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.

region2R		97.07 18	96.84 19	97.77 23	99.46 1	93.79 38	98.52 10	98.24 29	93.19 63	97.14 25	98.34 28	91.59 39	99.87 6	95.46 48	99.59 9	99.64 5

ACMMPR		97.07 18	96.84 19	97.79 20	99.44 2	93.88 34	98.52 10	98.31 22	93.21 60	97.15 24	98.33 31	91.35 41	99.86 7	95.63 43	99.59 9	99.62 7

HFP-MVS		97.14 15	96.92 16	97.83 16	99.42 3	94.12 28	98.52 10	98.32 20	93.21 60	97.18 22	98.29 37	92.08 28	99.83 14	95.63 43	99.59 9	99.54 19

#test#		97.02 21	96.75 26	97.83 16	99.42 3	94.12 28	98.15 29	98.32 20	92.57 84	97.18 22	98.29 37	92.08 28	99.83 14	95.12 53	99.59 9	99.54 19

HSP-MVS		97.53 5	97.49 5	97.63 35	99.40 5	93.77 41	98.53 9	97.85 91	95.55 5	98.56 5	97.81 62	93.90 5	99.65 41	96.62 15	99.21 50	99.48 28

mPP-MVS		96.86 27	96.60 29	97.64 33	99.40 5	93.44 48	98.50 13	98.09 51	93.27 59	95.95 63	98.33 31	91.04 45	99.88 4	95.20 50	99.57 13	99.60 10

MP-MVS		96.77 31	96.45 36	97.72 26	99.39 7	93.80 37	98.41 17	98.06 59	93.37 55	95.54 80	98.34 28	90.59 52	99.88 4	94.83 63	99.54 15	99.49 26
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
XVS		97.18 12	96.96 14	97.81 18	99.38 8	94.03 32	98.59 7	98.20 32	94.85 18	96.59 40	98.29 37	91.70 36	99.80 20	95.66 40	99.40 32	99.62 7

X-MVStestdata		91.71 187	89.67 244	97.81 18	99.38 8	94.03 32	98.59 7	98.20 32	94.85 18	96.59 40	32.69 364	91.70 36	99.80 20	95.66 40	99.40 32	99.62 7

zzz-MVS		97.07 18	96.77 25	97.97 12	99.37 10	94.42 19	97.15 134	98.08 52	95.07 15	96.11 54	98.59 7	90.88 49	99.90 1	96.18 30	99.50 21	99.58 11

MTAPA		97.08 17	96.78 24	97.97 12	99.37 10	94.42 19	97.24 121	98.08 52	95.07 15	96.11 54	98.59 7	90.88 49	99.90 1	96.18 30	99.50 21	99.58 11

HPM-MVS		96.69 34	96.45 36	97.40 41	99.36 12	93.11 56	98.87 1	98.06 59	91.17 129	96.40 48	97.99 51	90.99 46	99.58 55	95.61 45	99.61 8	99.49 26
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
PGM-MVS		96.81 29	96.53 32	97.65 31	99.35 13	93.53 46	97.65 74	98.98 1	92.22 90	97.14 25	98.44 17	91.17 43	99.85 10	94.35 70	99.46 25	99.57 13

CP-MVS		97.02 21	96.81 22	97.64 33	99.33 14	93.54 45	98.80 3	98.28 24	92.99 69	96.45 47	98.30 36	91.90 33	99.85 10	95.61 45	99.68 3	99.54 19

HPM-MVS_fast		96.51 39	96.27 40	97.22 52	99.32 15	92.74 64	98.74 4	98.06 59	90.57 151	96.77 32	98.35 25	90.21 56	99.53 71	94.80 65	99.63 6	99.38 39

MCST-MVS		97.18 12	96.84 19	98.20 6	99.30 16	95.35 6	97.12 136	98.07 57	93.54 53	96.08 56	97.69 69	93.86 6	99.71 29	96.50 19	99.39 34	99.55 17

test_part2					99.28 17	95.74 4				98.10 8

v1.0		40.67 341	54.22 337	0.00 358	99.28 17	0.00 373	0.00 364	98.26 26	93.81 46	98.10 8	98.53 13	0.00 375	0.00 370	0.00 367	0.00 368	0.00 368

CPTT-MVS		95.57 61	95.19 62	96.70 63	99.27 19	91.48 99	98.33 20	98.11 47	87.79 234	95.17 84	98.03 47	87.09 95	99.61 47	93.51 86	99.42 30	99.02 65

TSAR-MVS + MP.		97.42 6	97.33 7	97.69 29	99.25 20	94.24 24	98.07 34	97.85 91	93.72 47	98.57 4	98.35 25	93.69 8	99.40 89	97.06 3	99.46 25	99.44 32

CSCG		96.05 51	95.91 47	96.46 79	99.24 21	90.47 135	98.30 21	98.57 11	89.01 187	93.97 105	97.57 82	92.62 18	99.76 23	94.66 68	99.27 45	99.15 55

ACMMP		96.27 46	95.93 46	97.28 47	99.24 21	92.62 68	98.25 25	98.81 3	92.99 69	94.56 93	98.39 23	88.96 66	99.85 10	94.57 69	97.63 96	99.36 41
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
MP-MVS-pluss		96.70 33	96.27 40	97.98 11	99.23 23	94.71 14	96.96 146	98.06 59	90.67 141	95.55 79	98.78 4	91.07 44	99.86 7	96.58 17	99.55 14	99.38 39
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
DP-MVS Recon		95.68 59	95.12 64	97.37 42	99.19 24	94.19 25	97.03 139	98.08 52	88.35 219	95.09 85	97.65 73	89.97 59	99.48 79	92.08 112	98.59 75	98.44 118

ESAPD		97.86 1	97.65 2	98.47 1	99.17 25	95.78 3	97.21 127	98.35 19	95.16 13	98.71 3	98.80 3	95.05 1	99.89 3	96.70 14	99.73 1	99.73 2

APDe-MVS		97.82 2	97.73 1	98.08 9	99.15 26	94.82 13	98.81 2	98.30 23	94.76 25	98.30 6	98.90 2	93.77 7	99.68 37	97.93 1	99.69 2	99.75 1

ACMMP_Plus		97.20 11	96.86 18	98.23 5	99.09 27	95.16 9	97.60 87	98.19 34	92.82 78	97.93 12	98.74 5	91.60 38	99.86 7	96.26 23	99.52 17	99.67 3

HPM-MVS++		97.34 9	96.97 13	98.47 1	99.08 28	96.16 1	97.55 94	97.97 80	95.59 4	96.61 38	97.89 53	92.57 19	99.84 13	95.95 35	99.51 19	99.40 35

114514_t		93.95 105	93.06 114	96.63 66	99.07 29	91.61 95	97.46 103	97.96 81	77.99 337	93.00 132	97.57 82	86.14 107	99.33 94	89.22 161	99.15 54	98.94 76

SMA-MVS		97.35 8	97.03 10	98.30 4	99.06 30	95.42 5	97.94 44	98.18 36	90.57 151	98.85 2	98.94 1	93.33 10	99.83 14	96.72 13	99.68 3	99.63 6

APD-MVS		96.95 24	96.60 29	98.01 10	99.03 31	94.93 12	97.72 65	98.10 49	91.50 115	98.01 10	98.32 33	92.33 23	99.58 55	94.85 62	99.51 19	99.53 22
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
APD-MVS_3200maxsize		96.81 29	96.71 27	97.12 56	99.01 32	92.31 74	97.98 42	98.06 59	93.11 66	97.44 17	98.55 11	90.93 47	99.55 66	96.06 32	99.25 46	99.51 23

CDPH-MVS		95.97 54	95.38 57	97.77 23	98.93 33	94.44 18	96.35 212	97.88 86	86.98 255	96.65 36	97.89 53	91.99 32	99.47 80	92.26 103	99.46 25	99.39 36

CNVR-MVS		97.68 3	97.44 6	98.37 3	98.90 34	95.86 2	97.27 119	98.08 52	95.81 3	97.87 13	98.31 34	94.26 3	99.68 37	97.02 4	99.49 23	99.57 13

abl_6		96.40 42	96.21 42	96.98 60	98.89 35	92.20 79	97.89 48	98.03 68	93.34 58	97.22 21	98.42 19	87.93 80	99.72 28	95.10 54	99.07 61	99.02 65

PAPM_NR		95.01 73	94.59 75	96.26 95	98.89 35	90.68 130	97.24 121	97.73 97	91.80 109	92.93 137	96.62 130	89.13 65	99.14 110	89.21 162	97.78 93	98.97 72

NCCC		97.30 10	97.03 10	98.11 8	98.77 37	95.06 11	97.34 113	98.04 66	95.96 2	97.09 29	97.88 55	93.18 11	99.71 29	95.84 38	99.17 53	99.56 15

DP-MVS		92.76 146	91.51 169	96.52 71	98.77 37	90.99 119	97.38 110	96.08 233	82.38 310	89.29 233	97.87 56	83.77 131	99.69 35	81.37 291	96.69 124	98.89 82

MSLP-MVS++		96.94 25	97.06 9	96.59 69	98.72 39	91.86 89	97.67 71	98.49 12	94.66 28	97.24 20	98.41 22	92.31 26	98.94 134	96.61 16	99.46 25	98.96 73

TEST9					98.70 40	94.19 25	96.41 204	98.02 69	88.17 226	96.03 57	97.56 84	92.74 14	99.59 52

train_agg		96.30 45	95.83 48	97.72 26	98.70 40	94.19 25	96.41 204	98.02 69	88.58 205	96.03 57	97.56 84	92.73 15	99.59 52	95.04 55	99.37 39	99.39 36

test_8					98.67 42	94.06 31	96.37 211	98.01 71	88.58 205	95.98 62	97.55 86	92.73 15	99.58 55

agg_prior3		96.16 49	95.67 50	97.62 36	98.67 42	93.88 34	96.41 204	98.00 73	87.93 230	95.81 67	97.47 88	92.33 23	99.59 52	95.04 55	99.37 39	99.39 36

agg_prior1		96.22 48	95.77 49	97.56 37	98.67 42	93.79 38	96.28 220	98.00 73	88.76 202	95.68 73	97.55 86	92.70 17	99.57 63	95.01 57	99.32 41	99.32 43

agg_prior					98.67 42	93.79 38		98.00 73		95.68 73			99.57 63

test_prior3		96.46 41	96.20 43	97.23 50	98.67 42	92.99 58	96.35 212	98.00 73	92.80 79	96.03 57	97.59 80	92.01 30	99.41 87	95.01 57	99.38 35	99.29 45

test_prior				97.23 50	98.67 42	92.99 58		98.00 73					99.41 87			99.29 45

DeepC-MVS_fast	93.89 2	96.93 26	96.64 28	97.78 21	98.64 48	94.30 21	97.41 104	98.04 66	94.81 23	96.59 40	98.37 24	91.24 42	99.64 46	95.16 51	99.52 17	99.42 34
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
新几何1				97.32 44	98.60 49	93.59 44		97.75 95	81.58 317	95.75 70	97.85 59	90.04 58	99.67 39	86.50 215	99.13 56	98.69 97

原ACMM1				96.38 86	98.59 50	91.09 118		97.89 84	87.41 243	95.22 83	97.68 70	90.25 54	99.54 68	87.95 185	99.12 59	98.49 111

