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.

SMA-MVS		98.66 2	98.89 2	98.39 6	99.60 1	99.41 6	99.00 18	97.63 8	97.78 14	95.83 15	98.33 9	99.83 1	98.85 9	98.93 5	98.56 6	99.41 47	99.40 12

APDe-MVS		98.87 1	98.96 1	98.77 1	99.58 2	99.53 2	99.44 1	97.81 1	98.22 7	97.33 2	98.70 2	99.33 7	98.86 7	98.96 3	98.40 11	99.63 3	99.57 5

PGM-MVS		97.81 22	98.11 25	97.46 26	99.55 3	99.34 13	99.32 6	94.51 40	96.21 56	93.07 34	98.05 11	97.95 36	98.82 11	98.22 27	97.89 29	99.48 21	99.09 47

ACMMP_Plus		98.20 15	98.49 10	97.85 22	99.50 4	99.40 7	99.26 9	97.64 7	97.47 27	92.62 43	97.59 17	99.09 16	98.71 15	98.82 9	97.86 30	99.40 50	99.19 37

zzz-MVS		98.43 9	98.31 20	98.57 2	99.48 5	99.40 7	99.32 6	97.62 9	97.70 17	96.67 6	96.59 28	99.09 16	98.86 7	98.65 10	97.56 38	99.45 30	99.17 41

APD-MVS		98.36 12	98.32 19	98.41 5	99.47 6	99.26 19	99.12 12	97.77 4	96.73 43	96.12 12	97.27 24	98.88 19	98.46 22	98.47 15	98.39 12	99.52 13	99.22 33
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CSCG		97.44 29	97.18 36	97.75 24	99.47 6	99.52 3	98.55 29	95.41 35	97.69 19	95.72 16	94.29 47	95.53 54	98.10 27	96.20 101	97.38 45	99.24 78	99.62 2

ESAPD		98.59 3	98.77 4	98.39 6	99.46 8	99.50 4	99.11 13	97.80 2	97.20 34	96.06 13	98.56 3	99.83 1	98.43 23	98.84 7	98.03 23	99.45 30	99.45 10

HPM-MVS++		98.34 13	98.47 12	98.18 13	99.46 8	99.15 27	99.10 14	97.69 5	97.67 20	94.93 23	97.62 16	99.70 4	98.60 18	98.45 16	97.46 41	99.31 66	99.26 27

HSP-MVS		98.59 3	98.65 7	98.52 3	99.44 10	99.57 1	99.34 3	97.65 6	97.36 29	96.62 8	98.49 5	99.65 5	98.67 17	98.60 11	97.44 42	99.40 50	99.46 9

ACMMPR		98.40 10	98.49 10	98.28 10	99.41 11	99.40 7	99.36 2	97.35 17	98.30 4	95.02 22	97.79 14	98.39 31	99.04 2	98.26 24	98.10 18	99.50 19	99.22 33

X-MVS		97.84 21	98.19 24	97.42 27	99.40 12	99.35 10	99.06 15	97.25 21	97.38 28	90.85 52	96.06 32	98.72 24	98.53 21	98.41 19	98.15 17	99.46 26	99.28 22

MCST-MVS		98.20 15	98.36 15	98.01 19	99.40 12	99.05 30	99.00 18	97.62 9	97.59 24	93.70 30	97.42 23	99.30 8	98.77 13	98.39 20	97.48 40	99.59 4	99.31 21

CNVR-MVS		98.47 8	98.46 13	98.48 4	99.40 12	99.05 30	99.02 17	97.54 12	97.73 15	96.65 7	97.20 25	99.13 14	98.85 9	98.91 6	98.10 18	99.41 47	99.08 48

HFP-MVS		98.48 7	98.62 8	98.32 8	99.39 15	99.33 14	99.27 8	97.42 14	98.27 5	95.25 20	98.34 8	98.83 21	99.08 1	98.26 24	98.08 20	99.48 21	99.26 27

NCCC		98.10 18	98.05 27	98.17 15	99.38 16	99.05 30	99.00 18	97.53 13	98.04 10	95.12 21	94.80 44	99.18 12	98.58 19	98.49 14	97.78 32	99.39 52	98.98 64

MP-MVS		98.09 19	98.30 21	97.84 23	99.34 17	99.19 25	99.23 10	97.40 15	97.09 38	93.03 37	97.58 18	98.85 20	98.57 20	98.44 18	97.69 33	99.48 21	99.23 31
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CP-MVS		98.32 14	98.34 18	98.29 9	99.34 17	99.30 15	99.15 11	97.35 17	97.49 26	95.58 18	97.72 15	98.62 28	98.82 11	98.29 22	97.67 34	99.51 17	99.28 22

SteuartSystems-ACMMP		98.38 11	98.71 6	97.99 20	99.34 17	99.46 5	99.34 3	97.33 20	97.31 31	94.25 26	98.06 10	99.17 13	98.13 26	98.98 2	98.46 9	99.55 9	99.54 6
Skip Steuart: Steuart Systems R&D Blog.
mPP-MVS					99.21 20							98.29 32

AdaColmap		97.53 27	96.93 40	98.24 11	99.21 20	98.77 62	98.47 31	97.34 19	96.68 45	96.52 10	95.11 41	96.12 50	98.72 14	97.19 56	96.24 72	99.17 90	98.39 108

DeepC-MVS_fast	96.13 1	98.13 17	98.27 22	97.97 21	99.16 22	99.03 35	99.05 16	97.24 22	98.22 7	94.17 28	95.82 33	98.07 33	98.69 16	98.83 8	98.80 2	99.52 13	99.10 45
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MSLP-MVS++		98.04 20	97.93 29	98.18 13	99.10 23	99.09 29	98.34 33	96.99 27	97.54 25	96.60 9	94.82 43	98.45 30	98.89 5	97.46 48	98.77 4	99.17 90	99.37 14

3Dnovator	93.79 8	97.08 34	97.20 34	96.95 34	99.09 24	99.03 35	98.20 35	93.33 48	97.99 11	93.82 29	90.61 84	96.80 43	97.82 31	97.90 38	98.78 3	99.47 24	99.26 27

QAPM		96.78 41	97.14 37	96.36 39	99.05 25	99.14 28	98.02 38	93.26 50	97.27 33	90.84 55	91.16 76	97.31 38	97.64 36	97.70 42	98.20 15	99.33 61	99.18 40

OpenMVS	92.33 11	95.50 48	95.22 62	95.82 47	98.98 26	98.97 41	97.67 46	93.04 58	94.64 92	89.18 78	84.44 133	94.79 56	96.79 59	97.23 53	97.61 35	99.24 78	98.88 74

PLC	94.95 3	97.37 30	96.77 43	98.07 17	98.97 27	98.21 87	97.94 41	96.85 30	97.66 21	97.58 1	93.33 52	96.84 42	98.01 30	97.13 58	96.20 75	99.09 102	98.01 126
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
train_agg		97.65 26	98.06 26	97.18 30	98.94 28	98.91 55	98.98 22	97.07 26	96.71 44	90.66 57	97.43 22	99.08 18	98.20 24	97.96 36	97.14 50	99.22 84	99.19 37

CDPH-MVS		96.84 39	97.49 31	96.09 43	98.92 29	98.85 59	98.61 26	95.09 36	96.00 63	87.29 99	95.45 38	97.42 37	97.16 43	97.83 40	97.94 26	99.44 40	98.92 70

CPTT-MVS		97.78 23	97.54 30	98.05 18	98.91 30	99.05 30	99.00 18	96.96 28	97.14 36	95.92 14	95.50 36	98.78 23	98.99 4	97.20 54	96.07 76	98.54 169	99.04 56

3Dnovator+	93.91 7	97.23 32	97.22 33	97.24 29	98.89 31	98.85 59	98.26 34	93.25 52	97.99 11	95.56 19	90.01 90	98.03 35	98.05 28	97.91 37	98.43 10	99.44 40	99.35 16

