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 4	99.62 6	99.96 7	99.76 6	96.87 11	99.95 20	97.66 4	99.57 27	100.00 1	99.63 25	99.88 9	99.28 25	100.00 1	100.00 1

MSLP-MVS++		99.39 2	99.76 8	98.95 2	99.60 12	99.99 1	99.83 2	96.82 14	99.92 30	97.58 6	99.58 26	100.00 1	99.93 1	98.98 32	99.86 7	99.96 12	100.00 1

CNVR-MVS		99.39 2	99.75 11	98.98 1	99.69 1	99.95 12	99.76 6	96.91 7	99.98 3	97.59 5	99.64 19	100.00 1	99.93 1	99.94 2	98.75 47	99.97 11	99.97 82

ESAPD		99.37 4	99.74 14	98.94 3	99.60 12	99.94 17	99.87 1	96.95 3	99.94 22	97.42 7	99.62 21	100.00 1	99.80 13	99.91 5	98.78 44	99.98 9	100.00 1

HSP-MVS		99.36 5	99.79 4	98.85 7	99.61 10	99.96 7	99.71 20	96.94 5	99.97 7	97.11 9	99.60 23	100.00 1	99.70 17	99.96 1	99.12 30	100.00 1	99.96 101

SMA-MVS		99.34 6	99.79 4	98.81 9	99.69 1	99.94 17	99.75 12	96.91 7	99.98 3	96.76 11	99.37 38	100.00 1	99.90 4	99.88 9	99.46 16	99.84 31	99.92 124

APD-MVS		99.33 7	99.85 1	98.73 11	99.61 10	99.92 36	99.77 5	96.91 7	99.93 25	96.31 17	99.59 24	99.95 36	99.84 8	99.73 17	99.84 8	99.95 14	100.00 1
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
NCCC		99.24 8	99.75 11	98.65 12	99.63 5	99.96 7	99.76 6	96.91 7	99.97 7	95.86 20	99.67 11	100.00 1	99.75 14	99.85 12	98.80 42	99.98 9	99.97 82

CNLPA		99.24 8	99.58 29	98.85 7	99.34 29	99.95 12	99.32 32	96.65 23	99.96 16	98.44 2	98.97 52	100.00 1	99.57 28	98.66 40	99.56 14	99.76 75	99.97 82

AdaColmap		99.21 10	99.45 35	98.92 4	99.67 4	99.95 12	99.65 24	96.77 18	99.97 7	97.67 3	100.00 1	99.69 49	99.93 1	99.26 28	97.25 90	99.85 29	100.00 1

HFP-MVS		99.19 11	99.77 7	98.51 16	99.55 17	99.94 17	99.76 6	96.84 13	99.88 37	95.27 24	99.67 11	100.00 1	99.85 7	99.56 22	99.36 20	99.79 56	99.97 82

PLC	98.06 1	99.17 12	99.38 37	98.92 4	99.47 19	99.90 44	99.48 29	96.47 28	99.96 16	98.73 1	99.52 30	100.00 1	99.55 30	98.54 52	97.73 80	99.84 31	99.99 49
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
SD-MVS		99.16 13	99.73 15	98.49 17	97.93 49	99.95 12	99.74 15	96.94 5	99.96 16	96.60 13	99.47 33	100.00 1	99.88 6	99.15 30	99.59 12	99.84 31	100.00 1

CP-MVS		99.14 14	99.67 20	98.53 15	99.45 21	99.94 17	99.63 26	96.62 25	99.82 50	95.92 19	99.65 16	100.00 1	99.71 16	99.76 15	98.56 50	99.83 37	100.00 1

zzz-MVS		99.12 15	99.52 34	98.65 12	99.58 16	99.93 30	99.74 15	96.72 21	99.44 88	96.47 14	99.62 21	100.00 1	99.63 25	99.74 16	97.97 66	99.77 68	99.94 116

ACMMPR		99.12 15	99.76 8	98.36 18	99.45 21	99.94 17	99.75 12	96.70 22	99.93 25	94.65 28	99.65 16	99.96 34	99.84 8	99.51 24	99.35 21	99.79 56	99.96 101

MCST-MVS		99.08 17	99.72 17	98.33 19	99.59 15	99.97 3	99.78 4	96.96 2	99.95 20	93.72 32	99.67 11	100.00 1	99.90 4	99.91 5	98.55 51	100.00 1	100.00 1

CPTT-MVS		99.08 17	99.53 33	98.57 14	99.44 23	99.93 30	99.60 27	95.92 33	99.77 57	97.01 10	99.67 11	100.00 1	99.72 15	99.56 22	97.76 77	99.70 113	99.98 68

DeepC-MVS_fast	98.03 2	99.05 19	99.78 6	98.21 22	99.47 19	99.97 3	99.75 12	96.80 15	99.97 7	93.58 35	98.68 64	99.94 37	99.69 18	99.93 4	99.95 2	99.96 12	99.98 68
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 20	99.61 26	98.27 20	97.88 50	99.92 36	99.71 20	96.80 15	99.96 16	95.58 22	98.71 63	100.00 1	99.68 20	99.91 5	98.78 44	99.99 6	100.00 1

HPM-MVS++		98.98 21	99.62 25	98.22 21	99.62 6	99.94 17	99.74 15	96.95 3	99.87 40	93.76 31	99.49 32	100.00 1	99.39 36	99.73 17	98.35 56	99.89 25	99.96 101

SteuartSystems-ACMMP		98.95 22	99.80 3	97.95 25	99.43 24	99.96 7	99.76 6	96.45 29	99.82 50	93.63 33	99.64 19	100.00 1	98.56 77	99.90 8	99.31 23	99.84 31	100.00 1
Skip Steuart: Steuart Systems R&D Blog.
PHI-MVS		98.85 23	99.67 20	97.89 26	98.63 45	99.93 30	98.95 43	95.20 35	99.84 48	94.94 25	99.74 10	100.00 1	99.69 18	98.40 59	99.75 10	99.93 18	99.99 49

MP-MVS		98.82 24	99.63 23	97.88 27	99.41 25	99.91 43	99.74 15	96.76 19	99.88 37	91.89 43	99.50 31	99.94 37	99.65 23	99.71 20	98.49 55	99.82 41	99.97 82
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
ACMMP_Plus		98.68 25	99.58 29	97.62 28	99.62 6	99.92 36	99.72 19	96.78 17	99.71 65	90.13 71	99.66 15	99.99 27	99.64 24	99.78 14	98.14 62	99.82 41	99.89 142

train_agg		98.62 26	99.76 8	97.28 30	99.03 38	99.93 30	99.65 24	96.37 30	99.98 3	89.24 82	99.53 28	99.83 42	99.59 27	99.85 12	99.19 28	99.80 52	100.00 1

X-MVS		98.62 26	99.75 11	97.29 29	99.50 18	99.94 17	99.71 20	96.55 26	99.85 45	88.58 87	99.65 16	99.98 29	99.67 21	99.60 21	99.26 26	99.77 68	99.97 82

OMC-MVS		98.59 28	99.07 39	98.03 24	99.41 25	99.90 44	99.26 35	94.33 37	99.94 22	96.03 18	96.68 94	99.72 48	99.42 33	98.86 35	98.84 39	99.72 109	99.58 183

PGM-MVS		98.47 29	99.73 15	97.00 34	99.68 3	99.94 17	99.76 6	91.74 42	99.84 48	91.17 54	100.00 1	99.69 49	99.81 11	99.38 26	99.30 24	99.82 41	99.95 110

TSAR-MVS + ACMM		98.30 30	99.64 22	96.74 37	99.08 37	99.94 17	99.67 23	96.73 20	99.97 7	86.30 104	98.30 69	99.99 27	98.78 70	99.73 17	99.57 13	99.88 28	99.98 68

CSCG		98.22 31	98.37 59	98.04 23	99.60 12	99.82 57	99.45 30	93.59 38	99.16 104	96.46 15	98.22 77	95.86 93	99.41 35	96.33 131	99.22 27	99.75 88	99.94 116

