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	courty.	delive.	electro	facade	kicker	meadow	office	pipes	playgr.	relief	relief.	terrace	terrai.

APDe-MVS		99.49 1	99.64 1	99.32 1	99.74 4	99.74 3	99.75 1	98.34 2	99.56 9	98.72 3	99.57 5	99.97 5	99.53 15	99.65 2	99.25 14	99.84 6	99.77 54

SMA-MVS		99.38 3	99.60 2	99.12 7	99.76 2	99.62 34	99.39 28	98.23 15	99.52 14	98.03 13	99.45 9	99.98 1	99.64 5	99.58 6	99.30 11	99.68 96	99.76 59

TSAR-MVS + MP.		99.27 8	99.57 3	98.92 20	98.78 50	99.53 56	99.72 2	98.11 25	99.73 2	97.43 22	99.15 21	99.96 11	99.59 9	99.73 1	99.07 22	99.88 1	99.82 28

ESAPD		99.39 2	99.55 4	99.20 2	99.63 17	99.71 8	99.66 7	98.33 4	99.29 35	98.40 8	99.64 4	99.98 1	99.31 30	99.56 7	98.96 29	99.85 4	99.70 96

ACMMPR		99.30 7	99.54 5	99.03 14	99.66 14	99.64 26	99.68 5	98.25 13	99.56 9	97.12 27	99.19 18	99.95 16	99.72 1	99.43 15	99.25 14	99.72 66	99.77 54

HFP-MVS		99.32 4	99.53 6	99.07 11	99.69 8	99.59 46	99.63 11	98.31 6	99.56 9	97.37 23	99.27 15	99.97 5	99.70 3	99.35 20	99.24 16	99.71 76	99.76 59

SteuartSystems-ACMMP		99.20 13	99.51 7	98.83 24	99.66 14	99.66 20	99.71 4	98.12 24	99.14 57	96.62 31	99.16 20	99.98 1	99.12 51	99.63 3	99.19 20	99.78 39	99.83 27
Skip Steuart: Steuart Systems R&D Blog.
SD-MVS		99.25 10	99.50 8	98.96 18	98.79 49	99.55 54	99.33 31	98.29 10	99.75 1	97.96 15	99.15 21	99.95 16	99.61 6	99.17 27	99.06 23	99.81 27	99.84 23

DeepPCF-MVS	97.74 3	98.34 42	99.46 9	97.04 61	98.82 48	99.33 84	96.28 144	97.47 34	99.58 7	94.70 60	98.99 29	99.85 37	97.24 116	99.55 8	99.34 9	97.73 221	99.56 145

ACMMP_Plus		99.05 22	99.45 10	98.58 28	99.73 5	99.60 43	99.64 9	98.28 11	99.23 45	94.57 61	99.35 13	99.97 5	99.55 13	99.63 3	98.66 45	99.70 85	99.74 74

TSAR-MVS + ACMM		98.77 30	99.45 10	97.98 40	99.37 33	99.46 64	99.44 25	98.13 23	99.65 4	92.30 104	98.91 37	99.95 16	99.05 57	99.42 16	98.95 30	99.58 159	99.82 28

DeepC-MVS_fast	98.34 1	99.17 14	99.45 10	98.85 22	99.55 25	99.37 76	99.64 9	98.05 27	99.53 12	96.58 32	98.93 32	99.92 26	99.49 18	99.46 13	99.32 10	99.80 34	99.64 130
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
zzz-MVS		99.31 5	99.44 13	99.16 5	99.73 5	99.65 21	99.63 11	98.26 12	99.27 38	98.01 14	99.27 15	99.97 5	99.60 7	99.59 5	98.58 51	99.71 76	99.73 78

CP-MVS		99.27 8	99.44 13	99.08 10	99.62 19	99.58 49	99.53 16	98.16 18	99.21 48	97.79 17	99.15 21	99.96 11	99.59 9	99.54 9	98.86 37	99.78 39	99.74 74

HSP-MVS		99.31 5	99.43 15	99.17 3	99.68 11	99.75 2	99.72 2	98.31 6	99.45 18	98.16 10	99.28 14	99.98 1	99.30 32	99.34 21	98.41 61	99.81 27	99.81 33

PHI-MVS		99.08 19	99.43 15	98.67 26	99.15 42	99.59 46	99.11 40	97.35 35	99.14 57	97.30 24	99.44 10	99.96 11	99.32 29	98.89 44	99.39 7	99.79 36	99.58 139

MVS_111021_LR		98.67 34	99.41 17	97.81 43	99.37 33	99.53 56	98.51 61	95.52 43	99.27 38	94.85 56	99.56 6	99.69 45	99.04 58	99.36 19	98.88 35	99.60 149	99.58 139

APD-MVS		99.25 10	99.38 18	99.09 9	99.69 8	99.58 49	99.56 15	98.32 5	98.85 89	97.87 16	98.91 37	99.92 26	99.30 32	99.45 14	99.38 8	99.79 36	99.58 139
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
X-MVS		98.93 26	99.37 19	98.42 29	99.67 12	99.62 34	99.60 13	98.15 20	99.08 65	93.81 84	98.46 60	99.95 16	99.59 9	99.49 12	99.21 19	99.68 96	99.75 70

MP-MVS		99.07 20	99.36 20	98.74 25	99.63 17	99.57 51	99.66 7	98.25 13	99.00 77	95.62 40	98.97 30	99.94 24	99.54 14	99.51 11	98.79 42	99.71 76	99.73 78
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
TSAR-MVS + GP.		98.66 36	99.36 20	97.85 42	97.16 76	99.46 64	99.03 46	94.59 57	99.09 63	97.19 26	99.73 3	99.95 16	99.39 25	98.95 38	98.69 43	99.75 48	99.65 126

MVS_111021_HR		98.59 38	99.36 20	97.68 44	99.42 31	99.61 39	98.14 85	94.81 50	99.31 32	95.00 53	99.51 7	99.79 41	99.00 61	98.94 39	98.83 39	99.69 87	99.57 144

PGM-MVS		98.86 28	99.35 23	98.29 32	99.77 1	99.63 30	99.67 6	95.63 41	98.66 110	95.27 47	99.11 24	99.82 38	99.67 4	99.33 22	99.19 20	99.73 60	99.74 74

HPM-MVS++		99.10 18	99.30 24	98.86 21	99.69 8	99.48 62	99.59 14	98.34 2	99.26 41	96.55 34	99.10 25	99.96 11	99.36 26	99.25 25	98.37 66	99.64 126	99.66 123

CNVR-MVS		99.23 12	99.28 25	99.17 3	99.65 16	99.34 82	99.46 22	98.21 16	99.28 36	98.47 5	98.89 39	99.94 24	99.50 16	99.42 16	98.61 48	99.73 60	99.52 150

MCST-MVS		99.11 17	99.27 26	98.93 19	99.67 12	99.33 84	99.51 18	98.31 6	99.28 36	96.57 33	99.10 25	99.90 29	99.71 2	99.19 26	98.35 68	99.82 14	99.71 94

CHOSEN 280x420		97.99 49	99.24 27	96.53 82	98.34 55	99.61 39	98.36 75	89.80 149	99.27 38	95.08 51	99.81 1	98.58 58	98.64 72	99.02 35	98.92 32	98.93 203	99.48 159

MSLP-MVS++		99.15 15	99.24 27	99.04 13	99.52 28	99.49 61	99.09 42	98.07 26	99.37 23	98.47 5	97.79 78	99.89 31	99.50 16	98.93 40	99.45 4	99.61 141	99.76 59

CPTT-MVS		99.14 16	99.20 29	99.06 12	99.58 22	99.53 56	99.45 23	97.80 32	99.19 51	98.32 9	98.58 54	99.95 16	99.60 7	99.28 24	98.20 79	99.64 126	99.69 103

CANet		98.46 39	99.16 30	97.64 45	98.48 53	99.64 26	99.35 30	94.71 53	99.53 12	95.17 49	97.63 84	99.59 48	98.38 82	98.88 45	98.99 27	99.74 54	99.86 19