ACMMP		97.37 30	97.48 32	97.25 28	98.88 32	99.28 17	98.47 31	96.86 29	97.04 40	92.15 44	97.57 19	96.05 52	97.67 34	97.27 51	95.99 81	99.46 26	99.14 44
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
PHI-MVS		97.78 23	98.44 14	97.02 33	98.73 33	99.25 21	98.11 36	95.54 34	96.66 46	92.79 40	98.52 4	99.38 6	97.50 38	97.84 39	98.39 12	99.45 30	99.03 57

OMC-MVS		97.00 36	96.92 41	97.09 31	98.69 34	98.66 68	97.85 42	95.02 37	98.09 9	94.47 24	93.15 53	96.90 40	97.38 39	97.16 57	96.82 58	99.13 97	97.65 150

MAR-MVS		95.50 48	95.60 56	95.39 54	98.67 35	98.18 89	95.89 89	89.81 103	94.55 94	91.97 46	92.99 54	90.21 78	97.30 40	96.79 68	97.49 39	98.72 155	98.99 62
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
TSAR-MVS + ACMM		97.71 25	98.60 9	96.66 36	98.64 36	99.05 30	98.85 23	97.23 23	98.45 2	89.40 76	97.51 20	99.27 10	96.88 58	98.53 12	97.81 31	98.96 116	99.59 4

abl_6				96.82 35	98.60 37	98.74 63	97.74 44	93.73 44	96.25 54	94.37 25	94.55 46	98.60 29	97.25 41			99.27 73	98.61 88

CNLPA		96.90 38	96.28 49	97.64 25	98.56 38	98.63 72	96.85 58	96.60 31	97.73 15	97.08 4	89.78 92	96.28 49	97.80 33	96.73 73	96.63 60	98.94 117	98.14 122

EPNet		96.27 45	96.97 39	95.46 52	98.47 39	98.28 82	97.41 49	93.67 45	95.86 68	92.86 39	97.51 20	93.79 60	91.76 134	97.03 59	97.03 51	98.61 165	99.28 22
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MVS_111021_LR		97.16 33	98.01 28	96.16 42	98.47 39	98.98 40	96.94 55	93.89 43	97.64 22	91.44 48	98.89 1	96.41 45	97.20 42	98.02 35	97.29 49	99.04 111	98.85 77

MVS_111021_HR		97.04 35	98.20 23	95.69 48	98.44 41	99.29 16	96.59 73	93.20 53	97.70 17	89.94 69	98.46 6	96.89 41	96.71 62	98.11 32	97.95 25	99.27 73	99.01 60

MSDG		94.82 60	93.73 92	96.09 43	98.34 42	97.43 102	97.06 52	96.05 32	95.84 69	90.56 58	86.30 124	89.10 86	95.55 80	96.13 104	95.61 100	99.00 112	95.73 188

TAPA-MVS	94.18 5	96.38 43	96.49 47	96.25 40	98.26 43	98.66 68	98.00 39	94.96 38	97.17 35	89.48 74	92.91 56	96.35 46	97.53 37	96.59 80	95.90 84	99.28 71	97.82 139
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
DeepC-MVS	94.87 4	96.76 42	96.50 46	97.05 32	98.21 44	99.28 17	98.67 25	97.38 16	97.31 31	90.36 64	89.19 94	93.58 61	98.19 25	98.31 21	98.50 7	99.51 17	99.36 15
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SD-MVS		98.52 5	98.77 4	98.23 12	98.15 45	99.26 19	98.79 24	97.59 11	98.52 1	96.25 11	97.99 12	99.75 3	99.01 3	98.27 23	97.97 24	99.59 4	99.63 1

TSAR-MVS + MP.		98.49 6	98.78 3	98.15 16	98.14 46	99.17 26	99.34 3	97.18 24	98.44 3	95.72 16	97.84 13	99.28 9	98.87 6	99.05 1	98.05 21	99.66 1	99.60 3

EPNet_dtu		92.45 110	95.02 67	89.46 139	98.02 47	95.47 158	94.79 116	92.62 59	94.97 88	70.11 203	94.76 45	92.61 66	84.07 210	95.94 107	95.56 102	97.15 196	95.82 187
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CANet		96.84 39	97.20 34	96.42 37	97.92 48	99.24 23	98.60 27	93.51 47	97.11 37	93.07 34	91.16 76	97.24 39	96.21 70	98.24 26	98.05 21	99.22 84	99.35 16

LS3D		95.46 50	95.14 63	95.84 46	97.91 49	98.90 57	98.58 28	97.79 3	97.07 39	83.65 113	88.71 97	88.64 89	97.82 31	97.49 47	97.42 43	99.26 77	97.72 149

DELS-MVS		96.06 46	96.04 53	96.07 45	97.77 50	99.25 21	98.10 37	93.26 50	94.42 95	92.79 40	88.52 101	93.48 62	95.06 86	98.51 13	98.83 1	99.45 30	99.28 22
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
COLMAP_ROB	90.49 14	93.27 101	92.71 104	93.93 84	97.75 51	97.44 101	96.07 86	93.17 54	95.40 77	83.86 111	83.76 138	88.72 88	93.87 100	94.25 143	94.11 136	98.87 122	95.28 194
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
PCF-MVS	93.95 6	95.65 47	95.14 63	96.25 40	97.73 52	98.73 65	97.59 47	97.13 25	92.50 130	89.09 80	89.85 91	96.65 44	96.90 57	94.97 130	94.89 117	99.08 103	98.38 109
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PatchMatch-RL		94.69 66	94.41 76	95.02 58	97.63 53	98.15 91	94.50 121	91.99 77	95.32 80	91.31 49	95.47 37	83.44 118	96.02 73	96.56 83	95.23 110	98.69 159	96.67 178

PVSNet_BlendedMVS		95.41 52	95.28 60	95.57 50	97.42 54	99.02 37	95.89 89	93.10 55	96.16 57	93.12 32	91.99 68	85.27 106	94.66 88	98.09 33	97.34 46	99.24 78	99.08 48

PVSNet_Blended		95.41 52	95.28 60	95.57 50	97.42 54	99.02 37	95.89 89	93.10 55	96.16 57	93.12 32	91.99 68	85.27 106	94.66 88	98.09 33	97.34 46	99.24 78	99.08 48

DeepPCF-MVS	95.28 2	97.00 36	98.35 17	95.42 53	97.30 56	98.94 43	94.82 115	96.03 33	98.24 6	92.11 45	95.80 34	98.64 27	95.51 81	98.95 4	98.66 5	96.78 199	99.20 36

CHOSEN 280x420		95.46 50	97.01 38	93.66 91	97.28 57	97.98 96	96.40 80	85.39 156	96.10 61	91.07 50	96.53 29	96.34 48	95.61 77	97.65 43	96.95 54	96.21 203	97.49 152

MVS_0304		96.31 44	96.91 42	95.62 49	97.21 58	99.20 24	98.55 29	93.10 55	97.04 40	89.73 71	90.30 86	96.35 46	95.71 75	98.14 29	97.93 28	99.38 54	99.40 12

CHOSEN 1792x2688		92.66 107	92.49 110	92.85 101	97.13 59	98.89 58	95.90 87	88.50 118	95.32 80	83.31 114	71.99 206	88.96 87	94.10 99	96.69 74	96.49 62	98.15 181	99.10 45

HyFIR lowres test		92.03 111	91.55 130	92.58 105	97.13 59	98.72 66	94.65 118	86.54 141	93.58 110	82.56 117	67.75 221	90.47 76	95.67 76	95.87 109	95.54 103	98.91 120	98.93 69

OPM-MVS		93.61 89	92.43 113	95.00 60	96.94 61	97.34 103	97.78 43	94.23 41	89.64 165	85.53 104	88.70 98	82.81 124	96.28 69	96.28 98	95.00 116	99.24 78	97.22 161

