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 44	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 20	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 42	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 29	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 60	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 44	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 46	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 20	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 83	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 25	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 20	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 71	98.54 158	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 29	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 52	98.53 10	97.81 29	98.96 107	99.59 4

train_agg		97.65 24	98.06 24	97.18 28	98.94 26	98.91 50	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 77	99.19 33

AdaColmap		97.53 25	96.93 38	98.24 9	99.21 18	98.77 57	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 68	99.17 83	98.39 101

TSAR-MVS + GP.		97.45 26	98.36 13	96.39 36	95.56 75	98.93 44	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 37	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 95	97.38 42	99.24 71	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 75	99.46 25	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 81	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 70	99.09 95	98.01 117
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 54	98.26 32	93.25 50	97.99 11	95.56 17	90.01 84	98.03 33	98.05 26	97.91 35	98.43 9	99.44 37	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	99.04 103	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 78	96.80 41	97.82 29	97.90 36	98.78 3	99.47 23	99.26 24

MVS_111021_HR		97.04 33	98.20 21	95.69 46	98.44 39	99.29 14	96.59 67	93.20 51	97.70 16	89.94 64	98.46 5	96.89 39	96.71 55	98.11 30	97.95 23	99.27 66	99.01 55

DeepPCF-MVS	95.28 2	97.00 34	98.35 15	95.42 51	97.30 54	98.94 41	94.82 105	96.03 31	98.24 6	92.11 43	95.80 32	98.64 25	95.51 71	98.95 4	98.66 5	96.78 189	99.20 32

OMC-MVS		97.00 34	96.92 39	97.09 29	98.69 32	98.66 63	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 90	97.65 141

CNLPA		96.90 36	96.28 47	97.64 23	98.56 36	98.63 67	96.85 56	96.60 29	97.73 14	97.08 4	89.78 86	96.28 47	97.80 31	96.73 67	96.63 57	98.94 108	98.14 113

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 70	97.24 37	96.21 62	98.24 24	98.05 20	99.22 77	99.35 14

CDPH-MVS		96.84 37	97.49 29	96.09 41	98.92 27	98.85 54	98.61 24	95.09 34	96.00 59	87.29 92	95.45 36	97.42 35	97.16 41	97.83 38	97.94 24	99.44 37	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 70	97.31 36	97.64 34	97.70 40	98.20 14	99.33 55	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 88	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 63	98.00 37	94.96 36	97.17 32	89.48 69	92.91 54	96.35 44	97.53 35	96.59 74	95.90 79	99.28 64	97.82 130
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 80	96.35 44	95.71 66	98.14 27	97.93 26	99.38 48	99.40 11

EPNet		96.27 43	96.97 37	95.46 50	98.47 37	98.28 76	97.41 47	93.67 43	95.86 64	92.86 37	97.51 18	93.79 58	91.76 124	97.03 56	97.03 48	98.61 154	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 89	92.79 38	88.52 95	93.48 61	95.06 77	98.51 11	98.83 1	99.45 29	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 60	97.59 45	97.13 23	92.50 120	89.09 76	89.85 85	96.65 42	96.90 51	94.97 121	94.89 107	99.08 96	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 82	95.89 81	89.81 95	94.55 88	91.97 44	92.99 52	90.21 75	97.30 38	96.79 64	97.49 36	98.72 145	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 86	89.18 74	84.44 125	94.79 54	96.79 53	97.23 50	97.61 33	99.24 71	98.88 68

CHOSEN 280x420		95.46 48	97.01 36	93.66 83	97.28 55	97.98 86	96.40 74	85.39 146	96.10 57	91.07 48	96.53 27	96.34 46	95.61 68	97.65 41	96.95 51	96.21 193	97.49 143

LS3D		95.46 48	95.14 61	95.84 44	97.91 47	98.90 52	98.58 26	97.79 2	97.07 36	83.65 104	88.71 91	88.64 85	97.82 29	97.49 45	97.42 40	99.26 70	97.72 140

PVSNet_BlendedMVS		95.41 50	95.28 58	95.57 48	97.42 52	99.02 35	95.89 81	93.10 53	96.16 54	93.12 30	91.99 62	85.27 101	94.66 79	98.09 31	97.34 43	99.24 71	99.08 43

PVSNet_Blended		95.41 50	95.28 58	95.57 48	97.42 52	99.02 35	95.89 81	93.10 53	96.16 54	93.12 30	91.99 62	85.27 101	94.66 79	98.09 31	97.34 43	99.24 71	99.08 43

IS_MVSNet		95.28 52	96.43 46	93.94 75	95.30 85	99.01 37	95.90 79	91.12 82	94.13 95	87.50 89	91.23 69	94.45 56	94.17 88	98.45 14	98.50 6	99.65 2	99.23 27

EPP-MVSNet		95.27 53	96.18 49	94.20 72	94.88 99	98.64 65	94.97 100	90.70 84	95.34 74	89.67 68	91.66 66	93.84 57	95.42 73	97.32 48	97.00 49	99.58 6	99.47 8

canonicalmvs		95.25 54	95.45 57	95.00 57	95.27 87	98.72 61	96.89 54	89.82 94	96.51 45	90.84 52	93.72 46	86.01 96	97.66 33	95.78 106	97.94 24	99.54 11	99.50 7

UGNet		94.92 55	96.63 42	92.93 91	96.03 69	98.63 67	94.53 110	91.52 79	96.23 52	90.03 62	92.87 55	96.10 49	86.28 187	96.68 69	96.60 58	99.16 86	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 67	94.71 102	96.68 112	97.00 51	90.57 86	95.18 81	93.05 34	95.21 37	86.41 93	93.72 95	97.59 43	95.88 81	99.00 104	98.50 93

diffmvs		94.83 57	95.64 53	93.89 77	94.73 101	97.96 87	96.49 71	89.13 104	96.82 40	89.47 70	91.66 66	93.63 59	95.15 75	94.76 122	95.93 76	98.36 168	98.69 79

MVS_Test		94.82 58	95.66 52	93.84 79	94.79 100	98.35 75	96.49 71	89.10 105	96.12 56	87.09 93	92.58 57	90.61 73	96.48 58	96.51 83	96.89 52	99.11 93	98.54 88

MSDG		94.82 58	93.73 87	96.09 41	98.34 40	97.43 93	97.06 50	96.05 30	95.84 65	90.56 55	86.30 116	89.10 82	95.55 70	96.13 98	95.61 92	99.00 104	95.73 178