UA-Net		97.13 79	99.14 31	94.78 115	97.21 74	99.38 74	97.56 107	92.04 100	98.48 120	88.03 130	98.39 63	99.91 28	94.03 204	99.33 22	99.23 17	99.81 27	99.25 172

train_agg		98.73 32	99.11 32	98.28 33	99.36 35	99.35 80	99.48 21	97.96 29	98.83 93	93.86 83	98.70 49	99.86 34	99.44 22	99.08 33	98.38 64	99.61 141	99.58 139

CDPH-MVS		98.41 40	99.10 33	97.61 46	99.32 39	99.36 78	99.49 19	96.15 40	98.82 95	91.82 107	98.41 61	99.66 46	99.10 54	98.93 40	98.97 28	99.75 48	99.58 139

CANet_DTU		96.64 100	99.08 34	93.81 127	97.10 77	99.42 70	98.85 51	90.01 143	99.31 32	79.98 186	99.78 2	99.10 54	97.42 113	98.35 80	98.05 88	99.47 179	99.53 148

NCCC		99.05 22	99.08 34	99.02 15	99.62 19	99.38 74	99.43 26	98.21 16	99.36 25	97.66 20	97.79 78	99.90 29	99.45 21	99.17 27	98.43 59	99.77 44	99.51 154

UGNet		97.66 59	99.07 36	96.01 100	97.19 75	99.65 21	97.09 126	93.39 89	99.35 26	94.40 72	98.79 42	99.59 48	94.24 201	98.04 113	98.29 75	99.73 60	99.80 36
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
CNLPA		99.03 24	99.05 37	99.01 16	99.27 40	99.22 99	99.03 46	97.98 28	99.34 30	99.00 2	98.25 67	99.71 44	99.31 30	98.80 49	98.82 40	99.48 177	99.17 176

3Dnovator+	96.92 7	98.71 33	99.05 37	98.32 31	99.53 26	99.34 82	99.06 44	94.61 55	99.65 4	97.49 21	96.75 102	99.86 34	99.44 22	98.78 51	99.30 11	99.81 27	99.67 115

3Dnovator	96.92 7	98.67 34	99.05 37	98.23 35	99.57 23	99.45 66	99.11 40	94.66 54	99.69 3	96.80 30	96.55 112	99.61 47	99.40 24	98.87 46	99.49 3	99.85 4	99.66 123

QAPM		98.62 37	99.04 40	98.13 36	99.57 23	99.48 62	99.17 37	94.78 51	99.57 8	96.16 36	96.73 104	99.80 39	99.33 28	98.79 50	99.29 13	99.75 48	99.64 130

MVS_0304		98.14 47	99.03 41	97.10 57	98.05 60	99.63 30	99.27 33	94.33 60	99.63 6	93.06 95	97.32 87	99.05 55	98.09 95	98.82 48	98.87 36	99.81 27	99.89 8

ACMMP		98.74 31	99.03 41	98.40 30	99.36 35	99.64 26	99.20 35	97.75 33	98.82 95	95.24 48	98.85 40	99.87 33	99.17 43	98.74 56	97.50 116	99.71 76	99.76 59
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
OMC-MVS		98.84 29	99.01 43	98.65 27	99.39 32	99.23 98	99.22 34	96.70 37	99.40 20	97.77 18	97.89 77	99.80 39	99.21 36	99.02 35	98.65 46	99.57 163	99.07 183

AdaColmap		99.06 21	98.98 44	99.15 6	99.60 21	99.30 89	99.38 29	98.16 18	99.02 76	98.55 4	98.71 47	99.57 50	99.58 12	99.09 31	97.84 98	99.64 126	99.36 167

PLC	97.93 2	99.02 25	98.94 45	99.11 8	99.46 30	99.24 97	99.06 44	97.96 29	99.31 32	99.16 1	97.90 76	99.79 41	99.36 26	98.71 57	98.12 83	99.65 115	99.52 150
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CSCG		98.90 27	98.93 46	98.85 22	99.75 3	99.72 4	99.49 19	96.58 38	99.38 21	98.05 12	98.97 30	97.87 65	99.49 18	97.78 125	98.92 32	99.78 39	99.90 4

Vis-MVSNet (Re-imp)		97.40 67	98.89 47	95.66 108	95.99 111	99.62 34	97.82 99	93.22 94	98.82 95	91.40 111	96.94 99	98.56 59	95.70 155	99.14 29	99.41 6	99.79 36	99.75 70

EPNet		98.05 48	98.86 48	97.10 57	99.02 45	99.43 69	98.47 62	94.73 52	99.05 73	95.62 40	98.93 32	97.62 69	95.48 166	98.59 70	98.55 53	99.29 195	99.84 23
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
IS_MVSNet		97.86 51	98.86 48	96.68 77	96.02 107	99.72 4	98.35 76	93.37 91	98.75 107	94.01 77	96.88 101	98.40 61	98.48 79	99.09 31	99.42 5	99.83 10	99.80 36

TAPA-MVS	97.53 5	98.41 40	98.84 50	97.91 41	99.08 44	99.33 84	99.15 38	97.13 36	99.34 30	93.20 92	97.75 80	99.19 53	99.20 37	98.66 59	98.13 82	99.66 109	99.48 159
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
DELS-MVS		98.19 45	98.77 51	97.52 47	98.29 56	99.71 8	99.12 39	94.58 58	98.80 98	95.38 46	96.24 118	98.24 63	97.92 100	99.06 34	99.52 1	99.82 14	99.79 43
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
casdiffmvs1		97.69 58	98.72 52	96.49 86	96.00 109	99.40 72	98.26 80	91.54 112	99.52 14	94.56 62	98.61 53	96.41 79	98.79 66	98.60 68	98.58 51	99.80 34	99.91 3

EPP-MVSNet		97.75 55	98.71 53	96.63 80	95.68 122	99.56 52	97.51 109	93.10 95	99.22 46	94.99 54	97.18 93	97.30 72	98.65 71	98.83 47	98.93 31	99.84 6	99.92 1

DeepC-MVS	97.63 4	98.33 43	98.57 54	98.04 38	98.62 52	99.65 21	99.45 23	98.15 20	99.51 16	92.80 98	95.74 131	96.44 78	99.46 20	99.37 18	99.50 2	99.78 39	99.81 33
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MVS_Test		97.30 74	98.54 55	95.87 101	95.74 119	99.28 92	98.19 83	91.40 116	99.18 52	91.59 110	98.17 68	96.18 84	98.63 73	98.61 65	98.55 53	99.66 109	99.78 46

EPNet_dtu		96.30 109	98.53 56	93.70 131	98.97 46	98.24 157	97.36 113	94.23 63	98.85 89	79.18 200	99.19 18	98.47 60	94.09 203	97.89 120	98.21 78	98.39 211	98.85 192
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet_Blended_VisFu		97.41 66	98.49 57	96.15 91	97.49 66	99.76 1	96.02 147	93.75 85	99.26 41	93.38 90	93.73 152	99.35 51	96.47 139	98.96 37	98.46 57	99.77 44	99.90 4

OpenMVS	96.23 11	97.95 50	98.45 58	97.35 48	99.52 28	99.42 70	98.91 50	94.61 55	98.87 86	92.24 105	94.61 145	99.05 55	99.10 54	98.64 63	99.05 24	99.74 54	99.51 154

diffmvs1		97.31 72	98.41 59	96.03 98	95.86 114	99.31 87	98.04 92	90.88 123	99.35 26	93.31 91	98.71 47	95.25 94	98.56 76	98.22 92	98.14 81	99.54 171	99.87 14

PMMVS		97.52 63	98.39 60	96.51 84	95.82 117	98.73 127	97.80 101	93.05 96	98.76 105	94.39 73	99.07 28	97.03 75	98.55 77	98.31 82	97.61 111	99.43 185	99.21 175

tfpn_n400		97.32 69	98.38 61	96.09 94	96.07 104	99.30 89	98.00 93	93.84 80	99.35 26	90.50 117	98.93 32	94.24 112	98.30 87	98.65 60	98.60 49	99.83 10	99.60 135

tfpnconf		97.32 69	98.38 61	96.09 94	96.07 104	99.30 89	98.00 93	93.84 80	99.35 26	90.50 117	98.93 32	94.24 112	98.30 87	98.65 60	98.60 49	99.83 10	99.60 135