XVS					96.60 62	99.35 10	96.82 60			90.85 52		98.72 24				99.46 26

X-MVStestdata					96.60 62	99.35 10	96.82 60			90.85 52		98.72 24				99.46 26

TSAR-MVS + COLMAP		94.79 62	94.51 74	95.11 56	96.50 64	97.54 98	97.99 40	94.54 39	97.81 13	85.88 103	96.73 27	81.28 131	96.99 55	96.29 97	95.21 111	98.76 152	96.73 177

PVSNet_Blended_VisFu		94.77 64	95.54 58	93.87 85	96.48 65	98.97 41	94.33 123	91.84 81	94.93 89	90.37 63	85.04 129	94.99 55	90.87 150	98.12 31	97.30 48	99.30 68	99.45 10

LGP-MVS_train		94.12 74	94.62 71	93.53 92	96.44 66	97.54 98	97.40 50	91.84 81	94.66 91	81.09 128	95.70 35	83.36 122	95.10 85	96.36 95	95.71 95	99.32 63	99.03 57

HQP-MVS		94.43 69	94.57 72	94.27 78	96.41 67	97.23 105	96.89 56	93.98 42	95.94 65	83.68 112	95.01 42	84.46 112	95.58 78	95.47 120	94.85 119	99.07 105	99.00 61

ACMM	92.75 10	94.41 71	93.84 89	95.09 57	96.41 67	96.80 115	94.88 114	93.54 46	96.41 49	90.16 65	92.31 65	83.11 123	96.32 67	96.22 100	94.65 121	99.22 84	97.35 157
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
RPSCF		94.05 75	94.00 85	94.12 80	96.20 69	96.41 129	96.61 71	91.54 86	95.83 70	89.73 71	96.94 26	92.80 65	95.35 84	91.63 195	90.44 203	95.27 215	93.94 203

UA-Net		93.96 78	95.95 54	91.64 112	96.06 70	98.59 74	95.29 104	90.00 99	91.06 148	82.87 115	90.64 83	98.06 34	86.06 198	98.14 29	98.20 15	99.58 6	96.96 171

UGNet		94.92 58	96.63 44	92.93 100	96.03 71	98.63 72	94.53 120	91.52 87	96.23 55	90.03 67	92.87 57	96.10 51	86.28 197	96.68 75	96.60 61	99.16 93	99.32 20
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
ACMP	92.88 9	94.43 69	94.38 77	94.50 75	96.01 72	97.69 97	95.85 92	92.09 74	95.74 72	89.12 79	95.14 40	82.62 126	94.77 87	95.73 114	94.67 120	99.14 96	99.06 52
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
IB-MVS	89.56 15	91.71 116	92.50 109	90.79 122	95.94 73	98.44 79	87.05 209	91.38 88	93.15 114	92.98 38	84.78 130	85.14 109	78.27 217	92.47 170	94.44 132	99.10 101	99.08 48
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
MS-PatchMatch		91.82 114	92.51 108	91.02 116	95.83 74	96.88 110	95.05 108	84.55 171	93.85 105	82.01 118	82.51 146	91.71 68	90.52 163	95.07 128	93.03 157	98.13 182	94.52 196

CANet_DTU		93.92 80	96.57 45	90.83 120	95.63 75	98.39 80	96.99 54	87.38 133	96.26 53	71.97 190	96.31 30	93.02 63	94.53 91	97.38 49	96.83 57	98.49 172	97.79 141

ACMH	90.77 13	91.51 121	91.63 128	91.38 114	95.62 76	96.87 112	91.76 180	89.66 105	91.58 143	78.67 137	86.73 113	78.12 140	93.77 103	94.59 134	94.54 128	98.78 146	98.98 64
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
TSAR-MVS + GP.		97.45 28	98.36 15	96.39 38	95.56 77	98.93 49	97.74 44	93.31 49	97.61 23	94.24 27	98.44 7	99.19 11	98.03 29	97.60 44	97.41 44	99.44 40	99.33 18

view800		93.45 98	92.37 117	94.71 72	95.42 78	98.92 53	96.51 76	92.19 72	93.14 115	87.62 94	86.72 114	76.54 153	97.08 53	96.86 61	95.74 94	99.45 30	98.70 84

tfpn		92.91 104	91.44 132	94.63 74	95.42 78	98.92 53	96.41 79	92.10 73	93.19 113	87.34 98	86.85 111	69.20 210	97.01 54	96.88 60	96.28 68	99.47 24	98.75 83

thres600view7		93.49 96	92.37 117	94.79 71	95.42 78	98.93 49	96.58 74	92.31 64	93.04 117	87.88 92	86.62 117	76.94 150	97.09 52	96.82 63	95.63 98	99.45 30	98.63 86

view600		93.50 95	92.39 116	94.80 70	95.41 81	98.93 49	96.60 72	92.30 69	93.09 116	87.96 91	86.67 116	76.97 149	97.12 46	96.83 62	95.64 97	99.43 45	98.62 87

thres400		93.56 91	92.43 113	94.87 67	95.40 82	98.91 55	96.70 69	92.38 63	92.93 119	88.19 90	86.69 115	77.35 147	97.13 44	96.75 72	95.85 88	99.42 46	98.56 90

thres200		93.62 88	92.54 107	94.88 64	95.36 83	98.93 49	96.75 68	92.31 64	92.84 120	88.28 86	86.99 110	77.81 146	97.13 44	96.82 63	95.92 82	99.45 30	98.49 101

tfpn111		94.05 75	93.34 101	94.88 64	95.33 84	98.94 43	96.82 60	92.31 64	92.63 122	88.26 87	92.61 59	78.01 142	97.12 46	96.82 63	95.85 88	99.45 30	98.56 90

conf200view11		93.64 85	92.57 105	94.88 64	95.33 84	98.94 43	96.82 60	92.31 64	92.63 122	88.26 87	87.21 107	78.01 142	97.12 46	96.82 63	95.85 88	99.45 30	98.56 90

thres100view900		93.55 94	92.47 112	94.81 69	95.33 84	98.74 63	96.78 67	92.30 69	92.63 122	88.29 84	87.21 107	78.01 142	96.78 60	96.38 93	95.92 82	99.38 54	98.40 107

tfpn200view9		93.64 85	92.57 105	94.89 63	95.33 84	98.94 43	96.82 60	92.31 64	92.63 122	88.29 84	87.21 107	78.01 142	97.12 46	96.82 63	95.85 88	99.45 30	98.56 90

conf0.01		93.33 99	91.89 125	95.00 60	95.32 88	98.94 43	96.82 60	92.41 62	92.63 122	88.91 82	88.02 105	72.75 183	97.12 46	96.78 69	95.85 88	99.44 40	98.27 115

conf0.002		93.20 102	91.63 128	95.02 58	95.31 89	98.94 43	96.82 60	92.43 61	92.63 122	88.99 81	88.16 104	70.49 202	97.12 46	96.77 70	96.30 64	99.44 40	98.16 121

IS_MVSNet		95.28 54	96.43 48	93.94 83	95.30 90	99.01 39	95.90 87	91.12 90	94.13 101	87.50 96	91.23 75	94.45 58	94.17 97	98.45 16	98.50 7	99.65 2	99.23 31

CMPMVS	65.18 17	84.76 206	83.10 216	86.69 193	95.29 91	95.05 178	88.37 204	85.51 155	80.27 224	71.31 194	68.37 219	73.85 167	85.25 201	87.72 215	87.75 214	94.38 224	88.70 223
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
tfpn1000		94.14 73	94.54 73	93.67 89	95.27 92	98.50 78	95.36 103	91.84 81	96.31 52	87.38 97	92.98 55	84.04 114	92.60 121	96.49 90	95.62 99	99.55 9	97.82 139

canonicalmvs		95.25 56	95.45 59	95.00 60	95.27 92	98.72 66	96.89 56	89.82 102	96.51 47	90.84 55	93.72 48	86.01 101	97.66 35	95.78 113	97.94 26	99.54 11	99.50 7

