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		98.87 1	98.96 1	98.77 1	99.58 1	99.53 2	99.44 1	97.81 1	98.22 7	97.33 2	98.70 2	99.33 5	98.86 7	98.96 3	98.40 10	99.63 3	99.57 5

HSP-MVS		98.59 2	98.65 5	98.52 3	99.44 8	99.57 1	99.34 3	97.65 5	97.36 28	96.62 8	98.49 4	99.65 3	98.67 16	98.60 9	97.44 39	99.40 46	99.46 9

SD-MVS		98.52 3	98.77 3	98.23 10	98.15 43	99.26 17	98.79 22	97.59 9	98.52 1	96.25 11	97.99 10	99.75 1	99.01 3	98.27 21	97.97 22	99.59 4	99.63 1

TSAR-MVS + MP.		98.49 4	98.78 2	98.15 14	98.14 44	99.17 24	99.34 3	97.18 22	98.44 3	95.72 14	97.84 11	99.28 7	98.87 6	99.05 1	98.05 20	99.66 1	99.60 3

HFP-MVS		98.48 5	98.62 6	98.32 6	99.39 13	99.33 12	99.27 8	97.42 12	98.27 5	95.25 18	98.34 7	98.83 19	99.08 1	98.26 22	98.08 19	99.48 21	99.26 24

CNVR-MVS		98.47 6	98.46 11	98.48 4	99.40 10	99.05 28	99.02 16	97.54 10	97.73 14	96.65 7	97.20 23	99.13 12	98.85 9	98.91 5	98.10 17	99.41 44	99.08 43

MPTG		98.43 7	98.31 18	98.57 2	99.48 4	99.40 5	99.32 6	97.62 7	97.70 16	96.67 6	96.59 26	99.09 14	98.86 7	98.65 8	97.56 35	99.45 30	99.17 37

ACMMPR		98.40 8	98.49 8	98.28 8	99.41 9	99.40 5	99.36 2	97.35 15	98.30 4	95.02 20	97.79 12	98.39 29	99.04 2	98.26 22	98.10 17	99.50 19	99.22 29

SteuartSystems-ACMMP		98.38 9	98.71 4	97.99 18	99.34 15	99.46 4	99.34 3	97.33 18	97.31 29	94.25 24	98.06 8	99.17 11	98.13 24	98.98 2	98.46 8	99.55 9	99.54 6
Skip Steuart: Steuart Systems R&D Blog.
APD-MVS		98.36 10	98.32 17	98.41 5	99.47 5	99.26 17	99.12 12	97.77 3	96.73 41	96.12 12	97.27 22	98.88 17	98.46 21	98.47 13	98.39 11	99.52 13	99.22 29
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
HPM-MVS++		98.34 11	98.47 10	98.18 11	99.46 7	99.15 25	99.10 13	97.69 4	97.67 19	94.93 21	97.62 14	99.70 2	98.60 17	98.45 14	97.46 38	99.31 62	99.26 24

CP-MVS		98.32 12	98.34 16	98.29 7	99.34 15	99.30 13	99.15 11	97.35 15	97.49 25	95.58 16	97.72 13	98.62 26	98.82 10	98.29 20	97.67 32	99.51 17	99.28 19

ACMMP_Plus		98.20 13	98.49 8	97.85 20	99.50 3	99.40 5	99.26 9	97.64 6	97.47 26	92.62 41	97.59 15	99.09 14	98.71 14	98.82 7	97.86 28	99.40 46	99.19 33

MCST-MVS		98.20 13	98.36 13	98.01 17	99.40 10	99.05 28	99.00 17	97.62 7	97.59 23	93.70 28	97.42 21	99.30 6	98.77 12	98.39 18	97.48 37	99.59 4	99.31 18

DeepC-MVS_fast	96.13 1	98.13 15	98.27 20	97.97 19	99.16 20	99.03 33	99.05 15	97.24 20	98.22 7	94.17 26	95.82 31	98.07 31	98.69 15	98.83 6	98.80 2	99.52 13	99.10 40
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
NCCC		98.10 16	98.05 25	98.17 13	99.38 14	99.05 28	99.00 17	97.53 11	98.04 10	95.12 19	94.80 42	99.18 10	98.58 18	98.49 12	97.78 30	99.39 48	98.98 59

MP-MVS		98.09 17	98.30 19	97.84 21	99.34 15	99.19 23	99.23 10	97.40 13	97.09 35	93.03 35	97.58 16	98.85 18	98.57 19	98.44 16	97.69 31	99.48 21	99.23 27

MSLP-MVS++		98.04 18	97.93 27	98.18 11	99.10 21	99.09 27	98.34 31	96.99 25	97.54 24	96.60 9	94.82 41	98.45 28	98.89 5	97.46 46	98.77 4	99.17 85	99.37 12

X-MVS		97.84 19	98.19 22	97.42 25	99.40 10	99.35 8	99.06 14	97.25 19	97.38 27	90.85 49	96.06 30	98.72 22	98.53 20	98.41 17	98.15 16	99.46 26	99.28 19

PGM-MVS		97.81 20	98.11 23	97.46 24	99.55 2	99.34 11	99.32 6	94.51 38	96.21 53	93.07 32	98.05 9	97.95 34	98.82 10	98.22 25	97.89 27	99.48 21	99.09 42

CPTT-MVS		97.78 21	97.54 28	98.05 16	98.91 28	99.05 28	99.00 17	96.96 26	97.14 33	95.92 13	95.50 34	98.78 21	98.99 4	97.20 51	96.07 73	98.54 160	99.04 51

PHI-MVS		97.78 21	98.44 12	97.02 31	98.73 31	99.25 19	98.11 34	95.54 32	96.66 44	92.79 38	98.52 3	99.38 4	97.50 36	97.84 37	98.39 11	99.45 30	99.03 52

TSAR-MVS + ACMM		97.71 23	98.60 7	96.66 34	98.64 34	99.05 28	98.85 21	97.23 21	98.45 2	89.40 72	97.51 18	99.27 8	96.88 53	98.53 10	97.81 29	98.96 109	99.59 4

train_agg		97.65 24	98.06 24	97.18 28	98.94 26	98.91 51	98.98 20	97.07 24	96.71 42	90.66 54	97.43 20	99.08 16	98.20 22	97.96 34	97.14 47	99.22 79	99.19 33

AdaColmap		97.53 25	96.93 38	98.24 9	99.21 18	98.77 58	98.47 29	97.34 17	96.68 43	96.52 10	95.11 39	96.12 48	98.72 13	97.19 53	96.24 69	99.17 85	98.39 101

TSAR-MVS + GP.		97.45 26	98.36 13	96.39 36	95.56 75	98.93 45	97.74 42	93.31 47	97.61 22	94.24 25	98.44 6	99.19 9	98.03 27	97.60 42	97.41 41	99.44 38	99.33 16

CSCG		97.44 27	97.18 34	97.75 22	99.47 5	99.52 3	98.55 27	95.41 33	97.69 18	95.72 14	94.29 45	95.53 52	98.10 25	96.20 96	97.38 42	99.24 73	99.62 2

ACMMP		97.37 28	97.48 30	97.25 26	98.88 30	99.28 15	98.47 29	96.86 27	97.04 37	92.15 42	97.57 17	96.05 50	97.67 32	97.27 49	95.99 77	99.46 26	99.14 39
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
PLC	94.95 3	97.37 28	96.77 41	98.07 15	98.97 25	98.21 82	97.94 39	96.85 28	97.66 20	97.58 1	93.33 50	96.84 40	98.01 28	97.13 55	96.20 72	99.09 97	98.01 118
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
3Dnovator+	93.91 7	97.23 30	97.22 31	97.24 27	98.89 29	98.85 55	98.26 32	93.25 50	97.99 11	95.56 17	90.01 85	98.03 33	98.05 26	97.91 35	98.43 9	99.44 38	99.35 14