Anonymous20240521		97.56 62	98.36 63	96.62 81	96.44 84	98.36 152	98.37 73	91.73 106	99.11 62	94.80 58	98.36 64	96.28 82	98.60 74	98.12 100	98.44 58	99.76 46	99.87 14

PCF-MVS	97.50 6	98.18 46	98.35 64	97.99 39	98.65 51	99.36 78	98.94 49	98.14 22	98.59 112	93.62 87	96.61 108	99.76 43	99.03 59	97.77 126	97.45 120	99.57 163	98.89 191
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
tfpnview11		97.32 69	98.33 65	96.14 92	96.07 104	99.31 87	98.08 89	93.96 75	99.25 43	90.50 117	98.93 32	94.24 112	98.38 82	98.61 65	98.36 67	99.84 6	99.59 137

casdiffmvs		97.36 68	98.33 65	96.23 88	95.78 118	99.37 76	97.62 106	91.41 115	99.07 71	94.45 69	98.68 50	94.90 99	98.37 84	98.27 85	98.12 83	99.75 48	99.87 14

tfpn_ndepth		97.71 56	98.30 67	97.02 66	96.31 88	99.56 52	98.05 91	93.94 77	98.95 79	95.59 42	98.40 62	94.79 103	98.39 81	98.40 79	98.42 60	99.86 2	99.56 145

MSDG		98.27 44	98.29 68	98.24 34	99.20 41	99.22 99	99.20 35	97.82 31	99.37 23	94.43 70	95.90 126	97.31 71	99.12 51	98.76 53	98.35 68	99.67 104	99.14 180

thisisatest0530		97.23 75	98.25 69	96.05 96	95.60 125	99.59 46	96.96 130	93.23 92	99.17 53	92.60 100	98.75 45	96.19 83	98.17 90	98.19 96	96.10 154	99.72 66	99.77 54

PatchMatch-RL		97.77 54	98.25 69	97.21 54	99.11 43	99.25 95	97.06 128	94.09 66	98.72 108	95.14 50	98.47 59	96.29 81	98.43 80	98.65 60	97.44 121	99.45 181	98.94 186

LS3D		97.79 52	98.25 69	97.26 53	98.40 54	99.63 30	99.53 16	98.63 1	99.25 43	88.13 129	96.93 100	94.14 115	99.19 39	99.14 29	99.23 17	99.69 87	99.42 163

tttt0517		97.23 75	98.24 72	96.04 97	95.60 125	99.60 43	96.94 131	93.23 92	99.15 54	92.56 101	98.74 46	96.12 86	98.17 90	98.21 94	96.10 154	99.73 60	99.78 46

Vis-MVSNet		96.16 113	98.22 73	93.75 128	95.33 135	99.70 11	97.27 117	90.85 125	98.30 125	85.51 146	95.72 133	96.45 76	93.69 210	98.70 58	99.00 26	99.84 6	99.69 103
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
tfpn1000		97.60 61	98.21 74	96.89 75	96.32 86	99.60 43	97.99 95	93.85 79	99.21 48	95.03 52	98.49 57	93.69 119	98.31 86	98.50 75	98.31 74	99.86 2	99.70 96

Fast-Effi-MVS+-dtu		95.38 127	98.20 75	92.09 158	93.91 150	98.87 115	97.35 114	85.01 196	99.08 65	81.09 171	98.10 69	96.36 80	95.62 159	98.43 78	97.03 128	99.55 167	99.50 156

COLMAP_ROB	96.15 12	97.78 53	98.17 76	97.32 49	98.84 47	99.45 66	99.28 32	95.43 44	99.48 17	91.80 108	94.83 143	98.36 62	98.90 62	98.09 104	97.85 97	99.68 96	99.15 177
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
RPSCF		97.61 60	98.16 77	96.96 73	98.10 57	99.00 106	98.84 52	93.76 83	99.45 18	94.78 59	99.39 12	99.31 52	98.53 78	96.61 160	95.43 169	97.74 219	97.93 208

GG-mvs-BLEND		69.11 236	98.13 78	35.26 242	3.49 250	98.20 159	94.89 168	2.38 247	98.42 122	5.82 251	96.37 116	98.60 57	5.97 247	98.75 55	97.98 91	99.01 202	98.61 194

test0.0.03 1		96.69 97	98.12 79	95.01 113	95.49 129	98.99 108	95.86 149	90.82 126	98.38 123	92.54 102	96.66 106	97.33 70	95.75 153	97.75 128	98.34 70	99.60 149	99.40 165

FMVSNet3		97.02 82	98.12 79	95.73 107	93.59 159	97.98 162	98.34 77	91.32 117	98.80 98	93.92 80	97.21 90	95.94 89	97.63 108	98.61 65	98.62 47	99.61 141	99.65 126

Effi-MVS+-dtu		95.74 120	98.04 81	93.06 145	93.92 149	99.16 103	97.90 97	88.16 169	99.07 71	82.02 167	98.02 74	94.32 110	96.74 130	98.53 73	97.56 113	99.61 141	99.62 133

MAR-MVS		97.71 56	98.04 81	97.32 49	99.35 37	98.91 113	97.65 105	91.68 107	98.00 139	97.01 28	97.72 82	94.83 101	98.85 65	98.44 77	98.86 37	99.41 187	99.52 150
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
CDS-MVSNet		96.59 103	98.02 83	94.92 114	94.45 146	98.96 111	97.46 111	91.75 105	97.86 151	90.07 121	96.02 122	97.25 73	96.21 142	98.04 113	98.38 64	99.60 149	99.65 126
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
GBi-Net		96.98 83	98.00 84	95.78 103	93.81 153	97.98 162	98.09 86	91.32 117	98.80 98	93.92 80	97.21 90	95.94 89	97.89 101	98.07 107	98.34 70	99.68 96	99.67 115

test1		96.98 83	98.00 84	95.78 103	93.81 153	97.98 162	98.09 86	91.32 117	98.80 98	93.92 80	97.21 90	95.94 89	97.89 101	98.07 107	98.34 70	99.68 96	99.67 115

FC-MVSNet-test		96.07 115	97.94 86	93.89 125	93.60 158	98.67 130	96.62 137	90.30 138	98.76 105	88.62 126	95.57 137	97.63 68	94.48 197	97.97 116	97.48 119	99.71 76	99.52 150

FC-MVSNet-train		97.04 81	97.91 87	96.03 98	96.00 109	98.41 148	96.53 140	93.42 88	99.04 75	93.02 96	98.03 73	94.32 110	97.47 112	97.93 118	97.77 106	99.75 48	99.88 12

diffmvs		96.92 86	97.86 88	95.82 102	95.70 120	99.28 92	97.98 96	91.13 122	99.08 65	92.48 103	98.09 70	92.81 125	98.18 89	98.11 101	97.83 99	99.44 183	99.81 33

thresconf0.02		97.18 77	97.81 89	96.45 87	96.11 103	99.20 102	98.21 81	94.26 62	99.14 57	91.72 109	98.65 51	91.51 133	98.57 75	98.22 92	98.47 56	99.82 14	99.50 156

canonicalmvs		97.31 72	97.81 89	96.72 76	96.20 101	99.45 66	98.21 81	91.60 109	99.22 46	95.39 45	98.48 58	90.95 134	99.16 45	97.66 132	99.05 24	99.76 46	99.90 4

MVSTER		97.16 78	97.71 91	96.52 83	95.97 112	98.48 140	98.63 58	92.10 99	98.68 109	95.96 39	99.23 17	91.79 131	96.87 126	98.76 53	97.37 124	99.57 163	99.68 110

PVSNet_BlendedMVS		97.51 64	97.71 91	97.28 51	98.06 58	99.61 39	97.31 115	95.02 47	99.08 65	95.51 43	98.05 71	90.11 136	98.07 96	98.91 42	98.40 62	99.72 66	99.78 46