Vis-MVSNet (Re-imp)		94.46 68	96.24 50	92.40 106	95.23 94	98.64 70	95.56 99	90.99 91	94.42 95	85.02 106	90.88 82	94.65 57	88.01 187	98.17 28	98.37 14	99.57 8	98.53 95

conf0.05thres1000		92.47 109	91.39 133	93.73 88	95.21 95	98.52 76	95.66 95	91.56 85	90.87 151	84.27 108	82.79 144	76.12 154	96.29 68	96.59 80	95.68 96	99.39 52	99.19 37

CLD-MVS		94.79 62	94.36 78	95.30 55	95.21 95	97.46 100	97.23 51	92.24 71	96.43 48	91.77 47	92.69 58	84.31 113	96.06 71	95.52 118	95.03 113	99.31 66	99.06 52
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
tfpn_ndepth		94.36 72	94.64 70	94.04 82	95.16 97	98.51 77	95.58 97	92.09 74	95.78 71	88.52 83	92.38 64	85.74 103	93.34 111	96.39 91	95.90 84	99.54 11	97.79 141

TDRefinement		89.07 152	88.15 161	90.14 132	95.16 97	96.88 110	95.55 100	90.20 97	89.68 163	76.42 148	76.67 164	74.30 165	84.85 204	93.11 160	91.91 194	98.64 164	94.47 197

ACMH+	90.88 12	91.41 122	91.13 135	91.74 111	95.11 99	96.95 109	93.13 138	89.48 109	92.42 132	79.93 132	85.13 128	78.02 141	93.82 102	93.49 155	93.88 142	98.94 117	97.99 127

Anonymous20240521		94.76 65	95.12 65	94.35 77	95.10 100	95.81 147	96.46 78	89.49 108	96.33 50	90.16 65	92.55 62	90.26 77	95.83 74	95.52 118	96.03 79	99.06 108	99.33 18

tfpnview11		93.63 87	94.42 75	92.71 102	95.08 101	98.26 85	95.58 97	92.06 76	96.32 51	81.88 119	93.44 49	83.43 119	92.14 126	96.58 82	95.88 86	99.52 13	97.07 168

tfpn_n400		93.56 91	94.36 78	92.63 103	95.07 102	98.28 82	95.50 101	91.98 78	95.48 75	81.88 119	93.44 49	83.43 119	92.01 129	96.60 78	96.27 69	99.34 59	97.04 169

tfpnconf		93.56 91	94.36 78	92.63 103	95.07 102	98.28 82	95.50 101	91.98 78	95.48 75	81.88 119	93.44 49	83.43 119	92.01 129	96.60 78	96.27 69	99.34 59	97.04 169

Anonymous202405211			92.18 120		95.04 104	98.20 88	96.14 84	91.79 84	93.93 102		74.60 179	88.38 92	96.48 65	95.17 126	95.82 93	99.00 112	99.15 43

thresconf0.02		93.57 90	93.84 89	93.25 97	95.03 105	98.16 90	95.80 94	92.46 60	96.12 59	83.88 110	92.61 59	80.39 132	92.83 119	96.11 105	96.21 74	99.49 20	97.28 160

FC-MVSNet-train		93.85 81	93.91 86	93.78 87	94.94 106	96.79 118	94.29 124	91.13 89	93.84 106	88.26 87	90.40 85	85.23 108	94.65 90	96.54 85	95.31 108	99.38 54	99.28 22

EPP-MVSNet		95.27 55	96.18 52	94.20 79	94.88 107	98.64 70	94.97 110	90.70 92	95.34 79	89.67 73	91.66 73	93.84 59	95.42 83	97.32 50	97.00 52	99.58 6	99.47 8

casdiffmvs		95.22 57	96.19 51	94.09 81	94.85 108	98.57 75	96.83 59	89.37 110	97.36 29	87.24 100	91.72 72	91.84 67	96.99 55	97.27 51	97.60 36	99.29 70	98.94 68

MVS_Test		94.82 60	95.66 55	93.84 86	94.79 109	98.35 81	96.49 77	89.10 114	96.12 59	87.09 101	92.58 61	90.61 75	96.48 65	96.51 89	96.89 55	99.11 100	98.54 94

Anonymous20231211		93.49 96	92.33 119	94.84 68	94.78 110	98.00 95	96.11 85	91.85 80	94.86 90	90.91 51	74.69 178	89.18 84	96.73 61	94.82 132	95.51 104	98.67 160	99.24 30

MVSTER		94.89 59	95.07 66	94.68 73	94.71 111	96.68 121	97.00 53	90.57 94	95.18 86	93.05 36	95.21 39	86.41 98	93.72 104	97.59 45	95.88 86	99.00 112	98.50 100

EPMVS		90.88 127	92.12 121	89.44 140	94.71 111	97.24 104	93.55 130	76.81 210	95.89 66	81.77 123	91.49 74	86.47 97	93.87 100	90.21 204	90.07 205	95.92 205	93.49 209

DWT-MVSNet_training		91.30 123	89.73 146	93.13 99	94.64 113	96.87 112	94.93 111	86.17 146	94.22 99	93.18 31	89.11 95	73.28 177	93.59 107	88.00 214	90.73 201	96.26 202	95.87 185

DI_MVS_plusplus_trai		94.01 77	93.63 94	94.44 76	94.54 114	98.26 85	97.51 48	90.63 93	95.88 67	89.34 77	80.54 154	89.36 82	95.48 82	96.33 96	96.27 69	99.17 90	98.78 81

ADS-MVSNet		89.80 141	91.33 134	88.00 170	94.43 115	96.71 120	92.29 159	74.95 222	96.07 62	77.39 141	88.67 99	86.09 100	93.26 113	88.44 212	89.57 207	95.68 208	93.81 206

tpmrst		88.86 156	89.62 147	87.97 171	94.33 116	95.98 137	92.62 145	76.36 215	94.62 93	76.94 144	85.98 125	82.80 125	92.80 120	86.90 218	87.15 219	94.77 220	93.93 204

PMMVS		94.61 67	95.56 57	93.50 93	94.30 117	96.74 119	94.91 113	89.56 107	95.58 74	87.72 93	96.15 31	92.86 64	96.06 71	95.47 120	95.02 114	98.43 177	97.09 164

diffmvs		93.94 79	94.17 83	93.67 89	94.24 118	98.09 93	95.15 107	87.82 128	95.35 78	90.45 59	92.30 66	84.51 111	95.56 79	94.84 131	95.57 101	98.80 132	98.52 96

CostFormer		90.69 128	90.48 143	90.93 118	94.18 119	96.08 135	94.03 126	78.20 206	93.47 111	89.96 68	90.97 81	80.30 133	93.72 104	87.66 217	88.75 209	95.51 211	96.12 182

USDC		90.69 128	90.52 142	90.88 119	94.17 120	96.43 128	95.82 93	86.76 139	93.92 103	76.27 150	86.49 119	74.30 165	93.67 106	95.04 129	93.36 151	98.61 165	94.13 201

Vis-MVSNet		92.77 105	95.00 68	90.16 130	94.10 121	98.79 61	94.76 117	88.26 119	92.37 135	79.95 131	88.19 103	91.58 69	84.38 207	97.59 45	97.58 37	99.52 13	98.91 72
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
Effi-MVS+		92.93 103	93.86 88	91.86 108	94.07 122	98.09 93	95.59 96	85.98 149	94.27 98	79.54 135	91.12 79	81.81 128	96.71 62	96.67 76	96.06 77	99.27 73	98.98 64

tpmp4_e23		89.82 140	89.31 152	90.42 126	94.01 123	95.45 159	94.63 119	78.37 203	93.59 109	87.09 101	86.62 117	76.59 152	93.06 117	88.50 211	88.52 210	95.36 212	95.88 184

