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		99.40 1	99.81 2	98.92 3	99.62 5	99.96 7	99.76 4	96.87 9	99.95 19	97.66 4	99.57 25	100.00 1	99.63 22	99.88 8	99.28 24	100.00 1	100.00 1

MSLP-MVS++		99.39 2	99.76 7	98.95 2	99.60 10	99.99 1	99.83 1	96.82 11	99.92 27	97.58 6	99.58 24	100.00 1	99.93 1	98.98 29	99.86 7	99.96 10	100.00 1

CNVR-MVS		99.39 2	99.75 10	98.98 1	99.69 1	99.95 12	99.76 4	96.91 6	99.98 3	97.59 5	99.64 19	100.00 1	99.93 1	99.94 2	98.75 45	99.97 9	99.97 78

HSP-MVS		99.36 4	99.79 4	98.85 6	99.61 8	99.96 7	99.71 17	96.94 4	99.97 6	97.11 8	99.60 22	100.00 1	99.70 14	99.96 1	99.12 29	100.00 1	99.96 97

APD-MVS		99.33 5	99.85 1	98.73 8	99.61 8	99.92 33	99.77 3	96.91 6	99.93 23	96.31 14	99.59 23	99.95 31	99.84 7	99.73 14	99.84 8	99.95 12	100.00 1
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
NCCC		99.24 6	99.75 10	98.65 9	99.63 4	99.96 7	99.76 4	96.91 6	99.97 6	95.86 17	99.67 11	100.00 1	99.75 11	99.85 9	98.80 41	99.98 8	99.97 78

CNLPA		99.24 6	99.58 27	98.85 6	99.34 26	99.95 12	99.32 29	96.65 20	99.96 15	98.44 2	98.97 48	100.00 1	99.57 25	98.66 37	99.56 14	99.76 71	99.97 78

AdaColmap		99.21 8	99.45 33	98.92 3	99.67 3	99.95 12	99.65 21	96.77 15	99.97 6	97.67 3	100.00 1	99.69 44	99.93 1	99.26 25	97.25 83	99.85 26	100.00 1

HFP-MVS		99.19 9	99.77 6	98.51 13	99.55 14	99.94 17	99.76 4	96.84 10	99.88 32	95.27 21	99.67 11	100.00 1	99.85 6	99.56 19	99.36 19	99.79 52	99.97 78

PLC	98.06 1	99.17 10	99.38 35	98.92 3	99.47 16	99.90 41	99.48 26	96.47 25	99.96 15	98.73 1	99.52 28	100.00 1	99.55 27	98.54 49	97.73 74	99.84 28	99.99 46
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
SD-MVS		99.16 11	99.73 13	98.49 14	97.93 46	99.95 12	99.74 12	96.94 4	99.96 15	96.60 11	99.47 31	100.00 1	99.88 5	99.15 27	99.59 12	99.84 28	100.00 1

CP-MVS		99.14 12	99.67 18	98.53 12	99.45 18	99.94 17	99.63 23	96.62 22	99.82 44	95.92 16	99.65 16	100.00 1	99.71 13	99.76 12	98.56 49	99.83 33	100.00 1

MPTG		99.12 13	99.52 32	98.65 9	99.58 13	99.93 27	99.74 12	96.72 18	99.44 81	96.47 12	99.62 21	100.00 1	99.63 22	99.74 13	97.97 61	99.77 64	99.94 110

ACMMPR		99.12 13	99.76 7	98.36 15	99.45 18	99.94 17	99.75 10	96.70 19	99.93 23	94.65 25	99.65 16	99.96 29	99.84 7	99.51 21	99.35 20	99.79 52	99.96 97

MCST-MVS		99.08 15	99.72 15	98.33 16	99.59 12	99.97 3	99.78 2	96.96 2	99.95 19	93.72 29	99.67 11	100.00 1	99.90 4	99.91 5	98.55 50	100.00 1	100.00 1

CPTT-MVS		99.08 15	99.53 31	98.57 11	99.44 20	99.93 27	99.60 24	95.92 30	99.77 51	97.01 9	99.67 11	100.00 1	99.72 12	99.56 19	97.76 71	99.70 107	99.98 65

DeepC-MVS_fast	98.03 2	99.05 17	99.78 5	98.21 19	99.47 16	99.97 3	99.75 10	96.80 12	99.97 6	93.58 32	98.68 59	99.94 32	99.69 15	99.93 4	99.95 2	99.96 10	99.98 65
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TSAR-MVS + MP.		98.99 18	99.61 24	98.27 17	97.88 47	99.92 33	99.71 17	96.80 12	99.96 15	95.58 19	98.71 58	100.00 1	99.68 17	99.91 5	98.78 43	99.99 5	100.00 1

HPM-MVS++		98.98 19	99.62 23	98.22 18	99.62 5	99.94 17	99.74 12	96.95 3	99.87 35	93.76 28	99.49 30	100.00 1	99.39 33	99.73 14	98.35 53	99.89 21	99.96 97

SteuartSystems-ACMMP		98.95 20	99.80 3	97.95 22	99.43 21	99.96 7	99.76 4	96.45 26	99.82 44	93.63 30	99.64 19	100.00 1	98.56 69	99.90 7	99.31 22	99.84 28	100.00 1
Skip Steuart: Steuart Systems R&D Blog.
PHI-MVS		98.85 21	99.67 18	97.89 23	98.63 42	99.93 27	98.95 40	95.20 32	99.84 42	94.94 22	99.74 10	100.00 1	99.69 15	98.40 56	99.75 10	99.93 15	99.99 46

MP-MVS		98.82 22	99.63 21	97.88 24	99.41 22	99.91 40	99.74 12	96.76 16	99.88 32	91.89 39	99.50 29	99.94 32	99.65 20	99.71 17	98.49 52	99.82 37	99.97 78

ACMMP_Plus		98.68 23	99.58 27	97.62 25	99.62 5	99.92 33	99.72 16	96.78 14	99.71 59	90.13 65	99.66 15	99.99 24	99.64 21	99.78 11	98.14 58	99.82 37	99.89 133

train_agg		98.62 24	99.76 7	97.28 27	99.03 35	99.93 27	99.65 21	96.37 27	99.98 3	89.24 75	99.53 26	99.83 37	99.59 24	99.85 9	99.19 27	99.80 48	100.00 1

X-MVS		98.62 24	99.75 10	97.29 26	99.50 15	99.94 17	99.71 17	96.55 23	99.85 39	88.58 80	99.65 16	99.98 26	99.67 18	99.60 18	99.26 25	99.77 64	99.97 78

OMC-MVS		98.59 26	99.07 37	98.03 21	99.41 22	99.90 41	99.26 32	94.33 34	99.94 21	96.03 15	96.68 84	99.72 43	99.42 30	98.86 32	98.84 38	99.72 103	99.58 171

PGM-MVS		98.47 27	99.73 13	97.00 31	99.68 2	99.94 17	99.76 4	91.74 39	99.84 42	91.17 50	100.00 1	99.69 44	99.81 9	99.38 23	99.30 23	99.82 37	99.95 106

TSAR-MVS + ACMM		98.30 28	99.64 20	96.74 34	99.08 34	99.94 17	99.67 20	96.73 17	99.97 6	86.30 93	98.30 64	99.99 24	98.78 63	99.73 14	99.57 13	99.88 24	99.98 65

CSCG		98.22 29	98.37 58	98.04 20	99.60 10	99.82 54	99.45 27	93.59 35	99.16 96	96.46 13	98.22 70	95.86 89	99.41 32	96.33 120	99.22 26	99.75 83	99.94 110

3Dnovator+	95.21 7	98.17 30	99.08 36	97.12 29	99.28 29	99.78 67	98.61 48	89.93 54	99.93 23	95.36 20	95.50 95	100.00 1	99.56 26	98.58 44	99.80 9	99.95 12	99.97 78