TSAR-MVS + COLMAP		94.79 60	94.51 71	95.11 54	96.50 62	97.54 89	97.99 38	94.54 37	97.81 13	85.88 95	96.73 25	81.28 125	96.99 50	96.29 91	95.21 101	98.76 142	96.73 167

CLD-MVS		94.79 60	94.36 75	95.30 53	95.21 90	97.46 91	97.23 49	92.24 65	96.43 46	91.77 45	92.69 56	84.31 107	96.06 63	95.52 111	95.03 103	99.31 60	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 78	96.48 63	98.97 39	94.33 113	91.84 74	94.93 84	90.37 59	85.04 121	94.99 53	90.87 140	98.12 29	97.30 45	99.30 62	99.45 10

PatchMatch-RL		94.69 63	94.41 73	95.02 56	97.63 51	98.15 83	94.50 111	91.99 71	95.32 75	91.31 47	95.47 35	83.44 112	96.02 65	96.56 77	95.23 100	98.69 149	96.67 168

PMMVS		94.61 64	95.56 55	93.50 85	94.30 108	96.74 110	94.91 103	89.56 99	95.58 70	87.72 86	96.15 29	92.86 63	96.06 63	95.47 112	95.02 104	98.43 166	97.09 154

Vis-MVSNet (Re-imp)		94.46 65	96.24 48	92.40 97	95.23 89	98.64 65	95.56 90	90.99 83	94.42 89	85.02 98	90.88 76	94.65 55	88.01 177	98.17 26	98.37 13	99.57 8	98.53 89

HQP-MVS		94.43 66	94.57 69	94.27 71	96.41 65	97.23 96	96.89 54	93.98 40	95.94 61	83.68 103	95.01 40	84.46 106	95.58 69	95.47 112	94.85 109	99.07 98	99.00 56

ACMP	92.88 9	94.43 66	94.38 74	94.50 69	96.01 70	97.69 88	95.85 84	92.09 68	95.74 68	89.12 75	95.14 38	82.62 120	94.77 78	95.73 107	94.67 110	99.14 89	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	96.80 106	94.88 104	93.54 44	96.41 47	90.16 61	92.31 60	83.11 117	96.32 59	96.22 94	94.65 111	99.22 77	97.35 148
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 74	95.16 92	98.51 71	95.58 88	92.09 68	95.78 67	88.52 77	92.38 59	85.74 98	93.34 102	96.39 85	95.90 79	99.54 11	97.79 132

tfpn1000		94.14 70	94.54 70	93.67 82	95.27 87	98.50 72	95.36 94	91.84 74	96.31 49	87.38 90	92.98 53	84.04 108	92.60 111	96.49 84	95.62 91	99.55 9	97.82 130

LGP-MVS_train		94.12 71	94.62 68	93.53 84	96.44 64	97.54 89	97.40 48	91.84 74	94.66 85	81.09 119	95.70 33	83.36 116	95.10 76	96.36 89	95.71 87	99.32 57	99.03 52

RPSCF		94.05 72	94.00 81	94.12 73	96.20 67	96.41 120	96.61 65	91.54 78	95.83 66	89.73 66	96.94 24	92.80 64	95.35 74	91.63 185	90.44 193	95.27 205	93.94 194

DI_MVS_plusplus_trai		94.01 73	93.63 89	94.44 70	94.54 105	98.26 79	97.51 46	90.63 85	95.88 63	89.34 73	80.54 146	89.36 79	95.48 72	96.33 90	96.27 65	99.17 83	98.78 75

UA-Net		93.96 74	95.95 51	91.64 103	96.06 68	98.59 69	95.29 95	90.00 91	91.06 138	82.87 106	90.64 77	98.06 32	86.06 188	98.14 27	98.20 14	99.58 6	96.96 161

CANet_DTU		93.92 75	96.57 43	90.83 111	95.63 73	98.39 74	96.99 52	87.38 123	96.26 50	71.97 181	96.31 28	93.02 62	94.53 82	97.38 47	96.83 54	98.49 161	97.79 132

FC-MVSNet-train		93.85 76	93.91 82	93.78 80	94.94 98	96.79 109	94.29 114	91.13 81	93.84 99	88.26 81	90.40 79	85.23 103	94.65 81	96.54 79	95.31 98	99.38 48	99.28 19

GBi-Net		93.81 77	94.18 78	93.38 86	91.34 139	95.86 134	96.22 75	88.68 106	95.23 78	90.40 56	86.39 112	91.16 68	94.40 85	96.52 80	96.30 61	99.21 80	97.79 132

test1		93.81 77	94.18 78	93.38 86	91.34 139	95.86 134	96.22 75	88.68 106	95.23 78	90.40 56	86.39 112	91.16 68	94.40 85	96.52 80	96.30 61	99.21 80	97.79 132

FMVSNet3		93.79 79	94.17 80	93.35 88	91.21 142	95.99 127	96.62 64	88.68 106	95.23 78	90.40 56	86.39 112	91.16 68	94.11 89	95.96 99	96.67 56	99.07 98	97.79 132

conf200view11		93.64 80	92.57 99	94.88 60	95.33 82	98.94 41	96.82 57	92.31 59	92.63 115	88.26 81	87.21 99	78.01 135	97.12 44	96.82 60	95.85 83	99.45 29	98.56 85

tfpn200view9		93.64 80	92.57 99	94.89 59	95.33 82	98.94 41	96.82 57	92.31 59	92.63 115	88.29 78	87.21 99	78.01 135	97.12 44	96.82 60	95.85 83	99.45 29	98.56 85

tfpnview11		93.63 82	94.42 72	92.71 93	95.08 95	98.26 79	95.58 88	92.06 70	96.32 48	81.88 110	93.44 47	83.43 113	92.14 116	96.58 76	95.88 81	99.52 13	97.07 158

thres200		93.62 83	92.54 101	94.88 60	95.36 81	98.93 44	96.75 62	92.31 59	92.84 113	88.28 80	86.99 102	77.81 138	97.13 42	96.82 60	95.92 77	99.45 29	98.49 94