IterMVS-LS		92.56 108	93.18 102	91.84 109	93.90 124	94.97 180	94.99 109	86.20 145	94.18 100	82.68 116	85.81 126	87.36 95	94.43 92	95.31 122	96.02 80	98.87 122	98.60 89
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
dps		90.11 138	89.37 151	90.98 117	93.89 125	96.21 133	93.49 132	77.61 208	91.95 141	92.74 42	88.85 96	78.77 139	92.37 124	87.71 216	87.71 215	95.80 206	94.38 199

tpm cat1		88.90 154	87.78 174	90.22 129	93.88 126	95.39 168	93.79 129	78.11 207	92.55 129	89.43 75	81.31 150	79.84 135	91.40 137	84.95 222	86.34 224	94.68 223	94.09 202

PatchmatchNet		90.56 130	92.49 110	88.31 158	93.83 127	96.86 114	92.42 149	76.50 214	95.96 64	78.31 138	91.96 70	89.66 81	93.48 109	90.04 206	89.20 208	95.32 213	93.73 207
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
TinyColmap		89.42 144	88.58 156	90.40 127	93.80 128	95.45 159	93.96 128	86.54 141	92.24 138	76.49 147	80.83 152	70.44 203	93.37 110	94.45 138	93.30 154	98.26 180	93.37 211

RPMNet		90.19 136	92.03 123	88.05 167	93.46 129	95.95 140	93.41 133	74.59 223	92.40 133	75.91 152	84.22 134	86.41 98	92.49 122	94.42 139	93.85 144	98.44 175	96.96 171

gg-mvs-nofinetune		86.17 200	88.57 157	83.36 209	93.44 130	98.15 91	96.58 74	72.05 228	74.12 229	49.23 234	64.81 224	90.85 73	89.90 178	97.83 40	96.84 56	98.97 115	97.41 155

MDTV_nov1_ep13		91.57 119	93.18 102	89.70 136	93.39 131	96.97 108	93.53 131	80.91 198	95.70 73	81.86 122	92.40 63	89.93 79	93.25 114	91.97 193	90.80 200	95.25 216	94.46 198

CR-MVSNet		90.16 137	91.96 124	88.06 166	93.32 132	95.95 140	93.36 134	75.99 217	92.40 133	75.19 159	83.18 141	85.37 105	92.05 127	95.21 124	94.56 126	98.47 174	97.08 166

test-LLR		91.62 118	93.56 97	89.35 142	93.31 133	96.57 124	92.02 175	87.06 137	92.34 136	75.05 162	90.20 87	88.64 89	90.93 146	96.19 102	94.07 137	97.75 191	96.90 174

test0.0.03 1		91.97 112	93.91 86	89.72 135	93.31 133	96.40 130	91.34 185	87.06 137	93.86 104	81.67 124	91.15 78	89.16 85	86.02 199	95.08 127	95.09 112	98.91 120	96.64 180

CVMVSNet		89.77 142	91.66 127	87.56 184	93.21 135	95.45 159	91.94 179	89.22 112	89.62 166	69.34 208	83.99 136	85.90 102	84.81 205	94.30 142	95.28 109	96.85 198	97.09 164

PatchT		89.13 151	91.71 126	86.11 201	92.92 136	95.59 154	83.64 217	75.09 221	91.87 142	75.19 159	82.63 145	85.06 110	92.05 127	95.21 124	94.56 126	97.76 190	97.08 166

Fast-Effi-MVS+		91.87 113	92.08 122	91.62 113	92.91 137	97.21 106	94.93 111	84.60 168	93.61 108	81.49 126	83.50 139	78.95 137	96.62 64	96.55 84	96.22 73	99.16 93	98.51 99

IterMVS		90.20 135	92.43 113	87.61 182	92.82 138	94.31 198	94.11 125	81.54 196	92.97 118	69.90 204	84.71 131	88.16 94	89.96 177	95.25 123	94.17 135	97.31 194	97.46 153
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CDS-MVSNet		92.77 105	93.60 95	91.80 110	92.63 139	96.80 115	95.24 105	89.14 113	90.30 159	84.58 107	86.76 112	90.65 74	90.42 166	95.89 108	96.49 62	98.79 139	98.32 113
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
tpm		87.95 170	89.44 150	86.21 199	92.53 140	94.62 193	91.40 183	76.36 215	91.46 144	69.80 206	87.43 106	75.14 160	91.55 136	89.85 209	90.60 202	95.61 209	96.96 171

Effi-MVS+-dtu		91.78 115	93.59 96	89.68 138	92.44 141	97.11 107	94.40 122	84.94 164	92.43 131	75.48 154	91.09 80	83.75 117	93.55 108	96.61 77	95.47 105	97.24 195	98.67 85

testgi		89.42 144	91.50 131	87.00 191	92.40 142	95.59 154	89.15 203	85.27 161	92.78 121	72.42 188	91.75 71	76.00 157	84.09 209	94.38 140	93.82 146	98.65 163	96.15 181

LP		84.43 208	85.10 210	83.66 207	92.31 143	93.89 200	87.13 207	72.88 225	90.81 152	67.08 212	70.65 214	75.76 158	86.87 193	86.43 221	87.15 219	95.70 207	90.98 217

Fast-Effi-MVS+-dtu		91.19 124	93.64 93	88.33 157	92.19 144	96.46 127	93.99 127	81.52 197	92.59 128	71.82 191	92.17 67	85.54 104	91.68 135	95.73 114	94.64 122	98.80 132	98.34 110

FC-MVSNet-test		91.63 117	93.82 91	89.08 143	92.02 145	96.40 130	93.26 136	87.26 134	93.72 107	77.26 142	88.61 100	89.86 80	85.50 200	95.72 116	95.02 114	99.16 93	97.44 154

GA-MVS		89.28 147	90.75 141	87.57 183	91.77 146	96.48 126	92.29 159	87.58 132	90.61 156	65.77 214	84.48 132	76.84 151	89.46 179	95.84 110	93.68 147	98.52 170	97.34 158

TAMVS		90.54 132	90.87 140	90.16 130	91.48 147	96.61 123	93.26 136	86.08 147	87.71 194	81.66 125	83.11 143	84.04 114	90.42 166	94.54 135	94.60 123	98.04 186	95.48 192

tfpnnormal		88.50 157	87.01 194	90.23 128	91.36 148	95.78 149	92.74 142	90.09 98	83.65 217	76.33 149	71.46 211	69.58 208	91.84 132	95.54 117	94.02 139	99.06 108	99.03 57

GBi-Net		93.81 82	94.18 81	93.38 94	91.34 149	95.86 143	96.22 81	88.68 115	95.23 83	90.40 60	86.39 120	91.16 70	94.40 94	96.52 86	96.30 64	99.21 87	97.79 141

test1		93.81 82	94.18 81	93.38 94	91.34 149	95.86 143	96.22 81	88.68 115	95.23 83	90.40 60	86.39 120	91.16 70	94.40 94	96.52 86	96.30 64	99.21 87	97.79 141

FMVSNet2		93.30 100	93.36 100	93.22 98	91.34 149	95.86 143	96.22 81	88.24 120	95.15 87	89.92 70	81.64 148	89.36 82	94.40 94	96.77 70	96.98 53	99.21 87	97.79 141

FMVSNet3		93.79 84	94.17 83	93.35 96	91.21 152	95.99 136	96.62 70	88.68 115	95.23 83	90.40 60	86.39 120	91.16 70	94.11 98	95.96 106	96.67 59	99.07 105	97.79 141

testpf		83.57 211	85.70 205	81.08 212	90.99 153	88.96 224	82.71 220	65.32 236	90.22 161	73.86 174	81.58 149	76.10 155	81.19 214	84.14 226	85.41 226	92.43 229	93.45 210

TransMVSNet (Re)		87.73 179	86.79 196	88.83 148	90.76 154	94.40 196	91.33 186	89.62 106	84.73 213	75.41 156	72.73 202	71.41 198	86.80 194	94.53 136	93.93 141	99.06 108	95.83 186