ACMMP		98.16 31	99.01 38	97.18 28	98.86 37	99.92 33	98.77 45	95.73 31	99.31 92	91.15 51	100.00 1	99.81 39	98.82 62	98.11 70	95.91 119	99.77 64	99.97 78
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
MVS_111021_LR		98.15 32	99.69 17	96.36 39	99.23 31	99.93 27	97.79 59	91.84 38	99.87 35	90.53 60	100.00 1	99.57 49	98.93 56	99.44 22	99.08 31	99.85 26	99.95 106

EPNet		98.11 33	99.63 21	96.34 40	98.44 44	99.88 47	98.55 49	90.25 50	99.93 23	92.60 36	100.00 1	99.73 41	98.41 70	98.87 31	99.02 32	99.82 37	99.97 78
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TSAR-MVS + GP.		98.06 34	99.55 30	96.32 41	94.72 72	99.92 33	99.22 33	89.98 52	99.97 6	94.77 24	99.94 9	100.00 1	99.43 29	98.52 53	98.53 51	99.79 52	100.00 1

3Dnovator	95.01 8	97.98 35	98.89 41	96.92 33	99.36 24	99.76 69	98.72 46	89.98 52	99.98 3	93.99 27	94.60 109	99.43 54	99.50 28	98.55 46	99.91 4	99.99 5	99.98 65

MVS_111021_HR		97.94 36	99.59 25	96.02 43	99.27 30	99.97 3	97.03 81	90.44 47	99.89 29	90.75 54	100.00 1	99.73 41	98.68 68	98.67 36	98.89 36	99.95 12	99.97 78

QAPM		97.90 37	98.89 41	96.74 34	99.35 25	99.80 65	98.84 42	90.20 51	99.94 21	92.85 33	94.17 112	99.78 40	99.42 30	98.71 35	99.87 6	99.79 52	99.98 65

CDPH-MVS		97.88 38	99.59 25	95.89 44	98.90 36	99.95 12	99.40 28	92.86 37	99.86 38	85.33 96	98.62 60	99.45 53	99.06 54	99.29 24	99.94 3	99.81 45	100.00 1

CANet		97.62 39	98.94 40	96.08 42	97.19 51	99.93 27	99.29 31	90.38 48	99.87 35	91.00 52	95.79 94	99.51 50	98.72 67	98.53 50	99.00 33	99.90 20	99.99 46

TAPA-MVS	96.62 5	97.60 40	98.46 56	96.60 37	98.73 40	99.90 41	99.30 30	94.96 33	99.46 80	87.57 85	96.05 93	98.53 62	99.26 43	98.04 75	97.33 82	99.77 64	99.88 136
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
DeepPCF-MVS	97.16 4	97.58 41	99.72 15	95.07 55	98.45 43	99.96 7	93.83 131	95.93 29	100.00 1	90.79 53	98.38 63	99.85 36	95.28 121	99.94 2	99.97 1	96.15 217	99.97 78

PCF-MVS	97.20 3	97.49 42	98.20 64	96.66 36	97.62 49	99.92 33	98.93 41	96.64 21	98.53 122	88.31 83	94.04 114	99.58 48	98.94 55	97.53 89	97.79 69	99.54 132	99.97 78
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MSDG		97.29 43	97.55 79	97.00 31	98.66 41	99.71 71	99.03 38	96.15 28	99.59 67	89.67 72	92.77 126	94.86 93	98.75 64	98.22 65	97.94 62	99.72 103	99.76 156

CHOSEN 280x420		97.16 44	99.58 27	94.35 75	96.95 54	99.97 3	97.19 77	81.55 137	99.92 27	91.75 40	100.00 1	100.00 1	98.84 61	98.55 46	98.65 46	99.79 52	99.97 78

DELS-MVS		97.05 45	98.05 68	95.88 46	97.09 52	99.99 1	98.82 43	90.30 49	98.44 127	91.40 45	92.91 123	96.57 82	97.68 91	98.56 45	99.88 5	100.00 1	100.00 1
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
DeepC-MVS	96.33 6	97.05 45	97.59 78	96.42 38	97.37 50	99.92 33	99.10 36	96.54 24	99.34 91	86.64 92	91.93 130	93.15 105	99.11 52	99.11 28	99.68 11	99.73 99	99.97 78
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MVS_0304		97.04 47	98.72 49	95.08 54	96.32 58	99.90 41	99.15 34	89.61 58	99.89 29	87.22 90	95.47 96	98.22 72	98.22 75	98.63 41	98.90 35	99.93 15	100.00 1

MAR-MVS		97.03 48	98.00 70	95.89 44	99.32 27	99.74 70	96.76 87	84.89 99	99.97 6	94.86 23	98.29 65	90.58 112	99.67 18	98.02 77	99.50 15	99.82 37	99.92 117
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
MVSTER		97.00 49	98.85 44	94.83 63	92.71 96	97.43 143	99.03 38	85.52 92	99.82 44	92.74 35	99.15 40	99.94 32	99.19 46	98.66 37	96.99 97	99.79 52	99.98 65

OpenMVS	94.03 11	96.87 50	98.10 67	95.44 50	99.29 28	99.78 67	98.46 54	89.92 55	99.47 79	85.78 94	91.05 133	98.50 63	99.30 37	98.49 54	99.41 16	99.89 21	99.98 65

tfpn_ndepth		96.84 51	98.58 52	94.81 64	93.18 80	99.62 78	96.83 85	88.75 73	99.73 57	92.38 37	98.45 62	96.34 86	97.90 84	98.34 61	97.59 77	99.84 28	99.99 46

PatchMatch-RL		96.84 51	98.03 69	95.47 47	98.84 38	99.81 61	95.61 106	89.20 62	99.65 62	91.28 48	99.39 32	93.46 103	98.18 76	98.05 73	96.28 107	99.69 113	99.55 176

IS_MVSNet		96.66 53	98.62 51	94.38 72	92.41 106	99.70 72	97.19 77	87.67 86	99.05 103	91.27 49	95.09 101	98.46 67	97.95 83	98.64 39	99.37 17	99.79 52	100.00 1

tfpn1000		96.58 54	98.37 58	94.50 71	93.04 87	99.59 79	96.53 90	88.54 77	99.73 57	91.59 41	98.28 66	95.76 90	97.46 93	98.19 66	97.10 92	99.82 37	99.96 97

conf0.002		96.51 55	97.75 75	95.07 55	93.11 81	99.83 50	97.67 61	89.10 64	98.62 115	91.47 44	99.39 32	91.68 108	99.28 39	97.49 91	97.24 84	99.76 71	100.00 1

thresconf0.02		96.46 56	98.87 43	93.64 80	92.77 95	99.11 99	97.05 80	89.36 59	99.64 64	85.14 97	99.07 42	96.84 80	97.72 88	98.72 34	98.76 44	99.78 59	99.95 106

PMMVS		96.45 57	98.24 61	94.36 74	92.58 98	99.01 106	97.08 79	87.42 87	99.88 32	90.06 66	99.39 32	94.63 94	99.33 36	97.85 82	96.99 97	99.70 107	99.96 97

LS3D		96.44 58	97.31 84	95.41 51	97.06 53	99.87 48	99.51 25	97.48 1	99.57 68	79.00 119	95.39 97	89.19 118	99.81 9	98.55 46	98.84 38	99.62 121	99.78 154

diffmvs		96.35 59	98.76 48	93.54 82	92.41 106	99.55 81	97.22 76	83.75 112	99.57 68	89.64 73	96.86 80	98.33 68	98.37 71	98.42 55	98.61 47	99.88 24	99.99 46