OPM-MVS		93.61 84	92.43 107	95.00 57	96.94 59	97.34 94	97.78 41	94.23 39	89.64 155	85.53 96	88.70 92	82.81 118	96.28 61	96.28 92	95.00 106	99.24 71	97.22 151

tfpn_n400		93.56 85	94.36 75	92.63 94	95.07 96	98.28 76	95.50 92	91.98 72	95.48 71	81.88 110	93.44 47	83.43 113	92.01 119	96.60 72	96.27 65	99.34 53	97.04 159

tfpnconf		93.56 85	94.36 75	92.63 94	95.07 96	98.28 76	95.50 92	91.98 72	95.48 71	81.88 110	93.44 47	83.43 113	92.01 119	96.60 72	96.27 65	99.34 53	97.04 159

thres400		93.56 85	92.43 107	94.87 62	95.40 80	98.91 50	96.70 63	92.38 58	92.93 112	88.19 83	86.69 107	77.35 139	97.13 42	96.75 66	95.85 83	99.42 41	98.56 85

thres100view900		93.55 88	92.47 106	94.81 63	95.33 82	98.74 58	96.78 61	92.30 63	92.63 115	88.29 78	87.21 99	78.01 135	96.78 54	96.38 87	95.92 77	99.38 48	98.40 100

view600		93.50 89	92.39 110	94.80 64	95.41 79	98.93 44	96.60 66	92.30 63	93.09 109	87.96 84	86.67 108	76.97 141	97.12 44	96.83 59	95.64 89	99.43 40	98.62 82

thres600view7		93.49 90	92.37 111	94.79 65	95.42 76	98.93 44	96.58 68	92.31 59	93.04 110	87.88 85	86.62 109	76.94 142	97.09 47	96.82 60	95.63 90	99.45 29	98.63 81

view800		93.45 91	92.37 111	94.71 66	95.42 76	98.92 48	96.51 70	92.19 66	93.14 108	87.62 87	86.72 106	76.54 145	97.08 48	96.86 58	95.74 86	99.45 29	98.70 78

FMVSNet2		93.30 92	93.36 95	93.22 89	91.34 139	95.86 134	96.22 75	88.24 111	95.15 82	89.92 65	81.64 140	89.36 79	94.40 85	96.77 65	96.98 50	99.21 80	97.79 132

COLMAP_ROB	90.49 14	93.27 93	92.71 98	93.93 76	97.75 49	97.44 92	96.07 78	93.17 52	95.40 73	83.86 102	83.76 130	88.72 84	93.87 91	94.25 133	94.11 126	98.87 113	95.28 184
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Effi-MVS+		92.93 94	93.86 84	91.86 99	94.07 112	98.09 85	95.59 87	85.98 139	94.27 92	79.54 126	91.12 73	81.81 122	96.71 55	96.67 70	96.06 72	99.27 66	98.98 59

tfpn		92.91 95	91.44 122	94.63 68	95.42 76	98.92 48	96.41 73	92.10 67	93.19 106	87.34 91	86.85 103	69.20 200	97.01 49	96.88 57	96.28 64	99.47 23	98.75 77

CDS-MVSNet		92.77 96	93.60 90	91.80 101	92.63 129	96.80 106	95.24 96	89.14 103	90.30 149	84.58 99	86.76 104	90.65 72	90.42 156	95.89 101	96.49 59	98.79 129	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 96	95.00 65	90.16 121	94.10 111	98.79 56	94.76 107	88.26 110	92.37 125	79.95 122	88.19 97	91.58 67	84.38 197	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 98	92.49 104	92.85 92	97.13 57	98.89 53	95.90 79	88.50 109	95.32 75	83.31 105	71.99 196	88.96 83	94.10 90	96.69 68	96.49 59	98.15 171	99.10 40

IterMVS-LS		92.56 99	93.18 96	91.84 100	93.90 114	94.97 170	94.99 99	86.20 135	94.18 94	82.68 107	85.81 118	87.36 90	94.43 83	95.31 114	96.02 74	98.87 113	98.60 84
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
conf0.05thres1000		92.47 100	91.39 123	93.73 81	95.21 90	98.52 70	95.66 86	91.56 77	90.87 141	84.27 100	82.79 136	76.12 146	96.29 60	96.59 74	95.68 88	99.39 46	99.19 33

EPNet_dtu		92.45 101	95.02 64	89.46 130	98.02 45	95.47 148	94.79 106	92.62 57	94.97 83	70.11 194	94.76 43	92.61 65	84.07 200	95.94 100	95.56 93	97.15 186	95.82 177
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
HyFIR lowres test		92.03 102	91.55 120	92.58 96	97.13 57	98.72 61	94.65 108	86.54 131	93.58 103	82.56 108	67.75 211	90.47 74	95.67 67	95.87 102	95.54 94	98.91 111	98.93 63

test0.0.03 1		91.97 103	93.91 82	89.72 126	93.31 123	96.40 121	91.34 175	87.06 127	93.86 97	81.67 115	91.15 72	89.16 81	86.02 189	95.08 118	95.09 102	98.91 111	96.64 170

Fast-Effi-MVS+		91.87 104	92.08 114	91.62 104	92.91 127	97.21 97	94.93 101	84.60 158	93.61 101	81.49 117	83.50 131	78.95 130	96.62 57	96.55 78	96.22 69	99.16 86	98.51 92

MS-PatchMatch		91.82 105	92.51 102	91.02 107	95.83 72	96.88 101	95.05 98	84.55 161	93.85 98	82.01 109	82.51 138	91.71 66	90.52 153	95.07 119	93.03 147	98.13 172	94.52 187

Effi-MVS+-dtu		91.78 106	93.59 91	89.68 129	92.44 131	97.11 98	94.40 112	84.94 154	92.43 121	75.48 145	91.09 74	83.75 111	93.55 99	96.61 71	95.47 95	97.24 185	98.67 80

IB-MVS	89.56 15	91.71 107	92.50 103	90.79 113	95.94 71	98.44 73	87.05 199	91.38 80	93.15 107	92.98 36	84.78 122	85.14 104	78.27 207	92.47 160	94.44 122	99.10 94	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		91.63 108	93.82 86	89.08 134	92.02 135	96.40 121	93.26 126	87.26 124	93.72 100	77.26 133	88.61 94	89.86 77	85.50 190	95.72 109	95.02 104	99.16 86	97.44 145

test-LLR		91.62 109	93.56 92	89.35 133	93.31 123	96.57 115	92.02 165	87.06 127	92.34 126	75.05 153	90.20 81	88.64 85	90.93 136	96.19 96	94.07 127	97.75 181	96.90 164

MDTV_nov1_ep13		91.57 110	93.18 96	89.70 127	93.39 121	96.97 99	93.53 121	80.91 189	95.70 69	81.86 113	92.40 58	89.93 76	93.25 105	91.97 183	90.80 190	95.25 206	94.46 189