LTVRE_ROB	87.32 16	87.55 181	88.25 160	86.73 192	90.66 155	95.80 148	93.05 139	84.77 165	83.35 218	60.32 223	83.12 142	67.39 215	93.32 112	94.36 141	94.86 118	98.28 179	98.87 76
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
EG-PatchMatch MVS		86.68 194	87.24 186	86.02 202	90.58 156	96.26 132	91.08 189	81.59 195	84.96 212	69.80 206	71.35 212	75.08 162	84.23 208	94.24 144	93.35 152	98.82 125	95.46 193

TESTMET0.1,1		91.07 125	93.56 97	88.17 161	90.43 157	96.57 124	92.02 175	82.83 182	92.34 136	75.05 162	90.20 87	88.64 89	90.93 146	96.19 102	94.07 137	97.75 191	96.90 174

pm-mvs1		89.19 150	89.02 153	89.38 141	90.40 158	95.74 150	92.05 172	88.10 122	86.13 208	77.70 139	73.72 197	79.44 136	88.97 182	95.81 112	94.51 130	99.08 103	97.78 147

NR-MVSNet		89.34 146	88.66 155	90.13 133	90.40 158	95.61 152	93.04 140	89.91 100	91.22 146	78.96 136	77.72 162	68.90 212	89.16 181	94.24 144	93.95 140	99.32 63	98.99 62

FMVSNet1		91.54 120	90.93 138	92.26 107	90.35 160	95.27 172	95.22 106	87.16 136	91.37 145	87.62 94	75.45 167	83.84 116	94.43 92	96.52 86	96.30 64	98.82 125	97.74 148

test-mter		90.95 126	93.54 99	87.93 172	90.28 161	96.80 115	91.44 182	82.68 184	92.15 140	74.37 170	89.57 93	88.23 93	90.88 149	96.37 94	94.31 133	97.93 188	97.37 156

pmmvs4		90.55 131	89.91 145	91.30 115	90.26 162	94.95 181	92.73 143	87.94 126	93.44 112	85.35 105	82.28 147	76.09 156	93.02 118	93.56 153	92.26 192	98.51 171	96.77 176

MVS-HIRNet		85.36 204	86.89 195	83.57 208	90.13 163	94.51 194	83.57 218	72.61 226	88.27 190	71.22 195	68.97 217	81.81 128	88.91 183	93.08 161	91.94 193	94.97 219	89.64 222

SixPastTwentyTwo		88.37 161	89.47 149	87.08 189	90.01 164	95.93 142	87.41 206	85.32 158	90.26 160	70.26 201	86.34 123	71.95 193	90.93 146	92.89 165	91.72 196	98.55 168	97.22 161

UniMVSNet (Re)		90.03 139	89.61 148	90.51 125	89.97 165	96.12 134	92.32 155	89.26 111	90.99 149	80.95 129	78.25 161	75.08 162	91.14 141	93.78 148	93.87 143	99.41 47	99.21 35

our_test_3					89.78 166	93.84 201	85.59 213

v18		87.93 171	87.61 180	88.31 158	89.74 167	92.04 208	92.59 146	82.71 183	89.70 162	75.32 157	75.23 169	73.55 171	90.74 153	92.11 184	92.77 172	98.78 146	97.87 135

v16		87.87 176	87.60 181	88.19 160	89.70 168	92.01 210	92.37 150	82.54 186	89.67 164	75.00 164	75.02 173	73.65 169	90.73 155	92.14 180	92.80 166	98.77 150	97.90 132

UniMVSNet_NR-MVSNet		90.35 134	89.96 144	90.80 121	89.66 169	95.83 146	92.48 147	90.53 95	90.96 150	79.57 133	79.33 158	77.14 148	93.21 115	92.91 164	94.50 131	99.37 57	99.05 54

v17		87.83 177	87.56 182	88.13 162	89.65 170	92.02 209	92.34 154	82.55 185	89.38 167	74.76 165	75.14 170	73.59 170	90.70 156	92.15 179	92.78 170	98.78 146	97.89 133

v8		88.21 164	87.94 171	88.51 154	89.62 171	95.01 179	92.31 156	84.99 163	88.94 174	74.70 166	75.03 172	73.51 172	90.67 159	92.11 184	92.74 178	98.80 132	98.24 116

WR-MVS_H		87.93 171	87.85 172	88.03 169	89.62 171	95.58 156	90.47 194	85.55 154	87.20 200	76.83 145	74.42 184	72.67 189	86.37 196	93.22 159	93.04 156	99.33 61	98.83 78

v1neww		88.41 159	88.00 167	88.89 145	89.61 173	95.44 162	92.31 156	87.65 130	89.09 170	74.30 171	75.02 173	73.42 175	90.68 157	92.12 181	92.77 172	98.79 139	98.18 118

v7new		88.41 159	88.00 167	88.89 145	89.61 173	95.44 162	92.31 156	87.65 130	89.09 170	74.30 171	75.02 173	73.42 175	90.68 157	92.12 181	92.77 172	98.79 139	98.18 118

v6		88.43 158	88.01 164	88.92 144	89.60 175	95.43 164	92.36 151	87.66 129	89.07 172	74.50 168	75.06 171	73.47 173	90.59 162	92.11 184	92.76 176	98.79 139	98.18 118

pmmvs5		87.83 177	88.09 162	87.51 186	89.59 176	95.48 157	89.75 201	84.73 166	86.07 210	71.44 193	80.57 153	70.09 206	90.74 153	94.47 137	92.87 162	98.82 125	97.10 163

gm-plane-assit		83.26 212	85.29 208	80.89 213	89.52 177	89.89 222	70.26 230	78.24 205	77.11 227	58.01 227	74.16 189	66.90 217	90.63 161	97.20 54	96.05 78	98.66 162	95.68 189

v7		88.18 165	88.01 164	88.39 155	89.45 178	95.14 176	92.36 151	85.37 157	89.29 169	72.94 187	73.98 193	72.77 181	91.38 138	93.59 149	92.87 162	98.82 125	98.42 104

v1141		88.17 166	87.69 176	88.74 150	89.44 179	95.41 165	92.25 164	87.98 123	88.38 184	73.54 182	74.43 183	72.71 187	90.45 164	92.08 188	92.72 180	98.79 139	98.09 123

divwei89l23v2f112		88.17 166	87.69 176	88.74 150	89.44 179	95.41 165	92.26 162	87.97 125	88.29 188	73.57 181	74.45 182	72.75 183	90.42 166	92.08 188	92.72 180	98.81 129	98.09 123

v10		88.00 169	87.96 169	88.05 167	89.44 179	94.68 189	92.36 151	83.35 178	89.37 168	72.96 185	73.98 193	72.79 180	91.35 139	93.59 149	92.88 161	98.81 129	98.42 104

v1		88.17 166	87.66 178	88.77 149	89.44 179	95.40 167	92.29 159	87.98 123	88.21 191	73.75 176	74.41 185	72.75 183	90.36 172	92.07 191	92.71 183	98.80 132	98.09 123

V42		88.31 162	87.95 170	88.73 152	89.44 179	95.34 169	92.23 166	87.21 135	88.83 176	74.49 169	74.89 177	73.43 174	90.41 169	92.08 188	92.77 172	98.60 167	98.33 111

v15		87.46 185	87.16 189	87.81 173	89.41 184	91.96 211	92.26 162	82.28 189	88.42 182	73.72 177	74.29 188	72.73 186	90.41 169	92.17 178	92.76 176	98.79 139	97.83 138

v148		87.51 182	86.79 196	88.36 156	89.39 185	95.21 174	89.84 200	88.20 121	87.61 196	77.56 140	73.38 200	70.32 205	86.80 194	90.70 201	92.31 189	98.37 178	97.98 129