EPP-MVSNet		96.29 60	98.34 60	93.90 77	91.77 117	99.38 91	95.45 111	87.25 89	99.38 87	91.36 47	94.86 108	98.49 65	97.83 86	98.01 78	98.23 55	99.75 83	99.99 46

DWT-MVSNet_training		96.26 61	98.44 57	93.72 79	92.58 98	99.34 93	96.15 95	83.00 120	99.76 53	93.63 30	97.89 74	99.46 51	97.23 97	94.43 151	98.19 56	99.70 107	100.00 1

conf0.01		96.20 62	97.19 88	95.05 57	93.11 81	99.83 50	97.67 61	89.06 65	98.62 115	91.38 46	99.19 39	89.09 119	99.28 39	97.48 92	96.10 111	99.76 71	100.00 1

UGNet		96.05 63	98.55 53	93.13 86	94.64 73	99.65 75	94.70 120	87.78 84	99.40 86	89.69 71	98.25 67	99.25 57	92.12 153	96.50 112	97.08 93	99.84 28	99.72 160
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
COLMAP_ROB	93.56 12	96.03 64	96.83 98	95.11 53	97.87 48	99.52 82	98.81 44	91.40 42	99.42 83	84.97 98	90.46 135	96.82 81	98.05 78	96.46 116	96.19 110	99.54 132	98.92 193
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
PVSNet_BlendedMVS		96.01 65	96.48 107	95.46 48	96.47 56	99.89 45	95.64 103	91.23 43	99.75 55	91.59 41	96.80 81	82.44 142	98.05 78	98.53 50	97.92 66	99.80 48	100.00 1

PVSNet_Blended		96.01 65	96.48 107	95.46 48	96.47 56	99.89 45	95.64 103	91.23 43	99.75 55	91.59 41	96.80 81	82.44 142	98.05 78	98.53 50	97.92 66	99.80 48	100.00 1

tfpn		95.93 67	97.06 91	94.62 68	92.94 94	99.81 61	97.25 75	88.71 76	98.32 134	89.98 67	98.79 57	88.55 121	99.11 52	97.26 105	96.71 99	99.75 83	99.98 65

thres100view900		95.86 68	96.62 100	94.97 58	93.10 83	99.83 50	97.76 60	89.15 63	98.62 115	90.69 55	99.00 44	84.86 130	99.30 37	97.57 88	96.48 101	99.81 45	100.00 1

RPSCF		95.86 68	96.94 97	94.61 69	96.52 55	98.67 120	98.54 50	88.43 80	99.56 70	90.51 63	99.39 32	98.70 60	97.72 88	93.77 164	92.00 172	95.93 218	96.50 213

canonicalmvs		95.80 70	97.02 92	94.37 73	92.96 90	99.47 86	97.49 68	84.58 101	99.44 81	92.05 38	98.54 61	86.65 125	99.37 34	96.18 123	98.93 34	99.77 64	99.92 117

tfpnview11		95.78 71	98.17 66	93.01 90	92.58 98	99.04 105	96.64 88	88.72 75	99.63 66	83.08 106	98.90 49	94.24 98	97.25 96	98.35 60	97.21 85	99.77 64	99.80 153

conf200view11		95.78 71	96.54 103	94.89 59	93.10 83	99.82 54	97.67 61	88.85 68	98.62 115	90.69 55	99.00 44	84.86 130	99.28 39	97.41 96	96.10 111	99.76 71	99.99 46

tfpn200view9		95.78 71	96.54 103	94.89 59	93.10 83	99.82 54	97.67 61	88.85 68	98.62 115	90.69 55	99.00 44	84.86 130	99.28 39	97.41 96	96.10 111	99.76 71	99.99 46

thres200		95.77 74	96.55 102	94.86 61	93.09 86	99.82 54	97.63 66	88.85 68	98.49 123	90.66 58	98.99 47	84.86 130	99.20 44	97.41 96	96.28 107	99.76 71	100.00 1

tfpn_n400		95.76 75	98.21 62	92.90 92	92.57 102	99.05 103	96.42 91	88.50 78	99.49 74	83.08 106	98.90 49	94.24 98	97.07 98	98.10 71	97.93 64	99.74 87	99.76 156

tfpnconf		95.76 75	98.21 62	92.90 92	92.57 102	99.05 103	96.42 91	88.50 78	99.49 74	83.08 106	98.90 49	94.24 98	97.07 98	98.10 71	97.93 64	99.74 87	99.76 156

MVS_Test		95.74 77	98.18 65	92.90 92	92.16 110	99.49 85	97.36 74	84.30 106	99.79 48	84.94 99	96.65 85	93.63 102	98.85 60	98.61 43	99.10 30	99.81 45	100.00 1

thres400		95.72 78	96.48 107	94.84 62	93.00 89	99.83 50	97.55 67	88.93 66	98.49 123	90.61 59	98.86 52	84.63 134	99.20 44	97.45 93	96.10 111	99.77 64	99.99 46

view600		95.64 79	96.38 110	94.79 65	92.96 90	99.82 54	97.48 71	88.85 68	98.38 128	90.52 61	98.84 54	84.61 135	99.15 48	97.41 96	95.60 127	99.76 71	99.99 46

thres600view7		95.64 79	96.38 110	94.79 65	92.96 90	99.82 54	97.48 71	88.85 68	98.38 128	90.52 61	98.84 54	84.61 135	99.15 48	97.41 96	95.60 127	99.76 71	99.99 46

view800		95.62 81	96.38 110	94.73 67	92.96 90	99.81 61	97.38 73	88.75 73	98.35 133	90.43 64	98.81 56	84.54 137	99.13 51	97.35 101	95.82 122	99.76 71	99.98 65

Vis-MVSNet (Re-imp)		95.60 82	98.52 55	92.19 98	92.37 108	99.56 80	96.37 93	87.41 88	98.95 106	84.77 101	94.88 107	98.48 66	92.44 150	98.63 41	99.37 17	99.76 71	99.77 155

FMVSNet3		95.59 83	97.51 80	93.34 84	89.48 137	96.57 154	97.67 61	84.17 107	99.48 76	89.76 68	95.09 101	94.35 95	99.14 50	98.37 58	98.86 37	99.82 37	99.89 133

PVSNet_Blended_VisFu		95.37 84	97.44 82	92.95 91	95.20 65	99.80 65	92.68 138	88.41 81	99.12 98	87.64 84	88.31 143	99.10 58	94.07 136	98.27 63	97.51 79	99.73 99	100.00 1

DI_MVS_plusplus_trai		95.29 85	97.02 92	93.28 85	91.76 118	99.52 82	97.84 58	85.67 91	99.08 102	87.29 88	87.76 146	97.46 78	97.31 95	97.83 83	97.48 80	99.83 33	100.00 1

TSAR-MVS + COLMAP		95.20 86	95.03 128	95.41 51	96.17 59	98.69 119	99.11 35	93.40 36	99.97 6	84.89 100	98.23 69	75.01 161	99.34 35	97.27 104	96.37 106	99.58 126	99.64 166

GBi-Net		95.19 87	96.99 95	93.09 88	89.11 138	96.47 156	96.90 82	84.17 107	99.48 76	89.76 68	95.09 101	94.35 95	98.87 57	96.50 112	97.21 85	99.74 87	99.81 149

test1		95.19 87	96.99 95	93.09 88	89.11 138	96.47 156	96.90 82	84.17 107	99.48 76	89.76 68	95.09 101	94.35 95	98.87 57	96.50 112	97.21 85	99.74 87	99.81 149

test0.0.03 1		95.15 89	97.87 74	91.99 99	91.69 120	98.82 116	93.04 136	83.60 113	99.65 62	88.80 78	94.15 113	97.67 76	94.97 123	96.62 111	98.16 57	99.83 33	100.00 1