FMVSNet1		91.54 111	90.93 128	92.26 98	90.35 150	95.27 162	95.22 97	87.16 126	91.37 135	87.62 87	75.45 159	83.84 110	94.43 83	96.52 80	96.30 61	98.82 116	97.74 139

ACMH	90.77 13	91.51 112	91.63 119	91.38 105	95.62 74	96.87 103	91.76 170	89.66 97	91.58 133	78.67 128	86.73 105	78.12 133	93.77 94	94.59 124	94.54 118	98.78 136	98.98 59
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMH+	90.88 12	91.41 113	91.13 125	91.74 102	95.11 94	96.95 100	93.13 128	89.48 100	92.42 122	79.93 123	85.13 120	78.02 134	93.82 93	93.49 145	93.88 132	98.94 108	97.99 118

DWT-MVSNet_training		91.30 114	89.73 136	93.13 90	94.64 104	96.87 103	94.93 101	86.17 136	94.22 93	93.18 29	89.11 89	73.28 169	93.59 98	88.00 204	90.73 191	96.26 192	95.87 175

Fast-Effi-MVS+-dtu		91.19 115	93.64 88	88.33 148	92.19 134	96.46 118	93.99 117	81.52 187	92.59 118	71.82 182	92.17 61	85.54 99	91.68 125	95.73 107	94.64 112	98.80 123	98.34 103

TESTMET0.1,1		91.07 116	93.56 92	88.17 152	90.43 147	96.57 115	92.02 165	82.83 172	92.34 126	75.05 153	90.20 81	88.64 85	90.93 136	96.19 96	94.07 127	97.75 181	96.90 164

test-mter		90.95 117	93.54 94	87.93 163	90.28 151	96.80 106	91.44 172	82.68 174	92.15 130	74.37 161	89.57 87	88.23 88	90.88 139	96.37 88	94.31 123	97.93 178	97.37 147

EPMVS		90.88 118	92.12 113	89.44 131	94.71 102	97.24 95	93.55 120	76.81 201	95.89 62	81.77 114	91.49 68	86.47 92	93.87 91	90.21 194	90.07 195	95.92 195	93.49 200

CostFormer		90.69 119	90.48 133	90.93 109	94.18 109	96.08 126	94.03 116	78.20 197	93.47 104	89.96 63	90.97 75	80.30 126	93.72 95	87.66 207	88.75 199	95.51 201	96.12 172

USDC		90.69 119	90.52 132	90.88 110	94.17 110	96.43 119	95.82 85	86.76 129	93.92 96	76.27 141	86.49 111	74.30 157	93.67 97	95.04 120	93.36 141	98.61 154	94.13 192

PatchmatchNet		90.56 121	92.49 104	88.31 149	93.83 117	96.86 105	92.42 139	76.50 205	95.96 60	78.31 129	91.96 64	89.66 78	93.48 100	90.04 196	89.20 198	95.32 203	93.73 198
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
pmmvs4		90.55 122	89.91 135	91.30 106	90.26 152	94.95 171	92.73 133	87.94 117	93.44 105	85.35 97	82.28 139	76.09 148	93.02 109	93.56 143	92.26 182	98.51 160	96.77 166

TAMVS		90.54 123	90.87 130	90.16 121	91.48 137	96.61 114	93.26 126	86.08 137	87.71 184	81.66 116	83.11 135	84.04 108	90.42 156	94.54 125	94.60 113	98.04 176	95.48 182

FMVSNet5		90.36 124	90.93 128	89.70 127	87.99 199	92.25 196	92.03 164	83.51 165	92.20 129	84.13 101	85.59 119	86.48 91	92.43 113	94.61 123	94.52 119	98.13 172	90.85 209

UniMVSNet_NR-MVSNet		90.35 125	89.96 134	90.80 112	89.66 158	95.83 137	92.48 137	90.53 87	90.96 140	79.57 124	79.33 150	77.14 140	93.21 106	92.91 154	94.50 121	99.37 51	99.05 49

IterMVS		90.20 126	92.43 107	87.61 173	92.82 128	94.31 188	94.11 115	81.54 186	92.97 111	69.90 195	84.71 123	88.16 89	89.96 167	95.25 115	94.17 125	97.31 184	97.46 144
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
RPMNet		90.19 127	92.03 115	88.05 158	93.46 119	95.95 131	93.41 123	74.59 214	92.40 123	75.91 143	84.22 126	86.41 93	92.49 112	94.42 129	93.85 134	98.44 164	96.96 161

CR-MVSNet		90.16 128	91.96 116	88.06 157	93.32 122	95.95 131	93.36 124	75.99 208	92.40 123	75.19 150	83.18 133	85.37 100	92.05 117	95.21 116	94.56 116	98.47 163	97.08 156

dps		90.11 129	89.37 141	90.98 108	93.89 115	96.21 124	93.49 122	77.61 199	91.95 131	92.74 40	88.85 90	78.77 132	92.37 114	87.71 206	87.71 205	95.80 196	94.38 190

UniMVSNet (Re)		90.03 130	89.61 138	90.51 116	89.97 155	96.12 125	92.32 145	89.26 101	90.99 139	80.95 120	78.25 153	75.08 154	91.14 131	93.78 138	93.87 133	99.41 42	99.21 31

tpmp4_e23		89.82 131	89.31 142	90.42 117	94.01 113	95.45 149	94.63 109	78.37 194	93.59 102	87.09 93	86.62 109	76.59 144	93.06 108	88.50 201	88.52 200	95.36 202	95.88 174

ADS-MVSNet		89.80 132	91.33 124	88.00 161	94.43 106	96.71 111	92.29 149	74.95 213	96.07 58	77.39 132	88.67 93	86.09 95	93.26 104	88.44 202	89.57 197	95.68 198	93.81 197

CVMVSNet		89.77 133	91.66 118	87.56 175	93.21 125	95.45 149	91.94 169	89.22 102	89.62 156	69.34 199	83.99 128	85.90 97	84.81 195	94.30 132	95.28 99	96.85 188	97.09 154

DU-MVS		89.67 134	88.84 144	90.63 115	89.26 182	95.61 142	92.48 137	89.91 92	91.22 136	79.57 124	77.72 154	71.18 190	93.21 106	92.53 158	94.57 115	99.35 52	99.05 49

testgi		89.42 135	91.50 121	87.00 182	92.40 132	95.59 144	89.15 193	85.27 151	92.78 114	72.42 179	91.75 65	76.00 149	84.09 199	94.38 130	93.82 136	98.65 152	96.15 171