3Dnovator+	95.21 7	98.17 32	99.08 38	97.12 32	99.28 32	99.78 71	98.61 51	89.93 57	99.93 25	95.36 23	95.50 105	100.00 1	99.56 29	98.58 47	99.80 9	99.95 14	99.97 82

ACMMP		98.16 33	99.01 40	97.18 31	98.86 40	99.92 36	98.77 48	95.73 34	99.31 100	91.15 55	100.00 1	99.81 44	98.82 68	98.11 74	95.91 129	99.77 68	99.97 82
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 34	99.69 19	96.36 42	99.23 34	99.93 30	97.79 62	91.84 41	99.87 40	90.53 65	100.00 1	99.57 54	98.93 62	99.44 25	99.08 32	99.85 29	99.95 110

EPNet		98.11 35	99.63 23	96.34 43	98.44 47	99.88 50	98.55 52	90.25 53	99.93 25	92.60 40	100.00 1	99.73 46	98.41 79	98.87 34	99.02 33	99.82 41	99.97 82
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TSAR-MVS + GP.		98.06 36	99.55 32	96.32 44	94.72 75	99.92 36	99.22 36	89.98 55	99.97 7	94.77 27	99.94 9	100.00 1	99.43 32	98.52 56	98.53 52	99.79 56	100.00 1

3Dnovator	95.01 8	97.98 37	98.89 43	96.92 36	99.36 27	99.76 73	98.72 49	89.98 55	99.98 3	93.99 30	94.60 119	99.43 59	99.50 31	98.55 49	99.91 4	99.99 6	99.98 68

MVS_111021_HR		97.94 38	99.59 27	96.02 46	99.27 33	99.97 3	97.03 88	90.44 50	99.89 34	90.75 58	100.00 1	99.73 46	98.68 76	98.67 39	98.89 37	99.95 14	99.97 82

QAPM		97.90 39	98.89 43	96.74 37	99.35 28	99.80 69	98.84 45	90.20 54	99.94 22	92.85 36	94.17 122	99.78 45	99.42 33	98.71 38	99.87 6	99.79 56	99.98 68

CDPH-MVS		97.88 40	99.59 27	95.89 47	98.90 39	99.95 12	99.40 31	92.86 40	99.86 44	85.33 107	98.62 65	99.45 58	99.06 58	99.29 27	99.94 3	99.81 49	100.00 1

CANet		97.62 41	98.94 42	96.08 45	97.19 54	99.93 30	99.29 34	90.38 51	99.87 40	91.00 56	95.79 104	99.51 55	98.72 75	98.53 53	99.00 34	99.90 24	99.99 49

TAPA-MVS	96.62 5	97.60 42	98.46 57	96.60 40	98.73 43	99.90 44	99.30 33	94.96 36	99.46 87	87.57 93	96.05 103	98.53 67	99.26 47	98.04 80	97.33 89	99.77 68	99.88 147
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
DeepPCF-MVS	97.16 4	97.58 43	99.72 17	95.07 58	98.45 46	99.96 7	93.83 143	95.93 32	100.00 1	90.79 57	98.38 68	99.85 41	95.28 133	99.94 2	99.97 1	96.15 230	99.97 82

PCF-MVS	97.20 3	97.49 44	98.20 68	96.66 39	97.62 52	99.92 36	98.93 44	96.64 24	98.53 133	88.31 90	94.04 124	99.58 53	98.94 60	97.53 95	97.79 75	99.54 144	99.97 82
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MSDG		97.29 45	97.55 85	97.00 34	98.66 44	99.71 77	99.03 41	96.15 31	99.59 74	89.67 80	92.77 136	94.86 97	98.75 71	98.22 69	97.94 67	99.72 109	99.76 168

CHOSEN 280x420		97.16 46	99.58 29	94.35 80	96.95 57	99.97 3	97.19 82	81.55 150	99.92 30	91.75 44	100.00 1	100.00 1	98.84 67	98.55 49	98.65 48	99.79 56	99.97 82

DELS-MVS		97.05 47	98.05 72	95.88 49	97.09 55	99.99 1	98.82 46	90.30 52	98.44 138	91.40 49	92.91 133	96.57 86	97.68 103	98.56 48	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 47	97.59 84	96.42 41	97.37 53	99.92 36	99.10 39	96.54 27	99.34 99	86.64 101	91.93 140	93.15 111	99.11 56	99.11 31	99.68 11	99.73 105	99.97 82
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 49	98.72 50	95.08 57	96.32 61	99.90 44	99.15 37	89.61 61	99.89 34	87.22 98	95.47 106	98.22 76	98.22 85	98.63 44	98.90 36	99.93 18	100.00 1

MAR-MVS		97.03 50	98.00 74	95.89 47	99.32 30	99.74 76	96.76 95	84.89 107	99.97 7	94.86 26	98.29 70	90.58 122	99.67 21	98.02 82	99.50 15	99.82 41	99.92 124
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 51	98.85 46	94.83 68	92.71 103	97.43 154	99.03 41	85.52 100	99.82 50	92.74 38	99.15 43	99.94 37	99.19 50	98.66 40	96.99 105	99.79 56	99.98 68

OpenMVS	94.03 11	96.87 52	98.10 71	95.44 53	99.29 31	99.78 71	98.46 57	89.92 58	99.47 86	85.78 105	91.05 143	98.50 68	99.30 40	98.49 57	99.41 17	99.89 25	99.98 68

tfpn_ndepth		96.84 53	98.58 53	94.81 69	93.18 85	99.62 84	96.83 93	88.75 77	99.73 63	92.38 41	98.45 67	96.34 90	97.90 94	98.34 64	97.59 83	99.84 31	99.99 49

PatchMatch-RL		96.84 53	98.03 73	95.47 50	98.84 41	99.81 65	95.61 116	89.20 65	99.65 69	91.28 52	99.39 34	93.46 109	98.18 86	98.05 78	96.28 116	99.69 120	99.55 188

IS_MVSNet		96.66 55	98.62 52	94.38 77	92.41 114	99.70 78	97.19 82	87.67 92	99.05 112	91.27 53	95.09 111	98.46 72	97.95 93	98.64 42	99.37 18	99.79 56	100.00 1

tfpn1000		96.58 56	98.37 59	94.50 76	93.04 93	99.59 85	96.53 99	88.54 82	99.73 63	91.59 45	98.28 71	95.76 94	97.46 105	98.19 70	97.10 99	99.82 41	99.96 101

conf0.002		96.51 57	97.75 81	95.07 58	93.11 86	99.83 53	97.67 64	89.10 67	98.62 125	91.47 48	99.39 34	91.68 114	99.28 42	97.49 97	97.24 91	99.76 75	100.00 1

thresconf0.02		96.46 58	98.87 45	93.64 87	92.77 102	99.11 110	97.05 87	89.36 62	99.64 71	85.14 108	99.07 45	96.84 84	97.72 98	98.72 37	98.76 46	99.78 63	99.95 110

PMMVS		96.45 59	98.24 65	94.36 79	92.58 105	99.01 117	97.08 86	87.42 95	99.88 37	90.06 72	99.39 34	94.63 98	99.33 39	97.85 88	96.99 105	99.70 113	99.96 101

LS3D		96.44 60	97.31 91	95.41 54	97.06 56	99.87 51	99.51 28	97.48 1	99.57 75	79.00 130	95.39 107	89.19 129	99.81 11	98.55 49	98.84 39	99.62 132	99.78 166

EPP-MVSNet		96.29 61	98.34 61	93.90 82	91.77 127	99.38 102	95.45 121	87.25 97	99.38 95	91.36 51	94.86 118	98.49 70	97.83 96	98.01 83	98.23 58	99.75 88	99.99 49