EPNet_dtu		95.10 90	98.81 46	90.78 104	98.38 45	98.47 122	96.54 89	89.36 59	99.78 50	65.65 186	99.31 36	98.24 71	94.79 126	98.28 62	99.35 20	99.93 15	98.27 198
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
UA-Net		94.95 91	98.66 50	90.63 106	94.60 75	98.94 112	96.03 97	85.28 94	98.01 140	78.92 120	97.42 78	99.96 29	89.09 194	98.95 30	98.80 41	99.82 37	98.57 195

CANet_DTU		94.90 92	98.98 39	90.13 112	94.74 71	99.81 61	98.53 51	82.23 128	99.97 6	66.76 174	100.00 1	98.50 63	98.74 65	97.52 90	97.19 91	99.76 71	99.88 136

FC-MVSNet-train		94.61 93	96.27 114	92.68 96	92.35 109	97.14 146	93.45 135	87.73 85	98.93 107	87.31 87	96.42 87	89.35 116	95.67 116	96.06 129	96.01 117	99.56 129	99.98 65

CLD-MVS		94.53 94	94.45 135	94.61 69	93.85 78	98.36 124	98.12 56	89.68 56	99.35 90	89.62 74	95.19 99	77.08 151	96.66 108	95.51 134	95.67 124	99.74 87	100.00 1
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
conf0.05thres1000		94.50 95	95.70 122	93.11 87	92.68 97	99.67 74	96.04 96	87.81 83	97.52 145	83.71 102	96.20 91	84.52 138	98.73 66	96.39 118	95.66 125	99.71 105	99.92 117

FMVSNet2		94.48 96	95.95 119	92.77 95	89.11 138	96.47 156	96.90 82	83.38 115	99.11 99	88.64 79	87.50 151	92.26 107	98.87 57	97.91 80	98.60 48	99.74 87	99.81 149

HQP-MVS		94.48 96	95.39 126	93.42 83	95.10 66	98.35 125	98.19 55	91.41 41	99.77 51	79.79 116	99.30 37	77.08 151	96.25 111	96.93 106	96.28 107	99.76 71	99.99 46

MDTV_nov1_ep13		94.32 98	98.77 47	89.14 121	91.70 119	99.52 82	95.21 113	72.09 198	99.80 47	78.91 121	96.32 88	99.62 46	97.71 90	98.39 57	97.71 75	99.22 200	100.00 1

CDS-MVSNet		94.32 98	97.00 94	91.19 103	89.82 135	98.71 118	95.51 108	85.14 98	96.85 149	82.33 111	92.48 127	96.40 85	94.71 127	96.86 108	97.76 71	99.63 119	99.92 117
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
dps		94.29 100	97.33 83	90.75 105	92.02 113	99.21 96	94.31 125	66.97 206	99.50 73	95.61 18	96.22 90	98.64 61	96.08 112	93.71 166	94.03 148	99.52 136	99.98 65

ACMM	94.44 10	94.26 101	94.62 132	93.84 78	94.86 70	97.73 138	93.48 134	90.76 46	99.27 93	87.46 86	99.04 43	76.60 154	96.76 106	96.37 119	93.76 151	99.74 87	99.55 176
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMP	94.49 9	94.19 102	94.74 131	93.56 81	94.25 76	98.32 127	96.02 98	89.35 61	98.90 110	87.28 89	99.14 41	76.41 157	94.94 124	96.07 128	94.35 145	99.49 143	99.99 46
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
EPMVS		94.08 103	98.54 54	88.87 123	92.51 104	99.47 86	94.18 127	66.53 207	99.68 61	82.40 110	95.24 98	99.40 55	97.86 85	98.12 69	97.99 60	99.75 83	99.88 136

test-LLR		93.71 104	97.23 86	89.60 116	91.69 120	99.10 100	94.68 122	83.60 113	99.36 88	71.94 144	93.82 116	96.51 83	95.96 114	97.42 94	94.37 142	99.74 87	99.99 46

CHOSEN 1792x2688		93.69 105	94.89 130	92.28 97	96.17 59	99.84 49	95.69 102	83.17 118	98.54 121	82.04 112	77.58 201	91.15 110	96.90 101	98.36 59	98.82 40	99.73 99	99.98 65

LGP-MVS_train		93.60 106	95.05 127	91.90 100	94.90 69	98.29 128	97.93 57	88.06 82	99.14 97	74.83 134	99.26 38	76.50 155	96.07 113	96.31 121	95.90 121	99.59 124	99.97 78

FMVSNet5		93.53 107	96.09 118	90.56 108	86.74 151	92.84 203	92.64 139	77.50 164	99.41 85	88.97 77	98.02 72	97.81 74	98.00 81	94.85 145	95.43 129	99.50 142	94.25 218

OPM-MVS		93.50 108	93.00 145	94.07 76	95.82 62	98.26 129	98.49 53	91.62 40	94.69 171	81.93 113	92.82 125	76.18 159	96.82 103	96.12 125	94.57 136	99.74 87	98.39 196

CostFormer		93.50 108	96.50 106	90.00 113	91.69 120	98.65 121	93.88 130	67.64 203	98.97 104	89.16 76	97.79 75	88.92 120	97.97 82	95.14 142	96.06 115	99.63 119	100.00 1

IterMVS-LS		93.50 108	96.22 115	90.33 111	90.93 125	95.50 185	94.83 118	80.54 141	98.92 108	79.11 118	90.64 134	93.70 101	96.79 104	96.93 106	97.85 68	99.78 59	99.99 46
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PatchmatchNet		93.48 111	98.84 45	87.22 140	91.93 114	99.39 90	92.55 140	66.06 211	99.71 59	75.61 131	98.24 68	99.59 47	97.35 94	97.87 81	97.64 76	99.83 33	99.43 181
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MS-PatchMatch		93.46 112	95.91 121	90.61 107	95.48 63	99.31 94	95.62 105	77.23 166	99.42 83	81.88 114	88.92 140	96.06 88	93.80 138	96.45 117	93.11 160	99.65 117	98.10 202

tpm cat1		93.29 113	96.53 105	89.50 118	91.84 115	99.18 98	94.70 120	67.70 202	98.38 128	86.67 91	89.16 138	99.38 56	96.66 108	94.33 152	95.30 130	99.43 162	100.00 1

Effi-MVS+-dtu		93.13 114	97.13 89	88.47 131	88.86 144	99.19 97	96.79 86	79.08 154	99.64 64	70.01 154	97.51 77	89.38 115	96.53 110	97.60 86	96.55 100	99.57 127	100.00 1

HyFIR lowres test		93.13 114	94.48 134	91.56 101	96.12 61	99.68 73	93.52 133	79.98 145	97.24 146	81.73 115	72.66 212	95.74 91	98.29 74	98.27 63	97.79 69	99.70 107	100.00 1

Vis-MVSNet		93.08 116	96.76 99	88.78 127	91.14 124	99.63 77	94.85 117	83.34 116	97.19 147	74.78 135	91.92 131	93.15 105	88.81 197	97.59 87	98.35 53	99.78 59	99.49 180
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
Effi-MVS+		93.06 117	95.94 120	89.70 115	90.82 126	99.45 88	95.71 101	78.94 156	98.72 111	74.71 136	97.92 73	80.73 146	98.35 72	97.72 84	97.05 96	99.70 107	100.00 1

tpmp4_e23		92.95 118	96.28 113	89.06 122	91.80 116	98.81 117	94.95 116	67.56 205	99.21 94	82.97 109	96.54 86	88.52 122	97.47 92	94.47 150	96.42 104	99.61 122	100.00 1