AdaColmap		94.34 92	93.68 96	96.31 90	98.59 50	91.68 94	96.59 194	97.81 93	89.87 161	92.15 149	97.06 104	83.62 133	99.54 68	89.34 157	98.07 86	97.70 152

PLC	91.00 6	94.11 99	93.43 106	96.13 99	98.58 52	91.15 117	96.69 183	97.39 141	87.29 246	91.37 163	96.71 116	88.39 75	99.52 75	87.33 203	97.13 113	97.73 150
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
1121		94.71 87	93.83 91	97.34 43	98.57 53	93.64 43	96.04 234	97.73 97	81.56 319	95.68 73	97.85 59	90.23 55	99.65 41	87.68 192	99.12 59	98.73 92

SD-MVS		97.41 7	97.53 3	97.06 57	98.57 53	94.46 17	97.92 46	98.14 42	94.82 22	99.01 1	98.55 11	94.18 4	97.41 288	96.94 5	99.64 5	99.32 43

test12				97.65 31	98.46 55	94.26 22		97.66 107		95.52 81		90.89 48	99.46 81		99.25 46	99.22 50

MVS_111021_HR		96.68 36	96.58 31	96.99 59	98.46 55	92.31 74	96.20 228	98.90 2	94.30 36	95.86 65	97.74 67	92.33 23	99.38 92	96.04 33	99.42 30	99.28 48

OMC-MVS		95.09 72	94.70 73	96.25 96	98.46 55	91.28 106	96.43 201	97.57 116	92.04 104	94.77 91	97.96 52	87.01 96	99.09 121	91.31 131	96.77 120	98.36 126

MG-MVS		95.61 60	95.38 57	96.31 90	98.42 58	90.53 133	96.04 234	97.48 124	93.47 54	95.67 76	98.10 43	89.17 64	99.25 100	91.27 132	98.77 70	99.13 57

PHI-MVS		96.77 31	96.46 35	97.71 28	98.40 59	94.07 30	98.21 28	98.45 15	89.86 162	97.11 28	98.01 49	92.52 21	99.69 35	96.03 34	99.53 16	99.36 41

F-COLMAP		93.58 117	92.98 115	95.37 135	98.40 59	88.98 196	97.18 131	97.29 153	87.75 236	90.49 186	97.10 103	85.21 115	99.50 78	86.70 212	96.72 123	97.63 153

SteuartSystems-ACMMP		97.62 4	97.53 3	97.87 14	98.39 61	94.25 23	98.43 16	98.27 25	95.34 9	98.11 7	98.56 9	94.53 2	99.71 29	96.57 18	99.62 7	99.65 4
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旧先验1					98.38 62	93.38 50		97.75 95			98.09 44	92.30 27			99.01 64	99.16 53

CNLPA		94.28 93	93.53 101	96.52 71	98.38 62	92.55 70	96.59 194	96.88 199	90.13 158	91.91 153	97.24 96	85.21 115	99.09 121	87.64 195	97.83 91	97.92 140

Regformer-3		96.85 28	96.80 23	97.01 58	98.34 64	92.02 85	96.96 146	97.76 94	95.01 17	97.08 30	98.42 19	91.71 35	99.54 68	96.80 9	99.13 56	99.48 28

Regformer-4		96.97 23	96.87 17	97.25 49	98.34 64	92.66 67	96.96 146	98.01 71	95.12 14	97.14 25	98.42 19	91.82 34	99.61 47	96.90 6	99.13 56	99.50 24

TAPA-MVS	90.10 7	92.30 165	91.22 180	95.56 123	98.33 66	89.60 162	96.79 168	97.65 109	81.83 314	91.52 160	97.23 97	87.94 79	98.91 136	71.31 336	98.37 79	98.17 131
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
Regformer-1		97.10 16	96.96 14	97.54 38	98.32 67	93.48 47	96.83 161	97.99 78	95.20 12	97.46 16	98.25 40	92.48 22	99.58 55	96.79 11	99.29 43	99.55 17

Regformer-2		97.16 14	96.99 12	97.67 30	98.32 67	93.84 36	96.83 161	98.10 49	95.24 10	97.49 15	98.25 40	92.57 19	99.61 47	96.80 9	99.29 43	99.56 15

TSAR-MVS + GP.		96.69 34	96.49 33	97.27 48	98.31 69	93.39 49	96.79 168	96.72 205	94.17 37	97.44 17	97.66 72	92.76 13	99.33 94	96.86 8	97.76 95	99.08 62

CHOSEN 1792x2688		94.15 96	93.51 102	96.06 100	98.27 70	89.38 178	95.18 273	98.48 14	85.60 276	93.76 108	97.11 102	83.15 139	99.61 47	91.33 130	98.72 72	99.19 51

PVSNet_BlendedMVS		94.06 101	93.92 89	94.47 183	98.27 70	89.46 171	96.73 173	98.36 16	90.17 156	94.36 96	95.24 199	88.02 77	99.58 55	93.44 89	90.72 229	94.36 296

PVSNet_Blended		94.87 82	94.56 76	95.81 109	98.27 70	89.46 171	95.47 261	98.36 16	88.84 196	94.36 96	96.09 156	88.02 77	99.58 55	93.44 89	98.18 83	98.40 121

Anonymous20231211		90.63 241	89.42 249	94.27 191	98.24 73	89.19 192	98.05 35	97.89 84	79.95 327	88.25 253	94.96 205	72.56 300	98.13 203	89.70 150	85.14 279	95.49 231

EI-MVSNet-Vis-set		96.51 39	96.47 34	96.63 66	98.24 73	91.20 112	96.89 155	97.73 97	94.74 26	96.49 44	98.49 14	90.88 49	99.58 55	96.44 20	98.32 80	99.13 57

test222					98.24 73	92.21 77	95.33 265	97.60 112	79.22 332	95.25 82	97.84 61	88.80 69			99.15 54	98.72 93

HyFIR lowres test		93.66 114	92.92 117	95.87 107	98.24 73	89.88 149	94.58 280	98.49 12	85.06 283	93.78 107	95.78 172	82.86 160	98.67 159	91.77 119	95.71 143	99.07 63

MVS_111021_LR		96.24 47	96.19 44	96.39 85	98.23 77	91.35 105	96.24 226	98.79 4	93.99 40	95.80 68	97.65 73	89.92 60	99.24 101	95.87 36	99.20 51	98.58 101

EI-MVSNet-UG-set		96.34 44	96.30 39	96.47 77	98.20 78	90.93 123	96.86 157	97.72 100	94.67 27	96.16 53	98.46 15	90.43 53	99.58 55	96.23 24	97.96 89	98.90 80

PVSNet_Blended_VisFu		95.27 66	94.91 66	96.38 86	98.20 78	90.86 125	97.27 119	98.25 28	90.21 155	94.18 100	97.27 94	87.48 90	99.73 25	93.53 85	97.77 94	98.55 102

Anonymous202405211		92.07 175	90.83 196	95.76 111	98.19 80	88.75 200	97.58 90	95.00 284	86.00 272	93.64 109	97.45 89	66.24 330	99.53 71	90.68 139	92.71 195	99.01 69

PatchMatch-RL		92.90 140	92.02 145	95.56 123	98.19 80	90.80 127	95.27 270	97.18 157	87.96 229	91.86 155	95.68 179	80.44 209	98.99 132	84.01 256	97.54 99	96.89 178

testdata				95.46 132	98.18 82	88.90 198		97.66 107	82.73 308	97.03 31	98.07 45	90.06 57	98.85 142	89.67 151	98.98 65	98.64 99

Anonymous20240529		91.98 180	90.73 200	95.73 116	98.14 83	89.40 177	97.99 41	97.72 100	79.63 329	93.54 113	97.41 91	69.94 315	99.56 65	91.04 135	91.11 222	98.22 129

LFMVS		93.60 116	92.63 126	96.52 71	98.13 84	91.27 107	97.94 44	93.39 326	90.57 151	96.29 49	98.31 34	69.00 317	99.16 107	94.18 71	95.87 138	99.12 59

DeepPCF-MVS	93.97 1	96.61 37	97.09 8	95.15 145	98.09 85	86.63 266	96.00 238	98.15 40	95.43 7	97.95 11	98.56 9	93.40 9	99.36 93	96.77 12	99.48 24	99.45 30

VNet		95.89 56	95.45 54	97.21 53	98.07 86	92.94 61	97.50 97	98.15 40	93.87 42	97.52 14	97.61 79	85.29 114	99.53 71	95.81 39	95.27 149	99.16 53

MAR-MVS		94.22 94	93.46 104	96.51 74	98.00 87	92.19 80	97.67 71	97.47 127	88.13 228	93.00 132	95.84 165	84.86 120	99.51 76	87.99 184	98.17 84	97.83 147
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
view600		92.55 149	91.68 155	95.18 139	97.98 88	89.44 173	98.00 37	94.57 301	92.09 98	93.17 127	95.52 187	78.14 256	99.11 112	81.61 280	94.04 171	96.98 169

view800		92.55 149	91.68 155	95.18 139	97.98 88	89.44 173	98.00 37	94.57 301	92.09 98	93.17 127	95.52 187	78.14 256	99.11 112	81.61 280	94.04 171	96.98 169

conf0.05thres1000		92.55 149	91.68 155	95.18 139	97.98 88	89.44 173	98.00 37	94.57 301	92.09 98	93.17 127	95.52 187	78.14 256	99.11 112	81.61 280	94.04 171	96.98 169

tfpn		92.55 149	91.68 155	95.18 139	97.98 88	89.44 173	98.00 37	94.57 301	92.09 98	93.17 127	95.52 187	78.14 256	99.11 112	81.61 280	94.04 171	96.98 169

DeepC-MVS	93.07 3	96.06 50	95.66 51	97.29 46	97.96 92	93.17 55	97.30 118	98.06 59	93.92 41	93.38 118	98.66 6	86.83 97	99.73 25	95.60 47	99.22 49	98.96 73
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
COLMAP_ROB	87.81 15	90.40 245	89.28 252	93.79 214	97.95 93	87.13 255	96.92 153	95.89 246	82.83 307	86.88 280	97.18 98	73.77 295	99.29 98	78.44 314	93.62 181	94.95 267
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
AllTest		90.23 249	88.98 256	93.98 202	97.94 94	86.64 263	96.51 198	95.54 259	85.38 277	85.49 289	96.77 114	70.28 312	99.15 108	80.02 304	92.87 192	96.15 201

TestCases				93.98 202	97.94 94	86.64 263		95.54 259	85.38 277	85.49 289	96.77 114	70.28 312	99.15 108	80.02 304	92.87 192	96.15 201

tfpn111		92.45 156	91.58 162	95.06 149	97.92 96	89.37 179	97.71 67	94.66 296	92.20 92	93.31 120	94.90 209	78.06 260	99.11 112	81.37 291	94.06 169	96.70 184

conf200view11		92.45 156	91.58 162	95.05 150	97.92 96	89.37 179	97.71 67	94.66 296	92.20 92	93.31 120	94.90 209	78.06 260	99.08 123	81.40 287	94.08 165	96.70 184

thres100view900		92.43 158	91.58 162	94.98 155	97.92 96	89.37 179	97.71 67	94.66 296	92.20 92	93.31 120	94.90 209	78.06 260	99.08 123	81.40 287	94.08 165	96.48 192

thres600view7		92.49 155	91.60 161	95.18 139	97.91 99	89.47 169	97.65 74	94.66 296	92.18 97	93.33 119	94.91 208	78.06 260	99.10 118	81.61 280	94.06 169	96.98 169