TinyColmap		89.42 135	88.58 146	90.40 118	93.80 118	95.45 149	93.96 118	86.54 131	92.24 128	76.49 138	80.83 144	70.44 193	93.37 101	94.45 128	93.30 144	98.26 170	93.37 202

NR-MVSNet		89.34 137	88.66 145	90.13 124	90.40 148	95.61 142	93.04 130	89.91 92	91.22 136	78.96 127	77.72 154	68.90 202	89.16 171	94.24 134	93.95 130	99.32 57	98.99 57

GA-MVS		89.28 138	90.75 131	87.57 174	91.77 136	96.48 117	92.29 149	87.58 122	90.61 146	65.77 205	84.48 124	76.84 143	89.46 169	95.84 103	93.68 137	98.52 159	97.34 149

Baseline_NR-MVSNet		89.27 139	88.01 154	90.73 114	89.26 182	93.71 191	92.71 134	89.78 96	90.73 143	81.28 118	73.53 188	72.85 170	92.30 115	92.53 158	93.84 135	99.07 98	98.88 68

TranMVSNet+NR-MVSNet		89.23 140	88.48 148	90.11 125	89.07 188	95.25 163	92.91 131	90.43 88	90.31 148	77.10 134	76.62 157	71.57 188	91.83 123	92.12 171	94.59 114	99.32 57	98.92 64

pm-mvs1		89.19 141	89.02 143	89.38 132	90.40 148	95.74 140	92.05 162	88.10 113	86.13 198	77.70 130	73.72 187	79.44 129	88.97 172	95.81 105	94.51 120	99.08 96	97.78 138

PatchT		89.13 142	91.71 117	86.11 192	92.92 126	95.59 144	83.64 206	75.09 212	91.87 132	75.19 150	82.63 137	85.06 105	92.05 117	95.21 116	94.56 116	97.76 180	97.08 156

TDRefinement		89.07 143	88.15 151	90.14 123	95.16 92	96.88 101	95.55 91	90.20 89	89.68 153	76.42 139	76.67 156	74.30 157	84.85 194	93.11 150	91.91 184	98.64 153	94.47 188

MIMVSNet		88.99 144	91.07 126	86.57 185	86.78 207	95.62 141	91.20 178	75.40 211	90.65 145	76.57 137	84.05 127	82.44 121	91.01 135	95.84 103	95.38 97	98.48 162	93.50 199

anonymousdsp		88.90 145	91.00 127	86.44 188	88.74 195	95.97 129	90.40 185	82.86 171	88.77 168	67.33 202	81.18 143	81.44 124	90.22 165	96.23 93	94.27 124	99.12 92	99.16 38

tpm cat1		88.90 145	87.78 164	90.22 120	93.88 116	95.39 158	93.79 119	78.11 198	92.55 119	89.43 71	81.31 142	79.84 128	91.40 127	84.95 213	86.34 215	94.68 213	94.09 193

tpmrst		88.86 147	89.62 137	87.97 162	94.33 107	95.98 128	92.62 135	76.36 206	94.62 87	76.94 135	85.98 117	82.80 119	92.80 110	86.90 208	87.15 209	94.77 210	93.93 195

tfpnnormal		88.50 148	87.01 184	90.23 119	91.36 138	95.78 139	92.74 132	90.09 90	83.65 207	76.33 140	71.46 201	69.58 198	91.84 122	95.54 110	94.02 129	99.06 101	99.03 52

v6		88.43 149	88.01 154	88.92 135	89.60 164	95.43 154	92.36 141	87.66 119	89.07 162	74.50 159	75.06 163	73.47 165	90.59 152	92.11 174	92.76 166	98.79 129	98.18 110

v1neww		88.41 150	88.00 157	88.89 136	89.61 162	95.44 152	92.31 146	87.65 120	89.09 160	74.30 162	75.02 165	73.42 167	90.68 147	92.12 171	92.77 162	98.79 129	98.18 110

v7new		88.41 150	88.00 157	88.89 136	89.61 162	95.44 152	92.31 146	87.65 120	89.09 160	74.30 162	75.02 165	73.42 167	90.68 147	92.12 171	92.77 162	98.79 129	98.18 110

SixPastTwentyTwo		88.37 152	89.47 139	87.08 180	90.01 154	95.93 133	87.41 196	85.32 148	90.26 150	70.26 192	86.34 115	71.95 184	90.93 136	92.89 155	91.72 186	98.55 157	97.22 151

V42		88.31 153	87.95 160	88.73 143	89.44 168	95.34 159	92.23 156	87.21 125	88.83 166	74.49 160	74.89 169	73.43 166	90.41 159	92.08 178	92.77 162	98.60 156	98.33 104

v2v482		88.25 154	87.71 165	88.88 138	89.23 186	95.28 160	92.10 160	87.89 118	88.69 169	73.31 175	75.32 160	71.64 186	91.89 121	92.10 177	92.92 150	98.86 115	97.99 118

v8		88.21 155	87.94 161	88.51 145	89.62 160	95.01 169	92.31 146	84.99 153	88.94 164	74.70 157	75.03 164	73.51 164	90.67 149	92.11 174	92.74 168	98.80 123	98.24 108

v7		88.18 156	88.01 154	88.39 146	89.45 167	95.14 166	92.36 141	85.37 147	89.29 159	72.94 178	73.98 183	72.77 173	91.38 128	93.59 139	92.87 152	98.82 116	98.42 97

v1141		88.17 157	87.69 166	88.74 141	89.44 168	95.41 155	92.25 154	87.98 114	88.38 174	73.54 173	74.43 173	72.71 178	90.45 154	92.08 178	92.72 170	98.79 129	98.09 114

divwei89l23v2f112		88.17 157	87.69 166	88.74 141	89.44 168	95.41 155	92.26 152	87.97 116	88.29 178	73.57 172	74.45 172	72.75 175	90.42 156	92.08 178	92.72 170	98.81 120	98.09 114

v1		88.17 157	87.66 168	88.77 140	89.44 168	95.40 157	92.29 149	87.98 114	88.21 181	73.75 167	74.41 175	72.75 175	90.36 162	92.07 181	92.71 173	98.80 123	98.09 114

v10		88.00 160	87.96 159	88.05 158	89.44 168	94.68 179	92.36 141	83.35 168	89.37 158	72.96 176	73.98 183	72.79 172	91.35 129	93.59 139	92.88 151	98.81 120	98.42 97

tpm		87.95 161	89.44 140	86.21 190	92.53 130	94.62 183	91.40 173	76.36 206	91.46 134	69.80 197	87.43 98	75.14 152	91.55 126	89.85 199	90.60 192	95.61 199	96.96 161

v18		87.93 162	87.61 170	88.31 149	89.74 156	92.04 197	92.59 136	82.71 173	89.70 152	75.32 148	75.23 161	73.55 163	90.74 143	92.11 174	92.77 162	98.78 136	97.87 126