PVSNet_Blended		97.51 64	97.71 91	97.28 51	98.06 58	99.61 39	97.31 115	95.02 47	99.08 65	95.51 43	98.05 71	90.11 136	98.07 96	98.91 42	98.40 62	99.72 66	99.78 46

IterMVS		94.81 137	97.71 91	91.42 178	94.83 144	97.63 184	97.38 112	85.08 194	98.93 84	75.67 215	94.02 149	97.64 67	96.66 133	98.45 76	97.60 112	98.90 204	99.72 90
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MIMVSNet		94.49 146	97.59 95	90.87 195	91.74 197	98.70 129	94.68 189	78.73 229	97.98 140	83.71 155	97.71 83	94.81 102	96.96 123	97.97 116	97.92 93	99.40 189	98.04 206

FMVSNet2		96.64 100	97.50 96	95.63 109	93.81 153	97.98 162	98.09 86	90.87 124	98.99 78	93.48 88	93.17 160	95.25 94	97.89 101	98.63 64	98.80 41	99.68 96	99.67 115

DI_MVS_plusplus_trai		96.90 87	97.49 97	96.21 89	95.61 124	99.40 72	98.72 56	92.11 98	99.14 57	92.98 97	93.08 163	95.14 96	98.13 94	98.05 111	97.91 94	99.74 54	99.73 78

testgi		95.67 121	97.48 98	93.56 134	95.07 139	99.00 106	95.33 160	88.47 163	98.80 98	86.90 138	97.30 88	92.33 128	95.97 150	97.66 132	97.91 94	99.60 149	99.38 166

MDTV_nov1_ep13		95.57 122	97.48 98	93.35 142	95.43 131	98.97 110	97.19 121	83.72 209	98.92 85	87.91 132	97.75 80	96.12 86	97.88 104	96.84 159	95.64 167	97.96 217	98.10 204

IterMVS-LS		96.12 114	97.48 98	94.53 117	95.19 137	97.56 191	97.15 122	89.19 155	99.08 65	88.23 128	94.97 141	94.73 104	97.84 105	97.86 122	98.26 76	99.60 149	99.88 12
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Anonymous202405211			97.40 101		96.45 83	99.54 55	98.08 89	93.79 82	98.24 128		93.55 153	94.41 108	98.88 64	98.04 113	98.24 77	99.75 48	99.76 59

PatchT		93.96 154	97.36 102	90.00 207	94.76 145	98.65 131	90.11 224	78.57 230	97.96 143	80.42 179	96.07 121	94.10 116	96.85 127	98.10 102	97.49 117	99.26 196	99.15 177

CR-MVSNet		94.57 145	97.34 103	91.33 180	94.90 142	98.59 135	97.15 122	79.14 225	97.98 140	80.42 179	96.59 111	93.50 121	96.85 127	98.10 102	97.49 117	99.50 176	99.15 177

test-LLR		95.50 124	97.32 104	93.37 140	95.49 129	98.74 125	96.44 142	90.82 126	98.18 130	82.75 162	96.60 109	94.67 105	95.54 162	98.09 104	96.00 156	99.20 198	98.93 187

TESTMET0.1,1		94.95 135	97.32 104	92.20 155	92.62 164	98.74 125	96.44 142	86.67 182	98.18 130	82.75 162	96.60 109	94.67 105	95.54 162	98.09 104	96.00 156	99.20 198	98.93 187

test-mter		94.86 136	97.32 104	92.00 162	92.41 168	98.82 117	96.18 146	86.35 186	98.05 137	82.28 165	96.48 113	94.39 109	95.46 172	98.17 97	96.20 150	99.32 193	99.13 181

Effi-MVS+		95.81 118	97.31 107	94.06 123	95.09 138	99.35 80	97.24 119	88.22 166	98.54 116	85.38 147	98.52 55	88.68 140	98.70 69	98.32 81	97.93 92	99.74 54	99.84 23

MS-PatchMatch		95.99 116	97.26 108	94.51 118	97.46 67	98.76 123	97.27 117	86.97 179	99.09 63	89.83 124	93.51 155	97.78 66	96.18 144	97.53 139	95.71 166	99.35 191	98.41 199

RPMNet		94.66 139	97.16 109	91.75 172	94.98 140	98.59 135	97.00 129	78.37 231	97.98 140	83.78 152	96.27 117	94.09 117	96.91 124	97.36 144	96.73 134	99.48 177	99.09 182

CVMVSNet		95.33 130	97.09 110	93.27 143	95.23 136	98.39 150	95.49 156	92.58 97	97.71 158	83.00 161	94.44 148	93.28 122	93.92 207	97.79 124	98.54 55	99.41 187	99.45 161

PatchmatchNet		94.70 138	97.08 111	91.92 166	95.53 127	98.85 116	95.77 150	79.54 222	98.95 79	85.98 142	98.52 55	96.45 76	97.39 114	95.32 188	94.09 212	97.32 228	97.38 215
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
Anonymous20231211		97.10 80	97.06 112	97.14 55	96.32 86	99.52 59	98.16 84	93.76 83	98.84 92	95.98 38	90.92 169	94.58 107	98.90 62	97.72 130	98.10 85	99.71 76	99.75 70

ADS-MVSNet		94.65 140	97.04 113	91.88 169	95.68 122	98.99 108	95.89 148	79.03 227	99.15 54	85.81 144	96.96 98	98.21 64	97.10 119	94.48 215	94.24 210	97.74 219	97.21 216

CHOSEN 1792x2688		96.41 104	96.99 114	95.74 106	98.01 61	99.72 4	97.70 104	90.78 128	99.13 61	90.03 122	87.35 213	95.36 93	98.33 85	98.59 70	98.91 34	99.59 155	99.87 14

tfpn111		96.96 85	96.91 115	97.03 62	96.31 88	99.67 14	98.41 65	93.99 69	97.35 164	94.50 65	98.65 51	86.93 148	99.14 46	98.26 86	97.80 101	99.82 14	99.70 96

thisisatest0515		94.61 142	96.89 116	91.95 164	92.00 179	98.47 141	92.01 215	90.73 129	98.18 130	83.96 149	94.51 146	95.13 97	93.38 211	97.38 143	94.74 205	99.61 141	99.79 43

LGP-MVS_train		96.23 110	96.89 116	95.46 110	97.32 70	98.77 121	98.81 53	93.60 86	98.58 113	85.52 145	99.08 27	86.67 155	97.83 106	97.87 121	97.51 115	99.69 87	99.73 78

EPMVS		95.05 133	96.86 118	92.94 148	95.84 116	98.96 111	96.68 134	79.87 218	99.05 73	90.15 120	97.12 94	95.99 88	97.49 111	95.17 196	94.75 204	97.59 223	96.96 220

ACMP	96.25 10	96.62 102	96.72 119	96.50 85	96.96 79	98.75 124	97.80 101	94.30 61	98.85 89	93.12 94	98.78 43	86.61 156	97.23 117	97.73 129	96.61 138	99.62 138	99.71 94
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ACMM	96.26 9	96.67 99	96.69 120	96.66 78	97.29 73	98.46 142	96.48 141	95.09 46	99.21 48	93.19 93	98.78 43	86.73 154	98.17 90	97.84 123	96.32 146	99.74 54	99.49 158
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
HQP-MVS		96.37 105	96.58 121	96.13 93	97.31 72	98.44 145	98.45 63	95.22 45	98.86 87	88.58 127	98.33 65	87.00 147	97.67 107	97.23 148	96.56 140	99.56 166	99.62 133

HyFIR lowres test		95.99 116	96.56 122	95.32 111	97.99 62	99.65 21	96.54 138	88.86 157	98.44 121	89.77 125	84.14 225	97.05 74	99.03 59	98.55 72	98.19 80	99.73 60	99.86 19

TSAR-MVS + COLMAP		96.79 88	96.55 123	97.06 60	97.70 65	98.46 142	99.07 43	96.23 39	99.38 21	91.32 112	98.80 41	85.61 165	98.69 70	97.64 135	96.92 131	99.37 190	99.06 184

thres200		96.76 89	96.53 124	97.03 62	96.31 88	99.67 14	98.37 73	93.99 69	97.68 159	94.49 67	95.83 130	86.77 153	99.18 41	98.26 86	97.82 100	99.82 14	99.66 123