DWT-MVSNet_training		96.26 62	98.44 58	93.72 86	92.58 105	99.34 104	96.15 105	83.00 133	99.76 59	93.63 33	97.89 82	99.46 56	97.23 109	94.43 164	98.19 59	99.70 113	100.00 1

casdiffmvs1		96.22 63	98.26 64	93.85 84	92.52 111	99.45 95	97.35 79	84.50 113	99.87 40	89.96 74	97.60 86	93.89 105	98.79 69	98.49 57	98.51 53	99.95 14	100.00 1

conf0.01		96.20 64	97.19 95	95.05 60	93.11 86	99.83 53	97.67 64	89.06 68	98.62 125	91.38 50	99.19 42	89.09 130	99.28 42	97.48 98	96.10 120	99.76 75	100.00 1

UGNet		96.05 65	98.55 54	93.13 94	94.64 76	99.65 81	94.70 132	87.78 90	99.40 94	89.69 79	98.25 74	99.25 62	92.12 165	96.50 122	97.08 100	99.84 31	99.72 172
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 66	96.83 106	95.11 56	97.87 51	99.52 87	98.81 47	91.40 45	99.42 91	84.97 109	90.46 145	96.82 85	98.05 88	96.46 126	96.19 119	99.54 144	98.92 205
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 67	96.48 116	95.46 51	96.47 59	99.89 48	95.64 113	91.23 46	99.75 61	91.59 45	96.80 90	82.44 155	98.05 88	98.53 53	97.92 72	99.80 52	100.00 1

PVSNet_Blended		96.01 67	96.48 116	95.46 51	96.47 59	99.89 48	95.64 113	91.23 46	99.75 61	91.59 45	96.80 90	82.44 155	98.05 88	98.53 53	97.92 72	99.80 52	100.00 1

tfpn		95.93 69	97.06 98	94.62 73	92.94 101	99.81 65	97.25 81	88.71 80	98.32 145	89.98 73	98.79 62	88.55 132	99.11 56	97.26 112	96.71 108	99.75 88	99.98 68

thisisatest0530		95.89 70	98.32 62	93.06 98	91.76 128	99.75 74	94.94 127	87.60 93	99.91 32	86.66 100	98.28 71	99.98 29	97.72 98	97.10 113	93.24 168	99.65 124	99.95 110

tttt0517		95.88 71	98.31 63	93.04 99	91.75 130	99.75 74	94.90 128	87.60 93	99.91 32	86.63 102	98.28 71	99.98 29	97.72 98	97.10 113	93.24 168	99.65 124	99.95 110

thres100view900		95.86 72	96.62 108	94.97 61	93.10 88	99.83 53	97.76 63	89.15 66	98.62 125	90.69 59	99.00 48	84.86 142	99.30 40	97.57 94	96.48 110	99.81 49	100.00 1

RPSCF		95.86 72	96.94 105	94.61 74	96.52 58	98.67 131	98.54 53	88.43 86	99.56 76	90.51 68	99.39 34	98.70 65	97.72 98	93.77 177	92.00 184	95.93 231	96.50 224

Anonymous20240521		95.85 74	97.53 86	93.89 83	93.20 84	97.01 160	97.14 84	84.77 108	99.16 104	90.38 70	98.96 53	93.73 106	98.23 84	96.57 121	97.37 88	99.64 128	99.93 119

casdiffmvs		95.82 75	97.83 79	93.47 89	92.15 119	99.52 87	96.32 103	84.29 117	99.50 79	89.73 78	97.82 83	91.67 115	98.38 80	98.30 65	98.00 64	99.92 22	100.00 1

canonicalmvs		95.80 76	97.02 99	94.37 78	92.96 97	99.47 93	97.49 72	84.58 110	99.44 88	92.05 42	98.54 66	86.65 137	99.37 37	96.18 134	98.93 35	99.77 68	99.92 124

tfpn111		95.79 77	96.55 110	94.89 63	93.10 88	99.82 57	97.67 64	88.85 71	98.62 125	90.69 59	99.07 45	84.86 142	99.28 42	97.41 102	96.10 120	99.76 75	99.99 49

tfpnview11		95.78 78	98.17 70	93.01 100	92.58 105	99.04 116	96.64 97	88.72 79	99.63 73	83.08 117	98.90 54	94.24 102	97.25 108	98.35 63	97.21 92	99.77 68	99.80 165

conf200view11		95.78 78	96.54 112	94.89 63	93.10 88	99.82 57	97.67 64	88.85 71	98.62 125	90.69 59	99.00 48	84.86 142	99.28 42	97.41 102	96.10 120	99.76 75	99.99 49

tfpn200view9		95.78 78	96.54 112	94.89 63	93.10 88	99.82 57	97.67 64	88.85 71	98.62 125	90.69 59	99.00 48	84.86 142	99.28 42	97.41 102	96.10 120	99.76 75	99.99 49

thres200		95.77 81	96.55 110	94.86 66	93.09 92	99.82 57	97.63 70	88.85 71	98.49 134	90.66 63	98.99 51	84.86 142	99.20 48	97.41 102	96.28 116	99.76 75	100.00 1

tfpn_n400		95.76 82	98.21 66	92.90 103	92.57 109	99.05 114	96.42 100	88.50 83	99.49 81	83.08 117	98.90 54	94.24 102	97.07 110	98.10 75	97.93 69	99.74 93	99.76 168

tfpnconf		95.76 82	98.21 66	92.90 103	92.57 109	99.05 114	96.42 100	88.50 83	99.49 81	83.08 117	98.90 54	94.24 102	97.07 110	98.10 75	97.93 69	99.74 93	99.76 168

MVS_Test		95.74 84	98.18 69	92.90 103	92.16 118	99.49 92	97.36 78	84.30 116	99.79 54	84.94 110	96.65 95	93.63 108	98.85 66	98.61 46	99.10 31	99.81 49	100.00 1

thres400		95.72 85	96.48 116	94.84 67	93.00 96	99.83 53	97.55 71	88.93 69	98.49 134	90.61 64	98.86 57	84.63 147	99.20 48	97.45 99	96.10 120	99.77 68	99.99 49

diffmvs1		95.69 86	97.82 80	93.21 93	92.34 117	99.45 95	97.12 85	83.38 126	99.66 68	87.92 91	97.90 81	91.50 116	98.73 73	98.08 77	98.16 60	99.75 88	99.98 68

view600		95.64 87	96.38 119	94.79 70	92.96 97	99.82 57	97.48 75	88.85 71	98.38 139	90.52 66	98.84 59	84.61 148	99.15 52	97.41 102	95.60 137	99.76 75	99.99 49

thres600view7		95.64 87	96.38 119	94.79 70	92.96 97	99.82 57	97.48 75	88.85 71	98.38 139	90.52 66	98.84 59	84.61 148	99.15 52	97.41 102	95.60 137	99.76 75	99.99 49

view800		95.62 89	96.38 119	94.73 72	92.96 97	99.81 65	97.38 77	88.75 77	98.35 144	90.43 69	98.81 61	84.54 150	99.13 55	97.35 108	95.82 132	99.76 75	99.98 68

Vis-MVSNet (Re-imp)		95.60 90	98.52 56	92.19 109	92.37 115	99.56 86	96.37 102	87.41 96	98.95 115	84.77 112	94.88 117	98.48 71	92.44 162	98.63 44	99.37 18	99.76 75	99.77 167

FMVSNet3		95.59 91	97.51 87	93.34 91	89.48 149	96.57 167	97.67 64	84.17 118	99.48 83	89.76 75	95.09 111	94.35 99	99.14 54	98.37 61	98.86 38	99.82 41	99.89 142

PVSNet_Blended_VisFu		95.37 92	97.44 89	92.95 102	95.20 68	99.80 69	92.68 150	88.41 87	99.12 107	87.64 92	88.31 155	99.10 63	94.07 148	98.27 67	97.51 85	99.73 105	100.00 1

DI_MVS_plusplus_trai		95.29 93	97.02 99	93.28 92	91.76 128	99.52 87	97.84 61	85.67 99	99.08 111	87.29 96	87.76 158	97.46 82	97.31 107	97.83 89	97.48 86	99.83 37	100.00 1