ADS-MVSNet		92.91 119	97.97 71	87.01 142	92.07 112	99.27 95	92.70 137	65.39 216	99.85 39	75.40 132	94.93 106	98.26 69	96.86 102	96.09 126	97.52 78	99.65 117	99.84 145

TESTMET0.1,1		92.87 120	97.23 86	87.79 137	86.96 150	99.10 100	94.68 122	77.46 165	99.36 88	71.94 144	93.82 116	96.51 83	95.96 114	97.42 94	94.37 142	99.74 87	99.99 46

FC-MVSNet-test		92.78 121	96.19 117	88.80 126	88.00 147	97.54 140	93.60 132	82.36 127	98.16 135	79.71 117	91.55 132	95.41 92	89.65 189	96.09 126	95.23 131	99.49 143	99.31 184

Fast-Effi-MVS+-dtu		92.73 122	97.62 77	87.02 141	88.91 142	98.83 115	95.79 99	73.98 185	99.89 29	68.62 159	97.73 76	93.30 104	95.21 122	97.67 85	95.96 118	99.59 124	100.00 1

IB-MVS	90.59 15	92.70 123	95.70 122	89.21 120	94.62 74	99.45 88	83.77 206	88.92 67	99.53 71	92.82 34	98.86 52	86.08 127	75.24 218	92.81 182	93.17 158	99.89 21	100.00 1
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
test-mter		92.67 124	97.13 89	87.47 139	86.72 152	99.07 102	94.28 126	76.90 169	99.21 94	71.53 148	93.63 118	96.32 87	95.67 116	97.32 102	94.36 144	99.74 87	99.99 46

RPMNet		92.64 125	97.88 73	86.53 147	90.79 127	98.95 110	95.13 114	64.44 220	99.09 100	72.36 140	93.58 119	99.01 59	96.74 107	98.05 73	96.45 103	99.71 105	100.00 1

FMVSNet1		92.55 126	93.66 139	91.26 102	87.91 148	96.12 163	94.75 119	81.69 136	97.67 142	85.63 95	80.56 176	87.88 124	98.15 77	96.50 112	97.21 85	99.41 180	99.71 161

tpmrst		92.52 127	97.45 81	86.77 145	92.15 111	99.36 92	92.53 141	65.95 212	99.53 71	72.50 139	92.22 128	99.83 37	97.81 87	95.18 141	96.05 116	99.69 113	100.00 1

testgi		92.47 128	95.68 124	88.73 128	90.68 128	98.35 125	91.67 148	79.50 150	98.96 105	77.12 127	95.17 100	85.84 128	93.95 137	95.75 132	96.47 102	99.45 156	99.21 187

TAMVS		92.43 129	94.21 137	90.35 110	88.68 145	98.85 114	94.15 128	81.53 138	95.58 158	83.61 104	87.05 152	86.45 126	94.71 127	96.27 122	95.91 119	99.42 168	99.38 183

CR-MVSNet		92.32 130	97.97 71	85.74 159	90.63 130	98.95 110	95.46 109	65.50 214	99.09 100	67.51 165	94.20 111	98.18 73	95.59 119	98.16 67	97.20 89	99.74 87	100.00 1

CVMVSNet		92.13 131	95.40 125	88.32 134	91.29 123	97.29 145	91.85 145	86.42 90	96.71 151	71.84 146	89.56 137	91.18 109	88.98 196	96.17 124	97.76 71	99.51 140	99.14 189

Fast-Effi-MVS+		92.11 132	94.33 136	89.52 117	89.06 141	99.00 107	95.13 114	76.72 171	98.59 120	78.21 125	89.99 136	77.35 150	98.34 73	97.97 79	97.44 81	99.67 115	99.96 97

ACMH+	92.61 13	91.80 133	93.03 143	90.37 109	93.03 88	98.17 130	94.00 129	84.13 110	98.12 137	77.39 126	91.95 129	74.62 162	94.36 133	94.62 149	93.82 150	99.32 190	99.87 140

IterMVS		91.65 134	96.62 100	85.85 156	90.27 133	95.80 175	95.32 112	74.15 181	98.91 109	60.95 203	88.79 142	97.76 75	94.69 129	98.04 75	97.07 94	99.73 99	100.00 1
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ACMH	92.34 14	91.59 135	93.02 144	89.92 114	93.97 77	97.98 134	90.10 176	84.70 100	98.46 125	76.80 128	93.38 121	71.94 176	94.39 131	95.34 138	94.04 147	99.54 132	100.00 1
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs4		91.41 136	93.05 142	89.49 119	85.85 158	96.52 155	91.70 147	82.49 122	98.14 136	83.17 105	87.57 148	81.76 145	94.39 131	95.47 135	92.62 166	99.33 189	99.29 185

testpf		91.26 137	97.28 85	84.23 182	89.52 136	97.45 142	88.08 195	56.08 229	99.76 53	78.71 122	95.06 105	98.26 69	93.44 142	94.72 147	95.69 123	99.57 127	99.99 46

PatchT		91.06 138	97.66 76	83.36 195	90.32 132	98.96 109	82.30 211	64.72 219	98.45 126	67.51 165	93.28 122	97.60 77	95.59 119	98.16 67	97.20 89	99.70 107	100.00 1

MIMVSNet		91.01 139	96.22 115	84.93 170	85.24 168	98.09 131	90.40 163	64.96 218	97.55 144	72.65 137	96.23 89	90.81 111	96.79 104	96.69 109	97.06 95	99.52 136	97.09 209

UniMVSNet_NR-MVSNet		90.50 140	92.31 147	88.38 132	85.04 173	96.34 159	90.94 150	85.32 93	95.87 157	75.69 129	87.68 147	78.49 147	93.78 139	93.21 175	94.60 135	99.53 135	99.97 78

UniMVSNet (Re)		90.41 141	91.96 149	88.59 130	85.71 159	96.73 151	90.82 153	84.11 111	95.23 164	78.54 123	88.91 141	76.41 157	92.84 147	93.40 173	93.05 161	99.55 131	100.00 1

GA-MVS		90.38 142	94.59 133	85.46 164	88.30 146	98.44 123	92.18 142	83.30 117	97.89 141	58.05 210	92.86 124	84.25 140	91.27 178	96.65 110	92.61 167	99.66 116	99.43 181

USDC		90.36 143	91.68 151	88.82 125	92.58 98	98.02 132	96.27 94	79.83 146	98.37 131	70.61 153	89.05 139	67.50 208	94.17 134	95.77 131	94.43 140	99.46 153	98.62 194

TinyColmap		89.94 144	90.88 157	88.84 124	92.43 105	97.91 136	95.59 107	80.10 144	98.12 137	71.33 150	84.56 153	67.46 209	94.15 135	95.57 133	94.27 146	99.43 162	98.26 199

pm-mvs1		89.68 145	92.00 148	86.96 143	86.23 156	96.62 153	90.36 165	83.05 119	93.97 183	72.15 143	81.77 171	82.10 144	90.69 184	95.38 137	94.50 138	99.29 194	99.65 163

tpm		89.60 146	94.93 129	83.39 193	89.94 134	97.11 147	90.09 177	65.28 217	98.67 113	60.03 207	96.79 83	84.38 139	95.66 118	91.90 187	95.65 126	99.32 190	99.98 65

NR-MVSNet		89.52 147	90.71 158	88.14 136	86.19 157	96.20 160	92.07 143	84.58 101	95.54 159	75.27 133	87.52 149	67.96 207	91.24 180	94.33 152	93.45 155	99.49 143	99.97 78

DU-MVS		89.49 148	90.60 159	88.19 135	84.71 187	96.20 160	90.94 150	84.58 101	95.54 159	75.69 129	87.52 149	68.74 206	93.78 139	91.10 205	95.13 132	99.47 150	99.97 78