WR-MVS_H		87.93 162	87.85 162	88.03 160	89.62 160	95.58 146	90.47 184	85.55 144	87.20 190	76.83 136	74.42 174	72.67 180	86.37 186	93.22 149	93.04 146	99.33 55	98.83 72

WR-MVS		87.93 162	88.09 152	87.75 166	89.26 182	95.28 160	90.81 181	86.69 130	88.90 165	75.29 149	74.31 176	73.72 160	85.19 193	92.26 161	93.32 143	99.27 66	98.81 73

v1144		87.92 165	87.79 163	88.07 155	89.27 181	95.15 165	92.17 159	85.62 143	88.52 170	71.52 183	73.80 186	72.40 183	91.06 134	93.54 144	92.80 156	98.81 120	98.33 104

CP-MVSNet		87.89 166	87.27 175	88.62 144	89.30 178	95.06 167	90.60 183	85.78 141	87.43 188	75.98 142	74.60 170	68.14 204	90.76 141	93.07 152	93.60 138	99.30 62	98.98 59

v16		87.87 167	87.60 171	88.19 151	89.70 157	92.01 199	92.37 140	82.54 176	89.67 154	75.00 155	75.02 165	73.65 161	90.73 145	92.14 170	92.80 156	98.77 140	97.90 123

pmmvs5		87.83 168	88.09 152	87.51 177	89.59 165	95.48 147	89.75 191	84.73 156	86.07 200	71.44 184	80.57 145	70.09 196	90.74 143	94.47 127	92.87 152	98.82 116	97.10 153

v17		87.83 168	87.56 172	88.13 153	89.65 159	92.02 198	92.34 144	82.55 175	89.38 157	74.76 156	75.14 162	73.59 162	90.70 146	92.15 169	92.78 160	98.78 136	97.89 124

TransMVSNet (Re)		87.73 170	86.79 186	88.83 139	90.76 144	94.40 186	91.33 176	89.62 98	84.73 203	75.41 147	72.73 192	71.41 189	86.80 184	94.53 126	93.93 131	99.06 101	95.83 176

v11		87.58 171	87.50 173	87.67 170	89.34 176	91.91 204	92.22 158	81.63 184	89.01 163	72.95 177	74.11 181	72.51 182	91.08 133	94.01 137	93.00 148	98.77 140	97.93 121

LTVRE_ROB	87.32 16	87.55 172	88.25 150	86.73 183	90.66 145	95.80 138	93.05 129	84.77 155	83.35 208	60.32 214	83.12 134	67.39 205	93.32 103	94.36 131	94.86 108	98.28 169	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		87.51 173	87.31 174	87.74 167	89.04 189	94.87 177	92.07 161	85.03 152	88.49 171	70.32 191	72.65 193	70.35 194	91.21 130	93.59 139	92.80 156	98.78 136	98.42 97

v148		87.51 173	86.79 186	88.36 147	89.39 174	95.21 164	89.84 190	88.20 112	87.61 186	77.56 131	73.38 190	70.32 195	86.80 184	90.70 191	92.31 179	98.37 167	97.98 120

V14		87.47 175	87.19 178	87.80 165	89.37 175	91.95 201	92.25 154	82.12 180	88.39 173	73.83 166	74.31 176	72.84 171	90.44 155	92.20 166	92.78 160	98.80 123	97.84 128

v15		87.46 176	87.16 179	87.81 164	89.41 173	91.96 200	92.26 152	82.28 179	88.42 172	73.72 168	74.29 178	72.73 177	90.41 159	92.17 168	92.76 166	98.79 129	97.83 129

V9		87.41 177	87.15 180	87.72 168	89.33 177	91.93 202	92.23 156	82.02 181	88.35 175	73.59 171	74.13 180	72.77 173	90.37 161	92.21 165	92.80 156	98.79 129	97.86 127

v144192		87.40 178	87.20 177	87.64 171	88.89 190	94.88 176	91.65 171	84.70 157	87.80 183	71.17 188	73.20 191	70.91 191	90.75 142	92.69 156	92.49 175	98.71 146	98.43 96

v12		87.38 179	87.13 181	87.68 169	89.30 178	91.92 203	92.01 167	81.94 182	88.35 175	73.69 169	74.10 182	72.57 181	90.33 164	92.23 163	92.82 154	98.80 123	97.91 122

v13		87.34 180	87.11 183	87.62 172	89.30 178	91.91 204	92.04 163	81.86 183	88.35 175	73.36 174	73.88 185	72.69 179	90.34 163	92.23 163	92.82 154	98.80 123	97.88 125

PS-CasMVS		87.33 181	86.68 189	88.10 154	89.22 187	94.93 172	90.35 186	85.70 142	86.44 194	74.01 164	73.43 189	66.59 210	90.04 166	92.92 153	93.52 139	99.28 64	98.91 66

v1921920		87.31 182	87.13 181	87.52 176	88.87 192	94.72 178	91.96 168	84.59 159	88.28 179	69.86 196	72.50 194	70.03 197	91.10 132	93.33 147	92.61 174	98.71 146	98.44 95

PEN-MVS		87.22 183	86.50 193	88.07 155	88.88 191	94.44 185	90.99 180	86.21 133	86.53 193	73.66 170	74.97 168	66.56 211	89.42 170	91.20 187	93.48 140	99.24 71	98.31 107

v1240		86.89 184	86.75 188	87.06 181	88.75 194	94.65 181	91.30 177	84.05 162	87.49 187	68.94 200	71.96 197	68.86 203	90.65 150	93.33 147	92.72 170	98.67 150	98.24 108

EG-PatchMatch MVS		86.68 185	87.24 176	86.02 193	90.58 146	96.26 123	91.08 179	81.59 185	84.96 202	69.80 197	71.35 202	75.08 154	84.23 198	94.24 134	93.35 142	98.82 116	95.46 183