V14		87.47 184	87.19 188	87.80 174	89.37 186	91.95 212	92.25 164	82.12 190	88.39 183	73.83 175	74.31 186	72.84 179	90.44 165	92.20 176	92.78 170	98.80 132	97.84 137

v11		87.58 180	87.50 183	87.67 179	89.34 187	91.91 215	92.22 168	81.63 194	89.01 173	72.95 186	74.11 191	72.51 191	91.08 143	94.01 147	93.00 158	98.77 150	97.93 130

V9		87.41 186	87.15 190	87.72 177	89.33 188	91.93 213	92.23 166	82.02 191	88.35 185	73.59 180	74.13 190	72.77 181	90.37 171	92.21 175	92.80 166	98.79 139	97.86 136

v13		87.34 189	87.11 193	87.62 181	89.30 189	91.91 215	92.04 173	81.86 193	88.35 185	73.36 183	73.88 195	72.69 188	90.34 173	92.23 173	92.82 164	98.80 132	97.88 134

v12		87.38 188	87.13 191	87.68 178	89.30 189	91.92 214	92.01 177	81.94 192	88.35 185	73.69 178	74.10 192	72.57 190	90.33 174	92.23 173	92.82 164	98.80 132	97.91 131

CP-MVSNet		87.89 175	87.27 185	88.62 153	89.30 189	95.06 177	90.60 193	85.78 151	87.43 198	75.98 151	74.60 179	68.14 214	90.76 151	93.07 162	93.60 148	99.30 68	98.98 64

v1144		87.92 174	87.79 173	88.07 164	89.27 192	95.15 175	92.17 169	85.62 153	88.52 180	71.52 192	73.80 196	72.40 192	91.06 144	93.54 154	92.80 166	98.81 129	98.33 111

DU-MVS		89.67 143	88.84 154	90.63 124	89.26 193	95.61 152	92.48 147	89.91 100	91.22 146	79.57 133	77.72 162	71.18 199	93.21 115	92.53 168	94.57 125	99.35 58	99.05 54

WR-MVS		87.93 171	88.09 162	87.75 175	89.26 193	95.28 170	90.81 191	86.69 140	88.90 175	75.29 158	74.31 186	73.72 168	85.19 203	92.26 171	93.32 153	99.27 73	98.81 79

Baseline_NR-MVSNet		89.27 148	88.01 164	90.73 123	89.26 193	93.71 202	92.71 144	89.78 104	90.73 153	81.28 127	73.53 198	72.85 178	92.30 125	92.53 168	93.84 145	99.07 105	98.88 74

N_pmnet		84.80 205	85.10 210	84.45 205	89.25 196	92.86 205	84.04 216	86.21 143	88.78 177	66.73 213	72.41 205	74.87 164	85.21 202	88.32 213	86.45 222	95.30 214	92.04 213

v2v482		88.25 163	87.71 175	88.88 147	89.23 197	95.28 170	92.10 170	87.89 127	88.69 179	73.31 184	75.32 168	71.64 195	91.89 131	92.10 187	92.92 160	98.86 124	97.99 127

PS-CasMVS		87.33 190	86.68 199	88.10 163	89.22 198	94.93 182	90.35 196	85.70 152	86.44 204	74.01 173	73.43 199	66.59 220	90.04 176	92.92 163	93.52 149	99.28 71	98.91 72

TranMVSNet+NR-MVSNet		89.23 149	88.48 158	90.11 134	89.07 199	95.25 173	92.91 141	90.43 96	90.31 158	77.10 143	76.62 165	71.57 197	91.83 133	92.12 181	94.59 124	99.32 63	98.92 70

v1192		87.51 182	87.31 184	87.74 176	89.04 200	94.87 187	92.07 171	85.03 162	88.49 181	70.32 200	72.65 203	70.35 204	91.21 140	93.59 149	92.80 166	98.78 146	98.42 104

v144192		87.40 187	87.20 187	87.64 180	88.89 201	94.88 186	91.65 181	84.70 167	87.80 193	71.17 197	73.20 201	70.91 200	90.75 152	92.69 166	92.49 185	98.71 156	98.43 103

PEN-MVS		87.22 192	86.50 203	88.07 164	88.88 202	94.44 195	90.99 190	86.21 143	86.53 203	73.66 179	74.97 176	66.56 221	89.42 180	91.20 197	93.48 150	99.24 78	98.31 114

v1921920		87.31 191	87.13 191	87.52 185	88.87 203	94.72 188	91.96 178	84.59 169	88.28 189	69.86 205	72.50 204	70.03 207	91.10 142	93.33 157	92.61 184	98.71 156	98.44 102

pmmvs6		85.98 201	84.89 212	87.25 188	88.83 204	94.35 197	89.36 202	85.30 160	78.51 226	75.44 155	62.71 227	75.41 159	87.65 189	93.58 152	92.40 187	96.89 197	97.29 159

v1240		86.89 193	86.75 198	87.06 190	88.75 205	94.65 191	91.30 187	84.05 172	87.49 197	68.94 209	71.96 207	68.86 213	90.65 160	93.33 157	92.72 180	98.67 160	98.24 116

anonymousdsp		88.90 154	91.00 137	86.44 197	88.74 206	95.97 138	90.40 195	82.86 181	88.77 178	67.33 211	81.18 151	81.44 130	90.22 175	96.23 99	94.27 134	99.12 99	99.16 42

EU-MVSNet		85.62 203	87.65 179	83.24 210	88.54 207	92.77 206	87.12 208	85.32 158	86.71 201	64.54 216	78.52 160	75.11 161	78.35 216	92.25 172	92.28 191	95.58 210	95.93 183

DTE-MVSNet		86.67 195	86.09 204	87.35 187	88.45 208	94.08 199	90.65 192	86.05 148	86.13 208	72.19 189	74.58 181	66.77 219	87.61 190	90.31 203	93.12 155	99.13 97	97.62 151

v748		85.88 202	85.66 206	86.14 200	88.03 209	94.63 192	87.02 210	84.59 169	84.30 214	74.56 167	70.94 213	67.27 216	83.94 211	90.96 200	92.74 178	98.71 156	98.81 79

FMVSNet5		90.36 133	90.93 138	89.70 136	87.99 210	92.25 207	92.03 174	83.51 175	92.20 139	84.13 109	85.59 127	86.48 96	92.43 123	94.61 133	94.52 129	98.13 182	90.85 218

v7n		86.43 198	86.52 202	86.33 198	87.91 211	94.93 182	90.15 197	83.05 179	86.57 202	70.21 202	71.48 210	66.78 218	87.72 188	94.19 146	92.96 159	98.92 119	98.76 82

test20.03		82.92 213	85.52 207	79.90 216	87.75 212	91.84 217	82.80 219	82.99 180	82.65 222	60.32 223	78.90 159	70.50 201	67.10 227	92.05 192	90.89 199	98.44 175	91.80 214

MDTV_nov1_ep13_2view		86.30 199	88.27 159	84.01 206	87.71 213	94.67 190	88.08 205	76.78 211	90.59 157	68.66 210	80.46 155	80.12 134	87.58 191	89.95 208	88.20 212	95.25 216	93.90 205

V4		86.56 197	86.61 201	86.50 195	87.49 214	94.90 184	89.87 199	83.39 176	86.25 206	71.20 196	71.57 208	71.58 196	88.30 186	91.14 198	92.31 189	98.75 153	98.52 96

v52		86.57 196	86.63 200	86.50 195	87.47 215	94.89 185	89.90 198	83.39 176	86.36 205	71.17 197	71.53 209	71.65 194	88.34 185	91.14 198	92.32 188	98.74 154	98.52 96

Anonymous20231206		83.84 210	85.19 209	82.26 211	87.38 216	92.87 204	85.49 214	83.65 174	86.07 210	63.44 219	68.42 218	69.01 211	75.45 220	93.34 156	92.44 186	98.12 184	94.20 200