Baseline_NR-MVSNet		89.13 149	89.53 173	88.66 129	84.71 187	94.43 196	91.79 146	84.49 104	95.54 159	78.28 124	78.52 198	72.46 175	93.29 144	91.10 205	94.82 134	99.42 168	99.86 143

tfpnnormal		89.09 150	89.71 166	88.38 132	87.37 149	96.78 150	91.46 149	85.20 96	90.33 213	72.35 141	83.45 157	69.30 204	94.45 130	95.29 139	92.86 163	99.44 161	99.93 113

TranMVSNet+NR-MVSNet		88.88 151	89.90 164	87.69 138	84.06 199	95.68 177	91.88 144	85.23 95	95.16 165	72.54 138	83.06 160	70.14 198	92.93 146	90.81 208	94.53 137	99.48 147	99.89 133

WR-MVS_H		88.47 152	90.55 160	86.04 150	85.13 170	96.07 168	89.86 185	79.80 147	94.37 180	72.32 142	83.12 159	74.44 165	89.60 190	93.52 170	92.40 168	99.51 140	99.96 97

SixPastTwentyTwo		88.35 153	91.51 153	84.66 174	85.39 164	96.96 148	86.57 199	79.62 149	96.57 152	63.73 194	87.86 145	75.18 160	93.43 143	94.03 156	90.37 204	99.24 199	99.58 171

TransMVSNet (Re)		88.33 154	89.55 172	86.91 144	86.65 153	95.56 182	90.48 159	84.44 105	92.02 212	71.07 152	80.13 178	72.48 174	89.41 191	95.05 144	94.44 139	99.39 182	97.14 208

LP		88.31 155	93.18 141	82.63 198	90.66 129	97.98 134	87.32 198	63.49 223	97.17 148	63.02 197	82.08 163	90.47 113	91.92 155	92.75 183	93.42 156	99.38 184	98.37 197

MVS-HIRNet		88.27 156	94.05 138	81.51 202	88.90 143	98.93 113	83.38 209	60.52 228	98.06 139	63.78 193	80.67 175	90.36 114	92.94 145	97.29 103	96.41 105	99.56 129	96.66 211

WR-MVS		88.23 157	90.15 162	86.00 152	84.39 194	95.64 178	89.96 181	81.80 133	94.46 178	71.60 147	82.10 162	74.36 166	88.76 198	92.48 184	92.20 170	99.46 153	99.83 147

CP-MVSNet		88.09 158	89.57 170	86.36 148	84.63 190	95.46 187	89.48 187	80.53 142	93.42 198	71.26 151	81.25 173	69.90 199	92.78 148	93.30 174	93.69 152	99.47 150	99.96 97

anonymousdsp		87.98 159	92.38 146	82.85 196	83.68 203	96.79 149	90.78 154	74.06 184	95.29 163	57.91 211	83.33 158	83.12 141	91.15 182	95.96 130	92.37 169	99.52 136	99.76 156

v6		87.96 160	89.58 169	86.08 149	85.34 165	96.14 162	90.44 160	82.19 129	94.56 172	67.43 169	81.90 166	71.57 181	91.62 165	91.54 192	91.43 185	99.43 162	99.92 117

v1neww		87.88 161	89.51 175	85.97 154	85.32 166	96.12 163	90.33 167	82.17 130	94.51 173	66.96 171	81.84 168	71.21 184	91.64 162	91.52 194	91.43 185	99.42 168	99.92 117

v7new		87.88 161	89.51 175	85.97 154	85.32 166	96.12 163	90.33 167	82.17 130	94.51 173	66.96 171	81.84 168	71.21 184	91.64 162	91.52 194	91.43 185	99.42 168	99.92 117

LTVRE_ROB	88.65 16	87.87 163	91.11 156	84.10 185	86.64 154	97.47 141	94.40 124	78.41 160	96.13 155	52.02 218	87.95 144	65.92 214	93.59 141	95.29 139	95.09 133	99.52 136	99.95 106
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
V42		87.84 164	89.42 177	85.99 153	85.16 169	96.01 171	90.52 156	81.78 135	94.43 179	67.59 163	81.32 172	71.87 177	91.48 169	91.25 204	91.16 199	99.43 162	99.92 117

TDRefinement		87.79 165	88.76 191	86.66 146	93.54 79	98.02 132	95.76 100	85.18 97	96.57 152	67.90 160	80.51 177	66.51 213	78.37 215	93.20 176	89.73 208	99.22 200	96.75 210

MDTV_nov1_ep13_2view		87.75 166	93.32 140	81.26 204	83.74 202	96.64 152	85.66 202	66.20 210	98.36 132	61.61 201	84.34 155	87.95 123	91.12 183	94.01 157	92.66 165	99.22 200	99.27 186

v7		87.72 167	89.75 165	85.35 166	85.01 174	95.79 176	90.43 162	78.98 155	94.50 176	66.39 177	78.87 192	73.65 169	91.85 158	93.69 167	91.86 176	99.45 156	99.92 117

v8		87.54 168	89.33 178	85.45 165	85.41 163	95.50 185	90.32 170	78.94 156	94.35 181	66.93 173	81.90 166	70.99 189	91.62 165	91.49 197	91.22 196	99.48 147	99.87 140

v1144		87.49 169	89.64 167	84.97 169	84.73 186	95.84 174	90.17 175	79.30 151	93.96 184	64.65 191	78.83 194	73.38 171	91.51 168	93.77 164	91.77 177	99.45 156	99.93 113

v1		87.48 170	88.91 186	85.81 157	84.93 177	96.07 168	90.33 167	82.45 125	93.65 193	66.39 177	79.38 189	70.40 195	91.33 175	91.58 191	91.38 191	99.42 168	99.93 113

divwei89l23v2f112		87.46 171	88.97 183	85.70 161	84.85 182	96.08 166	90.23 173	82.46 123	93.69 192	65.83 184	79.57 186	70.54 192	91.39 174	91.60 190	91.39 189	99.43 162	99.92 117

v2v482		87.46 171	88.90 187	85.78 158	84.58 191	95.95 173	89.90 184	82.43 126	94.19 182	65.65 186	79.80 182	69.12 205	92.67 149	91.88 188	91.46 183	99.45 156	99.93 113

v1141		87.45 173	88.98 182	85.67 162	84.86 181	96.08 166	90.23 173	82.46 123	93.75 188	65.64 188	79.57 186	70.52 193	91.41 173	91.63 189	91.39 189	99.42 168	99.92 117

v10		87.40 174	89.62 168	84.80 172	84.93 177	95.07 193	90.44 160	75.63 175	94.51 173	66.52 175	78.87 192	73.47 170	91.86 157	93.69 167	91.87 175	99.45 156	99.86 143

pmmvs5		87.33 175	90.01 163	84.20 183	84.31 196	96.04 170	87.63 196	76.59 172	93.17 203	65.35 190	84.30 156	71.68 178	91.91 156	95.41 136	91.37 192	99.39 182	98.13 200

N_pmnet		87.31 176	91.51 153	82.41 201	85.13 170	95.57 181	80.59 213	81.79 134	96.20 154	58.52 209	78.62 196	85.66 129	89.36 192	94.64 148	92.14 171	99.08 205	97.72 207

PS-CasMVS		87.24 177	88.52 194	85.73 160	84.58 191	95.35 189	89.03 190	80.17 143	93.11 204	68.86 158	77.71 200	66.89 210	92.30 151	93.13 178	93.50 154	99.46 153	99.96 97

EU-MVSNet		87.20 178	90.47 161	83.38 194	85.11 172	93.85 201	86.10 201	79.76 148	93.30 202	65.39 189	84.41 154	78.43 148	85.04 208	92.20 186	93.03 162	98.86 207	98.05 203