API-MVS		94.84 83	94.49 80	95.90 106	97.90 100	92.00 86	97.80 56	97.48 124	89.19 177	94.81 89	96.71 116	88.84 68	99.17 106	88.91 170	98.76 71	96.53 189

VDD-MVS		93.82 109	93.08 113	96.02 102	97.88 101	89.96 147	97.72 65	95.85 247	92.43 87	95.86 65	98.44 17	68.42 321	99.39 90	96.31 21	94.85 155	98.71 95

tfpn200view9		92.38 161	91.52 167	94.95 158	97.85 102	89.29 185	97.41 104	94.88 291	92.19 95	93.27 124	94.46 233	78.17 253	99.08 123	81.40 287	94.08 165	96.48 192

thres400		92.42 159	91.52 167	95.12 148	97.85 102	89.29 185	97.41 104	94.88 291	92.19 95	93.27 124	94.46 233	78.17 253	99.08 123	81.40 287	94.08 165	96.98 169

DELS-MVS		96.61 37	96.38 38	97.30 45	97.79 104	93.19 54	95.96 239	98.18 36	95.23 11	95.87 64	97.65 73	91.45 40	99.70 34	95.87 36	99.44 29	99.00 71
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
tfpn1000		91.99 179	91.05 183	94.80 167	97.78 105	89.66 160	97.91 47	92.90 337	88.99 188	91.73 156	94.84 214	78.99 237	98.33 191	82.41 276	93.91 177	96.40 194

PVSNet	86.66 18	92.24 169	91.74 154	93.73 221	97.77 106	83.69 297	92.88 317	96.72 205	87.91 232	93.00 132	94.86 213	78.51 248	99.05 128	86.53 213	97.45 104	98.47 114

MVS_0304		96.05 51	95.45 54	97.85 15	97.75 107	94.50 16	96.87 156	97.95 83	95.46 6	95.60 77	98.01 49	80.96 196	99.83 14	97.23 2	99.25 46	99.23 49

0601test		94.78 85	94.23 85	96.43 81	97.74 108	91.22 108	96.85 158	97.10 168	91.23 126	95.71 71	96.93 107	84.30 126	99.31 96	93.10 95	95.12 151	98.75 89

Anonymous20240521		94.78 85	94.23 85	96.43 81	97.74 108	91.22 108	96.85 158	97.10 168	91.23 126	95.71 71	96.93 107	84.30 126	99.31 96	93.10 95	95.12 151	98.75 89

tfpn_ndepth		91.88 183	90.96 187	94.62 176	97.73 110	89.93 148	97.75 59	92.92 336	88.93 193	91.73 156	93.80 270	78.91 238	98.49 175	83.02 268	93.86 178	95.45 236

WTY-MVS		94.71 87	94.02 88	96.79 62	97.71 111	92.05 83	96.59 194	97.35 148	90.61 148	94.64 92	96.93 107	86.41 102	99.39 90	91.20 134	94.71 161	98.94 76

UA-Net		95.95 55	95.53 53	97.20 54	97.67 112	92.98 60	97.65 74	98.13 43	94.81 23	96.61 38	98.35 25	88.87 67	99.51 76	90.36 142	97.35 108	99.11 60

IS-MVSNet		94.90 80	94.52 79	96.05 101	97.67 112	90.56 132	98.44 15	96.22 228	93.21 60	93.99 103	97.74 67	85.55 112	98.45 176	89.98 144	97.86 90	99.14 56

PAPR		94.18 95	93.42 108	96.48 76	97.64 114	91.42 104	95.55 256	97.71 104	88.99 188	92.34 145	95.82 167	89.19 62	99.11 112	86.14 220	97.38 106	98.90 80

CANet		96.39 43	96.02 45	97.50 39	97.62 115	93.38 50	97.02 141	97.96 81	95.42 8	94.86 87	97.81 62	87.38 92	99.82 18	96.88 7	99.20 51	99.29 45

thres200		92.23 170	91.39 170	94.75 171	97.61 116	89.03 195	96.60 193	95.09 280	92.08 103	93.28 123	94.00 263	78.39 251	99.04 130	81.26 299	94.18 164	96.19 198

Vis-MVSNet (Re-imp)		94.15 96	93.88 90	94.95 158	97.61 116	87.92 237	98.10 31	95.80 250	92.22 90	93.02 131	97.45 89	84.53 124	97.91 252	88.24 179	97.97 88	99.02 65

canonicalmvs		96.02 53	95.45 54	97.75 25	97.59 118	95.15 10	98.28 22	97.60 112	94.52 30	96.27 50	96.12 152	87.65 86	99.18 105	96.20 29	94.82 157	98.91 79

LS3D		93.57 118	92.61 128	96.47 77	97.59 118	91.61 95	97.67 71	97.72 100	85.17 281	90.29 191	98.34 28	84.60 122	99.73 25	83.85 260	98.27 81	98.06 137

alignmvs		95.87 57	95.23 61	97.78 21	97.56 120	95.19 8	97.86 50	97.17 159	94.39 33	96.47 45	96.40 141	85.89 108	99.20 102	96.21 28	95.11 153	98.95 75

conf0.01		91.74 185	90.67 204	94.94 161	97.55 121	89.68 154	97.64 78	93.14 328	88.43 210	91.24 173	94.30 245	78.91 238	98.45 176	81.28 293	93.57 185	96.70 184

conf0.002		91.74 185	90.67 204	94.94 161	97.55 121	89.68 154	97.64 78	93.14 328	88.43 210	91.24 173	94.30 245	78.91 238	98.45 176	81.28 293	93.57 185	96.70 184

thresconf0.02		91.69 192	90.67 204	94.75 171	97.55 121	89.68 154	97.64 78	93.14 328	88.43 210	91.24 173	94.30 245	78.91 238	98.45 176	81.28 293	93.57 185	96.11 204

tfpn_n400		91.69 192	90.67 204	94.75 171	97.55 121	89.68 154	97.64 78	93.14 328	88.43 210	91.24 173	94.30 245	78.91 238	98.45 176	81.28 293	93.57 185	96.11 204

tfpnconf		91.69 192	90.67 204	94.75 171	97.55 121	89.68 154	97.64 78	93.14 328	88.43 210	91.24 173	94.30 245	78.91 238	98.45 176	81.28 293	93.57 185	96.11 204

tfpnview11		91.69 192	90.67 204	94.75 171	97.55 121	89.68 154	97.64 78	93.14 328	88.43 210	91.24 173	94.30 245	78.91 238	98.45 176	81.28 293	93.57 185	96.11 204

EPP-MVSNet		95.22 69	95.04 65	95.76 111	97.49 127	89.56 164	98.67 5	97.00 184	90.69 140	94.24 99	97.62 78	89.79 61	98.81 146	93.39 92	96.49 130	98.92 78

PS-MVSNAJ		95.37 63	95.33 59	95.49 128	97.35 128	90.66 131	95.31 267	97.48 124	93.85 43	96.51 43	95.70 178	88.65 71	99.65 41	94.80 65	98.27 81	96.17 199

casdiffmvs1		95.77 58	95.55 52	96.44 80	97.30 129	91.43 103	97.57 92	97.58 115	91.21 128	96.65 36	96.60 132	89.18 63	98.83 144	96.27 22	97.60 97	99.05 64

ab-mvs		93.57 118	92.55 130	96.64 64	97.28 130	91.96 88	95.40 263	97.45 133	89.81 166	93.22 126	96.28 145	79.62 223	99.46 81	90.74 137	93.11 191	98.50 109

xiu_mvs_v2_base		95.32 65	95.29 60	95.40 134	97.22 131	90.50 134	95.44 262	97.44 136	93.70 49	96.46 46	96.18 148	88.59 74	99.53 71	94.79 67	97.81 92	96.17 199

BH-untuned		92.94 138	92.62 127	93.92 210	97.22 131	86.16 270	96.40 208	96.25 226	90.06 159	89.79 213	96.17 151	83.19 137	98.35 188	87.19 206	97.27 110	97.24 166

Vis-MVSNet		95.23 67	94.81 68	96.51 74	97.18 133	91.58 98	98.26 24	98.12 44	94.38 34	94.90 86	98.15 42	82.28 175	98.92 135	91.45 129	98.58 76	99.01 69
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
BH-RMVSNet		92.72 147	91.97 147	94.97 156	97.16 134	87.99 232	96.15 229	95.60 256	90.62 146	91.87 154	97.15 101	78.41 250	98.57 167	83.16 265	97.60 97	98.36 126

MSDG		91.42 210	90.24 222	94.96 157	97.15 135	88.91 197	93.69 301	96.32 222	85.72 275	86.93 278	96.47 137	80.24 213	98.98 133	80.57 301	95.05 154	96.98 169

tttt0517		92.96 136	92.33 138	94.87 163	97.11 136	87.16 254	97.97 43	92.09 344	90.63 145	93.88 106	97.01 106	76.50 272	99.06 127	90.29 143	95.45 146	98.38 124

HY-MVS	89.66 9	93.87 107	92.95 116	96.63 66	97.10 137	92.49 72	95.64 254	96.64 213	89.05 186	93.00 132	95.79 171	85.77 111	99.45 83	89.16 164	94.35 162	97.96 138

thisisatest0530		93.03 133	92.21 140	95.49 128	97.07 138	89.11 194	97.49 101	92.19 343	90.16 157	94.09 101	96.41 140	76.43 275	99.05 128	90.38 141	95.68 144	98.31 128

XVG-OURS		93.72 113	93.35 109	94.80 167	97.07 138	88.61 203	94.79 277	97.46 129	91.97 107	93.99 103	97.86 58	81.74 187	98.88 141	92.64 102	92.67 197	96.92 177

sss		94.51 89	93.80 92	96.64 64	97.07 138	91.97 87	96.32 216	98.06 59	88.94 192	94.50 94	96.78 113	84.60 122	99.27 99	91.90 115	96.02 134	98.68 98

XVG-OURS-SEG-HR		93.86 108	93.55 99	94.81 166	97.06 141	88.53 205	95.28 268	97.45 133	91.68 112	94.08 102	97.68 70	82.41 173	98.90 137	93.84 81	92.47 198	96.98 169

1112_ss		93.37 122	92.42 136	96.21 97	97.05 142	90.99 119	96.31 217	96.72 205	86.87 261	89.83 211	96.69 120	86.51 101	99.14 110	88.12 181	93.67 179	98.50 109

Test_1112_low_res		92.84 144	91.84 150	95.85 108	97.04 143	89.97 145	95.53 258	96.64 213	85.38 277	89.65 221	95.18 200	85.86 109	99.10 118	87.70 190	93.58 184	98.49 111

BH-w/o		92.14 174	91.75 152	93.31 244	96.99 144	85.73 273	95.67 251	95.69 252	88.73 203	89.26 235	94.82 217	82.97 155	98.07 215	85.26 236	96.32 133	96.13 203

casdiffmvs		95.23 67	94.84 67	96.40 83	96.90 145	91.71 90	97.36 111	97.30 152	91.02 134	94.81 89	96.18 148	87.74 83	98.77 150	95.65 42	96.55 128	98.71 95

3Dnovator+	91.43 4	95.40 62	94.48 81	98.16 7	96.90 145	95.34 7	98.48 14	97.87 88	94.65 29	88.53 246	98.02 48	83.69 132	99.71 29	93.18 94	98.96 66	99.44 32