MVS_111021_LR		97.16 31	98.01 26	96.16 40	98.47 37	98.98 38	96.94 53	93.89 41	97.64 21	91.44 46	98.89 1	96.41 43	97.20 40	98.02 33	97.29 46	99.04 105	98.85 71

3Dnovator	93.79 8	97.08 32	97.20 32	96.95 32	99.09 22	99.03 33	98.20 33	93.33 46	97.99 11	93.82 27	90.61 79	96.80 41	97.82 29	97.90 36	98.78 3	99.47 24	99.26 24

MVS_111021_HR		97.04 33	98.20 21	95.69 46	98.44 39	99.29 14	96.59 68	93.20 51	97.70 16	89.94 64	98.46 5	96.89 39	96.71 56	98.11 30	97.95 23	99.27 68	99.01 55

DeepPCF-MVS	95.28 2	97.00 34	98.35 15	95.42 51	97.30 54	98.94 41	94.82 107	96.03 31	98.24 6	92.11 43	95.80 32	98.64 25	95.51 72	98.95 4	98.66 5	96.78 191	99.20 32

OMC-MVS		97.00 34	96.92 39	97.09 29	98.69 32	98.66 64	97.85 40	95.02 35	98.09 9	94.47 22	93.15 51	96.90 38	97.38 37	97.16 54	96.82 55	99.13 92	97.65 142

CNLPA		96.90 36	96.28 47	97.64 23	98.56 36	98.63 68	96.85 56	96.60 29	97.73 14	97.08 4	89.78 87	96.28 47	97.80 31	96.73 68	96.63 57	98.94 110	98.14 114

CANet		96.84 37	97.20 32	96.42 35	97.92 46	99.24 21	98.60 25	93.51 45	97.11 34	93.07 32	91.16 71	97.24 37	96.21 63	98.24 24	98.05 20	99.22 79	99.35 14

CDPH-MVS		96.84 37	97.49 29	96.09 41	98.92 27	98.85 55	98.61 24	95.09 34	96.00 60	87.29 93	95.45 36	97.42 35	97.16 41	97.83 38	97.94 24	99.44 38	98.92 64

QAPM		96.78 39	97.14 35	96.36 37	99.05 23	99.14 26	98.02 36	93.26 48	97.27 31	90.84 52	91.16 71	97.31 36	97.64 34	97.70 40	98.20 14	99.33 57	99.18 36

DeepC-MVS	94.87 4	96.76 40	96.50 44	97.05 30	98.21 42	99.28 15	98.67 23	97.38 14	97.31 29	90.36 60	89.19 89	93.58 60	98.19 23	98.31 19	98.50 6	99.51 17	99.36 13
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TAPA-MVS	94.18 5	96.38 41	96.49 45	96.25 38	98.26 41	98.66 64	98.00 37	94.96 36	97.17 32	89.48 69	92.91 54	96.35 44	97.53 35	96.59 75	95.90 81	99.28 66	97.82 131
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MVS_0304		96.31 42	96.91 40	95.62 47	97.21 56	99.20 22	98.55 27	93.10 53	97.04 37	89.73 66	90.30 81	96.35 44	95.71 67	98.14 27	97.93 26	99.38 50	99.40 11

EPNet		96.27 43	96.97 37	95.46 50	98.47 37	98.28 77	97.41 47	93.67 43	95.86 65	92.86 37	97.51 18	93.79 58	91.76 126	97.03 56	97.03 48	98.61 156	99.28 19
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DELS-MVS		96.06 44	96.04 50	96.07 43	97.77 48	99.25 19	98.10 35	93.26 48	94.42 90	92.79 38	88.52 96	93.48 61	95.06 78	98.51 11	98.83 1	99.45 30	99.28 19
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
PCF-MVS	93.95 6	95.65 45	95.14 61	96.25 38	97.73 50	98.73 61	97.59 45	97.13 23	92.50 122	89.09 76	89.85 86	96.65 42	96.90 52	94.97 123	94.89 109	99.08 98	98.38 102
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MAR-MVS		95.50 46	95.60 54	95.39 52	98.67 33	98.18 83	95.89 82	89.81 97	94.55 89	91.97 44	92.99 52	90.21 75	97.30 38	96.79 64	97.49 36	98.72 147	98.99 57
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
OpenMVS	92.33 11	95.50 46	95.22 60	95.82 45	98.98 24	98.97 39	97.67 44	93.04 56	94.64 87	89.18 74	84.44 127	94.79 54	96.79 54	97.23 50	97.61 33	99.24 73	98.88 68

CHOSEN 280x420		95.46 48	97.01 36	93.66 84	97.28 55	97.98 88	96.40 75	85.39 148	96.10 58	91.07 48	96.53 27	96.34 46	95.61 69	97.65 41	96.95 51	96.21 195	97.49 144

LS3D		95.46 48	95.14 61	95.84 44	97.91 47	98.90 53	98.58 26	97.79 2	97.07 36	83.65 106	88.71 92	88.64 85	97.82 29	97.49 45	97.42 40	99.26 72	97.72 141

PVSNet_BlendedMVS		95.41 50	95.28 58	95.57 48	97.42 52	99.02 35	95.89 82	93.10 53	96.16 54	93.12 30	91.99 63	85.27 101	94.66 80	98.09 31	97.34 43	99.24 73	99.08 43

PVSNet_Blended		95.41 50	95.28 58	95.57 48	97.42 52	99.02 35	95.89 82	93.10 53	96.16 54	93.12 30	91.99 63	85.27 101	94.66 80	98.09 31	97.34 43	99.24 73	99.08 43

IS_MVSNet		95.28 52	96.43 46	93.94 76	95.30 86	99.01 37	95.90 80	91.12 84	94.13 96	87.50 90	91.23 70	94.45 56	94.17 89	98.45 14	98.50 6	99.65 2	99.23 27

EPP-MVSNet		95.27 53	96.18 49	94.20 73	94.88 101	98.64 66	94.97 102	90.70 86	95.34 75	89.67 68	91.66 67	93.84 57	95.42 74	97.32 48	97.00 49	99.58 6	99.47 8

canonicalmvs		95.25 54	95.45 57	95.00 58	95.27 88	98.72 62	96.89 54	89.82 96	96.51 45	90.84 52	93.72 46	86.01 96	97.66 33	95.78 108	97.94 24	99.54 11	99.50 7

UGNet		94.92 55	96.63 42	92.93 93	96.03 69	98.63 68	94.53 112	91.52 81	96.23 52	90.03 62	92.87 55	96.10 49	86.28 189	96.68 70	96.60 58	99.16 88	99.32 17
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
MVSTER		94.89 56	95.07 63	94.68 68	94.71 104	96.68 114	97.00 51	90.57 88	95.18 82	93.05 34	95.21 37	86.41 93	93.72 96	97.59 43	95.88 83	99.00 106	98.50 93

diffmvs		94.83 57	95.64 53	93.89 78	94.73 103	97.96 89	96.49 72	89.13 106	96.82 40	89.47 70	91.66 67	93.63 59	95.15 76	94.76 124	95.93 78	98.36 170	98.69 79

MVS_Test		94.82 58	95.66 52	93.84 80	94.79 102	98.35 76	96.49 72	89.10 107	96.12 56	87.09 94	92.58 58	90.61 73	96.48 59	96.51 84	96.89 52	99.11 95	98.54 88

MSDG		94.82 58	93.73 88	96.09 41	98.34 40	97.43 95	97.06 50	96.05 30	95.84 66	90.56 55	86.30 118	89.10 82	95.55 71	96.13 99	95.61 94	99.00 106	95.73 180