PEN-MVS		87.20 178	88.22 198	86.01 151	84.01 201	94.93 195	90.00 179	81.52 140	93.46 197	69.29 156	79.69 184	65.51 215	91.72 159	91.01 207	93.12 159	99.49 143	99.84 145

v16		87.15 180	89.13 179	84.83 171	85.55 161	91.94 207	90.50 157	74.13 183	95.06 166	67.72 162	81.84 168	72.55 173	91.65 161	91.50 196	91.42 188	99.42 168	99.60 168

v18		87.14 181	88.96 184	85.01 168	85.57 160	92.03 205	90.89 152	74.62 179	94.80 170	67.90 160	82.02 164	71.28 183	91.63 164	91.53 193	91.44 184	99.47 150	99.60 168

v17		86.99 182	88.90 187	84.76 173	85.52 162	91.96 206	90.50 157	74.17 180	94.88 168	67.33 170	81.94 165	71.21 184	91.57 167	91.49 197	91.20 197	99.48 147	99.60 168

EG-PatchMatch MVS		86.96 183	89.56 171	83.93 189	86.29 155	97.61 139	90.75 155	73.31 190	95.43 162	66.08 182	75.88 209	71.31 182	87.55 203	94.79 146	92.74 164	99.61 122	99.13 190

v1192		86.93 184	89.01 180	84.50 175	84.46 193	95.51 184	89.93 183	78.65 158	93.75 188	62.29 199	77.19 202	70.88 190	92.28 152	93.84 161	91.96 173	99.38 184	99.90 129

v1921920		86.81 185	88.93 185	84.33 180	84.23 197	95.41 188	90.09 177	78.10 161	93.74 190	62.17 200	76.98 204	71.14 187	92.05 154	93.69 167	91.69 180	99.32 190	99.88 136

v144192		86.80 186	88.90 187	84.35 177	84.33 195	95.56 182	89.34 188	77.74 163	93.60 194	64.03 192	77.82 199	70.76 191	91.28 177	92.91 181	91.74 179	99.37 186	99.90 129

v11		86.74 187	89.01 180	84.09 187	84.79 184	91.79 212	90.39 164	72.53 197	94.47 177	65.75 185	78.64 195	72.96 172	91.66 160	93.92 159	91.69 180	99.42 168	99.61 167

DTE-MVSNet		86.70 188	87.66 206	85.58 163	83.30 204	94.29 197	89.74 186	81.53 138	92.77 206	68.93 157	80.13 178	64.00 218	90.62 185	89.45 212	93.34 157	99.32 190	99.67 162

gg-mvs-nofinetune		86.69 189	91.30 155	81.30 203	90.42 131	99.64 76	98.50 52	61.68 225	79.23 226	40.35 229	66.58 220	97.14 79	96.92 100	98.64 39	97.94 62	99.91 19	99.97 78

v148		86.63 190	87.79 202	85.28 167	84.65 189	95.97 172	86.46 200	82.84 121	92.91 205	71.52 149	78.99 191	66.74 212	86.83 205	89.28 213	90.69 202	99.41 180	99.94 110

V14		86.54 191	88.41 195	84.35 177	84.94 176	91.83 209	90.28 172	73.48 188	93.73 191	66.50 176	79.89 181	71.12 188	91.46 170	91.48 199	91.25 194	99.42 168	99.58 171

v15		86.50 192	88.32 196	84.37 176	85.00 175	91.86 208	90.30 171	73.76 186	93.90 186	66.28 180	79.78 183	70.37 196	91.45 171	91.48 199	91.27 193	99.43 162	99.58 171

V9		86.42 193	88.26 197	84.27 181	84.88 179	91.80 210	90.34 166	73.18 192	93.92 185	66.37 179	79.68 185	70.25 197	91.42 172	91.43 201	91.23 195	99.42 168	99.55 176

v12		86.32 194	88.22 198	84.10 185	84.76 185	91.80 210	89.94 182	72.97 194	93.85 187	66.18 181	79.98 180	69.72 203	91.33 175	91.40 202	91.20 197	99.42 168	99.56 175

v13		86.27 195	88.16 200	84.06 188	84.85 182	91.77 213	90.00 179	72.77 196	93.56 195	66.06 183	79.25 190	70.50 194	91.25 179	91.35 203	91.15 200	99.42 168	99.55 176

v1240		86.24 196	88.56 193	83.54 190	84.05 200	95.21 192	89.27 189	76.76 170	93.42 198	60.68 206	75.99 208	69.80 201	91.21 181	93.83 163	91.76 178	99.29 194	99.91 128

v52		85.80 197	87.74 203	83.53 191	82.87 207	95.31 191	88.71 191	77.04 168	92.23 209	63.53 195	76.91 205	69.80 201	89.78 187	90.05 210	90.07 206	99.26 198	99.82 148

V4		85.78 198	87.74 203	83.50 192	82.90 206	95.33 190	88.62 192	77.05 167	92.14 211	63.45 196	76.91 205	69.85 200	89.72 188	90.07 209	90.05 207	99.27 197	99.81 149

pmmvs6		85.75 199	86.97 207	84.34 179	84.88 179	95.59 180	87.41 197	79.19 153	87.81 219	67.56 164	63.05 223	77.76 149	89.15 193	93.45 172	91.90 174	97.83 214	99.21 187

v7n		85.39 200	87.70 205	82.70 197	82.77 209	95.64 178	88.27 194	74.83 177	92.30 208	62.58 198	76.37 207	64.80 217	88.38 200	94.29 154	90.61 203	99.34 187	99.87 140

gm-plane-assit		84.93 201	91.61 152	77.14 211	84.14 198	91.29 215	66.18 226	69.70 200	85.22 222	47.95 224	78.58 197	89.24 117	94.90 125	98.82 33	98.12 59	99.99 5	100.00 1

CMPMVS	65.66 17	84.62 202	85.02 210	84.15 184	95.40 64	97.79 137	88.35 193	79.22 152	89.66 217	60.71 205	72.20 213	73.94 167	87.32 204	86.73 217	84.55 222	93.90 224	90.31 222
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
v748		84.47 203	86.06 208	82.62 199	82.85 208	95.02 194	83.73 207	78.48 159	90.20 215	67.45 168	75.86 210	61.27 220	83.84 209	89.87 211	90.28 205	99.34 187	99.90 129

Anonymous20231206		84.28 204	89.53 173	78.17 208	82.31 211	94.16 199	82.57 210	76.51 173	93.38 201	52.98 216	79.47 188	73.74 168	75.45 217	95.07 143	94.41 141	99.18 203	96.46 214

new_pmnet		84.12 205	87.89 201	79.72 206	80.43 212	94.14 200	80.26 214	74.14 182	96.01 156	56.30 215	74.94 211	76.45 156	88.59 199	93.11 179	89.31 209	98.59 210	91.27 221

test20.03		83.86 206	88.73 192	78.16 209	82.60 210	93.00 202	81.61 212	74.68 178	92.36 207	57.50 212	83.01 161	74.48 164	73.30 221	92.40 185	91.14 201	99.29 194	94.75 217

test2356		83.84 207	91.77 150	74.59 215	78.71 214	89.10 219	78.24 218	72.07 199	96.78 150	45.18 227	96.19 92	76.77 153	74.87 219	93.17 177	94.01 149	98.44 211	96.38 215

pmmvs-eth3d		82.92 208	83.31 213	82.47 200	76.97 216	91.76 214	83.79 205	76.10 174	90.33 213	69.95 155	71.04 216	48.09 224	89.02 195	93.85 160	89.14 210	99.02 206	98.96 192