TSAR-MVS + COLMAP		95.20 94	95.03 138	95.41 54	96.17 62	98.69 130	99.11 38	93.40 39	99.97 7	84.89 111	98.23 76	75.01 174	99.34 38	97.27 111	96.37 115	99.58 137	99.64 178

GBi-Net		95.19 95	96.99 102	93.09 96	89.11 150	96.47 169	96.90 89	84.17 118	99.48 83	89.76 75	95.09 111	94.35 99	98.87 63	96.50 122	97.21 92	99.74 93	99.81 161

test1		95.19 95	96.99 102	93.09 96	89.11 150	96.47 169	96.90 89	84.17 118	99.48 83	89.76 75	95.09 111	94.35 99	98.87 63	96.50 122	97.21 92	99.74 93	99.81 161

test0.0.03 1		95.15 97	97.87 78	91.99 110	91.69 132	98.82 127	93.04 148	83.60 124	99.65 69	88.80 85	94.15 123	97.67 80	94.97 135	96.62 120	98.16 60	99.83 37	100.00 1

diffmvs		95.11 98	96.95 104	92.96 101	92.09 121	99.44 99	97.26 80	83.80 123	99.44 88	86.43 103	96.77 93	87.25 136	98.49 78	97.92 85	97.93 69	99.70 113	99.90 137

EPNet_dtu		95.10 99	98.81 48	90.78 115	98.38 48	98.47 133	96.54 98	89.36 62	99.78 56	65.65 198	99.31 39	98.24 75	94.79 138	98.28 66	99.35 21	99.93 18	98.27 210
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Anonymous20231211		94.96 100	94.99 139	94.91 62	93.01 95	99.44 99	96.85 92	88.49 85	98.78 120	92.61 39	83.94 169	90.25 125	98.94 60	95.87 142	96.77 107	99.58 137	99.89 142

UA-Net		94.95 101	98.66 51	90.63 117	94.60 78	98.94 123	96.03 107	85.28 102	98.01 151	78.92 131	97.42 88	99.96 34	89.09 206	98.95 33	98.80 42	99.82 41	98.57 207

CANet_DTU		94.90 102	98.98 41	90.13 123	94.74 74	99.81 65	98.53 54	82.23 141	99.97 7	66.76 186	100.00 1	98.50 68	98.74 72	97.52 96	97.19 98	99.76 75	99.88 147

FC-MVSNet-train		94.61 103	96.27 123	92.68 107	92.35 116	97.14 158	93.45 147	87.73 91	98.93 116	87.31 95	96.42 97	89.35 127	95.67 128	96.06 140	96.01 127	99.56 141	99.98 68

CLD-MVS		94.53 104	94.45 146	94.61 74	93.85 81	98.36 135	98.12 59	89.68 59	99.35 98	89.62 81	95.19 109	77.08 164	96.66 120	95.51 146	95.67 134	99.74 93	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		94.50 105	95.70 132	93.11 95	92.68 104	99.67 80	96.04 106	87.81 89	97.52 157	83.71 113	96.20 101	84.52 151	98.73 73	96.39 129	95.66 135	99.71 111	99.92 124

FMVSNet2		94.48 106	95.95 128	92.77 106	89.11 150	96.47 169	96.90 89	83.38 126	99.11 108	88.64 86	87.50 163	92.26 113	98.87 63	97.91 86	98.60 49	99.74 93	99.81 161

HQP-MVS		94.48 106	95.39 136	93.42 90	95.10 69	98.35 136	98.19 58	91.41 44	99.77 57	79.79 127	99.30 40	77.08 164	96.25 123	96.93 115	96.28 116	99.76 75	99.99 49

MDTV_nov1_ep13		94.32 108	98.77 49	89.14 132	91.70 131	99.52 87	95.21 123	72.09 210	99.80 53	78.91 132	96.32 98	99.62 51	97.71 102	98.39 60	97.71 81	99.22 213	100.00 1

CDS-MVSNet		94.32 108	97.00 101	91.19 114	89.82 147	98.71 129	95.51 118	85.14 106	96.85 161	82.33 122	92.48 137	96.40 89	94.71 139	96.86 117	97.76 77	99.63 130	99.92 124
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
dps		94.29 110	97.33 90	90.75 116	92.02 123	99.21 107	94.31 137	66.97 218	99.50 79	95.61 21	96.22 100	98.64 66	96.08 124	93.71 179	94.03 158	99.52 148	99.98 68

ACMM	94.44 10	94.26 111	94.62 143	93.84 85	94.86 73	97.73 149	93.48 146	90.76 49	99.27 101	87.46 94	99.04 47	76.60 167	96.76 118	96.37 130	93.76 161	99.74 93	99.55 188
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMP	94.49 9	94.19 112	94.74 142	93.56 88	94.25 79	98.32 138	96.02 108	89.35 64	98.90 119	87.28 97	99.14 44	76.41 170	94.94 136	96.07 139	94.35 155	99.49 155	99.99 49
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
EPMVS		94.08 113	98.54 55	88.87 134	92.51 112	99.47 93	94.18 139	66.53 219	99.68 67	82.40 121	95.24 108	99.40 60	97.86 95	98.12 73	97.99 65	99.75 88	99.88 147

test-LLR		93.71 114	97.23 93	89.60 127	91.69 132	99.10 111	94.68 134	83.60 124	99.36 96	71.94 156	93.82 126	96.51 87	95.96 126	97.42 100	94.37 152	99.74 93	99.99 49

CHOSEN 1792x2688		93.69 115	94.89 141	92.28 108	96.17 62	99.84 52	95.69 112	83.17 130	98.54 132	82.04 123	77.58 214	91.15 118	96.90 113	98.36 62	98.82 41	99.73 105	99.98 68

LGP-MVS_train		93.60 116	95.05 137	91.90 111	94.90 72	98.29 139	97.93 60	88.06 88	99.14 106	74.83 145	99.26 41	76.50 168	96.07 125	96.31 132	95.90 131	99.59 135	99.97 82

FMVSNet5		93.53 117	96.09 127	90.56 119	86.74 163	92.84 217	92.64 151	77.50 177	99.41 93	88.97 84	98.02 79	97.81 78	98.00 91	94.85 158	95.43 139	99.50 154	94.25 229

OPM-MVS		93.50 118	93.00 157	94.07 81	95.82 65	98.26 140	98.49 56	91.62 43	94.69 184	81.93 124	92.82 135	76.18 172	96.82 115	96.12 136	94.57 146	99.74 93	98.39 208

CostFormer		93.50 118	96.50 115	90.00 124	91.69 132	98.65 132	93.88 142	67.64 215	98.97 113	89.16 83	97.79 84	88.92 131	97.97 92	95.14 155	96.06 125	99.63 130	100.00 1

IterMVS-LS		93.50 118	96.22 124	90.33 122	90.93 137	95.50 199	94.83 130	80.54 154	98.92 117	79.11 129	90.64 144	93.70 107	96.79 116	96.93 115	97.85 74	99.78 63	99.99 49
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PatchmatchNet		93.48 121	98.84 47	87.22 151	91.93 124	99.39 101	92.55 152	66.06 223	99.71 65	75.61 142	98.24 75	99.59 52	97.35 106	97.87 87	97.64 82	99.83 37	99.43 193
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MS-PatchMatch		93.46 122	95.91 130	90.61 118	95.48 66	99.31 105	95.62 115	77.23 179	99.42 91	81.88 125	88.92 152	96.06 92	93.80 150	96.45 128	93.11 172	99.65 124	98.10 214

tpm cat1		93.29 123	96.53 114	89.50 129	91.84 125	99.18 109	94.70 132	67.70 214	98.38 139	86.67 99	89.16 149	99.38 61	96.66 120	94.33 165	95.30 140	99.43 174	100.00 1

Effi-MVS+-dtu		93.13 124	97.13 96	88.47 142	88.86 156	99.19 108	96.79 94	79.08 167	99.64 71	70.01 166	97.51 87	89.38 126	96.53 122	97.60 92	96.55 109	99.57 139	100.00 1