TSAR-MVS + COLMAP		94.79 60	94.51 71	95.11 54	96.50 62	97.54 91	97.99 38	94.54 37	97.81 13	85.88 96	96.73 25	81.28 125	96.99 51	96.29 92	95.21 103	98.76 144	96.73 169

CLD-MVS		94.79 60	94.36 75	95.30 53	95.21 91	97.46 93	97.23 49	92.24 67	96.43 46	91.77 45	92.69 56	84.31 107	96.06 64	95.52 113	95.03 105	99.31 62	99.06 47
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
PVSNet_Blended_VisFu		94.77 62	95.54 56	93.87 79	96.48 63	98.97 39	94.33 115	91.84 76	94.93 85	90.37 59	85.04 123	94.99 53	90.87 142	98.12 29	97.30 45	99.30 64	99.45 10

PatchMatch-RL		94.69 63	94.41 73	95.02 56	97.63 51	98.15 85	94.50 113	91.99 73	95.32 76	91.31 47	95.47 35	83.44 112	96.02 66	96.56 78	95.23 102	98.69 151	96.67 170

PMMVS		94.61 64	95.56 55	93.50 86	94.30 110	96.74 112	94.91 105	89.56 101	95.58 71	87.72 87	96.15 29	92.86 63	96.06 64	95.47 114	95.02 106	98.43 168	97.09 156

Vis-MVSNet (Re-imp)		94.46 65	96.24 48	92.40 99	95.23 90	98.64 66	95.56 92	90.99 85	94.42 90	85.02 99	90.88 77	94.65 55	88.01 179	98.17 26	98.37 13	99.57 8	98.53 89

HQP-MVS		94.43 66	94.57 69	94.27 72	96.41 65	97.23 98	96.89 54	93.98 40	95.94 62	83.68 105	95.01 40	84.46 106	95.58 70	95.47 114	94.85 111	99.07 100	99.00 56

ACMP	92.88 9	94.43 66	94.38 74	94.50 70	96.01 70	97.69 90	95.85 85	92.09 70	95.74 69	89.12 75	95.14 38	82.62 120	94.77 79	95.73 109	94.67 112	99.14 91	99.06 47
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ACMM	92.75 10	94.41 68	93.84 85	95.09 55	96.41 65	96.80 108	94.88 106	93.54 44	96.41 47	90.16 61	92.31 61	83.11 117	96.32 60	96.22 95	94.65 113	99.22 79	97.35 149
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tfpn_ndepth		94.36 69	94.64 67	94.04 75	95.16 93	98.51 72	95.58 90	92.09 70	95.78 68	88.52 78	92.38 60	85.74 98	93.34 103	96.39 86	95.90 81	99.54 11	97.79 133

tfpn1000		94.14 70	94.54 70	93.67 83	95.27 88	98.50 73	95.36 96	91.84 76	96.31 49	87.38 91	92.98 53	84.04 108	92.60 113	96.49 85	95.62 93	99.55 9	97.82 131

LGP-MVS_train		94.12 71	94.62 68	93.53 85	96.44 64	97.54 91	97.40 48	91.84 76	94.66 86	81.09 121	95.70 33	83.36 116	95.10 77	96.36 90	95.71 89	99.32 59	99.03 52

RPSCF		94.05 72	94.00 81	94.12 74	96.20 67	96.41 122	96.61 66	91.54 80	95.83 67	89.73 66	96.94 24	92.80 64	95.35 75	91.63 187	90.44 195	95.27 207	93.94 196

DI_MVS_plusplus_trai		94.01 73	93.63 90	94.44 71	94.54 107	98.26 80	97.51 46	90.63 87	95.88 64	89.34 73	80.54 148	89.36 79	95.48 73	96.33 91	96.27 66	99.17 85	98.78 75

UA-Net		93.96 74	95.95 51	91.64 105	96.06 68	98.59 70	95.29 97	90.00 93	91.06 140	82.87 108	90.64 78	98.06 32	86.06 190	98.14 27	98.20 14	99.58 6	96.96 163

CANet_DTU		93.92 75	96.57 43	90.83 113	95.63 73	98.39 75	96.99 52	87.38 125	96.26 50	71.97 183	96.31 28	93.02 62	94.53 83	97.38 47	96.83 54	98.49 163	97.79 133

FC-MVSNet-train		93.85 76	93.91 82	93.78 81	94.94 100	96.79 111	94.29 116	91.13 83	93.84 100	88.26 82	90.40 80	85.23 103	94.65 82	96.54 80	95.31 100	99.38 50	99.28 19

GBi-Net		93.81 77	94.18 78	93.38 87	91.34 141	95.86 136	96.22 76	88.68 108	95.23 79	90.40 56	86.39 114	91.16 68	94.40 86	96.52 81	96.30 61	99.21 82	97.79 133

test1		93.81 77	94.18 78	93.38 87	91.34 141	95.86 136	96.22 76	88.68 108	95.23 79	90.40 56	86.39 114	91.16 68	94.40 86	96.52 81	96.30 61	99.21 82	97.79 133

FMVSNet3		93.79 79	94.17 80	93.35 89	91.21 144	95.99 129	96.62 65	88.68 108	95.23 79	90.40 56	86.39 114	91.16 68	94.11 90	95.96 101	96.67 56	99.07 100	97.79 133

conf200view11		93.64 80	92.57 100	94.88 61	95.33 82	98.94 41	96.82 57	92.31 61	92.63 116	88.26 82	87.21 101	78.01 136	97.12 44	96.82 60	95.85 85	99.45 30	98.56 85

tfpn200view9		93.64 80	92.57 100	94.89 60	95.33 82	98.94 41	96.82 57	92.31 61	92.63 116	88.29 79	87.21 101	78.01 136	97.12 44	96.82 60	95.85 85	99.45 30	98.56 85

tfpnview11		93.63 82	94.42 72	92.71 95	95.08 96	98.26 80	95.58 90	92.06 72	96.32 48	81.88 112	93.44 47	83.43 113	92.14 118	96.58 77	95.88 83	99.52 13	97.07 160

thres200		93.62 83	92.54 102	94.88 61	95.36 81	98.93 45	96.75 63	92.31 61	92.84 114	88.28 81	86.99 104	77.81 139	97.13 42	96.82 60	95.92 79	99.45 30	98.49 94

OPM-MVS		93.61 84	92.43 108	95.00 58	96.94 59	97.34 96	97.78 41	94.23 39	89.64 157	85.53 97	88.70 93	82.81 118	96.28 62	96.28 93	95.00 108	99.24 73	97.22 153

thresconf0.02		93.57 85	93.84 85	93.25 90	95.03 99	98.16 84	95.80 87	92.46 58	96.12 56	83.88 103	92.61 57	80.39 126	92.83 111	96.11 100	96.21 71	99.49 20	97.28 152

tfpn_n400		93.56 86	94.36 75	92.63 96	95.07 97	98.28 77	95.50 94	91.98 74	95.48 72	81.88 112	93.44 47	83.43 113	92.01 121	96.60 73	96.27 66	99.34 55	97.04 161

tfpnconf		93.56 86	94.36 75	92.63 96	95.07 97	98.28 77	95.50 94	91.98 74	95.48 72	81.88 112	93.44 47	83.43 113	92.01 121	96.60 73	96.27 66	99.34 55	97.04 161

thres400		93.56 86	92.43 108	94.87 63	95.40 80	98.91 51	96.70 64	92.38 60	92.93 113	88.19 84	86.69 109	77.35 140	97.13 42	96.75 67	95.85 85	99.42 43	98.56 85

thres100view900		93.55 89	92.47 107	94.81 64	95.33 82	98.74 59	96.78 62	92.30 65	92.63 116	88.29 79	87.21 101	78.01 136	96.78 55	96.38 88	95.92 79	99.38 50	98.40 100

view600		93.50 90	92.39 111	94.80 65	95.41 79	98.93 45	96.60 67	92.30 65	93.09 110	87.96 85	86.67 110	76.97 142	97.12 44	96.83 59	95.64 91	99.43 42	98.62 82

thres600view7		93.49 91	92.37 112	94.79 66	95.42 76	98.93 45	96.58 69	92.31 61	93.04 111	87.88 86	86.62 111	76.94 143	97.09 48	96.82 60	95.63 92	99.45 30	98.63 81

view800		93.45 92	92.37 112	94.71 67	95.42 76	98.92 49	96.51 71	92.19 68	93.14 109	87.62 88	86.72 108	76.54 146	97.08 49	96.86 58	95.74 88	99.45 30	98.70 78