Fast-Effi-MVS+		95.38 127	96.52 125	94.05 124	94.15 148	99.14 104	97.24 119	86.79 180	98.53 117	87.62 134	94.51 146	87.06 145	98.76 67	98.60 68	98.04 89	99.72 66	99.77 54

conf200view11		96.75 90	96.51 126	97.03 62	96.31 88	99.67 14	98.41 65	93.99 69	97.35 164	94.50 65	95.90 126	86.93 148	99.14 46	98.26 86	97.80 101	99.82 14	99.70 96

tfpn200view9		96.75 90	96.51 126	97.03 62	96.31 88	99.67 14	98.41 65	93.99 69	97.35 164	94.52 63	95.90 126	86.93 148	99.14 46	98.26 86	97.80 101	99.82 14	99.70 96

CLD-MVS		96.74 92	96.51 126	97.01 68	96.71 81	98.62 133	98.73 55	94.38 59	98.94 82	94.46 68	97.33 86	87.03 146	98.07 96	97.20 150	96.87 132	99.72 66	99.54 147
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
TAMVS		95.53 123	96.50 129	94.39 120	93.86 152	99.03 105	96.67 135	89.55 152	97.33 170	90.64 115	93.02 164	91.58 132	96.21 142	97.72 130	97.43 122	99.43 185	99.36 167

thres100view900		96.72 93	96.47 130	97.00 70	96.31 88	99.52 59	98.28 79	94.01 67	97.35 164	94.52 63	95.90 126	86.93 148	99.09 56	98.07 107	97.87 96	99.81 27	99.63 132

conf0.05thres1000		96.34 107	96.47 130	96.17 90	96.16 102	99.71 8	97.82 99	93.46 87	98.10 135	90.69 114	96.75 102	85.26 170	99.11 53	98.05 111	97.65 108	99.82 14	99.80 36

FMVSNet5		95.42 125	96.47 130	94.20 121	92.26 171	95.99 216	95.66 152	87.15 176	97.87 149	93.46 89	96.68 105	93.79 118	97.52 109	97.10 154	97.21 126	99.11 201	96.62 226

view800		96.70 95	96.45 133	96.99 72	96.29 95	99.69 12	98.39 72	93.95 76	97.92 146	94.25 76	96.23 119	85.57 166	99.22 34	98.28 83	97.71 107	99.82 14	99.76 59

thres400		96.71 94	96.45 133	97.02 66	96.28 98	99.63 30	98.41 65	94.00 68	97.82 154	94.42 71	95.74 131	86.26 159	99.18 41	98.20 95	97.79 105	99.81 27	99.70 96

view600		96.70 95	96.44 135	97.01 68	96.28 98	99.67 14	98.42 64	93.99 69	97.87 149	94.34 74	95.99 123	85.94 162	99.20 37	98.26 86	97.64 109	99.82 14	99.73 78

IB-MVS	93.96 15	95.02 134	96.44 135	93.36 141	97.05 78	99.28 92	90.43 221	93.39 89	98.02 138	96.02 37	94.92 142	92.07 130	83.52 232	95.38 187	95.82 162	99.72 66	99.59 137
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
thres600view7		96.69 97	96.43 137	97.00 70	96.28 98	99.67 14	98.41 65	93.99 69	97.85 152	94.29 75	95.96 124	85.91 163	99.19 39	98.26 86	97.63 110	99.82 14	99.73 78

FMVSNet1		95.77 119	96.41 138	95.03 112	93.42 160	97.86 169	97.11 125	89.89 146	98.53 117	92.00 106	89.17 188	93.23 123	98.15 93	98.07 107	98.34 70	99.61 141	99.69 103

GA-MVS		93.93 155	96.31 139	91.16 186	93.61 157	98.79 118	95.39 159	90.69 131	98.25 127	73.28 223	96.15 120	88.42 141	94.39 199	97.76 127	95.35 173	99.58 159	99.45 161

ACMH+	95.51 13	95.40 126	96.00 140	94.70 116	96.33 85	98.79 118	96.79 133	91.32 117	98.77 104	87.18 136	95.60 136	85.46 167	96.97 122	97.15 151	96.59 139	99.59 155	99.65 126

MVS-HIRNet		92.51 188	95.97 141	88.48 216	93.73 156	98.37 151	90.33 222	75.36 239	98.32 124	77.78 206	89.15 189	94.87 100	95.14 189	97.62 136	96.39 144	98.51 207	97.11 217

ACMH	95.42 14	95.27 131	95.96 142	94.45 119	96.83 80	98.78 120	94.72 187	91.67 108	98.95 79	86.82 139	96.42 115	83.67 182	97.00 121	97.48 141	96.68 136	99.69 87	99.76 59
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs4		95.09 132	95.90 143	94.14 122	92.29 170	97.70 176	95.45 157	90.31 136	98.60 111	90.70 113	93.25 158	89.90 138	96.67 132	97.13 152	95.42 170	99.44 183	99.28 170

tpmrst		93.86 157	95.88 144	91.50 176	95.69 121	98.62 133	95.64 153	79.41 223	98.80 98	83.76 154	95.63 135	96.13 85	97.25 115	92.92 221	92.31 226	97.27 229	96.74 223

anonymousdsp		93.12 166	95.86 145	89.93 209	91.09 216	98.25 156	95.12 161	85.08 194	97.44 162	73.30 222	90.89 170	90.78 135	95.25 187	97.91 119	95.96 160	99.71 76	99.82 28

OPM-MVS		96.22 111	95.85 146	96.65 79	97.75 63	98.54 138	99.00 48	95.53 42	96.88 188	89.88 123	95.95 125	86.46 158	98.07 96	97.65 134	96.63 137	99.67 104	98.83 193

conf0.01		96.35 106	95.71 147	97.10 57	96.30 94	99.65 21	98.41 65	94.10 65	97.35 164	94.82 57	95.44 139	81.88 209	99.14 46	98.16 98	97.80 101	99.82 14	99.69 103

MDTV_nov1_ep13_2view		92.44 190	95.66 148	88.68 214	91.05 217	97.92 166	92.17 213	79.64 220	98.83 93	76.20 213	91.45 166	93.51 120	95.04 190	95.68 185	93.70 215	97.96 217	98.53 196

conf0.002		96.31 108	95.63 149	97.11 56	96.29 95	99.64 26	98.41 65	94.11 64	97.35 164	94.86 55	95.49 138	81.06 214	99.14 46	98.14 99	98.02 90	99.82 14	99.69 103

tfpn		96.22 111	95.62 150	96.93 74	96.29 95	99.72 4	98.34 77	93.94 77	97.96 143	93.94 79	96.45 114	79.09 224	99.22 34	98.28 83	98.06 87	99.83 10	99.78 46

pm-mvs1		94.27 147	95.57 151	92.75 149	92.58 165	98.13 160	94.87 172	90.71 130	96.70 194	83.78 152	89.94 183	89.85 139	94.96 192	97.58 137	97.07 127	99.61 141	99.72 90

UniMVSNet_NR-MVSNet		94.59 143	95.47 152	93.55 135	91.85 186	97.89 168	95.03 163	92.00 101	97.33 170	86.12 140	93.19 159	87.29 144	96.60 135	96.12 178	96.70 135	99.72 66	99.80 36

UniMVSNet (Re)		94.58 144	95.34 153	93.71 130	92.25 172	98.08 161	94.97 165	91.29 121	97.03 181	87.94 131	93.97 151	86.25 160	96.07 147	96.27 175	95.97 159	99.72 66	99.79 43

SixPastTwentyTwo		93.44 163	95.32 154	91.24 184	92.11 175	98.40 149	92.77 210	88.64 162	98.09 136	77.83 205	93.51 155	85.74 164	96.52 138	96.91 157	94.89 201	99.59 155	99.73 78

dps		94.63 141	95.31 155	93.84 126	95.53 127	98.71 128	96.54 138	80.12 217	97.81 156	97.21 25	96.98 97	92.37 127	96.34 141	92.46 226	91.77 230	97.26 230	97.08 218