UGNet		94.04 103	93.28 111	96.31 90	96.85 147	91.19 113	97.88 49	97.68 106	94.40 32	93.00 132	96.18 148	73.39 298	99.61 47	91.72 120	98.46 77	98.13 132
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
VDDNet		93.05 132	92.07 142	96.02 102	96.84 148	90.39 137	98.08 33	95.85 247	86.22 269	95.79 69	98.46 15	67.59 324	99.19 103	94.92 61	94.85 155	98.47 114

RPSCF		90.75 234	90.86 192	90.42 314	96.84 148	76.29 338	95.61 255	96.34 221	83.89 297	91.38 162	97.87 56	76.45 273	98.78 148	87.16 208	92.23 201	96.20 197

MVS_Test		94.89 81	94.62 74	95.68 118	96.83 150	89.55 165	96.70 181	97.17 159	91.17 129	95.60 77	96.11 154	87.87 81	98.76 152	93.01 99	97.17 112	98.72 93

LCM-MVSNet-Re		92.50 153	92.52 133	92.44 267	96.82 151	81.89 310	96.92 153	93.71 321	92.41 88	84.30 298	94.60 226	85.08 117	97.03 301	91.51 126	97.36 107	98.40 121

Fast-Effi-MVS+		93.46 120	92.75 122	95.59 122	96.77 152	90.03 139	96.81 165	97.13 164	88.19 224	91.30 168	94.27 255	86.21 104	98.63 161	87.66 194	96.46 132	98.12 133

QAPM		93.45 121	92.27 139	96.98 60	96.77 152	92.62 68	98.39 18	98.12 44	84.50 291	88.27 252	97.77 65	82.39 174	99.81 19	85.40 234	98.81 69	98.51 107

CHOSEN 280x420		93.12 129	92.72 124	94.34 189	96.71 154	87.27 248	90.29 338	97.72 100	86.61 265	91.34 165	95.29 196	84.29 128	98.41 183	93.25 93	98.94 67	97.35 165

Effi-MVS+		94.93 79	94.45 82	96.36 88	96.61 155	91.47 100	96.41 204	97.41 140	91.02 134	94.50 94	95.92 161	87.53 89	98.78 148	93.89 79	96.81 119	98.84 87

thisisatest0515		92.29 166	91.30 175	95.25 137	96.60 156	88.90 198	94.36 285	92.32 341	87.92 231	93.43 117	94.57 227	77.28 269	99.00 131	89.42 156	95.86 139	97.86 144

PCF-MVS	89.48 11	91.56 203	89.95 233	96.36 88	96.60 156	92.52 71	92.51 322	97.26 154	79.41 330	88.90 238	96.56 133	84.04 129	99.55 66	77.01 320	97.30 109	97.01 168
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
xiu_mvs_v1_base_debu		95.01 73	94.76 70	95.75 113	96.58 158	91.71 90	96.25 223	97.35 148	92.99 69	96.70 33	96.63 127	82.67 164	99.44 84	96.22 25	97.46 100	96.11 204

xiu_mvs_v1_base		95.01 73	94.76 70	95.75 113	96.58 158	91.71 90	96.25 223	97.35 148	92.99 69	96.70 33	96.63 127	82.67 164	99.44 84	96.22 25	97.46 100	96.11 204

xiu_mvs_v1_base_debi		95.01 73	94.76 70	95.75 113	96.58 158	91.71 90	96.25 223	97.35 148	92.99 69	96.70 33	96.63 127	82.67 164	99.44 84	96.22 25	97.46 100	96.11 204

MVSTER		93.20 127	92.81 119	94.37 187	96.56 161	89.59 163	97.06 138	97.12 165	91.24 125	91.30 168	95.96 158	82.02 181	98.05 223	93.48 88	90.55 231	95.47 234

3Dnovator	91.36 5	95.19 71	94.44 83	97.44 40	96.56 161	93.36 52	98.65 6	98.36 16	94.12 38	89.25 236	98.06 46	82.20 178	99.77 22	93.41 91	99.32 41	99.18 52

FMVSNet3		91.78 184	90.69 203	95.03 152	96.53 163	92.27 76	97.02 141	96.93 194	89.79 167	89.35 230	94.65 224	77.01 270	97.47 283	86.12 221	88.82 246	95.35 246

diffmvs1		94.99 77	94.79 69	95.60 121	96.52 164	89.20 189	96.43 201	97.36 146	92.59 83	94.85 88	96.10 155	87.85 82	98.74 154	93.99 74	97.41 105	98.86 84

GBi-Net		91.35 214	90.27 220	94.59 177	96.51 165	91.18 114	97.50 97	96.93 194	88.82 198	89.35 230	94.51 229	73.87 292	97.29 295	86.12 221	88.82 246	95.31 248

test1		91.35 214	90.27 220	94.59 177	96.51 165	91.18 114	97.50 97	96.93 194	88.82 198	89.35 230	94.51 229	73.87 292	97.29 295	86.12 221	88.82 246	95.31 248

FMVSNet2		91.31 216	90.08 227	94.99 153	96.51 165	92.21 77	97.41 104	96.95 192	88.82 198	88.62 243	94.75 220	73.87 292	97.42 287	85.20 237	88.55 252	95.35 246

ACMH+	87.92 14	90.20 250	89.18 254	93.25 246	96.48 168	86.45 267	96.99 144	96.68 210	88.83 197	84.79 295	96.22 147	70.16 314	98.53 169	84.42 249	88.04 254	94.77 285

CANet_DTU		94.37 91	93.65 97	96.55 70	96.46 169	92.13 81	96.21 227	96.67 212	94.38 34	93.53 114	97.03 105	79.34 226	99.71 29	90.76 136	98.45 78	97.82 148

mvs_anonymous		93.82 109	93.74 93	94.06 198	96.44 170	85.41 278	95.81 246	97.05 176	89.85 164	90.09 202	96.36 143	87.44 91	97.75 265	93.97 75	96.69 124	99.02 65

TR-MVS		91.48 207	90.59 212	94.16 195	96.40 171	87.33 246	95.67 251	95.34 269	87.68 238	91.46 161	95.52 187	76.77 271	98.35 188	82.85 270	93.61 182	96.79 181

ACMP	89.59 10	92.62 148	92.14 141	94.05 199	96.40 171	88.20 218	97.36 111	97.25 156	91.52 114	88.30 250	96.64 123	78.46 249	98.72 157	91.86 118	91.48 216	95.23 255
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MVSFormer		95.37 63	95.16 63	95.99 104	96.34 173	91.21 110	98.22 26	97.57 116	91.42 119	96.22 51	97.32 92	86.20 105	97.92 249	94.07 72	99.05 62	98.85 85

lupinMVS		94.99 77	94.56 76	96.29 93	96.34 173	91.21 110	95.83 245	96.27 224	88.93 193	96.22 51	96.88 111	86.20 105	98.85 142	95.27 49	99.05 62	98.82 88

diffmvs		94.47 90	94.23 85	95.18 139	96.32 175	88.22 215	96.27 221	97.04 179	92.55 85	93.60 110	95.94 160	86.79 98	98.70 158	92.98 100	96.61 126	98.63 100

ACMM	89.79 8	92.96 136	92.50 134	94.35 188	96.30 176	88.71 201	97.58 90	97.36 146	91.40 121	90.53 185	96.65 122	79.77 220	98.75 153	91.24 133	91.64 212	95.59 230
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
IterMVS-LS		92.29 166	91.94 148	93.34 243	96.25 177	86.97 259	96.57 197	97.05 176	90.67 141	89.50 227	94.80 218	86.59 99	97.64 273	89.91 145	86.11 269	95.40 242
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
HQP_MVS		93.78 111	93.43 106	94.82 164	96.21 178	89.99 142	97.74 61	97.51 122	94.85 18	91.34 165	96.64 123	81.32 192	98.60 164	93.02 97	92.23 201	95.86 214

plane_prior7					96.21 178	89.98 144

ACMH	87.59 16	90.53 243	89.42 249	93.87 211	96.21 178	87.92 237	97.24 121	96.94 193	88.45 209	83.91 304	96.27 146	71.92 301	98.62 163	84.43 248	89.43 242	95.05 266
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CDS-MVSNet		94.14 98	93.54 100	95.93 105	96.18 181	91.46 101	96.33 215	97.04 179	88.97 191	93.56 111	96.51 135	87.55 88	97.89 253	89.80 147	95.95 136	98.44 118
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
LTVRE_ROB	88.41 13	90.99 226	89.92 234	94.19 193	96.18 181	89.55 165	96.31 217	97.09 170	87.88 233	85.67 287	95.91 162	78.79 246	98.57 167	81.50 285	89.98 237	94.44 294
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
LPG-MVS_test		92.94 138	92.56 129	94.10 196	96.16 183	88.26 211	97.65 74	97.46 129	91.29 122	90.12 199	97.16 99	79.05 230	98.73 155	92.25 105	91.89 209	95.31 248

LGP-MVS_train				94.10 196	96.16 183	88.26 211		97.46 129	91.29 122	90.12 199	97.16 99	79.05 230	98.73 155	92.25 105	91.89 209	95.31 248

TAMVS		94.01 104	93.46 104	95.64 119	96.16 183	90.45 136	96.71 178	96.89 198	89.27 175	93.46 116	96.92 110	87.29 93	97.94 245	88.70 176	95.74 141	98.53 104

plane_prior1					96.14 186

CLD-MVS		92.98 135	92.53 132	94.32 190	96.12 187	89.20 189	95.28 268	97.47 127	92.66 81	89.90 206	95.62 181	80.58 206	98.40 184	92.73 101	92.40 199	95.38 244
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
plane_prior6					96.10 188	90.00 140						81.32 192

Effi-MVS+-dtu		93.08 130	93.21 112	92.68 264	96.02 189	83.25 301	97.14 135	96.72 205	93.85 43	91.20 180	93.44 285	83.08 145	98.30 193	91.69 123	95.73 142	96.50 191

mvs-test1		93.63 115	93.69 95	93.46 238	96.02 189	84.61 288	97.24 121	96.72 205	93.85 43	92.30 146	95.76 173	83.08 145	98.89 139	91.69 123	96.54 129	96.87 179

NP-MVS					95.99 191	89.81 151					95.87 163

ADS-MVSNet2		89.45 263	88.59 261	92.03 282	95.86 192	82.26 308	90.93 334	94.32 311	83.23 305	91.28 171	91.81 310	79.01 234	95.99 323	79.52 306	91.39 218	97.84 145

ADS-MVSNet		89.89 256	88.68 260	93.53 234	95.86 192	84.89 285	90.93 334	95.07 282	83.23 305	91.28 171	91.81 310	79.01 234	97.85 255	79.52 306	91.39 218	97.84 145

HQP-NCC					95.86 192		96.65 186		93.55 50	90.14 193

ACMP_Plane					95.86 192		96.65 186		93.55 50	90.14 193

HQP-MVS		93.19 128	92.74 123	94.54 182	95.86 192	89.33 182	96.65 186	97.39 141	93.55 50	90.14 193	95.87 163	80.95 197	98.50 172	92.13 109	92.10 206	95.78 221

EI-MVSNet		93.03 133	92.88 118	93.48 236	95.77 197	86.98 258	96.44 199	97.12 165	90.66 143	91.30 168	97.64 76	86.56 100	98.05 223	89.91 145	90.55 231	95.41 238