FMVSNet2		93.30 93	93.36 96	93.22 91	91.34 141	95.86 136	96.22 76	88.24 113	95.15 83	89.92 65	81.64 142	89.36 79	94.40 86	96.77 65	96.98 50	99.21 82	97.79 133

COLMAP_ROB	90.49 14	93.27 94	92.71 99	93.93 77	97.75 49	97.44 94	96.07 79	93.17 52	95.40 74	83.86 104	83.76 132	88.72 84	93.87 92	94.25 135	94.11 128	98.87 115	95.28 186
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
conf0.002		93.20 95	91.63 120	95.02 56	95.31 85	98.94 41	96.82 57	92.43 59	92.63 116	88.99 77	88.16 99	70.49 194	97.12 44	96.77 65	96.30 61	99.44 38	98.16 113

Effi-MVS+		92.93 96	93.86 84	91.86 101	94.07 114	98.09 87	95.59 89	85.98 141	94.27 93	79.54 128	91.12 74	81.81 122	96.71 56	96.67 71	96.06 74	99.27 68	98.98 59

tfpn		92.91 97	91.44 124	94.63 69	95.42 76	98.92 49	96.41 74	92.10 69	93.19 107	87.34 92	86.85 105	69.20 202	97.01 50	96.88 57	96.28 65	99.47 24	98.75 77

CDS-MVSNet		92.77 98	93.60 91	91.80 103	92.63 131	96.80 108	95.24 98	89.14 105	90.30 151	84.58 100	86.76 106	90.65 72	90.42 158	95.89 103	96.49 59	98.79 131	98.32 106
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Vis-MVSNet		92.77 98	95.00 65	90.16 123	94.10 113	98.79 57	94.76 109	88.26 112	92.37 127	79.95 124	88.19 98	91.58 67	84.38 199	97.59 43	97.58 34	99.52 13	98.91 66
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CHOSEN 1792x2688		92.66 100	92.49 105	92.85 94	97.13 57	98.89 54	95.90 80	88.50 111	95.32 76	83.31 107	71.99 198	88.96 83	94.10 91	96.69 69	96.49 59	98.15 173	99.10 40

IterMVS-LS		92.56 101	93.18 97	91.84 102	93.90 116	94.97 172	94.99 101	86.20 137	94.18 95	82.68 109	85.81 120	87.36 90	94.43 84	95.31 116	96.02 76	98.87 115	98.60 84
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
conf0.05thres1000		92.47 102	91.39 125	93.73 82	95.21 91	98.52 71	95.66 88	91.56 79	90.87 143	84.27 101	82.79 138	76.12 147	96.29 61	96.59 75	95.68 90	99.39 48	99.19 33

EPNet_dtu		92.45 103	95.02 64	89.46 132	98.02 45	95.47 150	94.79 108	92.62 57	94.97 84	70.11 196	94.76 43	92.61 65	84.07 202	95.94 102	95.56 95	97.15 188	95.82 179
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
HyFIR lowres test		92.03 104	91.55 122	92.58 98	97.13 57	98.72 62	94.65 110	86.54 133	93.58 104	82.56 110	67.75 213	90.47 74	95.67 68	95.87 104	95.54 96	98.91 113	98.93 63

test0.0.03 1		91.97 105	93.91 82	89.72 128	93.31 125	96.40 123	91.34 177	87.06 129	93.86 98	81.67 117	91.15 73	89.16 81	86.02 191	95.08 120	95.09 104	98.91 113	96.64 172

Fast-Effi-MVS+		91.87 106	92.08 115	91.62 106	92.91 129	97.21 99	94.93 103	84.60 160	93.61 102	81.49 119	83.50 133	78.95 131	96.62 58	96.55 79	96.22 70	99.16 88	98.51 92

MS-PatchMatch		91.82 107	92.51 103	91.02 109	95.83 72	96.88 103	95.05 100	84.55 163	93.85 99	82.01 111	82.51 140	91.71 66	90.52 155	95.07 121	93.03 149	98.13 174	94.52 189

Effi-MVS+-dtu		91.78 108	93.59 92	89.68 131	92.44 133	97.11 100	94.40 114	84.94 156	92.43 123	75.48 147	91.09 75	83.75 111	93.55 100	96.61 72	95.47 97	97.24 187	98.67 80

IB-MVS	89.56 15	91.71 109	92.50 104	90.79 115	95.94 71	98.44 74	87.05 201	91.38 82	93.15 108	92.98 36	84.78 124	85.14 104	78.27 209	92.47 162	94.44 124	99.10 96	99.08 43
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
FC-MVSNet-test		91.63 110	93.82 87	89.08 136	92.02 137	96.40 123	93.26 128	87.26 126	93.72 101	77.26 135	88.61 95	89.86 77	85.50 192	95.72 111	95.02 106	99.16 88	97.44 146

test-LLR		91.62 111	93.56 93	89.35 135	93.31 125	96.57 117	92.02 167	87.06 129	92.34 128	75.05 155	90.20 82	88.64 85	90.93 138	96.19 97	94.07 129	97.75 183	96.90 166

MDTV_nov1_ep13		91.57 112	93.18 97	89.70 129	93.39 123	96.97 101	93.53 123	80.91 191	95.70 70	81.86 115	92.40 59	89.93 76	93.25 106	91.97 185	90.80 192	95.25 208	94.46 191

FMVSNet1		91.54 113	90.93 130	92.26 100	90.35 152	95.27 164	95.22 99	87.16 128	91.37 137	87.62 88	75.45 161	83.84 110	94.43 84	96.52 81	96.30 61	98.82 118	97.74 140

ACMH	90.77 13	91.51 114	91.63 120	91.38 107	95.62 74	96.87 105	91.76 172	89.66 99	91.58 135	78.67 130	86.73 107	78.12 134	93.77 95	94.59 126	94.54 120	98.78 138	98.98 59
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMH+	90.88 12	91.41 115	91.13 127	91.74 104	95.11 95	96.95 102	93.13 130	89.48 102	92.42 124	79.93 125	85.13 122	78.02 135	93.82 94	93.49 147	93.88 134	98.94 110	97.99 119

DWT-MVSNet_training		91.30 116	89.73 138	93.13 92	94.64 106	96.87 105	94.93 103	86.17 138	94.22 94	93.18 29	89.11 90	73.28 170	93.59 99	88.00 206	90.73 193	96.26 194	95.87 177

Fast-Effi-MVS+-dtu		91.19 117	93.64 89	88.33 150	92.19 136	96.46 120	93.99 119	81.52 189	92.59 120	71.82 184	92.17 62	85.54 99	91.68 127	95.73 109	94.64 114	98.80 125	98.34 103