DWT-MVSNet_training		95.38 127	95.05 156	95.78 103	95.86 114	98.88 114	97.55 108	90.09 142	98.23 129	96.49 35	97.62 85	86.92 152	97.16 118	92.03 229	94.12 211	97.52 224	97.50 211

LTVRE_ROB	93.20 16	92.84 174	94.92 157	90.43 203	92.83 162	98.63 132	97.08 127	87.87 172	97.91 147	68.42 229	93.54 154	79.46 223	96.62 134	97.55 138	97.40 123	99.74 54	99.92 1
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
tpm cat1		94.06 150	94.90 158	93.06 145	95.42 133	98.52 139	96.64 136	80.67 213	97.82 154	92.63 99	93.39 157	95.00 98	96.06 148	91.36 233	91.58 232	96.98 233	96.66 225

CostFormer		94.25 149	94.88 159	93.51 137	95.43 131	98.34 153	96.21 145	80.64 214	97.94 145	94.01 77	98.30 66	86.20 161	97.52 109	92.71 222	92.69 222	97.23 232	98.02 207

USDC		94.26 148	94.83 160	93.59 133	96.02 107	98.44 145	97.84 98	88.65 161	98.86 87	82.73 164	94.02 149	80.56 215	96.76 129	97.28 147	96.15 153	99.55 167	98.50 197

tpm		92.38 194	94.79 161	89.56 210	94.30 147	97.50 196	94.24 201	78.97 228	97.72 157	74.93 219	97.97 75	82.91 193	96.60 135	93.65 220	94.81 202	98.33 212	98.98 185

EU-MVSNet		92.80 177	94.76 162	90.51 201	91.88 184	96.74 213	92.48 212	88.69 160	96.21 209	79.00 201	91.51 165	87.82 142	91.83 219	95.87 183	96.27 147	99.21 197	98.92 190

WR-MVS_H		93.54 161	94.67 163	92.22 153	91.95 182	97.91 167	94.58 195	88.75 159	96.64 198	83.88 151	90.66 173	85.13 171	94.40 198	96.54 165	95.91 161	99.73 60	99.89 8

LP		92.12 204	94.60 164	89.22 212	94.96 141	98.45 144	93.01 208	77.58 232	97.85 152	77.26 209	89.80 184	93.00 124	94.54 194	93.69 218	92.58 223	98.00 216	96.83 222

N_pmnet		92.21 201	94.60 164	89.42 211	91.88 184	97.38 203	89.15 227	89.74 150	97.89 148	73.75 221	87.94 210	92.23 129	93.85 208	96.10 179	93.20 218	98.15 215	97.43 214

tpmp4_e23		93.84 159	94.58 166	92.98 147	95.41 134	98.29 154	96.81 132	80.57 215	98.15 133	90.53 116	97.00 96	84.39 178	96.91 124	93.69 218	92.45 224	97.67 222	98.06 205

NR-MVSNet		94.01 151	94.51 167	93.44 138	92.56 166	97.77 170	95.67 151	91.57 110	97.17 175	85.84 143	93.13 161	80.53 216	95.29 185	97.01 155	96.17 151	99.69 87	99.75 70

WR-MVS		93.43 164	94.48 168	92.21 154	91.52 208	97.69 180	94.66 191	89.98 144	96.86 189	83.43 156	90.12 175	85.03 172	93.94 206	96.02 181	95.82 162	99.71 76	99.82 28

DU-MVS		93.98 153	94.44 169	93.44 138	91.66 201	97.77 170	95.03 163	91.57 110	97.17 175	86.12 140	93.13 161	81.13 213	96.60 135	95.10 207	97.01 130	99.67 104	99.80 36

testpf		91.80 209	94.43 170	88.74 213	93.89 151	95.30 229	92.05 214	71.77 240	97.52 161	87.24 135	94.77 144	92.68 126	91.48 220	91.75 232	92.11 229	96.02 237	96.89 221

TinyColmap		94.00 152	94.35 171	93.60 132	95.89 113	98.26 155	97.49 110	88.82 158	98.56 115	83.21 158	91.28 168	80.48 217	96.68 131	97.34 145	96.26 149	99.53 173	98.24 202

pmmvs5		92.71 184	94.27 172	90.90 193	91.42 210	97.74 172	93.23 206	86.66 183	95.99 216	78.96 202	91.45 166	83.44 184	95.55 161	97.30 146	95.05 182	99.58 159	98.93 187

gg-mvs-nofinetune		90.85 213	94.14 173	87.02 219	94.89 143	99.25 95	98.64 57	76.29 236	88.24 238	57.50 240	79.93 233	95.45 92	95.18 188	98.77 52	98.07 86	99.62 138	99.24 173

TranMVSNet+NR-MVSNet		93.67 160	94.14 173	93.13 144	91.28 215	97.58 189	95.60 154	91.97 102	97.06 179	84.05 148	90.64 174	82.22 204	96.17 145	94.94 211	96.78 133	99.69 87	99.78 46

tfpnnormal		93.85 158	94.12 175	93.54 136	93.22 161	98.24 157	95.45 157	91.96 103	94.61 226	83.91 150	90.74 171	81.75 211	97.04 120	97.49 140	96.16 152	99.68 96	99.84 23

v7		92.97 172	94.11 176	91.65 175	91.83 187	97.55 193	94.86 175	88.19 168	96.96 184	79.72 191	88.16 205	84.68 175	95.63 157	96.33 172	95.30 175	99.65 115	99.77 54

TransMVSNet (Re)		93.45 162	94.08 177	92.72 150	92.83 162	97.62 187	94.94 166	91.54 112	95.65 222	83.06 160	88.93 191	83.53 183	94.25 200	97.41 142	97.03 128	99.67 104	98.40 201

v10		92.79 179	94.06 178	91.31 182	91.78 192	97.29 207	94.87 172	86.10 187	96.97 183	79.82 188	88.16 205	84.56 176	95.63 157	96.33 172	95.31 174	99.65 115	99.80 36

v1144		92.81 175	94.03 179	91.40 179	91.68 200	97.60 188	94.73 186	88.40 164	96.71 193	78.48 203	88.14 207	84.46 177	95.45 173	96.31 174	95.22 177	99.65 115	99.76 59

CP-MVSNet		93.25 165	94.00 180	92.38 152	91.65 203	97.56 191	94.38 198	89.20 154	96.05 214	83.16 159	89.51 186	81.97 208	96.16 146	96.43 167	96.56 140	99.71 76	99.89 8

v6		93.11 167	93.98 181	92.10 157	92.01 178	97.71 173	94.86 175	90.15 139	96.96 184	80.47 178	90.01 178	83.26 186	95.48 166	95.17 196	95.01 188	99.64 126	99.76 59

Baseline_NR-MVSNet		93.87 156	93.98 181	93.75 128	91.66 201	97.02 208	95.53 155	91.52 114	97.16 177	87.77 133	87.93 211	83.69 181	96.35 140	95.10 207	97.23 125	99.68 96	99.73 78

v1neww		93.06 168	93.94 183	92.03 160	91.99 180	97.70 176	94.79 179	90.14 140	96.93 186	80.13 183	89.97 180	83.01 190	95.48 166	95.16 200	95.01 188	99.63 132	99.76 59

v7new		93.06 168	93.94 183	92.03 160	91.99 180	97.70 176	94.79 179	90.14 140	96.93 186	80.13 183	89.97 180	83.01 190	95.48 166	95.16 200	95.01 188	99.63 132	99.76 59

Anonymous20231206		90.70 215	93.93 185	86.92 220	90.21 225	96.79 211	90.30 223	86.61 184	96.05 214	69.25 228	88.46 202	84.86 174	85.86 228	97.11 153	96.47 143	99.30 194	97.80 210

EG-PatchMatch MVS		92.45 189	93.92 186	90.72 198	92.56 166	98.43 147	94.88 171	84.54 200	97.18 174	79.55 194	86.12 223	83.23 187	93.15 214	97.22 149	96.00 156	99.67 104	99.27 171