CVMVSNet		91.23 218	91.75 152	89.67 320	95.77 197	74.69 340	96.44 199	94.88 291	85.81 273	92.18 148	97.64 76	79.07 229	95.58 331	88.06 182	95.86 139	98.74 91

FIs		94.09 100	93.70 94	95.27 136	95.70 199	92.03 84	98.10 31	98.68 7	93.36 57	90.39 189	96.70 118	87.63 87	97.94 245	92.25 105	90.50 233	95.84 217

VPA-MVSNet		93.24 126	92.48 135	95.51 126	95.70 199	92.39 73	97.86 50	98.66 9	92.30 89	92.09 151	95.37 194	80.49 208	98.40 184	93.95 76	85.86 270	95.75 225

Patchmatch-test1		91.54 205	90.85 193	93.59 230	95.59 201	84.95 284	94.72 278	95.58 258	90.82 136	92.25 147	93.58 278	75.80 278	97.41 288	83.35 262	95.98 135	98.40 121

VPNet		92.23 170	91.31 174	94.99 153	95.56 202	90.96 121	97.22 126	97.86 90	92.96 75	90.96 181	96.62 130	75.06 284	98.20 197	91.90 115	83.65 306	95.80 220

semantic-postprocess				91.82 287	95.52 203	84.20 291		96.15 231	90.61 148	87.39 268	94.27 255	75.63 280	96.44 310	87.34 202	86.88 265	94.82 279

jason		94.84 83	94.39 84	96.18 98	95.52 203	90.93 123	96.09 232	96.52 217	89.28 174	96.01 61	97.32 92	84.70 121	98.77 150	95.15 52	98.91 68	98.85 85
jason: jason.
FC-MVSNet-test		93.94 106	93.57 98	95.04 151	95.48 205	91.45 102	98.12 30	98.71 5	93.37 55	90.23 192	96.70 118	87.66 85	97.85 255	91.49 127	90.39 234	95.83 218

IterMVS		90.15 252	89.67 244	91.61 294	95.48 205	83.72 294	94.33 287	96.12 232	89.99 160	87.31 271	94.15 260	75.78 279	96.27 313	86.97 210	86.89 264	94.83 277
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
FMVSNet1		89.88 257	88.31 265	94.59 177	95.41 207	91.18 114	97.50 97	96.93 194	86.62 264	87.41 267	94.51 229	65.94 332	97.29 295	83.04 267	87.43 260	95.31 248

UniMVSNet (Re)		93.31 124	92.55 130	95.61 120	95.39 208	93.34 53	97.39 108	98.71 5	93.14 65	90.10 201	94.83 216	87.71 84	98.03 228	91.67 125	83.99 299	95.46 235

MVS-HIRNet		82.47 318	81.21 319	86.26 330	95.38 209	69.21 350	88.96 346	89.49 355	66.28 352	80.79 319	74.08 355	68.48 320	97.39 290	71.93 334	95.47 145	92.18 336

PatchmatchNet		91.91 181	91.35 171	93.59 230	95.38 209	84.11 292	93.15 313	95.39 263	89.54 168	92.10 150	93.68 274	82.82 162	98.13 203	84.81 240	95.32 148	98.52 105
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
UniMVSNet_NR-MVSNet		93.37 122	92.67 125	95.47 131	95.34 211	92.83 62	97.17 132	98.58 10	92.98 74	90.13 197	95.80 168	88.37 76	97.85 255	91.71 121	83.93 300	95.73 227

ITE_SJBPF				92.43 268	95.34 211	85.37 279		95.92 239	91.47 116	87.75 260	96.39 142	71.00 308	97.96 243	82.36 277	89.86 240	93.97 305

OpenMVS	89.19 12	92.86 142	91.68 155	96.40 83	95.34 211	92.73 65	98.27 23	98.12 44	84.86 286	85.78 286	97.75 66	78.89 245	99.74 24	87.50 199	98.65 73	96.73 182

1314		92.81 145	92.03 144	95.14 146	95.33 214	89.52 168	96.04 234	97.44 136	87.72 237	86.25 283	95.33 195	83.84 130	98.79 147	89.26 159	97.05 114	97.11 167

PAPM		91.52 206	90.30 218	95.20 138	95.30 215	89.83 150	93.38 308	96.85 201	86.26 268	88.59 245	95.80 168	84.88 119	98.15 202	75.67 324	95.93 137	97.63 153

Fast-Effi-MVS+-dtu		92.29 166	91.99 146	93.21 249	95.27 216	85.52 277	97.03 139	96.63 215	92.09 98	89.11 237	95.14 202	80.33 212	98.08 211	87.54 198	94.74 160	96.03 211

Patchmatch-test		89.42 264	87.99 268	93.70 224	95.27 216	85.11 280	88.98 345	94.37 309	81.11 320	87.10 275	93.69 273	82.28 175	97.50 281	74.37 327	94.76 158	98.48 113

PVSNet_0	82.17 19	85.46 308	83.64 309	90.92 305	95.27 216	79.49 329	90.55 337	95.60 256	83.76 300	83.00 307	89.95 317	71.09 307	97.97 239	82.75 272	60.79 354	95.31 248

IB-MVS	87.33 17	89.91 255	88.28 266	94.79 169	95.26 219	87.70 243	95.12 274	93.95 320	89.35 173	87.03 276	92.49 298	70.74 310	99.19 103	89.18 163	81.37 319	97.49 162
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
PatchFormer-LS_test		91.68 197	91.18 182	93.19 250	95.24 220	83.63 298	95.53 258	95.44 262	89.82 165	91.37 163	92.58 297	80.85 204	98.52 170	89.65 153	90.16 236	97.42 164

nrg030		94.05 102	93.31 110	96.27 94	95.22 221	94.59 15	98.34 19	97.46 129	92.93 76	91.21 179	96.64 123	87.23 94	98.22 196	94.99 60	85.80 271	95.98 212

MDTV_nov1_ep13			90.76 198		95.22 221	80.33 322	93.03 316	95.28 270	88.14 227	92.84 138	93.83 268	81.34 191	98.08 211	82.86 269	94.34 163

MVS		91.71 187	90.44 214	95.51 126	95.20 223	91.59 97	96.04 234	97.45 133	73.44 349	87.36 269	95.60 182	85.42 113	99.10 118	85.97 225	97.46 100	95.83 218

tpmp4_e23		89.58 261	88.59 261	92.54 266	95.16 224	81.53 312	94.11 293	95.09 280	81.66 315	88.60 244	93.44 285	75.11 283	98.33 191	82.45 275	91.72 211	97.75 149

tfpnnormal		89.70 260	88.40 264	93.60 229	95.15 225	90.10 138	97.56 93	98.16 39	87.28 247	86.16 284	94.63 225	77.57 267	98.05 223	74.48 325	84.59 294	92.65 320

tpmrst		91.44 209	91.32 173	91.79 289	95.15 225	79.20 332	93.42 307	95.37 265	88.55 207	93.49 115	93.67 275	82.49 170	98.27 194	90.41 140	89.34 243	97.90 141

WR-MVS		92.34 162	91.53 166	94.77 170	95.13 227	90.83 126	96.40 208	97.98 79	91.88 108	89.29 233	95.54 186	82.50 169	97.80 260	89.79 148	85.27 277	95.69 228

tpm cat1		88.36 285	87.21 285	91.81 288	95.13 227	80.55 320	92.58 321	95.70 251	74.97 345	87.45 265	91.96 308	78.01 264	98.17 201	80.39 303	88.74 249	96.72 183

WR-MVS_H		92.00 178	91.35 171	93.95 206	95.09 229	89.47 169	98.04 36	98.68 7	91.46 117	88.34 248	94.68 222	85.86 109	97.56 277	85.77 228	84.24 297	94.82 279

CP-MVSNet		91.89 182	91.24 178	93.82 212	95.05 230	88.57 204	97.82 55	98.19 34	91.70 111	88.21 254	95.76 173	81.96 182	97.52 280	87.86 186	84.65 293	95.37 245

DWT-MVSNet_test		90.76 232	89.89 235	93.38 241	95.04 231	83.70 296	95.85 244	94.30 312	88.19 224	90.46 187	92.80 292	73.61 296	98.50 172	88.16 180	90.58 230	97.95 139

test_0402		86.46 300	84.79 303	91.45 297	95.02 232	85.55 276	96.29 219	94.89 290	80.90 321	82.21 308	93.97 264	68.21 322	97.29 295	62.98 346	88.68 251	91.51 340

cascas		91.20 219	90.08 227	94.58 181	94.97 233	89.16 193	93.65 303	97.59 114	79.90 328	89.40 228	92.92 291	75.36 282	98.36 187	92.14 108	94.75 159	96.23 196

PS-CasMVS		91.55 204	90.84 195	93.69 225	94.96 234	88.28 210	97.84 54	98.24 29	91.46 117	88.04 256	95.80 168	79.67 222	97.48 282	87.02 209	84.54 295	95.31 248

DU-MVS		92.90 140	92.04 143	95.49 128	94.95 235	92.83 62	97.16 133	98.24 29	93.02 68	90.13 197	95.71 176	83.47 134	97.85 255	91.71 121	83.93 300	95.78 221

NR-MVSNet		92.34 162	91.27 177	95.53 125	94.95 235	93.05 57	97.39 108	98.07 57	92.65 82	84.46 296	95.71 176	85.00 118	97.77 264	89.71 149	83.52 307	95.78 221

tpmvs		89.83 259	89.15 255	91.89 285	94.92 237	80.30 323	93.11 314	95.46 261	86.28 267	88.08 255	92.65 294	80.44 209	98.52 170	81.47 286	89.92 239	96.84 180

PMMVS		92.86 142	92.34 137	94.42 186	94.92 237	86.73 262	94.53 282	96.38 220	84.78 288	94.27 98	95.12 204	83.13 141	98.40 184	91.47 128	96.49 130	98.12 133

tpm2		89.96 254	89.21 253	92.23 273	94.91 239	81.25 314	93.78 298	94.42 307	80.62 325	91.56 159	93.44 285	76.44 274	97.94 245	85.60 231	92.08 208	97.49 162

TinyColmap		86.82 298	85.35 300	91.21 300	94.91 239	82.99 302	93.94 296	94.02 319	83.58 301	81.56 315	94.68 222	62.34 339	98.13 203	75.78 322	87.35 263	92.52 323

CostFormer		91.18 222	90.70 202	92.62 265	94.84 241	81.76 311	94.09 294	94.43 306	84.15 294	92.72 139	93.77 271	79.43 225	98.20 197	90.70 138	92.18 204	97.90 141

MIMVSNet		88.50 280	86.76 289	93.72 223	94.84 241	87.77 241	91.39 329	94.05 317	86.41 266	87.99 257	92.59 296	63.27 336	95.82 327	77.44 316	92.84 194	97.57 160

FMVSNet5		87.29 295	85.79 296	91.78 290	94.80 243	87.28 247	95.49 260	95.28 270	84.09 295	83.85 305	91.82 309	62.95 337	94.17 338	78.48 313	85.34 276	93.91 306

TranMVSNet+NR-MVSNet		92.50 153	91.63 160	95.14 146	94.76 244	92.07 82	97.53 95	98.11 47	92.90 77	89.56 224	96.12 152	83.16 138	97.60 276	89.30 158	83.20 310	95.75 225

XXY-MVS		92.16 172	91.23 179	94.95 158	94.75 245	90.94 122	97.47 102	97.43 138	89.14 184	88.90 238	96.43 139	79.71 221	98.24 195	89.56 154	87.68 257	95.67 229