TESTMET0.1,1		91.07 118	93.56 93	88.17 154	90.43 149	96.57 117	92.02 167	82.83 174	92.34 128	75.05 155	90.20 82	88.64 85	90.93 138	96.19 97	94.07 129	97.75 183	96.90 166

test-mter		90.95 119	93.54 95	87.93 165	90.28 153	96.80 108	91.44 174	82.68 176	92.15 132	74.37 163	89.57 88	88.23 88	90.88 141	96.37 89	94.31 125	97.93 180	97.37 148

EPMVS		90.88 120	92.12 114	89.44 133	94.71 104	97.24 97	93.55 122	76.81 203	95.89 63	81.77 116	91.49 69	86.47 92	93.87 92	90.21 196	90.07 197	95.92 197	93.49 202

CostFormer		90.69 121	90.48 135	90.93 111	94.18 111	96.08 128	94.03 118	78.20 199	93.47 105	89.96 63	90.97 76	80.30 127	93.72 96	87.66 209	88.75 201	95.51 203	96.12 174

USDC		90.69 121	90.52 134	90.88 112	94.17 112	96.43 121	95.82 86	86.76 131	93.92 97	76.27 143	86.49 113	74.30 158	93.67 98	95.04 122	93.36 143	98.61 156	94.13 194

PatchmatchNet		90.56 123	92.49 105	88.31 151	93.83 119	96.86 107	92.42 141	76.50 207	95.96 61	78.31 131	91.96 65	89.66 78	93.48 101	90.04 198	89.20 200	95.32 205	93.73 200
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
pmmvs4		90.55 124	89.91 137	91.30 108	90.26 154	94.95 173	92.73 135	87.94 119	93.44 106	85.35 98	82.28 141	76.09 149	93.02 110	93.56 145	92.26 184	98.51 162	96.77 168

TAMVS		90.54 125	90.87 132	90.16 123	91.48 139	96.61 116	93.26 128	86.08 139	87.71 186	81.66 118	83.11 137	84.04 108	90.42 158	94.54 127	94.60 115	98.04 178	95.48 184

FMVSNet5		90.36 126	90.93 130	89.70 129	87.99 201	92.25 198	92.03 166	83.51 167	92.20 131	84.13 102	85.59 121	86.48 91	92.43 115	94.61 125	94.52 121	98.13 174	90.85 211

UniMVSNet_NR-MVSNet		90.35 127	89.96 136	90.80 114	89.66 160	95.83 139	92.48 139	90.53 89	90.96 142	79.57 126	79.33 152	77.14 141	93.21 107	92.91 156	94.50 123	99.37 53	99.05 49

IterMVS		90.20 128	92.43 108	87.61 175	92.82 130	94.31 190	94.11 117	81.54 188	92.97 112	69.90 197	84.71 125	88.16 89	89.96 169	95.25 117	94.17 127	97.31 186	97.46 145
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
RPMNet		90.19 129	92.03 116	88.05 160	93.46 121	95.95 133	93.41 125	74.59 216	92.40 125	75.91 145	84.22 128	86.41 93	92.49 114	94.42 131	93.85 136	98.44 166	96.96 163

CR-MVSNet		90.16 130	91.96 117	88.06 159	93.32 124	95.95 133	93.36 126	75.99 210	92.40 125	75.19 152	83.18 135	85.37 100	92.05 119	95.21 118	94.56 118	98.47 165	97.08 158

dps		90.11 131	89.37 143	90.98 110	93.89 117	96.21 126	93.49 124	77.61 201	91.95 133	92.74 40	88.85 91	78.77 133	92.37 116	87.71 208	87.71 207	95.80 198	94.38 192

UniMVSNet (Re)		90.03 132	89.61 140	90.51 118	89.97 157	96.12 127	92.32 147	89.26 103	90.99 141	80.95 122	78.25 155	75.08 155	91.14 133	93.78 140	93.87 135	99.41 44	99.21 31

tpmp4_e23		89.82 133	89.31 144	90.42 119	94.01 115	95.45 151	94.63 111	78.37 196	93.59 103	87.09 94	86.62 111	76.59 145	93.06 109	88.50 203	88.52 202	95.36 204	95.88 176

ADS-MVSNet		89.80 134	91.33 126	88.00 163	94.43 108	96.71 113	92.29 151	74.95 215	96.07 59	77.39 134	88.67 94	86.09 95	93.26 105	88.44 204	89.57 199	95.68 200	93.81 199

CVMVSNet		89.77 135	91.66 119	87.56 177	93.21 127	95.45 151	91.94 171	89.22 104	89.62 158	69.34 201	83.99 130	85.90 97	84.81 197	94.30 134	95.28 101	96.85 190	97.09 156

DU-MVS		89.67 136	88.84 146	90.63 117	89.26 184	95.61 144	92.48 139	89.91 94	91.22 138	79.57 126	77.72 156	71.18 191	93.21 107	92.53 160	94.57 117	99.35 54	99.05 49

testgi		89.42 137	91.50 123	87.00 184	92.40 134	95.59 146	89.15 195	85.27 153	92.78 115	72.42 181	91.75 66	76.00 150	84.09 201	94.38 132	93.82 138	98.65 154	96.15 173

TinyColmap		89.42 137	88.58 148	90.40 120	93.80 120	95.45 151	93.96 120	86.54 133	92.24 130	76.49 140	80.83 146	70.44 195	93.37 102	94.45 130	93.30 146	98.26 172	93.37 204

NR-MVSNet		89.34 139	88.66 147	90.13 126	90.40 150	95.61 144	93.04 132	89.91 94	91.22 138	78.96 129	77.72 156	68.90 204	89.16 173	94.24 136	93.95 132	99.32 59	98.99 57

GA-MVS		89.28 140	90.75 133	87.57 176	91.77 138	96.48 119	92.29 151	87.58 124	90.61 148	65.77 207	84.48 126	76.84 144	89.46 171	95.84 105	93.68 139	98.52 161	97.34 150

Baseline_NR-MVSNet		89.27 141	88.01 156	90.73 116	89.26 184	93.71 193	92.71 136	89.78 98	90.73 145	81.28 120	73.53 190	72.85 171	92.30 117	92.53 160	93.84 137	99.07 100	98.88 68

TranMVSNet+NR-MVSNet		89.23 142	88.48 150	90.11 127	89.07 190	95.25 165	92.91 133	90.43 90	90.31 150	77.10 136	76.62 159	71.57 189	91.83 125	92.12 173	94.59 116	99.32 59	98.92 64

pm-mvs1		89.19 143	89.02 145	89.38 134	90.40 150	95.74 142	92.05 164	88.10 115	86.13 200	77.70 132	73.72 189	79.44 130	88.97 174	95.81 107	94.51 122	99.08 98	97.78 139

PatchT		89.13 144	91.71 118	86.11 194	92.92 128	95.59 146	83.64 208	75.09 214	91.87 134	75.19 152	82.63 139	85.06 105	92.05 119	95.21 118	94.56 118	97.76 182	97.08 158

TDRefinement		89.07 145	88.15 153	90.14 125	95.16 93	96.88 103	95.55 93	90.20 91	89.68 155	76.42 141	76.67 158	74.30 158	84.85 196	93.11 152	91.91 186	98.64 155	94.47 190