DTE-MVSNet		86.67 186	86.09 194	87.35 178	88.45 197	94.08 189	90.65 182	86.05 138	86.13 198	72.19 180	74.58 171	66.77 209	87.61 180	90.31 193	93.12 145	99.13 90	97.62 142

v52		86.57 187	86.63 190	86.50 186	87.47 204	94.89 175	89.90 188	83.39 166	86.36 195	71.17 188	71.53 199	71.65 185	88.34 175	91.14 188	92.32 178	98.74 144	98.52 90

V4		86.56 188	86.61 191	86.50 186	87.49 203	94.90 174	89.87 189	83.39 166	86.25 196	71.20 187	71.57 198	71.58 187	88.30 176	91.14 188	92.31 179	98.75 143	98.52 90

v7n		86.43 189	86.52 192	86.33 189	87.91 200	94.93 172	90.15 187	83.05 169	86.57 192	70.21 193	71.48 200	66.78 208	87.72 178	94.19 136	92.96 149	98.92 110	98.76 76

MDTV_nov1_ep13_2view		86.30 190	88.27 149	84.01 197	87.71 202	94.67 180	88.08 195	76.78 202	90.59 147	68.66 201	80.46 147	80.12 127	87.58 181	89.95 198	88.20 202	95.25 206	93.90 196

gg-mvs-nofinetune		86.17 191	88.57 147	83.36 200	93.44 120	98.15 83	96.58 68	72.05 219	74.12 219	49.23 226	64.81 214	90.85 71	89.90 168	97.83 38	96.84 53	98.97 106	97.41 146

pmmvs6		85.98 192	84.89 202	87.25 179	88.83 193	94.35 187	89.36 192	85.30 150	78.51 216	75.44 146	62.71 217	75.41 151	87.65 179	93.58 142	92.40 177	96.89 187	97.29 150

v748		85.88 193	85.66 196	86.14 191	88.03 198	94.63 182	87.02 200	84.59 159	84.30 204	74.56 158	70.94 203	67.27 206	83.94 201	90.96 190	92.74 168	98.71 146	98.81 73

EU-MVSNet		85.62 194	87.65 169	83.24 201	88.54 196	92.77 195	87.12 198	85.32 148	86.71 191	64.54 207	78.52 152	75.11 153	78.35 206	92.25 162	92.28 181	95.58 200	95.93 173

MVS-HIRNet		85.36 195	86.89 185	83.57 199	90.13 153	94.51 184	83.57 207	72.61 217	88.27 180	71.22 186	68.97 207	81.81 122	88.91 173	93.08 151	91.94 183	94.97 209	89.64 213

N_pmnet		84.80 196	85.10 200	84.45 196	89.25 185	92.86 194	84.04 205	86.21 133	88.78 167	66.73 204	72.41 195	74.87 156	85.21 192	88.32 203	86.45 213	95.30 204	92.04 204

CMPMVS	65.18 17	84.76 197	83.10 206	86.69 184	95.29 86	95.05 168	88.37 194	85.51 145	80.27 214	71.31 185	68.37 209	73.85 159	85.25 191	87.72 205	87.75 204	94.38 214	88.70 214
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
PM-MVS		84.72 198	84.47 203	85.03 195	84.67 209	91.57 207	86.27 202	82.31 178	87.65 185	70.62 190	76.54 158	56.41 222	88.75 174	92.59 157	89.85 196	97.54 183	96.66 169

LP		84.43 199	85.10 200	83.66 198	92.31 133	93.89 190	87.13 197	72.88 216	90.81 142	67.08 203	70.65 204	75.76 150	86.87 183	86.43 211	87.15 209	95.70 197	90.98 208

pmmvs-eth3d		84.33 200	82.94 207	85.96 194	84.16 212	90.94 208	86.55 201	83.79 163	84.25 205	75.85 144	70.64 205	56.43 221	87.44 182	92.20 166	90.41 194	97.97 177	95.68 179

Anonymous20231206		83.84 201	85.19 199	82.26 202	87.38 205	92.87 193	85.49 203	83.65 164	86.07 200	63.44 210	68.42 208	69.01 201	75.45 210	93.34 146	92.44 176	98.12 174	94.20 191

testpf		83.57 202	85.70 195	81.08 203	90.99 143	88.96 213	82.71 209	65.32 227	90.22 151	73.86 165	81.58 141	76.10 147	81.19 204	84.14 217	85.41 217	92.43 220	93.45 201

gm-plane-assit		83.26 203	85.29 198	80.89 204	89.52 166	89.89 211	70.26 220	78.24 196	77.11 217	58.01 219	74.16 179	66.90 207	90.63 151	97.20 51	96.05 73	98.66 151	95.68 179

test20.03		82.92 204	85.52 197	79.90 207	87.75 201	91.84 206	82.80 208	82.99 170	82.65 212	60.32 214	78.90 151	70.50 192	67.10 218	92.05 182	90.89 189	98.44 164	91.80 205

new_pmnet		81.53 205	82.68 208	80.20 205	83.47 214	89.47 212	82.21 211	78.36 195	87.86 182	60.14 216	67.90 210	69.43 199	82.03 203	89.22 200	87.47 206	94.99 208	87.39 215

testus		81.33 206	84.13 204	78.06 210	84.54 210	87.72 214	79.66 213	80.42 190	87.36 189	54.13 225	83.83 129	56.63 220	73.21 215	90.51 192	91.74 185	96.40 190	91.11 207

test2356		81.26 207	84.10 205	77.95 212	84.35 211	87.38 216	79.56 214	79.53 193	86.17 197	54.14 224	83.24 132	60.71 214	73.77 211	90.01 197	91.18 188	96.33 191	90.01 211

MDA-MVSNet-bldmvs		80.11 208	80.24 210	79.94 206	77.01 223	93.21 192	78.86 217	85.94 140	82.71 211	60.86 211	79.71 149	51.77 224	83.71 202	75.60 222	86.37 214	93.28 218	92.35 203

MIMVSNet1		80.03 209	80.93 209	78.97 208	72.46 226	90.73 209	80.81 212	82.44 177	80.39 213	63.64 209	57.57 219	64.93 212	76.37 208	91.66 184	91.55 187	98.07 175	89.70 212

pmmvs3		79.16 210	80.12 211	78.05 211	79.36 218	86.59 218	78.13 218	73.87 215	76.42 218	57.51 220	70.59 206	57.02 219	84.66 196	90.10 195	88.32 201	94.75 211	91.77 206

new-patchmatchnet		78.49 211	78.19 212	78.84 209	84.13 213	90.06 210	77.11 219	80.39 191	79.57 215	59.64 218	66.01 212	55.65 223	75.62 209	84.55 216	80.70 219	96.14 194	90.77 210