HyFIR lowres test		93.13 124	94.48 145	91.56 112	96.12 64	99.68 79	93.52 145	79.98 158	97.24 158	81.73 126	72.66 225	95.74 95	98.29 83	98.27 67	97.79 75	99.70 113	100.00 1

Vis-MVSNet		93.08 126	96.76 107	88.78 138	91.14 136	99.63 83	94.85 129	83.34 128	97.19 159	74.78 146	91.92 141	93.15 111	88.81 209	97.59 93	98.35 56	99.78 63	99.49 192
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
Effi-MVS+		93.06 127	95.94 129	89.70 126	90.82 138	99.45 95	95.71 111	78.94 169	98.72 121	74.71 147	97.92 80	80.73 159	98.35 81	97.72 90	97.05 103	99.70 113	100.00 1

tpmp4_e23		92.95 128	96.28 122	89.06 133	91.80 126	98.81 128	94.95 126	67.56 217	99.21 102	82.97 120	96.54 96	88.52 133	97.47 104	94.47 163	96.42 113	99.61 133	100.00 1

ADS-MVSNet		92.91 129	97.97 75	87.01 153	92.07 122	99.27 106	92.70 149	65.39 228	99.85 45	75.40 143	94.93 116	98.26 73	96.86 114	96.09 137	97.52 84	99.65 124	99.84 157

TESTMET0.1,1		92.87 130	97.23 93	87.79 148	86.96 162	99.10 111	94.68 134	77.46 178	99.36 96	71.94 156	93.82 126	96.51 87	95.96 126	97.42 100	94.37 152	99.74 93	99.99 49

FC-MVSNet-test		92.78 131	96.19 126	88.80 137	88.00 159	97.54 151	93.60 144	82.36 140	98.16 146	79.71 128	91.55 142	95.41 96	89.65 201	96.09 137	95.23 141	99.49 155	99.31 196

Fast-Effi-MVS+-dtu		92.73 132	97.62 83	87.02 152	88.91 154	98.83 126	95.79 109	73.98 198	99.89 34	68.62 171	97.73 85	93.30 110	95.21 134	97.67 91	95.96 128	99.59 135	100.00 1

IB-MVS	90.59 15	92.70 133	95.70 132	89.21 131	94.62 77	99.45 95	83.77 219	88.92 70	99.53 77	92.82 37	98.86 57	86.08 139	75.24 231	92.81 195	93.17 170	99.89 25	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		92.67 134	97.13 96	87.47 150	86.72 164	99.07 113	94.28 138	76.90 182	99.21 102	71.53 160	93.63 128	96.32 91	95.67 128	97.32 109	94.36 154	99.74 93	99.99 49

RPMNet		92.64 135	97.88 77	86.53 158	90.79 139	98.95 121	95.13 124	64.44 232	99.09 109	72.36 152	93.58 129	99.01 64	96.74 119	98.05 78	96.45 112	99.71 111	100.00 1

FMVSNet1		92.55 136	93.66 151	91.26 113	87.91 160	96.12 176	94.75 131	81.69 149	97.67 154	85.63 106	80.56 189	87.88 135	98.15 87	96.50 122	97.21 92	99.41 192	99.71 173

tpmrst		92.52 137	97.45 88	86.77 156	92.15 119	99.36 103	92.53 153	65.95 224	99.53 77	72.50 150	92.22 138	99.83 42	97.81 97	95.18 154	96.05 126	99.69 120	100.00 1

testgi		92.47 138	95.68 134	88.73 139	90.68 140	98.35 136	91.67 160	79.50 163	98.96 114	77.12 138	95.17 110	85.84 140	93.95 149	95.75 144	96.47 111	99.45 168	99.21 199

TAMVS		92.43 139	94.21 149	90.35 121	88.68 157	98.85 125	94.15 140	81.53 151	95.58 171	83.61 115	87.05 164	86.45 138	94.71 139	96.27 133	95.91 129	99.42 180	99.38 195

CR-MVSNet		92.32 140	97.97 75	85.74 170	90.63 142	98.95 121	95.46 119	65.50 226	99.09 109	67.51 177	94.20 121	98.18 77	95.59 131	98.16 71	97.20 96	99.74 93	100.00 1

CVMVSNet		92.13 141	95.40 135	88.32 145	91.29 135	97.29 156	91.85 157	86.42 98	96.71 164	71.84 158	89.56 148	91.18 117	88.98 208	96.17 135	97.76 77	99.51 152	99.14 201

Fast-Effi-MVS+		92.11 142	94.33 147	89.52 128	89.06 153	99.00 118	95.13 124	76.72 184	98.59 131	78.21 136	89.99 146	77.35 163	98.34 82	97.97 84	97.44 87	99.67 122	99.96 101

ACMH+	92.61 13	91.80 143	93.03 155	90.37 120	93.03 94	98.17 141	94.00 141	84.13 121	98.12 148	77.39 137	91.95 139	74.62 175	94.36 145	94.62 162	93.82 160	99.32 203	99.87 152

IterMVS		91.65 144	96.62 108	85.85 167	90.27 145	95.80 189	95.32 122	74.15 194	98.91 118	60.95 215	88.79 154	97.76 79	94.69 141	98.04 80	97.07 101	99.73 105	100.00 1
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ACMH	92.34 14	91.59 145	93.02 156	89.92 125	93.97 80	97.98 145	90.10 188	84.70 109	98.46 136	76.80 139	93.38 131	71.94 189	94.39 143	95.34 150	94.04 157	99.54 144	100.00 1
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v1.0		91.56 146	85.41 222	98.74 10	99.62 6	99.94 17	99.79 3	96.87 11	99.93 25	96.33 16	99.59 24	100.00 1	99.84 8	99.88 9	98.50 54	100.00 1	0.00 246

pmmvs4		91.41 147	93.05 154	89.49 130	85.85 171	96.52 168	91.70 159	82.49 135	98.14 147	83.17 116	87.57 160	81.76 158	94.39 143	95.47 147	92.62 178	99.33 201	99.29 197

testpf		91.26 148	97.28 92	84.23 194	89.52 148	97.45 153	88.08 207	56.08 241	99.76 59	78.71 133	95.06 115	98.26 73	93.44 154	94.72 160	95.69 133	99.57 139	99.99 49

PatchT		91.06 149	97.66 82	83.36 207	90.32 144	98.96 120	82.30 224	64.72 231	98.45 137	67.51 177	93.28 132	97.60 81	95.59 131	98.16 71	97.20 96	99.70 113	100.00 1

MIMVSNet		91.01 150	96.22 124	84.93 182	85.24 181	98.09 142	90.40 175	64.96 230	97.55 156	72.65 148	96.23 99	90.81 120	96.79 116	96.69 118	97.06 102	99.52 148	97.09 220

UniMVSNet_NR-MVSNet		90.50 151	92.31 159	88.38 143	85.04 187	96.34 172	90.94 162	85.32 101	95.87 170	75.69 140	87.68 159	78.49 160	93.78 151	93.21 188	94.60 145	99.53 147	99.97 82

UniMVSNet (Re)		90.41 152	91.96 161	88.59 141	85.71 172	96.73 164	90.82 165	84.11 122	95.23 177	78.54 134	88.91 153	76.41 170	92.84 159	93.40 186	93.05 173	99.55 143	100.00 1

GA-MVS		90.38 153	94.59 144	85.46 176	88.30 158	98.44 134	92.18 154	83.30 129	97.89 153	58.05 222	92.86 134	84.25 153	91.27 190	96.65 119	92.61 179	99.66 123	99.43 193

USDC		90.36 154	91.68 163	88.82 136	92.58 105	98.02 143	96.27 104	79.83 159	98.37 142	70.61 165	89.05 150	67.50 221	94.17 146	95.77 143	94.43 150	99.46 165	98.62 206

thisisatest0515		90.28 155	94.32 148	85.57 175	85.23 182	97.23 157	85.44 215	83.09 131	96.80 162	72.41 151	89.82 147	90.87 119	87.93 214	95.27 153	90.39 216	99.33 201	99.88 147