MIMVSNet		88.99 146	91.07 128	86.57 187	86.78 209	95.62 143	91.20 180	75.40 213	90.65 147	76.57 139	84.05 129	82.44 121	91.01 137	95.84 105	95.38 99	98.48 164	93.50 201

anonymousdsp		88.90 147	91.00 129	86.44 190	88.74 197	95.97 131	90.40 187	82.86 173	88.77 170	67.33 204	81.18 145	81.44 124	90.22 167	96.23 94	94.27 126	99.12 94	99.16 38

tpm cat1		88.90 147	87.78 166	90.22 122	93.88 118	95.39 160	93.79 121	78.11 200	92.55 121	89.43 71	81.31 144	79.84 129	91.40 129	84.95 215	86.34 217	94.68 215	94.09 195

tpmrst		88.86 149	89.62 139	87.97 164	94.33 109	95.98 130	92.62 137	76.36 208	94.62 88	76.94 137	85.98 119	82.80 119	92.80 112	86.90 210	87.15 211	94.77 212	93.93 197

tfpnnormal		88.50 150	87.01 186	90.23 121	91.36 140	95.78 141	92.74 134	90.09 92	83.65 209	76.33 142	71.46 203	69.58 200	91.84 124	95.54 112	94.02 131	99.06 103	99.03 52

v6		88.43 151	88.01 156	88.92 137	89.60 166	95.43 156	92.36 143	87.66 121	89.07 164	74.50 161	75.06 165	73.47 166	90.59 154	92.11 176	92.76 168	98.79 131	98.18 110

v1neww		88.41 152	88.00 159	88.89 138	89.61 164	95.44 154	92.31 148	87.65 122	89.09 162	74.30 164	75.02 167	73.42 168	90.68 149	92.12 173	92.77 164	98.79 131	98.18 110

v7new		88.41 152	88.00 159	88.89 138	89.61 164	95.44 154	92.31 148	87.65 122	89.09 162	74.30 164	75.02 167	73.42 168	90.68 149	92.12 173	92.77 164	98.79 131	98.18 110

SixPastTwentyTwo		88.37 154	89.47 141	87.08 182	90.01 156	95.93 135	87.41 198	85.32 150	90.26 152	70.26 194	86.34 117	71.95 185	90.93 138	92.89 157	91.72 188	98.55 159	97.22 153

V42		88.31 155	87.95 162	88.73 145	89.44 170	95.34 161	92.23 158	87.21 127	88.83 168	74.49 162	74.89 171	73.43 167	90.41 161	92.08 180	92.77 164	98.60 158	98.33 104

v2v482		88.25 156	87.71 167	88.88 140	89.23 188	95.28 162	92.10 162	87.89 120	88.69 171	73.31 177	75.32 162	71.64 187	91.89 123	92.10 179	92.92 152	98.86 117	97.99 119

v8		88.21 157	87.94 163	88.51 147	89.62 162	95.01 171	92.31 148	84.99 155	88.94 166	74.70 159	75.03 166	73.51 165	90.67 151	92.11 176	92.74 170	98.80 125	98.24 108

v7		88.18 158	88.01 156	88.39 148	89.45 169	95.14 168	92.36 143	85.37 149	89.29 161	72.94 180	73.98 185	72.77 174	91.38 130	93.59 141	92.87 154	98.82 118	98.42 97

v1141		88.17 159	87.69 168	88.74 143	89.44 170	95.41 157	92.25 156	87.98 116	88.38 176	73.54 175	74.43 175	72.71 179	90.45 156	92.08 180	92.72 172	98.79 131	98.09 115

divwei89l23v2f112		88.17 159	87.69 168	88.74 143	89.44 170	95.41 157	92.26 154	87.97 118	88.29 180	73.57 174	74.45 174	72.75 176	90.42 158	92.08 180	92.72 172	98.81 122	98.09 115

v1		88.17 159	87.66 170	88.77 142	89.44 170	95.40 159	92.29 151	87.98 116	88.21 183	73.75 169	74.41 177	72.75 176	90.36 164	92.07 183	92.71 175	98.80 125	98.09 115

v10		88.00 162	87.96 161	88.05 160	89.44 170	94.68 181	92.36 143	83.35 170	89.37 160	72.96 178	73.98 185	72.79 173	91.35 131	93.59 141	92.88 153	98.81 122	98.42 97

tpm		87.95 163	89.44 142	86.21 192	92.53 132	94.62 185	91.40 175	76.36 208	91.46 136	69.80 199	87.43 100	75.14 153	91.55 128	89.85 201	90.60 194	95.61 201	96.96 163

v18		87.93 164	87.61 172	88.31 151	89.74 158	92.04 199	92.59 138	82.71 175	89.70 154	75.32 150	75.23 163	73.55 164	90.74 145	92.11 176	92.77 164	98.78 138	97.87 127

WR-MVS_H		87.93 164	87.85 164	88.03 162	89.62 162	95.58 148	90.47 186	85.55 146	87.20 192	76.83 138	74.42 176	72.67 181	86.37 188	93.22 151	93.04 148	99.33 57	98.83 72

WR-MVS		87.93 164	88.09 154	87.75 168	89.26 184	95.28 162	90.81 183	86.69 132	88.90 167	75.29 151	74.31 178	73.72 161	85.19 195	92.26 163	93.32 145	99.27 68	98.81 73

v1144		87.92 167	87.79 165	88.07 157	89.27 183	95.15 167	92.17 161	85.62 145	88.52 172	71.52 185	73.80 188	72.40 184	91.06 136	93.54 146	92.80 158	98.81 122	98.33 104

CP-MVSNet		87.89 168	87.27 177	88.62 146	89.30 180	95.06 169	90.60 185	85.78 143	87.43 190	75.98 144	74.60 172	68.14 206	90.76 143	93.07 154	93.60 140	99.30 64	98.98 59

v16		87.87 169	87.60 173	88.19 153	89.70 159	92.01 201	92.37 142	82.54 178	89.67 156	75.00 157	75.02 167	73.65 162	90.73 147	92.14 172	92.80 158	98.77 142	97.90 124

pmmvs5		87.83 170	88.09 154	87.51 179	89.59 167	95.48 149	89.75 193	84.73 158	86.07 202	71.44 186	80.57 147	70.09 198	90.74 145	94.47 129	92.87 154	98.82 118	97.10 155

v17		87.83 170	87.56 174	88.13 155	89.65 161	92.02 200	92.34 146	82.55 177	89.38 159	74.76 158	75.14 164	73.59 163	90.70 148	92.15 171	92.78 162	98.78 138	97.89 125

TransMVSNet (Re)		87.73 172	86.79 188	88.83 141	90.76 146	94.40 188	91.33 178	89.62 100	84.73 205	75.41 149	72.73 194	71.41 190	86.80 186	94.53 128	93.93 133	99.06 103	95.83 178

v11		87.58 173	87.50 175	87.67 172	89.34 178	91.91 206	92.22 160	81.63 186	89.01 165	72.95 179	74.11 183	72.51 183	91.08 135	94.01 139	93.00 150	98.77 142	97.93 122

LTVRE_ROB	87.32 16	87.55 174	88.25 152	86.73 185	90.66 147	95.80 140	93.05 131	84.77 157	83.35 210	60.32 216	83.12 136	67.39 207	93.32 104	94.36 133	94.86 110	98.28 171	98.87 70
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
v1192		87.51 175	87.31 176	87.74 169	89.04 191	94.87 179	92.07 163	85.03 154	88.49 173	70.32 193	72.65 195	70.35 196	91.21 132	93.59 141	92.80 158	98.78 138	98.42 97

v148		87.51 175	86.79 188	88.36 149	89.39 176	95.21 166	89.84 192	88.20 114	87.61 188	77.56 133	73.38 192	70.32 197	86.80 186	90.70 193	92.31 181	98.37 169	97.98 121

V14		87.47 177	87.19 180	87.80 167	89.37 177	91.95 203	92.25 156	82.12 182	88.39 175	73.83 168	74.31 178	72.84 172	90.44 157	92.20 168	92.78 162	98.80 125	97.84 129

v15		87.46 178	87.16 181	87.81 166	89.41 175	91.96 202	92.26 154	82.28 181	88.42 174	73.72 170	74.29 180	72.73 178	90.41 161	92.17 170	92.76 168	98.79 131	97.83 130