PM-MVS		82.79 209	84.51 211	80.77 205	77.22 215	92.13 204	83.61 208	73.31 190	93.50 196	61.06 202	77.15 203	46.52 227	90.55 186	94.14 155	89.05 212	98.85 208	99.12 191

testus		82.22 210	88.82 190	74.52 216	79.14 213	89.37 218	78.38 216	72.99 193	97.57 143	44.54 228	93.44 120	58.13 222	74.20 220	92.96 180	93.67 153	97.89 213	96.58 212

pmmvs3		80.91 211	85.62 209	75.42 213	75.01 218	89.09 220	75.31 220	68.70 201	86.99 220	46.74 226	81.18 174	62.91 219	87.95 201	93.84 161	89.06 211	98.80 209	96.23 216

MIMVSNet1		80.64 212	83.97 212	76.76 212	68.91 227	91.15 217	78.32 217	75.47 176	89.58 218	56.64 214	65.10 221	65.17 216	82.14 210	93.51 171	91.64 182	99.10 204	91.66 220

MDA-MVSNet-bldmvs		80.30 213	82.83 214	77.34 210	69.16 226	94.29 197	72.16 221	81.97 132	90.14 216	57.32 213	94.01 115	47.97 225	86.81 206	68.74 228	86.82 218	96.63 216	97.86 205

new-patchmatchnet		78.17 214	80.82 215	75.07 214	76.93 217	91.20 216	71.90 222	73.32 189	86.59 221	48.91 221	67.11 219	47.85 226	81.19 211	88.18 214	87.02 217	98.19 212	97.79 206

Anonymous20231211		74.10 215	74.22 223	73.97 217	74.36 219	87.76 221	75.92 219	72.78 195	74.83 231	52.25 217	44.18 230	42.42 230	73.07 222	86.16 218	86.24 220	95.44 222	97.94 204

FPMVS		73.80 216	74.62 221	72.84 218	83.09 205	84.44 223	83.89 204	73.64 187	92.20 210	48.50 222	72.19 214	59.51 221	63.16 224	69.13 227	66.26 232	84.74 229	78.59 232

1111		73.79 217	78.62 217	68.16 220	69.34 224	81.48 225	59.42 230	52.46 231	78.55 227	50.42 219	62.43 224	71.67 179	80.43 213	86.79 215	88.22 213	96.87 215	81.17 231

testmv		71.50 218	77.62 218	64.36 221	72.64 220	81.28 227	59.32 232	66.24 208	83.91 223	35.02 233	69.74 217	46.18 228	57.12 227	85.60 220	87.48 215	95.84 219	89.16 224

test1235678		71.50 218	77.61 219	64.36 221	72.64 220	81.26 228	59.31 233	66.22 209	83.90 224	35.02 233	69.74 217	46.18 228	57.12 227	85.60 220	87.47 216	95.84 219	89.15 225

Gipuma		71.02 220	72.60 225	69.19 219	71.31 222	75.11 231	66.36 225	61.65 226	94.93 167	47.29 225	38.74 231	38.52 232	75.52 216	86.09 219	85.92 221	93.01 225	88.87 226
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
.test1245		70.78 221	79.90 216	60.13 226	69.34 224	81.48 225	59.42 230	52.46 231	78.55 227	50.42 219	62.43 224	71.67 179	80.43 213	86.79 215	78.71 224	48.74 235	99.65 163

test12356		69.94 222	75.85 220	63.04 223	70.04 223	79.32 230	61.62 228	65.84 213	80.56 225	36.30 232	71.45 215	39.38 231	48.79 233	83.64 222	88.02 214	95.64 221	88.56 227

GG-mvs-BLEND		69.85 223	99.39 34	35.39 233	3.67 237	99.94 17	99.10 36	1.69 235	99.85 39	3.19 240	98.13 71	99.46 51	4.92 235	99.23 26	99.14 28	99.80 48	100.00 1

PMMVS2		65.18 224	68.25 226	61.59 224	61.37 230	79.72 229	59.18 234	61.80 224	64.72 232	37.33 230	53.82 227	35.59 233	54.46 231	73.94 226	80.52 223	95.40 223	89.43 223

PMVS	60.14 18	62.67 225	64.05 227	61.06 225	68.32 228	53.27 238	52.23 235	67.63 204	75.07 230	48.30 223	58.27 226	57.43 223	49.99 232	67.20 229	62.42 233	79.87 233	74.68 234
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
testmvs		61.76 226	72.90 224	48.76 230	21.21 235	68.61 233	66.11 227	37.38 233	94.83 169	33.06 235	64.31 222	29.72 234	86.08 207	74.44 225	78.71 224	48.74 235	99.65 163

E-PMN		55.33 227	55.79 229	54.81 228	59.81 232	57.23 236	38.83 236	63.59 221	64.06 234	24.66 237	35.33 233	26.40 236	58.69 226	55.41 231	70.54 229	83.26 230	81.56 230

EMVS		55.14 228	55.29 230	54.97 227	60.87 231	57.52 235	38.58 237	63.57 222	64.54 233	23.36 238	36.96 232	27.99 235	60.69 225	51.17 232	66.61 231	82.73 232	82.25 229

no-one		52.34 229	53.36 232	51.14 229	57.63 233	69.39 232	35.07 239	61.58 227	44.14 236	37.06 231	34.80 234	26.36 237	32.65 234	50.68 233	70.83 228	82.88 231	77.30 233

MVE	58.81 19	52.07 230	55.15 231	48.48 231	42.45 234	62.35 234	36.41 238	54.70 230	49.88 235	27.65 236	29.98 235	18.08 238	54.87 230	65.93 230	77.26 226	74.79 234	82.59 228
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test123		48.14 231	58.11 228	36.51 232	8.71 236	56.81 237	59.55 229	24.08 234	77.50 229	14.41 239	49.20 228	11.94 240	80.98 212	41.62 234	69.81 230	31.32 237	99.90 129

ESAPD		0.00 232	0.00 233	0.00 234	0.00 238	0.00 239	0.00 240	0.00 236	0.00 237	0.00 241	0.00 236	0.00 241	0.00 236	0.00 235	0.00 234	0.00 238	0.00 235

sosnet-low-res		0.00 232	0.00 233	0.00 234	0.00 238	0.00 239	0.00 240	0.00 236	0.00 237	0.00 241	0.00 236	0.00 241	0.00 236	0.00 235	0.00 234	0.00 238	0.00 235

sosnet		0.00 232	0.00 233	0.00 234	0.00 238	0.00 239	0.00 240	0.00 236	0.00 237	0.00 241	0.00 236	0.00 241	0.00 236	0.00 235	0.00 234	0.00 238	0.00 235

ambc			74.33 222		66.84 229	84.26 224	84.17 203		93.39 200	58.99 208	45.93 229	18.06 239	70.61 223	93.94 158	86.62 219	92.61 227	98.13 200

MTAPA										96.61 10		100.00 1

MTMP										97.42 7		100.00 1

Patchmatch-RL test							68.01 224

tmp_tt				78.81 207	98.80 39	85.73 222	70.08 223	77.87 162	98.68 112	83.71 102	99.53 26	74.55 163	54.97 229	78.28 224	72.43 227	87.45 228

XVS					95.09 67	99.94 17	97.49 68			88.58 80		99.98 26				99.78 59

X-MVStestdata					95.09 67	99.94 17	97.49 68			88.58 80		99.98 26				99.78 59

abl_6				97.06 30	99.17 33	99.82 54	98.68 47	90.86 45	100.00 1	94.53 26	97.40 79	100.00 1	99.17 47			99.93 15	99.99 46

mPP-MVS					99.23 31							99.87 35

NP-MVS									99.79 48

Patchmtry						99.00 107	95.46 109	65.50 214		67.51 165

DeepMVS_CX						97.31 144	79.48 215	89.65 57	98.66 114	60.89 204	94.40 110	66.89 210	87.65 202	81.69 223		92.76 226	94.24 219

High-resolution multi-view benchmark