FPMVS		75.84 221	74.59 223	77.29 222	86.92 217	83.89 230	85.01 215	80.05 201	82.91 220	60.61 222	65.25 223	60.41 225	63.86 228	75.60 231	73.60 233	87.29 234	80.47 230

MIMVSNet		88.99 153	91.07 136	86.57 194	86.78 218	95.62 151	91.20 188	75.40 220	90.65 155	76.57 146	84.05 135	82.44 127	91.01 145	95.84 110	95.38 107	98.48 173	93.50 208

tmp_tt				66.88 229	86.07 219	73.86 237	68.22 231	33.38 238	96.88 42	80.67 130	88.23 102	78.82 138	49.78 234	82.68 228	77.47 230	83.19 237

PM-MVS		84.72 207	84.47 213	85.03 204	84.67 220	91.57 218	86.27 212	82.31 188	87.65 195	70.62 199	76.54 166	56.41 232	88.75 184	92.59 167	89.85 206	97.54 193	96.66 179

testus		81.33 215	84.13 214	78.06 219	84.54 221	87.72 225	79.66 224	80.42 199	87.36 199	54.13 233	83.83 137	56.63 230	73.21 225	90.51 202	91.74 195	96.40 200	91.11 216

test2356		81.26 216	84.10 215	77.95 221	84.35 222	87.38 226	79.56 225	79.53 202	86.17 207	54.14 232	83.24 140	60.71 224	73.77 221	90.01 207	91.18 198	96.33 201	90.01 220

pmmvs-eth3d		84.33 209	82.94 217	85.96 203	84.16 223	90.94 219	86.55 211	83.79 173	84.25 215	75.85 153	70.64 215	56.43 231	87.44 192	92.20 176	90.41 204	97.97 187	95.68 189

new-patchmatchnet		78.49 220	78.19 222	78.84 218	84.13 224	90.06 221	77.11 229	80.39 200	79.57 225	59.64 226	66.01 222	55.65 233	75.62 219	84.55 225	80.70 228	96.14 204	90.77 219

new_pmnet		81.53 214	82.68 218	80.20 214	83.47 225	89.47 223	82.21 222	78.36 204	87.86 192	60.14 225	67.90 220	69.43 209	82.03 213	89.22 210	87.47 216	94.99 218	87.39 224

testmv		72.66 223	74.40 224	70.62 224	80.64 226	81.51 233	64.99 235	76.60 212	68.76 232	44.81 235	63.78 225	48.00 235	62.52 229	84.74 223	87.17 217	94.19 225	86.86 225

test1235678		72.65 224	74.40 224	70.62 224	80.64 226	81.50 234	64.99 235	76.59 213	68.74 233	44.81 235	63.78 225	47.99 236	62.51 230	84.73 224	87.17 217	94.19 225	86.85 226

pmmvs3		79.16 219	80.12 221	78.05 220	79.36 228	86.59 228	78.13 228	73.87 224	76.42 228	57.51 228	70.59 216	57.02 229	84.66 206	90.10 205	88.32 211	94.75 221	91.77 215

1111		73.35 222	74.40 224	72.12 223	78.22 229	82.24 231	65.06 233	65.61 234	70.28 230	55.42 229	56.30 230	57.35 227	73.66 222	86.73 219	88.16 213	94.75 221	79.76 232

.test1245		56.65 230	56.09 232	57.30 231	78.22 229	82.24 231	65.06 233	65.61 234	70.28 230	55.42 229	56.30 230	57.35 227	73.66 222	86.73 219	15.01 237	5.84 241	24.75 238

PMVS	63.12 18	67.27 227	66.39 230	68.30 227	77.98 231	60.24 240	59.53 239	76.82 209	66.65 235	60.74 221	54.39 232	59.82 226	51.24 233	73.92 234	70.52 234	83.48 236	79.17 233
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test12356		69.55 225	71.53 228	67.24 228	77.70 232	78.48 235	65.92 232	75.55 219	68.39 234	44.26 237	61.80 228	40.70 238	47.92 237	81.45 229	87.01 221	92.09 230	82.89 228

MDA-MVSNet-bldmvs		80.11 217	80.24 220	79.94 215	77.01 233	93.21 203	78.86 227	85.94 150	82.71 221	60.86 220	79.71 157	51.77 234	83.71 212	75.60 231	86.37 223	93.28 227	92.35 212

ambc			73.83 227		76.23 234	85.13 229	82.27 221		84.16 216	65.58 215	52.82 233	23.31 243	73.55 224	91.41 196	85.26 227	92.97 228	94.70 195

Gipuma		68.35 226	66.71 229	70.27 226	74.16 235	68.78 239	63.93 238	71.77 229	83.34 219	54.57 231	34.37 235	31.88 239	68.69 226	83.30 227	85.53 225	88.48 233	79.78 231
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MIMVSNet1		80.03 218	80.93 219	78.97 217	72.46 236	90.73 220	80.81 223	82.44 187	80.39 223	63.64 218	57.57 229	64.93 222	76.37 218	91.66 194	91.55 197	98.07 185	89.70 221

no-one		55.96 231	55.63 233	56.35 232	68.48 237	73.29 238	43.03 240	72.52 227	44.01 239	34.80 238	32.83 236	29.11 240	35.21 238	56.63 236	75.72 231	84.04 235	77.79 234

PMMVS2		64.36 229	65.94 231	62.52 230	67.37 238	77.44 236	64.39 237	69.32 233	61.47 236	34.59 239	46.09 234	41.03 237	48.02 236	74.56 233	78.23 229	91.43 231	82.76 229

EMVS		49.98 233	46.76 236	53.74 234	64.96 239	51.29 242	37.81 242	69.35 232	51.83 237	22.69 242	29.57 238	25.06 241	57.28 231	44.81 238	56.11 236	70.32 239	68.64 237

E-PMN		50.67 232	47.85 235	53.96 233	64.13 240	50.98 243	38.06 241	69.51 231	51.40 238	24.60 241	29.46 239	24.39 242	56.07 232	48.17 237	59.70 235	71.40 238	70.84 236

MVE	50.86 19	49.54 234	51.43 234	47.33 235	44.14 241	59.20 241	36.45 243	60.59 237	41.47 240	31.14 240	29.58 237	17.06 244	48.52 235	62.22 235	74.63 232	63.12 240	75.87 235
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		12.09 235	16.94 237	6.42 237	3.15 242	6.08 244	9.51 245	3.84 239	21.46 241	5.31 243	27.49 240	6.76 245	10.89 239	17.06 239	15.01 237	5.84 241	24.75 238

GG-mvs-BLEND		66.17 228	94.91 69	32.63 236	1.32 243	96.64 122	91.40 183	0.85 241	94.39 97	2.20 244	90.15 89	95.70 53	2.27 241	96.39 91	95.44 106	97.78 189	95.68 189

test123		9.58 236	13.53 238	4.97 238	1.31 244	5.47 245	8.32 246	2.95 240	18.14 242	2.03 245	20.82 241	2.34 246	10.60 240	10.00 240	14.16 239	4.60 243	23.77 240

sosnet-low-res		0.00 237	0.00 239	0.00 239	0.00 245	0.00 246	0.00 247	0.00 242	0.00 243	0.00 246	0.00 242	0.00 247	0.00 242	0.00 241	0.00 240	0.00 244	0.00 241

sosnet		0.00 237	0.00 239	0.00 239	0.00 245	0.00 246	0.00 247	0.00 242	0.00 243	0.00 246	0.00 242	0.00 247	0.00 242	0.00 241	0.00 240	0.00 244	0.00 241

MTAPA										96.83 5		99.12 15

MTMP										97.18 3		98.83 21

Patchmatch-RL test							34.61 244

NP-MVS									95.32 80

Patchmtry						95.96 139	93.36 134	75.99 217		75.19 159

DeepMVS_CX						86.86 227	79.50 226	70.43 230	90.73 153	63.66 217	80.36 156	60.83 223	79.68 215	76.23 230		89.46 232	86.53 227

High-resolution multi-view benchmark