V9		87.41 179	87.15 182	87.72 170	89.33 179	91.93 204	92.23 158	82.02 183	88.35 177	73.59 173	74.13 182	72.77 174	90.37 163	92.21 167	92.80 158	98.79 131	97.86 128

v144192		87.40 180	87.20 179	87.64 173	88.89 192	94.88 178	91.65 173	84.70 159	87.80 185	71.17 190	73.20 193	70.91 192	90.75 144	92.69 158	92.49 177	98.71 148	98.43 96

v12		87.38 181	87.13 183	87.68 171	89.30 180	91.92 205	92.01 169	81.94 184	88.35 177	73.69 171	74.10 184	72.57 182	90.33 166	92.23 165	92.82 156	98.80 125	97.91 123

v13		87.34 182	87.11 185	87.62 174	89.30 180	91.91 206	92.04 165	81.86 185	88.35 177	73.36 176	73.88 187	72.69 180	90.34 165	92.23 165	92.82 156	98.80 125	97.88 126

PS-CasMVS		87.33 183	86.68 191	88.10 156	89.22 189	94.93 174	90.35 188	85.70 144	86.44 196	74.01 166	73.43 191	66.59 212	90.04 168	92.92 155	93.52 141	99.28 66	98.91 66

v1921920		87.31 184	87.13 183	87.52 178	88.87 194	94.72 180	91.96 170	84.59 161	88.28 181	69.86 198	72.50 196	70.03 199	91.10 134	93.33 149	92.61 176	98.71 148	98.44 95

PEN-MVS		87.22 185	86.50 195	88.07 157	88.88 193	94.44 187	90.99 182	86.21 135	86.53 195	73.66 172	74.97 170	66.56 213	89.42 172	91.20 189	93.48 142	99.24 73	98.31 107

v1240		86.89 186	86.75 190	87.06 183	88.75 196	94.65 183	91.30 179	84.05 164	87.49 189	68.94 202	71.96 199	68.86 205	90.65 152	93.33 149	92.72 172	98.67 152	98.24 108

EG-PatchMatch MVS		86.68 187	87.24 178	86.02 195	90.58 148	96.26 125	91.08 181	81.59 187	84.96 204	69.80 199	71.35 204	75.08 155	84.23 200	94.24 136	93.35 144	98.82 118	95.46 185

DTE-MVSNet		86.67 188	86.09 196	87.35 180	88.45 199	94.08 191	90.65 184	86.05 140	86.13 200	72.19 182	74.58 173	66.77 211	87.61 182	90.31 195	93.12 147	99.13 92	97.62 143

v52		86.57 189	86.63 192	86.50 188	87.47 206	94.89 177	89.90 190	83.39 168	86.36 197	71.17 190	71.53 201	71.65 186	88.34 177	91.14 190	92.32 180	98.74 146	98.52 90

V4		86.56 190	86.61 193	86.50 188	87.49 205	94.90 176	89.87 191	83.39 168	86.25 198	71.20 189	71.57 200	71.58 188	88.30 178	91.14 190	92.31 181	98.75 145	98.52 90

v7n		86.43 191	86.52 194	86.33 191	87.91 202	94.93 174	90.15 189	83.05 171	86.57 194	70.21 195	71.48 202	66.78 210	87.72 180	94.19 138	92.96 151	98.92 112	98.76 76

MDTV_nov1_ep13_2view		86.30 192	88.27 151	84.01 199	87.71 204	94.67 182	88.08 197	76.78 204	90.59 149	68.66 203	80.46 149	80.12 128	87.58 183	89.95 200	88.20 204	95.25 208	93.90 198

gg-mvs-nofinetune		86.17 193	88.57 149	83.36 202	93.44 122	98.15 85	96.58 69	72.05 221	74.12 221	49.23 228	64.81 216	90.85 71	89.90 170	97.83 38	96.84 53	98.97 108	97.41 147

pmmvs6		85.98 194	84.89 204	87.25 181	88.83 195	94.35 189	89.36 194	85.30 152	78.51 218	75.44 148	62.71 219	75.41 152	87.65 181	93.58 144	92.40 179	96.89 189	97.29 151

v748		85.88 195	85.66 198	86.14 193	88.03 200	94.63 184	87.02 202	84.59 161	84.30 206	74.56 160	70.94 205	67.27 208	83.94 203	90.96 192	92.74 170	98.71 148	98.81 73

EU-MVSNet		85.62 196	87.65 171	83.24 203	88.54 198	92.77 197	87.12 200	85.32 150	86.71 193	64.54 209	78.52 154	75.11 154	78.35 208	92.25 164	92.28 183	95.58 202	95.93 175

MVS-HIRNet		85.36 197	86.89 187	83.57 201	90.13 155	94.51 186	83.57 209	72.61 219	88.27 182	71.22 188	68.97 209	81.81 122	88.91 175	93.08 153	91.94 185	94.97 211	89.64 215

N_pmnet		84.80 198	85.10 202	84.45 198	89.25 187	92.86 196	84.04 207	86.21 135	88.78 169	66.73 206	72.41 197	74.87 157	85.21 194	88.32 205	86.45 215	95.30 206	92.04 206

CMPMVS	65.18 17	84.76 199	83.10 208	86.69 186	95.29 87	95.05 170	88.37 196	85.51 147	80.27 216	71.31 187	68.37 211	73.85 160	85.25 193	87.72 207	87.75 206	94.38 216	88.70 216
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
PM-MVS		84.72 200	84.47 205	85.03 197	84.67 211	91.57 209	86.27 204	82.31 180	87.65 187	70.62 192	76.54 160	56.41 224	88.75 176	92.59 159	89.85 198	97.54 185	96.66 171

LP		84.43 201	85.10 202	83.66 200	92.31 135	93.89 192	87.13 199	72.88 218	90.81 144	67.08 205	70.65 206	75.76 151	86.87 185	86.43 213	87.15 211	95.70 199	90.98 210

pmmvs-eth3d		84.33 202	82.94 209	85.96 196	84.16 214	90.94 210	86.55 203	83.79 165	84.25 207	75.85 146	70.64 207	56.43 223	87.44 184	92.20 168	90.41 196	97.97 179	95.68 181

Anonymous20231206		83.84 203	85.19 201	82.26 204	87.38 207	92.87 195	85.49 205	83.65 166	86.07 202	63.44 212	68.42 210	69.01 203	75.45 212	93.34 148	92.44 178	98.12 176	94.20 193

testpf		83.57 204	85.70 197	81.08 205	90.99 145	88.96 215	82.71 211	65.32 229	90.22 153	73.86 167	81.58 143	76.10 148	81.19 206	84.14 219	85.41 219	92.43 222	93.45 203

gm-plane-assit		83.26 205	85.29 200	80.89 206	89.52 168	89.89 213	70.26 222	78.24 198	77.11 219	58.01 221	74.16 181	66.90 209	90.63 153	97.20 51	96.05 75	98.66 153	95.68 181

test20.03		82.92 206	85.52 199	79.90 209	87.75 203	91.84 208	82.80 210	82.99 172	82.65 214	60.32 216	78.90 153	70.50 193	67.10 220	92.05 184	90.89 191	98.44 166	91.80 207

new_pmnet		81.53 207	82.68 210	80.20 207	83.47 216	89.47 214	82.21 213	78.36 197	87.86 184	60.14 218	67.90 212	69.43 201	82.03 205	89.22 202	87.47 208	94.99 210	87.39 217

testus		81.33 208	84.13 206	78.06 212	84.54 212	87.72 216	79.66 215	80.42 192	87.36 191	54.13 227	83.83 131	56.63 222	73.21 217	90.51 194	91.74 187	96.40 192	91.11 209

test2356		81.26 209	84.10 207	77.95 214	84.35 213	87.38 218	79.56 216	79.53 195	86.17 199	54.14 226	83.24 134	60.71 216	73.77 213	90.01 199	91.18 190	96.33 193	90.01 213