V42		93.05 170	93.90 187	92.04 159	91.91 183	97.66 182	94.91 167	89.91 145	96.85 190	80.58 176	89.66 185	83.43 185	95.37 178	95.03 210	94.90 199	99.59 155	99.78 46

v8		92.87 173	93.87 188	91.72 174	92.05 177	97.50 196	94.79 179	88.20 167	96.85 190	80.11 185	90.01 178	82.86 195	95.48 166	95.15 204	94.90 199	99.66 109	99.80 36

v16		92.66 185	93.80 189	91.32 181	92.13 173	95.62 219	94.89 168	85.12 193	97.20 173	80.66 174	89.96 182	83.93 180	95.49 165	95.17 196	95.04 183	99.63 132	99.68 110

v11		92.43 191	93.77 190	90.85 196	91.72 198	95.58 224	94.87 172	84.07 208	96.98 182	79.28 197	88.03 208	84.22 179	95.53 164	96.55 164	95.36 172	99.65 115	99.70 96

test20.03		90.65 216	93.71 191	87.09 218	90.44 223	96.24 214	89.74 226	85.46 190	95.59 223	72.99 224	90.68 172	85.33 168	84.41 231	95.94 182	95.10 181	99.52 174	97.06 219

v18		92.63 186	93.67 192	91.43 177	92.13 173	95.65 217	95.09 162	85.44 191	97.06 179	80.78 173	90.06 176	83.06 188	95.47 171	95.16 200	95.01 188	99.64 126	99.67 115

v17		92.55 187	93.65 193	91.27 183	92.11 175	95.63 218	94.89 168	85.15 192	97.12 178	80.39 182	90.02 177	83.02 189	95.45 173	95.17 196	94.92 198	99.66 109	99.68 110

v1192		92.43 191	93.61 194	91.05 187	91.53 207	97.43 200	94.61 193	87.99 170	96.60 199	76.72 211	87.11 215	82.74 196	95.85 152	96.35 171	95.30 175	99.60 149	99.74 74

v1141		92.79 179	93.61 194	91.84 171	91.75 194	97.71 173	94.74 185	90.33 133	96.58 201	79.21 199	88.59 198	82.53 200	95.36 179	95.16 200	94.96 195	99.63 132	99.72 90

divwei89l23v2f112		92.80 177	93.60 196	91.86 170	91.75 194	97.71 173	94.75 184	90.32 134	96.54 203	79.35 196	88.59 198	82.55 199	95.35 180	95.15 204	94.96 195	99.63 132	99.72 90

v1921920		92.36 196	93.57 197	90.94 192	91.39 211	97.39 202	94.70 188	87.63 174	96.60 199	76.63 212	86.98 216	82.89 194	95.75 153	96.26 176	95.14 180	99.55 167	99.73 78

v1		92.81 175	93.57 197	91.94 165	91.79 191	97.70 176	94.80 178	90.32 134	96.52 204	79.75 189	88.47 201	82.46 201	95.32 182	95.14 206	94.96 195	99.63 132	99.73 78

TDRefinement		93.04 171	93.57 197	92.41 151	96.58 82	98.77 121	97.78 103	91.96 103	98.12 134	80.84 172	89.13 190	79.87 221	87.78 224	96.44 166	94.50 209	99.54 171	98.15 203

v144192		92.38 194	93.55 200	91.00 190	91.44 209	97.47 199	94.27 199	87.41 175	96.52 204	78.03 204	87.50 212	82.65 197	95.32 182	95.82 184	95.15 179	99.55 167	99.78 46

v2v482		92.77 181	93.52 201	91.90 168	91.59 206	97.63 184	94.57 196	90.31 136	96.80 192	79.22 198	88.74 195	81.55 212	96.04 149	95.26 189	94.97 194	99.66 109	99.69 103

V14		92.31 198	93.41 202	91.03 189	91.80 190	95.59 222	94.79 179	84.70 198	96.58 201	79.83 187	88.79 194	82.98 192	95.41 175	95.22 190	95.02 187	99.65 115	99.67 115

PS-CasMVS		92.72 182	93.36 203	91.98 163	91.62 205	97.52 194	94.13 202	88.98 156	95.94 217	81.51 170	87.35 213	79.95 220	95.91 151	96.37 169	96.49 142	99.70 85	99.89 8

v1240		91.99 205	93.33 204	90.44 202	91.29 213	97.30 206	94.25 200	86.79 180	96.43 208	75.49 217	86.34 221	81.85 210	95.29 185	96.42 168	95.22 177	99.52 174	99.73 78

v15		92.27 199	93.33 204	91.04 188	91.83 187	95.60 220	94.79 179	84.88 197	96.66 196	79.66 192	88.72 196	82.45 202	95.40 176	95.19 195	95.00 192	99.65 115	99.67 115

V9		92.24 200	93.32 206	90.98 191	91.76 193	95.58 224	94.83 177	84.50 202	96.68 195	79.73 190	88.66 197	82.39 203	95.39 177	95.22 190	95.03 185	99.65 115	99.67 115

v12		92.18 202	93.29 207	90.88 194	91.70 199	95.59 222	94.61 193	84.36 204	96.65 197	79.59 193	88.85 192	82.03 207	95.35 180	95.22 190	95.04 183	99.65 115	99.68 110

v13		92.16 203	93.28 208	90.85 196	91.75 194	95.58 224	94.65 192	84.23 206	96.49 207	79.51 195	88.40 203	82.58 198	95.31 184	95.21 193	95.03 185	99.66 109	99.68 110

PEN-MVS		92.72 182	93.20 209	92.15 156	91.29 213	97.31 205	94.67 190	89.81 147	96.19 210	81.83 168	88.58 200	79.06 225	95.61 160	95.21 193	96.27 147	99.72 66	99.82 28

v52		91.94 206	93.10 210	90.57 199	90.62 220	97.50 196	93.98 203	87.02 177	95.86 219	77.67 207	86.93 217	82.16 206	94.53 195	94.71 213	94.70 206	99.61 141	99.85 21

V4		91.92 207	93.10 210	90.55 200	90.64 219	97.51 195	93.93 204	87.02 177	95.81 221	77.61 208	86.93 217	82.19 205	94.50 196	94.72 212	94.68 207	99.62 138	99.85 21

v7n		91.61 210	92.95 212	90.04 206	90.56 222	97.69 180	93.74 205	85.59 189	95.89 218	76.95 210	86.60 220	78.60 227	93.76 209	97.01 155	94.99 193	99.65 115	99.87 14

v148		92.36 196	92.88 213	91.75 172	91.63 204	97.66 182	92.64 211	90.55 132	96.09 212	83.34 157	88.19 204	80.00 219	92.74 215	93.98 217	94.58 208	99.58 159	99.69 103

test2356		88.81 221	92.86 214	84.09 227	87.85 228	93.46 234	87.07 232	83.60 210	96.50 206	62.08 238	97.06 95	75.04 231	85.17 229	95.08 209	95.42 170	98.75 206	97.46 212

DTE-MVSNet		92.42 193	92.85 215	91.91 167	90.87 218	96.97 209	94.53 197	89.81 147	95.86 219	81.59 169	88.83 193	77.88 228	95.01 191	94.34 216	96.35 145	99.64 126	99.73 78

new_pmnet		90.45 217	92.84 216	87.66 217	88.96 226	96.16 215	88.71 228	84.66 199	97.56 160	71.91 227	85.60 224	86.58 157	93.28 212	96.07 180	93.54 216	98.46 209	94.39 230

gm-plane-assit		89.44 220	92.82 217	85.49 223	91.37 212	95.34 228	79.55 238	82.12 211	91.68 234	64.79 235	87.98 209	80.26 218	95.66 156	98.51 74	97.56 113	99.45 181	98.41 199

testus		88.77 222	92.77 218	84.10 226	88.24 227	93.95 232	87.16 231	84.24 205	97.37 163	61.54 239	95.70 134	73.10 233	84.90 230	95.56 186	95.82 162	98.51 207	97.88 209

pmmvs6		91.90 208	92.53 219	91.17 185	91.81 189	97.63 184	93.23 206	88.37 165	93.43 231	80.61 175	77.32 235	87.47 143	94.12 202	96.58 162	95.72 165	98.88 205	99.53 148

v748		91.12 212	91.95 220	90.16 205	90.60 221	97.35 204	91.11 216	87.92 171	94.75 225	80.54 177	86.26 222	75.97 230	91.13 221	94.63 214	94.81 202	99.65 115	99.90 4

pmmvs3		88.19 224	91.27 221	84.60 225	85.60 232	93.66 233	85.68 233	81.13 212	92.36 233	63.66 237	89.51 186	77.10 229	93.22 213	96.37 169	92.40 225	98.30 213	97.46 212

