0.02 460	0.03 463	0.00 473	0.00 496	0.00 498	0.00 485	0.00 497	0.00 491	0.00 492	0.27 492	0.00 495	0.00 492	0.00 491	0.00 490	0.00 488

ab-mvs-re		8.30 457	11.06 460	0.00 473	0.00 496	0.00 498	0.00 485	0.00 497	0.00 491	0.00 492	99.58 277	0.00 495	0.00 492	0.00 491	0.00 490	0.00 488

uanet		0.02 460	0.03 463	0.00 473	0.00 496	0.00 498	0.00 485	0.00 497	0.00 491	0.00 492	0.27 492	0.00 495	0.00 492	0.00 491	0.00 490	0.00 488

TestfortrainingZip							99.69 62

WAC-MVS						97.16 335								95.47 408

FOURS1					99.91 1	99.93 1	99.87 8	99.56 90	99.10 48	99.81 69

test_one_0601					99.81 57	99.88 10		99.49 191	98.97 75	99.65 138	99.81 133	99.09 16

eth-test2					0.00 496

eth-test					0.00 496

RE-MVS-def			99.34 50		99.76 82	99.82 28	99.63 101	99.52 133	98.38 137	99.76 91	99.82 118	98.75 60		98.61 203	99.81 120	99.77 100

IU-MVS					99.84 38	99.88 10		99.32 331	98.30 149	99.84 56				98.86 163	99.85 94	99.89 29

save fliter					99.76 82	99.59 88	99.14 368	99.40 280	99.00 67

test0726					99.85 31	99.89 6	99.62 106	99.50 178	99.10 48	99.86 53	99.82 118	98.94 34

GSMVS																99.52 225

test_part2					99.81 57	99.83 22				99.77 85

sam_mvs1												94.86 261				99.52 225

sam_mvs												94.72 274

MTGPA								99.47 225

MTMP							99.54 169	98.88 411

test9_res														97.49 326	99.72 148	99.75 112

agg_prior2														97.21 345	99.73 147	99.75 112

test_prior4						99.56 94	98.99 404

test_prior2							98.96 411		98.34 143	99.01 299	99.52 301	98.68 70		97.96 275	99.74 145

新几何2							99.01 401

旧先验1					99.74 100	99.59 88		99.54 109			99.69 226	98.47 86			99.68 156	99.73 126

原ACMM2							98.95 414

test222					99.75 92	99.49 109	98.91 420	99.49 191	96.42 380	99.34 228	99.65 247	98.28 100			99.69 153	99.72 136

segment_acmp												98.96 27

testdata1							98.85 425		98.32 147

plane_prior7					99.29 303	97.03 350

plane_prior6					99.27 308	96.98 354						92.71 347

plane_prior4											99.61 268

plane_prior3						97.00 352			98.69 107	99.11 279

plane_prior2							99.39 281		98.97 75

plane_prior1					99.26 311

plane_prior						96.97 355	99.21 354		98.45 130						97.60 325

n2								0.00 497

nn								0.00 497

door-mid								98.05 458

test11								99.35 308

door								97.92 459

HQP5-MVS						96.83 366

HQP-NCC					99.19 329		98.98 407		98.24 162	98.66 355

ACMP_Plane					99.19 329		98.98 407		98.24 162	98.66 355

BP-MVS														97.19 349

HQP3-MVS								99.39 283							97.58 327

HQP2-MVS												92.47 356

NP-MVS					99.23 319	96.92 362					99.40 339

MDTV_nov1_ep13_2view						95.18 423	99.35 299		96.84 346	99.58 162		95.19 246		97.82 288		99.46 253

ACMMP++_ref															97.19 355

ACMMP++															97.43 345

Test By Simon												98.75 60

High-resolution multi-view benchmark