MDA-MVSNet-bldmvs		80.11 210	80.24 212	79.94 208	77.01 225	93.21 194	78.86 219	85.94 142	82.71 213	60.86 213	79.71 151	51.77 226	83.71 204	75.60 224	86.37 216	93.28 220	92.35 205

MIMVSNet1		80.03 211	80.93 211	78.97 210	72.46 228	90.73 211	80.81 214	82.44 179	80.39 215	63.64 211	57.57 221	64.93 214	76.37 210	91.66 186	91.55 189	98.07 177	89.70 214

pmmvs3		79.16 212	80.12 213	78.05 213	79.36 220	86.59 220	78.13 220	73.87 217	76.42 220	57.51 222	70.59 208	57.02 221	84.66 198	90.10 197	88.32 203	94.75 213	91.77 208

new-patchmatchnet		78.49 213	78.19 214	78.84 211	84.13 215	90.06 212	77.11 221	80.39 193	79.57 217	59.64 220	66.01 214	55.65 225	75.62 211	84.55 218	80.70 221	96.14 196	90.77 212

Anonymous20231211		75.89 214	74.18 219	77.88 215	81.42 217	87.72 216	79.33 218	81.05 190	66.49 228	60.00 219	45.74 227	51.46 227	71.22 218	85.70 214	86.91 214	94.25 217	95.25 187

FPMVS		75.84 215	74.59 215	77.29 216	86.92 208	83.89 222	85.01 206	80.05 194	82.91 212	60.61 215	65.25 215	60.41 217	63.86 221	75.60 224	73.60 226	87.29 227	80.47 223

1111		73.35 216	74.40 216	72.12 217	78.22 221	82.24 223	65.06 225	65.61 227	70.28 222	55.42 223	56.30 222	57.35 219	73.66 214	86.73 211	88.16 205	94.75 213	79.76 225

testmv		72.66 217	74.40 216	70.62 218	80.64 218	81.51 225	64.99 227	76.60 205	68.76 224	44.81 229	63.78 217	48.00 228	62.52 222	84.74 216	87.17 209	94.19 218	86.86 218

test1235678		72.65 218	74.40 216	70.62 218	80.64 218	81.50 226	64.99 227	76.59 206	68.74 225	44.81 229	63.78 217	47.99 229	62.51 223	84.73 217	87.17 209	94.19 218	86.85 219

test12356		69.55 219	71.53 221	67.24 222	77.70 224	78.48 227	65.92 224	75.55 212	68.39 226	44.26 231	61.80 220	40.70 231	47.92 230	81.45 222	87.01 213	92.09 223	82.89 221

Gipuma		68.35 220	66.71 222	70.27 220	74.16 227	68.78 231	63.93 230	71.77 222	83.34 211	54.57 225	34.37 228	31.88 232	68.69 219	83.30 220	85.53 218	88.48 226	79.78 224
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	63.12 18	67.27 221	66.39 223	68.30 221	77.98 223	60.24 232	59.53 231	76.82 202	66.65 227	60.74 214	54.39 224	59.82 218	51.24 226	73.92 227	70.52 227	83.48 229	79.17 226
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
GG-mvs-BLEND		66.17 222	94.91 66	32.63 230	1.32 235	96.64 115	91.40 175	0.85 234	94.39 92	2.20 238	90.15 84	95.70 51	2.27 234	96.39 86	95.44 98	97.78 181	95.68 181

PMMVS2		64.36 223	65.94 224	62.52 224	67.37 230	77.44 228	64.39 229	69.32 226	61.47 229	34.59 233	46.09 226	41.03 230	48.02 229	74.56 226	78.23 222	91.43 224	82.76 222

.test1245		56.65 224	56.09 225	57.30 225	78.22 221	82.24 223	65.06 225	65.61 227	70.28 222	55.42 223	56.30 222	57.35 219	73.66 214	86.73 211	15.01 230	5.84 234	24.75 231

no-one		55.96 225	55.63 226	56.35 226	68.48 229	73.29 230	43.03 232	72.52 220	44.01 232	34.80 232	32.83 229	29.11 233	35.21 231	56.63 229	75.72 224	84.04 228	77.79 227

E-PMN		50.67 226	47.85 228	53.96 227	64.13 232	50.98 235	38.06 233	69.51 224	51.40 231	24.60 235	29.46 232	24.39 235	56.07 225	48.17 230	59.70 228	71.40 231	70.84 229

EMVS		49.98 227	46.76 229	53.74 228	64.96 231	51.29 234	37.81 234	69.35 225	51.83 230	22.69 236	29.57 231	25.06 234	57.28 224	44.81 231	56.11 229	70.32 232	68.64 230

MVE	50.86 19	49.54 228	51.43 227	47.33 229	44.14 233	59.20 233	36.45 235	60.59 230	41.47 233	31.14 234	29.58 230	17.06 237	48.52 228	62.22 228	74.63 225	63.12 233	75.87 228
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		12.09 229	16.94 230	6.42 231	3.15 234	6.08 236	9.51 237	3.84 232	21.46 234	5.31 237	27.49 233	6.76 238	10.89 232	17.06 232	15.01 230	5.84 234	24.75 231

test123		9.58 230	13.53 231	4.97 232	1.31 236	5.47 237	8.32 238	2.95 233	18.14 235	2.03 239	20.82 234	2.34 239	10.60 233	10.00 233	14.16 232	4.60 236	23.77 233

ESAPD		0.00 231	0.00 232	0.00 233	0.00 237	0.00 238	0.00 239	0.00 235	0.00 236	0.00 240	0.00 235	0.00 240	0.00 235	0.00 234	0.00 233	0.00 237	0.00 234

sosnet-low-res		0.00 231	0.00 232	0.00 233	0.00 237	0.00 238	0.00 239	0.00 235	0.00 236	0.00 240	0.00 235	0.00 240	0.00 235	0.00 234	0.00 233	0.00 237	0.00 234

sosnet		0.00 231	0.00 232	0.00 233	0.00 237	0.00 238	0.00 239	0.00 235	0.00 236	0.00 240	0.00 235	0.00 240	0.00 235	0.00 234	0.00 233	0.00 237	0.00 234

ambc			73.83 220		76.23 226	85.13 221	82.27 212		84.16 208	65.58 208	52.82 225	23.31 236	73.55 216	91.41 188	85.26 220	92.97 221	94.70 188

MTAPA										96.83 5		99.12 13

MTMP										97.18 3		98.83 19

Patchmatch-RL test							34.61 236

tmp_tt				66.88 223	86.07 210	73.86 229	68.22 223	33.38 231	96.88 39	80.67 123	88.23 97	78.82 132	49.78 227	82.68 221	77.47 223	83.19 230

XVS					96.60 60	99.35 8	96.82 57			90.85 49		98.72 22				99.46 26

X-MVStestdata					96.60 60	99.35 8	96.82 57			90.85 49		98.72 22				99.46 26

abl_6				96.82 33	98.60 35	98.74 59	97.74 42	93.73 42	96.25 51	94.37 23	94.55 44	98.60 27	97.25 39			99.27 68	98.61 83

mPP-MVS					99.21 18							98.29 30

NP-MVS									95.32 76

Patchmtry						95.96 132	93.36 126	75.99 210		75.19 152

DeepMVS_CX						86.86 219	79.50 217	70.43 223	90.73 145	63.66 210	80.36 150	60.83 215	79.68 207	76.23 223		89.46 225	86.53 220

High-resolution multi-view benchmark