EPMVS		90.70 238	89.81 239	93.37 242	94.73 246	84.21 290	93.67 302	88.02 356	89.50 170	92.38 143	93.49 282	77.82 266	97.78 262	86.03 224	92.68 196	98.11 136

USDC		88.94 267	87.83 270	92.27 269	94.66 247	84.96 283	93.86 297	95.90 241	87.34 245	83.40 306	95.56 184	67.43 325	98.19 199	82.64 274	89.67 241	93.66 308

GA-MVS		91.38 212	90.31 217	94.59 177	94.65 248	87.62 244	94.34 286	96.19 229	90.73 139	90.35 190	93.83 268	71.84 302	97.96 243	87.22 205	93.61 182	98.21 130

OPM-MVS		93.28 125	92.76 120	94.82 164	94.63 249	90.77 129	96.65 186	97.18 157	93.72 47	91.68 158	97.26 95	79.33 227	98.63 161	92.13 109	92.28 200	95.07 261

test-LLR		91.42 210	91.19 181	92.12 279	94.59 250	80.66 317	94.29 288	92.98 334	91.11 131	90.76 183	92.37 300	79.02 232	98.07 215	88.81 174	96.74 121	97.63 153

test-mter		90.19 251	89.54 247	92.12 279	94.59 250	80.66 317	94.29 288	92.98 334	87.68 238	90.76 183	92.37 300	67.67 323	98.07 215	88.81 174	96.74 121	97.63 153

dp		88.90 269	88.26 267	90.81 307	94.58 252	76.62 337	92.85 318	94.93 289	85.12 282	90.07 204	93.07 289	75.81 277	98.12 206	80.53 302	87.42 261	97.71 151

PEN-MVS		91.20 219	90.44 214	93.48 236	94.49 253	87.91 239	97.76 58	98.18 36	91.29 122	87.78 259	95.74 175	80.35 211	97.33 293	85.46 233	82.96 311	95.19 257

gg-mvs-nofinetune		87.82 290	85.61 297	94.44 184	94.46 254	89.27 188	91.21 333	84.61 362	80.88 322	89.89 208	74.98 353	71.50 304	97.53 279	85.75 229	97.21 111	96.51 190

CR-MVSNet		90.82 231	89.77 240	93.95 206	94.45 255	87.19 252	90.23 339	95.68 254	86.89 260	92.40 141	92.36 303	80.91 200	97.05 299	81.09 300	93.95 175	97.60 158

RPMNet		88.52 278	86.72 291	93.95 206	94.45 255	87.19 252	90.23 339	94.99 286	77.87 339	92.40 141	87.55 343	80.17 215	97.05 299	68.84 340	93.95 175	97.60 158

TESTMET0.1,1		90.06 253	89.42 249	91.97 283	94.41 257	80.62 319	94.29 288	91.97 346	87.28 247	90.44 188	92.47 299	68.79 318	97.67 270	88.50 178	96.60 127	97.61 157

TransMVSNet (Re)		88.94 267	87.56 271	93.08 252	94.35 258	88.45 208	97.73 63	95.23 274	87.47 241	84.26 299	95.29 196	79.86 219	97.33 293	79.44 310	74.44 345	93.45 311

MS-PatchMatch		90.27 247	89.77 240	91.78 290	94.33 259	84.72 287	95.55 256	96.73 204	86.17 270	86.36 282	95.28 198	71.28 306	97.80 260	84.09 253	98.14 85	92.81 319

XVG-ACMP-BASELINE		90.93 228	90.21 225	93.09 251	94.31 260	85.89 271	95.33 265	97.26 154	91.06 133	89.38 229	95.44 193	68.61 319	98.60 164	89.46 155	91.05 224	94.79 283

pcd1.5k->3k		38.37 342	40.51 343	31.96 354	94.29 261	0.00 373	0.00 364	97.69 105	0.00 368	0.00 370	0.00 370	81.45 190	0.00 370	0.00 367	91.11 222	95.89 213

pm-mvs1		90.72 236	89.65 246	93.96 205	94.29 261	89.63 161	97.79 57	96.82 202	89.07 185	86.12 285	95.48 192	78.61 247	97.78 262	86.97 210	81.67 317	94.46 293

v1neww		91.70 190	91.01 184	93.75 218	94.19 263	88.14 223	97.20 128	96.98 185	89.18 179	89.87 209	94.44 235	83.10 143	98.06 220	89.06 166	85.09 282	95.06 264

v7new		91.70 190	91.01 184	93.75 218	94.19 263	88.14 223	97.20 128	96.98 185	89.18 179	89.87 209	94.44 235	83.10 143	98.06 220	89.06 166	85.09 282	95.06 264

v16		88.69 274	87.50 273	92.26 271	94.19 263	88.11 227	96.81 165	95.95 237	87.01 253	80.71 322	89.80 321	83.08 145	96.20 315	84.61 245	75.34 335	92.48 326

v18		88.71 273	87.52 272	92.27 269	94.16 266	88.11 227	96.82 164	95.96 236	87.03 251	80.76 320	89.81 320	83.15 139	96.22 314	84.69 242	75.31 336	92.49 324

v8		91.29 217	90.53 213	93.57 233	94.15 267	88.12 225	97.34 113	97.06 175	88.99 188	88.32 249	94.26 257	83.08 145	98.01 232	87.62 196	83.92 302	94.57 290

v6		91.69 192	91.00 186	93.75 218	94.14 268	88.12 225	97.20 128	96.98 185	89.19 177	89.90 206	94.42 237	83.04 149	98.07 215	89.07 165	85.10 281	95.07 261

v17		88.67 275	87.47 275	92.26 271	94.13 269	88.09 229	96.81 165	95.95 237	87.02 252	80.72 321	89.75 322	83.11 142	96.20 315	84.61 245	75.15 338	92.49 324

v7		91.47 208	90.73 200	93.68 226	94.13 269	88.16 221	97.09 137	97.05 176	88.38 217	89.80 212	94.52 228	82.21 177	98.01 232	88.00 183	85.42 274	94.87 273

V14		88.52 278	87.30 278	92.17 276	94.12 271	87.99 232	96.72 176	95.91 240	86.98 255	80.50 326	89.63 323	83.03 150	96.12 319	84.23 251	74.60 341	92.40 331

v10		91.04 225	90.23 223	93.49 235	94.12 271	88.16 221	97.32 116	97.08 172	88.26 221	88.29 251	94.22 258	82.17 179	97.97 239	86.45 216	84.12 298	94.33 297

V9		88.49 281	87.26 280	92.18 275	94.12 271	87.97 235	96.73 173	95.90 241	86.95 257	80.40 328	89.61 324	82.98 154	96.13 317	84.14 252	74.55 342	92.44 328

v12		88.46 282	87.23 283	92.17 276	94.10 274	87.99 232	96.71 178	95.90 241	86.91 258	80.34 330	89.58 327	82.92 158	96.11 321	84.09 253	74.50 344	92.42 329

v15		88.53 277	87.31 277	92.20 274	94.09 275	88.05 230	96.72 176	95.90 241	87.01 253	80.53 325	89.60 326	83.02 151	96.13 317	84.29 250	74.64 339	92.41 330

Patchmtry		88.64 276	87.25 281	92.78 260	94.09 275	86.64 263	89.82 342	95.68 254	80.81 324	87.63 264	92.36 303	80.91 200	97.03 301	78.86 312	85.12 280	94.67 287

v13		88.45 283	87.22 284	92.16 278	94.08 277	87.95 236	96.71 178	95.90 241	86.86 262	80.27 332	89.55 328	82.92 158	96.12 319	84.02 255	74.63 340	92.40 331

v11		88.41 284	87.19 287	92.08 281	94.08 277	87.77 241	96.75 171	95.85 247	86.74 263	80.50 326	89.50 329	82.49 170	96.08 322	83.55 261	75.20 337	92.38 333

PatchT		88.87 270	87.42 276	93.22 248	94.08 277	85.10 281	89.51 343	94.64 300	81.92 313	92.36 144	88.15 339	80.05 216	97.01 303	72.43 332	93.65 180	97.54 161

V42		91.58 202	90.87 191	93.73 221	94.05 280	88.50 206	97.32 116	96.97 188	88.80 201	89.71 217	94.33 242	82.54 168	98.05 223	89.01 168	85.07 284	94.64 289

v1141		91.61 198	90.89 188	93.78 215	94.01 281	88.24 213	96.96 146	96.96 189	89.17 181	89.75 215	94.29 251	82.99 153	98.03 228	88.85 172	85.00 287	95.07 261

divwei89l23v2f112		91.61 198	90.89 188	93.78 215	94.01 281	88.22 215	96.96 146	96.96 189	89.17 181	89.75 215	94.28 253	83.02 151	98.03 228	88.86 171	84.98 290	95.08 259

v1		91.61 198	90.89 188	93.78 215	94.01 281	88.21 217	96.96 146	96.96 189	89.17 181	89.78 214	94.29 251	82.97 155	98.05 223	88.85 172	84.99 288	95.08 259

DTE-MVSNet		90.56 242	89.75 242	93.01 253	93.95 284	87.25 249	97.64 78	97.65 109	90.74 138	87.12 273	95.68 179	79.97 218	97.00 304	83.33 264	81.66 318	94.78 284

tpm		90.25 248	89.74 243	91.76 292	93.92 285	79.73 328	93.98 295	93.54 325	88.28 220	91.99 152	93.25 288	77.51 268	97.44 285	87.30 204	87.94 255	98.12 133

PS-MVSNAJss		93.74 112	93.51 102	94.44 184	93.91 286	89.28 187	97.75 59	97.56 119	92.50 86	89.94 205	96.54 134	88.65 71	98.18 200	93.83 82	90.90 226	95.86 214

v1144		91.37 213	90.60 211	93.68 226	93.89 287	88.23 214	96.84 160	97.03 182	88.37 218	89.69 219	94.39 238	82.04 180	97.98 236	87.80 188	85.37 275	94.84 275

v2v482		91.59 201	90.85 193	93.80 213	93.87 288	88.17 220	96.94 152	96.88 199	89.54 168	89.53 225	94.90 209	81.70 188	98.02 231	89.25 160	85.04 286	95.20 256

v148		90.99 226	90.38 216	92.81 259	93.83 289	85.80 272	96.78 170	96.68 210	89.45 171	88.75 242	93.93 266	82.96 157	97.82 259	87.83 187	83.25 308	94.80 281

Baseline_NR-MVSNet		91.20 219	90.62 210	92.95 255	93.83 289	88.03 231	97.01 143	95.12 279	88.42 216	89.70 218	95.13 203	83.47 134	97.44 285	89.66 152	83.24 309	93.37 313

EPNet_dtu		91.71 187	91.28 176	92.99 254	93.76 291	83.71 295	96.69 183	95.28 270	93.15 64	87.02 277	95.95 159	83.37 136	97.38 291	79.46 309	96.84 117	97.88 143
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
v1192		91.07 223	90.23 223	93.58 232	93.70 292	87.82 240	96.73 173	97.07 173	87.77 235	89.58 222	94.32 243	80.90 203	97.97 239	86.52 214	85.48 272	94.95 267