TinyColmap		89.94 156	90.88 169	88.84 135	92.43 113	97.91 147	95.59 117	80.10 157	98.12 148	71.33 162	84.56 165	67.46 222	94.15 147	95.57 145	94.27 156	99.43 174	98.26 211

pm-mvs1		89.68 157	92.00 160	86.96 154	86.23 168	96.62 166	90.36 177	83.05 132	93.97 196	72.15 155	81.77 184	82.10 157	90.69 196	95.38 149	94.50 148	99.29 207	99.65 175

tpm		89.60 158	94.93 140	83.39 205	89.94 146	97.11 159	90.09 189	65.28 229	98.67 123	60.03 219	96.79 92	84.38 152	95.66 130	91.90 200	95.65 136	99.32 203	99.98 68

NR-MVSNet		89.52 159	90.71 170	88.14 147	86.19 169	96.20 173	92.07 155	84.58 110	95.54 172	75.27 144	87.52 161	67.96 220	91.24 192	94.33 165	93.45 165	99.49 155	99.97 82

DU-MVS		89.49 160	90.60 171	88.19 146	84.71 201	96.20 173	90.94 162	84.58 110	95.54 172	75.69 140	87.52 161	68.74 219	93.78 151	91.10 218	95.13 142	99.47 162	99.97 82

Baseline_NR-MVSNet		89.13 161	89.53 185	88.66 140	84.71 201	94.43 210	91.79 158	84.49 114	95.54 172	78.28 135	78.52 211	72.46 188	93.29 156	91.10 218	94.82 144	99.42 180	99.86 155

tfpnnormal		89.09 162	89.71 178	88.38 143	87.37 161	96.78 163	91.46 161	85.20 104	90.33 226	72.35 153	83.45 170	69.30 217	94.45 142	95.29 151	92.86 175	99.44 173	99.93 119

TranMVSNet+NR-MVSNet		88.88 163	89.90 176	87.69 149	84.06 213	95.68 191	91.88 156	85.23 103	95.16 178	72.54 149	83.06 173	70.14 211	92.93 158	90.81 221	94.53 147	99.48 159	99.89 142

WR-MVS_H		88.47 164	90.55 172	86.04 161	85.13 184	96.07 182	89.86 197	79.80 160	94.37 193	72.32 154	83.12 172	74.44 178	89.60 202	93.52 183	92.40 180	99.51 152	99.96 101

SixPastTwentyTwo		88.35 165	91.51 165	84.66 186	85.39 177	96.96 161	86.57 211	79.62 162	96.57 165	63.73 206	87.86 157	75.18 173	93.43 155	94.03 169	90.37 217	99.24 212	99.58 183

TransMVSNet (Re)		88.33 166	89.55 184	86.91 155	86.65 165	95.56 196	90.48 171	84.44 115	92.02 225	71.07 164	80.13 191	72.48 187	89.41 203	95.05 157	94.44 149	99.39 194	97.14 219

LP		88.31 167	93.18 153	82.63 210	90.66 141	97.98 145	87.32 210	63.49 235	97.17 160	63.02 209	82.08 176	90.47 123	91.92 167	92.75 196	93.42 166	99.38 196	98.37 209

MVS-HIRNet		88.27 168	94.05 150	81.51 214	88.90 155	98.93 124	83.38 222	60.52 240	98.06 150	63.78 205	80.67 188	90.36 124	92.94 157	97.29 110	96.41 114	99.56 141	96.66 222

WR-MVS		88.23 169	90.15 174	86.00 163	84.39 208	95.64 192	89.96 193	81.80 146	94.46 191	71.60 159	82.10 175	74.36 179	88.76 210	92.48 197	92.20 182	99.46 165	99.83 159

CP-MVSNet		88.09 170	89.57 182	86.36 159	84.63 204	95.46 201	89.48 199	80.53 155	93.42 211	71.26 163	81.25 186	69.90 212	92.78 160	93.30 187	93.69 162	99.47 162	99.96 101

anonymousdsp		87.98 171	92.38 158	82.85 208	83.68 217	96.79 162	90.78 166	74.06 197	95.29 176	57.91 223	83.33 171	83.12 154	91.15 194	95.96 141	92.37 181	99.52 148	99.76 168

v6		87.96 172	89.58 181	86.08 160	85.34 178	96.14 175	90.44 172	82.19 142	94.56 185	67.43 181	81.90 179	71.57 194	91.62 177	91.54 205	91.43 197	99.43 174	99.92 124

v1neww		87.88 173	89.51 187	85.97 165	85.32 179	96.12 176	90.33 179	82.17 143	94.51 186	66.96 183	81.84 181	71.21 197	91.64 174	91.52 207	91.43 197	99.42 180	99.92 124

v7new		87.88 173	89.51 187	85.97 165	85.32 179	96.12 176	90.33 179	82.17 143	94.51 186	66.96 183	81.84 181	71.21 197	91.64 174	91.52 207	91.43 197	99.42 180	99.92 124

LTVRE_ROB	88.65 16	87.87 175	91.11 168	84.10 197	86.64 166	97.47 152	94.40 136	78.41 173	96.13 168	52.02 229	87.95 156	65.92 227	93.59 153	95.29 151	95.09 143	99.52 148	99.95 110
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		87.84 176	89.42 189	85.99 164	85.16 183	96.01 185	90.52 168	81.78 148	94.43 192	67.59 175	81.32 185	71.87 190	91.48 181	91.25 217	91.16 211	99.43 174	99.92 124

TDRefinement		87.79 177	88.76 203	86.66 157	93.54 82	98.02 143	95.76 110	85.18 105	96.57 165	67.90 172	80.51 190	66.51 226	78.37 228	93.20 189	89.73 221	99.22 213	96.75 221

MDTV_nov1_ep13_2view		87.75 178	93.32 152	81.26 216	83.74 216	96.64 165	85.66 214	66.20 222	98.36 143	61.61 213	84.34 167	87.95 134	91.12 195	94.01 170	92.66 177	99.22 213	99.27 198

v7		87.72 179	89.75 177	85.35 178	85.01 188	95.79 190	90.43 174	78.98 168	94.50 189	66.39 189	78.87 205	73.65 182	91.85 170	93.69 180	91.86 188	99.45 168	99.92 124

v8		87.54 180	89.33 190	85.45 177	85.41 176	95.50 199	90.32 182	78.94 169	94.35 194	66.93 185	81.90 179	70.99 202	91.62 177	91.49 210	91.22 208	99.48 159	99.87 152

v1144		87.49 181	89.64 179	84.97 181	84.73 200	95.84 188	90.17 187	79.30 164	93.96 197	64.65 203	78.83 207	73.38 184	91.51 180	93.77 177	91.77 189	99.45 168	99.93 119

v1		87.48 182	88.91 198	85.81 168	84.93 191	96.07 182	90.33 179	82.45 138	93.65 206	66.39 189	79.38 202	70.40 208	91.33 187	91.58 204	91.38 203	99.42 180	99.93 119

divwei89l23v2f112		87.46 183	88.97 195	85.70 172	84.85 196	96.08 180	90.23 185	82.46 136	93.69 205	65.83 196	79.57 199	70.54 205	91.39 186	91.60 203	91.39 201	99.43 174	99.92 124

v2v482		87.46 183	88.90 199	85.78 169	84.58 205	95.95 187	89.90 196	82.43 139	94.19 195	65.65 198	79.80 195	69.12 218	92.67 161	91.88 201	91.46 195	99.45 168	99.93 119

v1141		87.45 185	88.98 194	85.67 173	84.86 195	96.08 180	90.23 185	82.46 136	93.75 201	65.64 200	79.57 199	70.52 206	91.41 185	91.63 202	91.39 201	99.42 180	99.92 124

v10		87.40 186	89.62 180	84.80 184	84.93 191	95.07 207	90.44 172	75.63 188	94.51 186	66.52 187	78.87 205	73.47 183	91.86 169	93.69 180	91.87 187	99.45 168	99.86 155

pmmvs5		87.33 187	90.01 175	84.20 195	84.31 210	96.04 184	87.63 208	76.59 185	93.17 216	65.35 202	84.30 168	71.68 191	91.91 168	95.41 148	91.37 204	99.39 194	98.13 212