CMPMVS	70.31 18	90.74 214	91.06 222	90.36 204	97.32 70	97.43 200	92.97 209	87.82 173	93.50 230	75.34 218	83.27 228	84.90 173	92.19 218	92.64 225	91.21 233	96.50 235	94.46 229
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MIMVSNet1		88.61 223	90.68 223	86.19 222	81.56 240	95.30 229	87.78 229	85.98 188	94.19 229	72.30 226	78.84 234	78.90 226	90.06 222	96.59 161	95.47 168	99.46 180	95.49 228

PM-MVS		89.55 219	90.30 224	88.67 215	87.06 229	95.60 220	90.88 219	84.51 201	96.14 211	75.75 214	86.89 219	63.47 240	94.64 193	96.85 158	93.89 213	99.17 200	99.29 169

pmmvs-eth3d		89.81 218	89.65 225	90.00 207	86.94 230	95.38 227	91.08 217	86.39 185	94.57 227	82.27 166	83.03 229	64.94 237	93.96 205	96.57 163	93.82 214	99.35 191	99.24 173

MDA-MVSNet-bldmvs		87.84 225	89.22 226	86.23 221	81.74 239	96.77 212	83.74 234	89.57 151	94.50 228	72.83 225	96.64 107	64.47 239	92.71 216	81.43 239	92.28 227	96.81 234	98.47 198

new-patchmatchnet		86.12 226	87.30 227	84.74 224	86.92 231	95.19 231	83.57 235	84.42 203	92.67 232	65.66 232	80.32 232	64.72 238	89.41 223	92.33 228	89.21 234	98.43 210	96.69 224

testmv		81.83 229	86.26 228	76.66 232	84.10 233	89.42 239	74.29 242	79.65 219	90.61 235	51.85 244	82.11 230	63.06 242	72.61 237	91.94 230	92.75 220	97.49 225	93.94 232

test1235678		81.83 229	86.26 228	76.66 232	84.10 233	89.41 240	74.29 242	79.64 220	90.60 236	51.84 245	82.11 230	63.07 241	72.61 237	91.94 230	92.75 220	97.49 225	93.94 232

1111		82.87 228	85.67 230	79.62 231	81.86 237	89.62 237	74.44 240	68.81 242	87.44 239	66.59 230	76.83 236	70.33 235	87.71 225	92.65 223	93.37 217	98.28 214	89.42 236

v1.0		91.56 211	85.17 231	99.01 16	99.70 7	99.69 12	99.40 27	98.31 6	98.94 82	97.70 19	99.40 11	99.97 5	99.17 43	99.54 9	98.67 44	99.78 39	0.00 246

test12356		80.53 232	84.80 232	75.54 234	82.31 236	88.05 243	75.99 239	79.31 224	88.53 237	53.24 243	83.30 227	56.38 243	65.16 243	90.87 234	93.10 219	97.25 231	93.34 235

FPMVS		83.82 227	84.61 233	82.90 228	90.39 224	90.71 236	90.85 220	84.10 207	95.47 224	65.15 233	83.44 226	74.46 232	75.48 234	81.63 238	79.42 240	91.42 241	87.14 238

Gipuma		81.40 231	81.78 234	80.96 230	83.21 235	85.61 244	79.73 237	76.25 237	97.33 170	64.21 236	55.32 241	55.55 244	86.04 227	92.43 227	92.20 228	96.32 236	93.99 231
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
ambc			80.99 235		80.04 242	90.84 235	90.91 218		96.09 212	74.18 220	62.81 240	30.59 250	82.44 233	96.25 177	91.77 230	95.91 238	98.56 195

PMMVS2		77.26 233	79.47 236	74.70 236	76.00 243	88.37 242	74.22 244	76.34 235	78.31 242	54.13 241	69.96 239	52.50 245	70.14 240	84.83 237	88.71 235	97.35 227	93.58 234

PMVS	72.60 17	76.39 234	77.66 237	74.92 235	81.04 241	69.37 249	68.47 245	80.54 216	85.39 241	65.07 234	73.52 238	72.91 234	65.67 242	80.35 240	76.81 241	88.71 243	85.25 242
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
.test1245		69.67 235	72.22 238	66.70 239	81.86 237	89.62 237	74.44 240	68.81 242	87.44 239	66.59 230	76.83 236	70.33 235	87.71 225	92.65 223	37.65 243	20.79 247	51.04 243

E-PMN		68.30 237	68.43 239	68.15 237	74.70 245	71.56 248	55.64 247	77.24 233	77.48 244	39.46 247	51.95 244	41.68 248	73.28 236	70.65 242	79.51 239	88.61 244	86.20 241

EMVS		68.12 238	68.11 240	68.14 238	75.51 244	71.76 247	55.38 248	77.20 234	77.78 243	37.79 248	53.59 242	43.61 246	74.72 235	67.05 244	76.70 242	88.27 245	86.24 240

no-one		66.79 239	67.62 241	65.81 240	73.06 246	81.79 245	51.90 250	76.20 238	61.07 246	54.05 242	51.62 245	41.72 247	49.18 244	67.26 243	82.83 238	90.47 242	87.07 239

MVE	67.97 19	65.53 240	67.43 242	63.31 241	59.33 247	74.20 246	53.09 249	70.43 241	66.27 245	43.13 246	45.98 246	30.62 249	70.65 239	79.34 241	86.30 236	83.25 246	89.33 237
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		31.24 241	40.15 243	20.86 243	12.61 248	17.99 250	25.16 251	13.30 245	48.42 247	24.82 249	53.07 243	30.13 251	28.47 245	42.73 245	37.65 243	20.79 247	51.04 243

test123		26.75 242	34.25 244	18.01 244	7.93 249	17.18 251	24.85 252	12.36 246	44.83 248	16.52 250	41.80 247	18.10 252	28.29 246	33.08 246	34.79 245	18.10 249	49.95 245

sosnet-low-res		0.00 243	0.00 245	0.00 245	0.00 251	0.00 252	0.00 253	0.00 248	0.00 249	0.00 252	0.00 248	0.00 253	0.00 248	0.00 247	0.00 246	0.00 250	0.00 246

sosnet		0.00 243	0.00 245	0.00 245	0.00 251	0.00 252	0.00 253	0.00 248	0.00 249	0.00 252	0.00 248	0.00 253	0.00 248	0.00 247	0.00 246	0.00 250	0.00 246

our_test_3					92.30 169	97.58 189	90.09 225

MTAPA										98.09 11		99.97 5

MTMP										98.46 7		99.96 11

Patchmatch-RL test							66.86 246

tmp_tt				82.25 229	97.73 64	88.71 241	80.18 236	68.65 244	99.15 54	86.98 137	99.47 8	85.31 169	68.35 241	87.51 236	83.81 237	91.64 240

XVS					97.42 68	99.62 34	98.59 59			93.81 84		99.95 16				99.69 87

X-MVStestdata					97.42 68	99.62 34	98.59 59			93.81 84		99.95 16				99.69 87

abl_6				98.09 37	99.33 38	99.22 99	98.79 54	94.96 49	98.52 119	97.00 29	97.30 88	99.86 34	98.76 67			99.69 87	99.41 164

mPP-MVS					99.53 26							99.89 31

NP-MVS									98.57 114

Patchmtry						98.59 135	97.15 122	79.14 225		80.42 179

DeepMVS_CX						96.85 210	87.43 230	89.27 153	98.30 125	75.55 216	95.05 140	79.47 222	92.62 217	89.48 235		95.18 239	95.96 227

High-resolution multi-view benchmark