GG-mvs-BLEND				93.62 228	93.69 293	89.20 189	92.39 325	83.33 363		87.98 258	89.84 319	71.00 308	96.87 306	82.08 279	95.40 147	94.80 281

v144192		91.06 224	90.28 219	93.39 240	93.66 294	87.23 251	96.83 161	97.07 173	87.43 242	89.69 219	94.28 253	81.48 189	98.00 235	87.18 207	84.92 291	94.93 271

v1921920		90.85 230	90.03 230	93.29 245	93.55 295	86.96 260	96.74 172	97.04 179	87.36 244	89.52 226	94.34 241	80.23 214	97.97 239	86.27 217	85.21 278	94.94 269

v7n		90.76 232	89.86 236	93.45 239	93.54 296	87.60 245	97.70 70	97.37 144	88.85 195	87.65 263	94.08 262	81.08 194	98.10 208	84.68 243	83.79 305	94.66 288

JIA-IIPM		88.26 287	87.04 288	91.91 284	93.52 297	81.42 313	89.38 344	94.38 308	80.84 323	90.93 182	80.74 350	79.22 228	97.92 249	82.76 271	91.62 213	96.38 195

v1240		90.70 238	89.85 237	93.23 247	93.51 298	86.80 261	96.61 191	97.02 183	87.16 249	89.58 222	94.31 244	79.55 224	97.98 236	85.52 232	85.44 273	94.90 272

test_djsdf		93.07 131	92.76 120	94.00 201	93.49 299	88.70 202	98.22 26	97.57 116	91.42 119	90.08 203	95.55 185	82.85 161	97.92 249	94.07 72	91.58 214	95.40 242

SixPastTwentyTwo		89.15 266	88.54 263	90.98 303	93.49 299	80.28 324	96.70 181	94.70 295	90.78 137	84.15 301	95.57 183	71.78 303	97.71 268	84.63 244	85.07 284	94.94 269

mvs_tets		92.31 164	91.76 151	93.94 209	93.41 301	88.29 209	97.63 85	97.53 120	92.04 104	88.76 241	96.45 138	74.62 288	98.09 210	93.91 78	91.48 216	95.45 236

OurMVSNet-221017-0		90.51 244	90.19 226	91.44 298	93.41 301	81.25 314	96.98 145	96.28 223	91.68 112	86.55 281	96.30 144	74.20 291	97.98 236	88.96 169	87.40 262	95.09 258

pmmvs4		90.93 228	89.85 237	94.17 194	93.34 303	90.79 128	94.60 279	96.02 234	84.62 289	87.45 265	95.15 201	81.88 185	97.45 284	87.70 190	87.87 256	94.27 301

DI_MVS_plusplus_test		92.01 176	90.77 197	95.73 116	93.34 303	89.78 152	96.14 230	96.18 230	90.58 150	81.80 313	93.50 281	74.95 286	98.90 137	93.51 86	96.94 116	98.51 107

jajsoiax		92.42 159	91.89 149	94.03 200	93.33 305	88.50 206	97.73 63	97.53 120	92.00 106	88.85 240	96.50 136	75.62 281	98.11 207	93.88 80	91.56 215	95.48 232

v748		90.34 246	89.54 247	92.75 261	93.25 306	85.71 274	97.61 86	97.17 159	88.54 208	87.20 272	93.54 279	81.02 195	98.01 232	85.73 230	81.80 315	94.52 291

test_normal		92.01 176	90.75 199	95.80 110	93.24 307	89.97 145	95.93 241	96.24 227	90.62 146	81.63 314	93.45 284	74.98 285	98.89 139	93.61 84	97.04 115	98.55 102

v52		90.70 238	90.00 231	92.82 256	93.24 307	87.03 256	97.60 87	97.14 163	88.21 222	87.69 261	93.94 265	80.91 200	98.07 215	87.39 200	83.87 304	93.36 314

gm-plane-assit					93.22 309	78.89 334			84.82 287		93.52 280		98.64 160	87.72 189

V4		90.71 237	90.00 231	92.82 256	93.21 310	87.03 256	97.59 89	97.16 162	88.21 222	87.69 261	93.92 267	80.93 199	98.06 220	87.39 200	83.90 303	93.39 312

LP		84.13 312	81.85 317	90.97 304	93.20 311	82.12 309	87.68 349	94.27 314	76.80 340	81.93 311	88.52 334	72.97 299	95.95 324	59.53 350	81.73 316	94.84 275

MVP-Stereo		90.74 235	90.08 227	92.71 262	93.19 312	88.20 218	95.86 243	96.27 224	86.07 271	84.86 294	94.76 219	77.84 265	97.75 265	83.88 259	98.01 87	92.17 337
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
EU-MVSNet		88.72 272	88.90 257	88.20 323	93.15 313	74.21 341	96.63 190	94.22 315	85.18 280	87.32 270	95.97 157	76.16 276	94.98 335	85.27 235	86.17 267	95.41 238

MDA-MVSNet-bldmvs		85.00 309	82.95 311	91.17 302	93.13 314	83.33 300	94.56 281	95.00 284	84.57 290	65.13 353	92.65 294	70.45 311	95.85 325	73.57 330	77.49 328	94.33 297

K. test v3		87.64 292	86.75 290	90.32 315	93.02 315	79.48 330	96.61 191	92.08 345	90.66 143	80.25 333	94.09 261	67.21 327	96.65 309	85.96 226	80.83 322	94.83 277

pmmvs5		89.86 258	88.87 258	92.82 256	92.86 316	86.23 269	96.26 222	95.39 263	84.24 293	87.12 273	94.51 229	74.27 290	97.36 292	87.61 197	87.57 258	94.86 274

testgi		87.97 288	87.21 285	90.24 316	92.86 316	80.76 316	96.67 185	94.97 287	91.74 110	85.52 288	95.83 166	62.66 338	94.47 337	76.25 321	88.36 253	95.48 232

EPNet		95.20 70	94.56 76	97.14 55	92.80 318	92.68 66	97.85 53	94.87 294	96.64 1	92.46 140	97.80 64	86.23 103	99.65 41	93.72 83	98.62 74	99.10 61
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
N_pmnet		78.73 323	78.71 322	78.79 340	92.80 318	46.50 367	94.14 292	43.71 371	78.61 335	80.83 317	91.66 313	74.94 287	96.36 311	67.24 341	84.45 296	93.50 309

EG-PatchMatch MVS		87.02 297	85.44 298	91.76 292	92.67 320	85.00 282	96.08 233	96.45 218	83.41 304	79.52 335	93.49 282	57.10 346	97.72 267	79.34 311	90.87 227	92.56 322

Gipuma		67.86 331	65.41 332	75.18 344	92.66 321	73.45 342	66.50 362	94.52 305	53.33 357	57.80 357	66.07 359	30.81 360	89.20 354	48.15 359	78.88 326	62.90 360
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
anonymousdsp		92.16 172	91.55 165	93.97 204	92.58 322	89.55 165	97.51 96	97.42 139	89.42 172	88.40 247	94.84 214	80.66 205	97.88 254	91.87 117	91.28 220	94.48 292

test0.0.03 1		89.37 265	88.70 259	91.41 299	92.47 323	85.63 275	95.22 272	92.70 339	91.11 131	86.91 279	93.65 276	79.02 232	93.19 343	78.00 315	89.18 244	95.41 238

our_test_3		88.78 271	87.98 269	91.20 301	92.45 324	82.53 304	93.61 305	95.69 252	85.77 274	84.88 293	93.71 272	79.99 217	96.78 308	79.47 308	86.24 266	94.28 300

ppachtmachnet_test		88.35 286	87.29 279	91.53 295	92.45 324	83.57 299	93.75 299	95.97 235	84.28 292	85.32 292	94.18 259	79.00 236	96.93 305	75.71 323	84.99 288	94.10 302

YYNet1		85.87 305	84.23 307	90.78 310	92.38 326	82.46 306	93.17 311	95.14 278	82.12 312	67.69 348	92.36 303	78.16 255	95.50 333	77.31 318	79.73 324	94.39 295

MDA-MVSNet_test_wron		85.87 305	84.23 307	90.80 309	92.38 326	82.57 303	93.17 311	95.15 277	82.15 311	67.65 349	92.33 306	78.20 252	95.51 332	77.33 317	79.74 323	94.31 299

LF4IMVS		87.94 289	87.25 281	89.98 318	92.38 326	80.05 327	94.38 284	95.25 273	87.59 240	84.34 297	94.74 221	64.31 335	97.66 272	84.83 239	87.45 259	92.23 335

lessismore_v0				90.45 313	91.96 329	79.09 333		87.19 359		80.32 331	94.39 238	66.31 329	97.55 278	84.00 257	76.84 330	94.70 286

testpf		80.97 320	81.40 318	79.65 338	91.53 330	72.43 344	73.47 360	89.55 354	78.63 334	80.81 318	89.06 331	61.36 340	91.36 349	83.34 263	84.89 292	75.15 356

pmmvs6		87.81 291	86.19 293	92.69 263	91.32 331	86.30 268	97.34 113	96.41 219	80.59 326	84.05 303	94.37 240	67.37 326	97.67 270	84.75 241	79.51 325	94.09 304

Anonymous20231206		87.09 296	86.14 294	89.93 319	91.22 332	80.35 321	96.11 231	95.35 266	83.57 302	84.16 300	93.02 290	73.54 297	95.61 329	72.16 333	86.14 268	93.84 307

DeepMVS_CX				74.68 345	90.84 333	64.34 356		81.61 366	65.34 353	67.47 351	88.01 340	48.60 355	80.13 361	62.33 347	73.68 347	79.58 354

Test4		89.48 262	87.50 273	95.44 133	90.76 334	89.72 153	95.78 249	97.09 170	90.28 154	77.67 339	91.74 312	55.42 350	98.08 211	91.92 114	96.83 118	98.52 105

test20.03		86.14 303	85.40 299	88.35 321	90.12 335	80.06 326	95.90 242	95.20 275	88.59 204	81.29 316	93.62 277	71.43 305	92.65 344	71.26 337	81.17 320	92.34 334

OpenMVS_ROB	81.14 20	84.42 311	82.28 312	90.83 306	90.06 336	84.05 293	95.73 250	94.04 318	73.89 348	80.17 334	91.53 314	59.15 343	97.64 273	66.92 342	89.05 245	90.80 343

UnsupCasMVSNet_eth		85.99 304	84.45 305	90.62 311	89.97 337	82.40 307	93.62 304	97.37 144	89.86 162	78.59 338	92.37 300	65.25 334	95.35 334	82.27 278	70.75 348	94.10 302

DSMNet-mixed		86.34 301	86.12 295	87.00 328	89.88 338	70.43 345	94.93 276	90.08 353	77.97 338	85.42 291	92.78 293	74.44 289	93.96 339	74.43 326	95.14 150	96.62 188

new_pmnet		82.89 315	81.12 320	88.18 324	89.63 339	80.18 325	91.77 328	92.57 340	76.79 341	75.56 342	88.23 338	61.22 341	94.48 336	71.43 335	82.92 312	89.87 345

MIMVSNet1		84.93 310	83.05 310	90.56 312	89.56 340	84.84 286	95.40 263	95.35 266	83.91 296	80.38 329	92.21 307	57.23 345	93.34 342	70.69 339	82.75 314	93.50 309