N_pmnet		87.31 188	91.51 165	82.41 213	85.13 184	95.57 195	80.59 227	81.79 147	96.20 167	58.52 221	78.62 209	85.66 141	89.36 204	94.64 161	92.14 183	99.08 218	97.72 218

PS-CasMVS		87.24 189	88.52 206	85.73 171	84.58 205	95.35 203	89.03 202	80.17 156	93.11 217	68.86 170	77.71 213	66.89 223	92.30 163	93.13 191	93.50 164	99.46 165	99.96 101

EU-MVSNet		87.20 190	90.47 173	83.38 206	85.11 186	93.85 215	86.10 213	79.76 161	93.30 215	65.39 201	84.41 166	78.43 161	85.04 221	92.20 199	93.03 174	98.86 220	98.05 215

PEN-MVS		87.20 190	88.22 210	86.01 162	84.01 215	94.93 209	90.00 191	81.52 153	93.46 210	69.29 168	79.69 197	65.51 228	91.72 171	91.01 220	93.12 171	99.49 155	99.84 157

v16		87.15 192	89.13 191	84.83 183	85.55 174	91.94 221	90.50 169	74.13 196	95.06 179	67.72 174	81.84 181	72.55 186	91.65 173	91.50 209	91.42 200	99.42 180	99.60 180

v18		87.14 193	88.96 196	85.01 180	85.57 173	92.03 219	90.89 164	74.62 192	94.80 183	67.90 172	82.02 177	71.28 196	91.63 176	91.53 206	91.44 196	99.47 162	99.60 180

v17		86.99 194	88.90 199	84.76 185	85.52 175	91.96 220	90.50 169	74.17 193	94.88 181	67.33 182	81.94 178	71.21 197	91.57 179	91.49 210	91.20 209	99.48 159	99.60 180

EG-PatchMatch MVS		86.96 195	89.56 183	83.93 201	86.29 167	97.61 150	90.75 167	73.31 203	95.43 175	66.08 194	75.88 222	71.31 195	87.55 216	94.79 159	92.74 176	99.61 133	99.13 202

v1192		86.93 196	89.01 192	84.50 187	84.46 207	95.51 198	89.93 195	78.65 171	93.75 201	62.29 211	77.19 215	70.88 203	92.28 164	93.84 174	91.96 185	99.38 196	99.90 137

v1921920		86.81 197	88.93 197	84.33 192	84.23 211	95.41 202	90.09 189	78.10 174	93.74 203	62.17 212	76.98 217	71.14 200	92.05 166	93.69 180	91.69 192	99.32 203	99.88 147

v144192		86.80 198	88.90 199	84.35 189	84.33 209	95.56 196	89.34 200	77.74 176	93.60 207	64.03 204	77.82 212	70.76 204	91.28 189	92.91 194	91.74 191	99.37 198	99.90 137

v11		86.74 199	89.01 192	84.09 199	84.79 198	91.79 226	90.39 176	72.53 209	94.47 190	65.75 197	78.64 208	72.96 185	91.66 172	93.92 172	91.69 192	99.42 180	99.61 179

DTE-MVSNet		86.70 200	87.66 218	85.58 174	83.30 218	94.29 211	89.74 198	81.53 151	92.77 219	68.93 169	80.13 191	64.00 231	90.62 197	89.45 225	93.34 167	99.32 203	99.67 174

gg-mvs-nofinetune		86.69 201	91.30 167	81.30 215	90.42 143	99.64 82	98.50 55	61.68 237	79.23 239	40.35 240	66.58 233	97.14 83	96.92 112	98.64 42	97.94 67	99.91 23	99.97 82

v148		86.63 202	87.79 214	85.28 179	84.65 203	95.97 186	86.46 212	82.84 134	92.91 218	71.52 161	78.99 204	66.74 225	86.83 218	89.28 226	90.69 214	99.41 192	99.94 116

V14		86.54 203	88.41 207	84.35 189	84.94 190	91.83 223	90.28 184	73.48 201	93.73 204	66.50 188	79.89 194	71.12 201	91.46 182	91.48 212	91.25 206	99.42 180	99.58 183

v15		86.50 204	88.32 208	84.37 188	85.00 189	91.86 222	90.30 183	73.76 199	93.90 199	66.28 192	79.78 196	70.37 209	91.45 183	91.48 212	91.27 205	99.43 174	99.58 183

V9		86.42 205	88.26 209	84.27 193	84.88 193	91.80 224	90.34 178	73.18 205	93.92 198	66.37 191	79.68 198	70.25 210	91.42 184	91.43 214	91.23 207	99.42 180	99.55 188

v12		86.32 206	88.22 210	84.10 197	84.76 199	91.80 224	89.94 194	72.97 207	93.85 200	66.18 193	79.98 193	69.72 216	91.33 187	91.40 215	91.20 209	99.42 180	99.56 187

v13		86.27 207	88.16 212	84.06 200	84.85 196	91.77 227	90.00 191	72.77 208	93.56 208	66.06 195	79.25 203	70.50 207	91.25 191	91.35 216	91.15 212	99.42 180	99.55 188

v1240		86.24 208	88.56 205	83.54 202	84.05 214	95.21 206	89.27 201	76.76 183	93.42 211	60.68 218	75.99 221	69.80 214	91.21 193	93.83 176	91.76 190	99.29 207	99.91 136

v52		85.80 209	87.74 215	83.53 203	82.87 221	95.31 205	88.71 203	77.04 181	92.23 222	63.53 207	76.91 218	69.80 214	89.78 199	90.05 223	90.07 219	99.26 211	99.82 160

V4		85.78 210	87.74 215	83.50 204	82.90 220	95.33 204	88.62 204	77.05 180	92.14 224	63.45 208	76.91 218	69.85 213	89.72 200	90.07 222	90.05 220	99.27 210	99.81 161

pmmvs6		85.75 211	86.97 219	84.34 191	84.88 193	95.59 194	87.41 209	79.19 166	87.81 232	67.56 176	63.05 236	77.76 162	89.15 205	93.45 185	91.90 186	97.83 227	99.21 199

v7n		85.39 212	87.70 217	82.70 209	82.77 223	95.64 192	88.27 206	74.83 190	92.30 221	62.58 210	76.37 220	64.80 230	88.38 212	94.29 167	90.61 215	99.34 199	99.87 152

gm-plane-assit		84.93 213	91.61 164	77.14 223	84.14 212	91.29 229	66.18 239	69.70 212	85.22 235	47.95 235	78.58 210	89.24 128	94.90 137	98.82 36	98.12 63	99.99 6	100.00 1

CMPMVS	65.66 17	84.62 214	85.02 223	84.15 196	95.40 67	97.79 148	88.35 205	79.22 165	89.66 230	60.71 217	72.20 226	73.94 180	87.32 217	86.73 230	84.55 234	93.90 236	90.31 233
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
v748		84.47 215	86.06 220	82.62 211	82.85 222	95.02 208	83.73 220	78.48 172	90.20 228	67.45 180	75.86 223	61.27 233	83.84 222	89.87 224	90.28 218	99.34 199	99.90 137