Anonymous20231211		75.89 212	74.18 217	77.88 213	81.42 215	87.72 214	79.33 216	81.05 188	66.49 226	60.00 217	45.74 225	51.46 225	71.22 216	85.70 212	86.91 212	94.25 215	95.25 185

FPMVS		75.84 213	74.59 213	77.29 214	86.92 206	83.89 220	85.01 204	80.05 192	82.91 210	60.61 213	65.25 213	60.41 215	63.86 219	75.60 222	73.60 224	87.29 225	80.47 221

1111		73.35 214	74.40 214	72.12 215	78.22 219	82.24 221	65.06 223	65.61 225	70.28 220	55.42 221	56.30 220	57.35 217	73.66 212	86.73 209	88.16 203	94.75 211	79.76 223

testmv		72.66 215	74.40 214	70.62 216	80.64 216	81.51 223	64.99 225	76.60 203	68.76 222	44.81 227	63.78 215	48.00 226	62.52 220	84.74 214	87.17 207	94.19 216	86.86 216

test1235678		72.65 216	74.40 214	70.62 216	80.64 216	81.50 224	64.99 225	76.59 204	68.74 223	44.81 227	63.78 215	47.99 227	62.51 221	84.73 215	87.17 207	94.19 216	86.85 217

test12356		69.55 217	71.53 219	67.24 220	77.70 222	78.48 225	65.92 222	75.55 210	68.39 224	44.26 229	61.80 218	40.70 229	47.92 228	81.45 220	87.01 211	92.09 221	82.89 219

Gipuma		68.35 218	66.71 220	70.27 218	74.16 225	68.78 229	63.93 228	71.77 220	83.34 209	54.57 223	34.37 226	31.88 230	68.69 217	83.30 218	85.53 216	88.48 224	79.78 222
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	63.12 18	67.27 219	66.39 221	68.30 219	77.98 221	60.24 230	59.53 229	76.82 200	66.65 225	60.74 212	54.39 222	59.82 216	51.24 224	73.92 225	70.52 225	83.48 227	79.17 224
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
GG-mvs-BLEND		66.17 220	94.91 66	32.63 228	1.32 233	96.64 113	91.40 173	0.85 232	94.39 91	2.20 236	90.15 83	95.70 51	2.27 232	96.39 85	95.44 96	97.78 179	95.68 179

PMMVS2		64.36 221	65.94 222	62.52 222	67.37 228	77.44 226	64.39 227	69.32 224	61.47 227	34.59 231	46.09 224	41.03 228	48.02 227	74.56 224	78.23 220	91.43 222	82.76 220

.test1245		56.65 222	56.09 223	57.30 223	78.22 219	82.24 221	65.06 223	65.61 225	70.28 220	55.42 221	56.30 220	57.35 217	73.66 212	86.73 209	15.01 228	5.84 232	24.75 229

no-one		55.96 223	55.63 224	56.35 224	68.48 227	73.29 228	43.03 230	72.52 218	44.01 230	34.80 230	32.83 227	29.11 231	35.21 229	56.63 227	75.72 222	84.04 226	77.79 225

E-PMN		50.67 224	47.85 226	53.96 225	64.13 230	50.98 233	38.06 231	69.51 222	51.40 229	24.60 233	29.46 230	24.39 233	56.07 223	48.17 228	59.70 226	71.40 229	70.84 227

EMVS		49.98 225	46.76 227	53.74 226	64.96 229	51.29 232	37.81 232	69.35 223	51.83 228	22.69 234	29.57 229	25.06 232	57.28 222	44.81 229	56.11 227	70.32 230	68.64 228

MVE	50.86 19	49.54 226	51.43 225	47.33 227	44.14 231	59.20 231	36.45 233	60.59 228	41.47 231	31.14 232	29.58 228	17.06 235	48.52 226	62.22 226	74.63 223	63.12 231	75.87 226
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		12.09 227	16.94 228	6.42 229	3.15 232	6.08 234	9.51 235	3.84 230	21.46 232	5.31 235	27.49 231	6.76 236	10.89 230	17.06 230	15.01 228	5.84 232	24.75 229

test123		9.58 228	13.53 229	4.97 230	1.31 234	5.47 235	8.32 236	2.95 231	18.14 233	2.03 237	20.82 232	2.34 237	10.60 231	10.00 231	14.16 230	4.60 234	23.77 231

test_all		0.00 229	0.00 230	0.00 231	0.00 235	0.00 236	0.00 237	0.00 233	0.00 234	0.00 238	0.00 233	0.00 238	0.00 233	0.00 232	0.00 231	0.00 235	0.00 232

sosnet-low-res		0.00 229	0.00 230	0.00 231	0.00 235	0.00 236	0.00 237	0.00 233	0.00 234	0.00 238	0.00 233	0.00 238	0.00 233	0.00 232	0.00 231	0.00 235	0.00 232

sosnet		0.00 229	0.00 230	0.00 231	0.00 235	0.00 236	0.00 237	0.00 233	0.00 234	0.00 238	0.00 233	0.00 238	0.00 233	0.00 232	0.00 231	0.00 235	0.00 232

ambc			73.83 218		76.23 224	85.13 219	82.27 210		84.16 206	65.58 206	52.82 223	23.31 234	73.55 214	91.41 186	85.26 218	92.97 219	94.70 186

MTAPA										96.83 5		99.12 13

MTMP										97.18 3		98.83 19

Patchmatch-RL test							34.61 234

tmp_tt				66.88 221	86.07 208	73.86 227	68.22 221	33.38 229	96.88 39	80.67 121	88.23 96	78.82 131	49.78 225	82.68 219	77.47 221	83.19 228

XVS					96.60 60	99.35 8	96.82 57			90.85 49		98.72 22				99.46 25

X-MVStestdata					96.60 60	99.35 8	96.82 57			90.85 49		98.72 22				99.46 25

abl_6				96.82 33	98.60 35	98.74 58	97.74 42	93.73 42	96.25 51	94.37 23	94.55 44	98.60 27	97.25 39			99.27 66	98.61 83

mPP-MVS					99.21 18							98.29 30

NP-MVS									95.32 75

Patchmtry						95.96 130	93.36 124	75.99 208		75.19 150

DeepMVS_CX						86.86 217	79.50 215	70.43 221	90.73 143	63.66 208	80.36 148	60.83 213	79.68 205	76.23 221		89.46 223	86.53 218

High-resolution multi-view benchmark