CMPMVS	62.92 21	85.62 307	84.92 302	87.74 325	89.14 341	73.12 343	94.17 291	96.80 203	73.98 347	73.65 343	94.93 207	66.36 328	97.61 275	83.95 258	91.28 220	92.48 326
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Patchmatch-RL test		87.38 293	86.24 292	90.81 307	88.74 342	78.40 335	88.12 348	93.17 327	87.11 250	82.17 309	89.29 330	81.95 183	95.60 330	88.64 177	77.02 329	98.41 120

pmmvs-eth3d		86.22 302	84.45 305	91.53 295	88.34 343	87.25 249	94.47 283	95.01 283	83.47 303	79.51 336	89.61 324	69.75 316	95.71 328	83.13 266	76.73 331	91.64 338

UnsupCasMVSNet_bld		82.13 319	79.46 321	90.14 317	88.00 344	82.47 305	90.89 336	96.62 216	78.94 333	75.61 341	84.40 348	56.63 347	96.31 312	77.30 319	66.77 353	91.63 339

PM-MVS		83.48 313	81.86 316	88.31 322	87.83 345	77.59 336	93.43 306	91.75 347	86.91 258	80.63 323	89.91 318	44.42 357	95.84 326	85.17 238	76.73 331	91.50 341

testing_2		87.33 294	85.03 301	94.22 192	87.77 346	89.32 184	94.97 275	97.11 167	89.22 176	71.64 347	88.73 333	55.16 351	97.94 245	91.95 113	88.73 250	95.41 238

testus		82.63 317	82.15 313	84.07 332	87.31 347	67.67 351	93.18 309	94.29 313	82.47 309	82.14 310	90.69 315	53.01 352	91.94 347	66.30 343	89.96 238	92.62 321

new-patchmatchnet		83.18 314	81.87 315	87.11 327	86.88 348	75.99 339	93.70 300	95.18 276	85.02 284	77.30 340	88.40 336	65.99 331	93.88 340	74.19 329	70.18 349	91.47 342

test2356		82.77 316	82.14 314	84.65 331	85.77 349	70.36 346	91.22 332	93.69 324	81.58 317	81.82 312	89.00 332	60.63 342	90.77 350	64.74 344	90.80 228	92.82 317

1111		78.29 324	77.55 324	80.50 336	83.89 350	59.98 359	91.89 326	93.71 321	75.06 343	73.60 344	87.67 341	55.66 348	92.60 345	58.54 352	77.92 327	88.93 347

.test1245		65.38 332	69.22 330	53.86 352	83.89 350	59.98 359	91.89 326	93.71 321	75.06 343	73.60 344	87.67 341	55.66 348	92.60 345	58.54 352	2.96 365	9.00 365

ambc				86.56 329	83.60 352	70.00 349	85.69 352	94.97 287		80.60 324	88.45 335	37.42 358	96.84 307	82.69 273	75.44 334	92.86 316

pmmvs3		79.97 321	77.50 325	87.39 326	82.80 353	79.38 331	92.70 320	90.75 351	70.69 351	78.66 337	87.47 344	51.34 354	93.40 341	73.39 331	69.65 350	89.38 346

test1235678		79.82 322	78.53 323	83.69 333	82.55 354	67.55 352	92.50 323	94.13 316	79.28 331	72.10 346	86.45 346	57.27 344	90.68 351	61.60 348	80.90 321	92.82 317

TDRefinement		86.53 299	84.76 304	91.85 286	82.23 355	84.25 289	96.38 210	95.35 266	84.97 285	84.09 302	94.94 206	65.76 333	98.34 190	84.60 247	74.52 343	92.97 315

test12356		74.97 325	74.13 326	77.49 341	78.81 356	56.23 363	88.53 347	92.75 338	75.14 342	67.50 350	85.07 347	44.88 356	89.96 352	58.71 351	75.75 333	86.26 348

PMMVS2		70.19 329	66.92 331	80.01 337	76.35 357	65.67 354	86.22 351	87.58 358	64.83 354	62.38 354	80.29 352	26.78 365	88.49 356	63.79 345	54.07 355	85.88 350

FPMVS		71.27 328	69.85 328	75.50 343	74.64 358	59.03 361	91.30 330	91.50 348	58.80 355	57.92 356	88.28 337	29.98 363	85.53 358	53.43 356	82.84 313	81.95 352

E-PMN		53.28 337	52.56 339	55.43 350	74.43 359	47.13 366	83.63 355	76.30 367	42.23 361	42.59 361	62.22 361	28.57 364	74.40 363	31.53 362	31.51 360	44.78 361

no-one		68.12 330	63.78 333	81.13 335	74.01 360	70.22 348	87.61 350	90.71 352	72.63 350	53.13 358	71.89 356	30.29 361	91.45 348	61.53 349	32.21 359	81.72 353

PNet_i23d		59.01 334	55.87 335	68.44 347	73.98 361	51.37 364	81.36 356	82.41 364	52.37 358	42.49 362	70.39 358	11.39 368	79.99 362	49.77 358	38.71 357	73.97 357

wuyk23d		25.11 343	24.57 345	26.74 355	73.98 361	39.89 370	57.88 363	9.80 372	12.27 365	10.39 367	6.97 369	7.03 370	36.44 367	25.43 364	17.39 364	3.89 367

testmv		72.22 327	70.02 327	78.82 339	73.06 363	61.75 357	91.24 331	92.31 342	74.45 346	61.06 355	80.51 351	34.21 359	88.63 355	55.31 355	68.07 352	86.06 349

EMVS		52.08 339	51.31 340	54.39 351	72.62 364	45.39 368	83.84 354	75.51 368	41.13 362	40.77 363	59.65 362	30.08 362	73.60 364	28.31 363	29.90 362	44.18 362

LCM-MVSNet		72.55 326	69.39 329	82.03 334	70.81 365	65.42 355	90.12 341	94.36 310	55.02 356	65.88 352	81.72 349	24.16 367	89.96 352	74.32 328	68.10 351	90.71 344

MVE	50.73 23	53.25 338	48.81 342	66.58 349	65.34 366	57.50 362	72.49 361	70.94 369	40.15 363	39.28 364	63.51 360	6.89 372	73.48 365	38.29 361	42.38 356	68.76 359
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
wuykxyi23d		56.92 336	51.11 341	74.38 346	62.30 367	61.47 358	80.09 357	84.87 361	49.62 359	30.80 366	57.20 363	7.03 370	82.94 359	55.69 354	32.36 358	78.72 355

ANet_high		63.94 333	59.58 334	77.02 342	61.24 368	66.06 353	85.66 353	87.93 357	78.53 336	42.94 360	71.04 357	25.42 366	80.71 360	52.60 357	30.83 361	84.28 351

PMVS	53.92 22	58.58 335	55.40 336	68.12 348	51.00 369	48.64 365	78.86 358	87.10 360	46.77 360	35.84 365	74.28 354	8.76 369	86.34 357	42.07 360	73.91 346	69.38 358
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
tmp_tt		51.94 340	53.82 338	46.29 353	33.73 370	45.30 369	78.32 359	67.24 370	18.02 364	50.93 359	87.05 345	52.99 353	53.11 366	70.76 338	25.29 363	40.46 363

testmvs		13.36 345	16.33 346	4.48 357	5.04 371	2.26 372	93.18 309	3.28 373	2.70 366	8.24 368	21.66 365	2.29 374	2.19 368	7.58 365	2.96 365	9.00 365

test123		13.04 346	15.66 347	5.18 356	4.51 372	3.45 371	92.50 323	1.81 374	2.50 367	7.58 369	20.15 366	3.67 373	2.18 369	7.13 366	1.07 367	9.90 364

cdsmvs_eth3d_5k		23.24 344	30.99 344	0.00 358	0.00 373	0.00 373	0.00 364	97.63 111	0.00 368	0.00 370	96.88 111	84.38 125	0.00 370	0.00 367	0.00 368	0.00 368

pcd_1.5k_mvsjas		7.39 348	9.85 349	0.00 358	0.00 373	0.00 373	0.00 364	0.00 375	0.00 368	0.00 370	0.00 370	88.65 71	0.00 370	0.00 367	0.00 368	0.00 368

sosnet-low-res		0.00 349	0.00 350	0.00 358	0.00 373	0.00 373	0.00 364	0.00 375	0.00 368	0.00 370	0.00 370	0.00 375	0.00 370	0.00 367	0.00 368	0.00 368

sosnet		0.00 349	0.00 350	0.00 358	0.00 373	0.00 373	0.00 364	0.00 375	0.00 368	0.00 370	0.00 370	0.00 375	0.00 370	0.00 367	0.00 368	0.00 368

uncertanet		0.00 349	0.00 350	0.00 358	0.00 373	0.00 373	0.00 364	0.00 375	0.00 368	0.00 370	0.00 370	0.00 375	0.00 370	0.00 367	0.00 368	0.00 368

Regformer		0.00 349	0.00 350	0.00 358	0.00 373	0.00 373	0.00 364	0.00 375	0.00 368	0.00 370	0.00 370	0.00 375	0.00 370	0.00 367	0.00 368	0.00 368

ab-mvs-re		8.06 347	10.74 348	0.00 358	0.00 373	0.00 373	0.00 364	0.00 375	0.00 368	0.00 370	96.69 120	0.00 375	0.00 370	0.00 367	0.00 368	0.00 368

uanet		0.00 349	0.00 350	0.00 358	0.00 373	0.00 373	0.00 364	0.00 375	0.00 368	0.00 370	0.00 370	0.00 375	0.00 370	0.00 367	0.00 368	0.00 368

GSMVS																98.45 116

test_part1				0.00 358		0.00 373	0.00 364	98.26 26				0.00 375	0.00 370	0.00 367	0.00 368	0.00 368

sam_mvs1												82.76 163				98.45 116

sam_mvs												81.94 184

MTGPA								98.08 52

test_post1							92.81 319				16.58 368	80.53 207	97.68 269	86.20 219

test_post											17.58 367	81.76 186	98.08 211

patchmatchnet-post											90.45 316	82.65 167	98.10 208

MTMP							97.86 50	82.03 365

test9_res														94.81 64	99.38 35	99.45 30

agg_prior2														93.94 77	99.38 35	99.50 24

test_prior4						93.66 42	96.42 203

test_prior2							96.35 212		92.80 79	96.03 57	97.59 80	92.01 30		95.01 57	99.38 35

旧先验2							95.94 240		81.66 315	97.34 19			98.82 145	92.26 103

新几何2							95.79 247

无先验							95.79 247	97.87 88	83.87 299				99.65 41	87.68 192		98.89 82

原ACMM2							95.67 251

testdata2													99.67 39	85.96 226

segment_acmp												92.89 12

testdata1							95.26 271		93.10 67

plane_prior5								97.51 122					98.60 164	93.02 97	92.23 201	95.86 214

plane_prior4											96.64 123

plane_prior3						90.00 140			94.46 31	91.34 165

plane_prior2							97.74 61		94.85 18

plane_prior						89.99 142	97.24 121		94.06 39						92.16 205

n2								0.00 375

nn								0.00 375

door-mid								91.06 350

test11								97.88 86

door								91.13 349

HQP5-MVS						89.33 182

BP-MVS														92.13 109

HQP4-MVS										90.14 193			98.50 172			95.78 221

HQP3-MVS								97.39 141							92.10 206

HQP2-MVS												80.95 197

MDTV_nov1_ep13_2view						70.35 347	93.10 315		83.88 298	93.55 112		82.47 172		86.25 218		98.38 124

ACMMP++_ref															90.30 235

ACMMP++															91.02 225

Test By Simon												88.73 70

High-resolution multi-view benchmark