Anonymous20231206		84.28 216	89.53 185	78.17 220	82.31 225	94.16 213	82.57 223	76.51 186	93.38 214	52.98 228	79.47 201	73.74 181	75.45 230	95.07 156	94.41 151	99.18 216	96.46 225

new_pmnet		84.12 217	87.89 213	79.72 218	80.43 226	94.14 214	80.26 228	74.14 195	96.01 169	56.30 227	74.94 224	76.45 169	88.59 211	93.11 192	89.31 222	98.59 223	91.27 232

test20.03		83.86 218	88.73 204	78.16 221	82.60 224	93.00 216	81.61 226	74.68 191	92.36 220	57.50 224	83.01 174	74.48 177	73.30 234	92.40 198	91.14 213	99.29 207	94.75 228

test2356		83.84 219	91.77 162	74.59 227	78.71 228	89.10 233	78.24 232	72.07 211	96.78 163	45.18 238	96.19 102	76.77 166	74.87 232	93.17 190	94.01 159	98.44 224	96.38 226

pmmvs-eth3d		82.92 220	83.31 226	82.47 212	76.97 230	91.76 228	83.79 218	76.10 187	90.33 226	69.95 167	71.04 229	48.09 237	89.02 207	93.85 173	89.14 223	99.02 219	98.96 204

PM-MVS		82.79 221	84.51 224	80.77 217	77.22 229	92.13 218	83.61 221	73.31 203	93.50 209	61.06 214	77.15 216	46.52 240	90.55 198	94.14 168	89.05 225	98.85 221	99.12 203

testus		82.22 222	88.82 202	74.52 228	79.14 227	89.37 232	78.38 230	72.99 206	97.57 155	44.54 239	93.44 130	58.13 235	74.20 233	92.96 193	93.67 163	97.89 226	96.58 223

pmmvs3		80.91 223	85.62 221	75.42 225	75.01 232	89.09 234	75.31 233	68.70 213	86.99 233	46.74 237	81.18 187	62.91 232	87.95 213	93.84 174	89.06 224	98.80 222	96.23 227

MIMVSNet1		80.64 224	83.97 225	76.76 224	68.91 240	91.15 231	78.32 231	75.47 189	89.58 231	56.64 226	65.10 234	65.17 229	82.14 223	93.51 184	91.64 194	99.10 217	91.66 231

MDA-MVSNet-bldmvs		80.30 225	82.83 227	77.34 222	69.16 239	94.29 211	72.16 234	81.97 145	90.14 229	57.32 225	94.01 125	47.97 238	86.81 219	68.74 240	86.82 231	96.63 229	97.86 216

new-patchmatchnet		78.17 226	80.82 228	75.07 226	76.93 231	91.20 230	71.90 235	73.32 202	86.59 234	48.91 232	67.11 232	47.85 239	81.19 224	88.18 227	87.02 230	98.19 225	97.79 217

FPMVS		73.80 227	74.62 234	72.84 229	83.09 219	84.44 236	83.89 217	73.64 200	92.20 223	48.50 233	72.19 227	59.51 234	63.16 236	69.13 239	66.26 244	84.74 241	78.59 243

1111		73.79 228	78.62 230	68.16 231	69.34 237	81.48 238	59.42 243	52.46 243	78.55 240	50.42 230	62.43 237	71.67 192	80.43 226	86.79 228	88.22 226	96.87 228	81.17 242

testmv		71.50 229	77.62 231	64.36 232	72.64 233	81.28 240	59.32 245	66.24 220	83.91 236	35.02 244	69.74 230	46.18 241	57.12 239	85.60 232	87.48 228	95.84 232	89.16 235

test1235678		71.50 229	77.61 232	64.36 232	72.64 233	81.26 241	59.31 246	66.22 221	83.90 237	35.02 244	69.74 230	46.18 241	57.12 239	85.60 232	87.47 229	95.84 232	89.15 236

Gipuma		71.02 231	72.60 237	69.19 230	71.31 235	75.11 244	66.36 238	61.65 238	94.93 180	47.29 236	38.74 243	38.52 244	75.52 229	86.09 231	85.92 233	93.01 237	88.87 237
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
.test1245		70.78 232	79.90 229	60.13 237	69.34 237	81.48 238	59.42 243	52.46 243	78.55 240	50.42 230	62.43 237	71.67 192	80.43 226	86.79 228	78.71 236	48.74 247	99.65 175

test12356		69.94 233	75.85 233	63.04 234	70.04 236	79.32 243	61.62 241	65.84 225	80.56 238	36.30 243	71.45 228	39.38 243	48.79 245	83.64 234	88.02 227	95.64 234	88.56 238

GG-mvs-BLEND		69.85 234	99.39 36	35.39 244	3.67 250	99.94 17	99.10 39	1.69 247	99.85 45	3.19 251	98.13 78	99.46 56	4.92 247	99.23 29	99.14 29	99.80 52	100.00 1

PMMVS2		65.18 235	68.25 238	61.59 235	61.37 243	79.72 242	59.18 247	61.80 236	64.72 244	37.33 241	53.82 240	35.59 245	54.46 243	73.94 238	80.52 235	95.40 235	89.43 234

PMVS	60.14 18	62.67 236	64.05 239	61.06 236	68.32 241	53.27 251	52.23 248	67.63 216	75.07 243	48.30 234	58.27 239	57.43 236	49.99 244	67.20 241	62.42 245	79.87 245	74.68 245
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
testmvs		61.76 237	72.90 236	48.76 241	21.21 248	68.61 246	66.11 240	37.38 245	94.83 182	33.06 246	64.31 235	29.72 246	86.08 220	74.44 237	78.71 236	48.74 247	99.65 175

E-PMN		55.33 238	55.79 241	54.81 239	59.81 245	57.23 249	38.83 249	63.59 233	64.06 246	24.66 248	35.33 245	26.40 248	58.69 238	55.41 243	70.54 241	83.26 242	81.56 241

EMVS		55.14 239	55.29 242	54.97 238	60.87 244	57.52 248	38.58 250	63.57 234	64.54 245	23.36 249	36.96 244	27.99 247	60.69 237	51.17 244	66.61 243	82.73 244	82.25 240

no-one		52.34 240	53.36 244	51.14 240	57.63 246	69.39 245	35.07 252	61.58 239	44.14 248	37.06 242	34.80 246	26.36 249	32.65 246	50.68 245	70.83 240	82.88 243	77.30 244

MVE	58.81 19	52.07 241	55.15 243	48.48 242	42.45 247	62.35 247	36.41 251	54.70 242	49.88 247	27.65 247	29.98 247	18.08 250	54.87 242	65.93 242	77.26 238	74.79 246	82.59 239
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test123		48.14 242	58.11 240	36.51 243	8.71 249	56.81 250	59.55 242	24.08 246	77.50 242	14.41 250	49.20 241	11.94 252	80.98 225	41.62 246	69.81 242	31.32 249	99.90 137

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

Anonymous202405211			95.78 131		93.26 83	99.52 87	96.70 96	88.55 81	97.93 152		88.99 151	90.68 121	98.99 59	96.46 126	97.02 104	99.64 128	99.89 142

our_test_3					85.89 170	96.09 179	82.15 225

ambc			74.33 235		66.84 242	84.26 237	84.17 216		93.39 213	58.99 220	45.93 242	18.06 251	70.61 235	93.94 171	86.62 232	92.61 239	98.13 212

MTAPA										96.61 12		100.00 1

MTMP										97.42 7		100.00 1

Patchmatch-RL test							68.01 237

tmp_tt				78.81 219	98.80 42	85.73 235	70.08 236	77.87 175	98.68 122	83.71 113	99.53 28	74.55 176	54.97 241	78.28 236	72.43 239	87.45 240

XVS					95.09 70	99.94 17	97.49 72			88.58 87		99.98 29				99.78 63

X-MVStestdata					95.09 70	99.94 17	97.49 72			88.58 87		99.98 29				99.78 63

abl_6				97.06 33	99.17 36	99.82 57	98.68 50	90.86 48	100.00 1	94.53 29	97.40 89	100.00 1	99.17 51			99.93 18	99.99 49

mPP-MVS					99.23 34							99.87 40

NP-MVS									99.79 54

Patchmtry						99.00 118	95.46 119	65.50 226		67.51 177

DeepMVS_CX						97.31 155	79.48 229	89.65 60	98.66 124	60.89 216	94.40 120	66.89 223	87.65 215	81.69 235		92.76 238	94.24 230

High-resolution multi-view benchmark