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 3	98.89 3	98.39 7	99.60 1	99.41 7	99.00 19	97.63 9	97.78 14	95.83 16	98.33 10	99.83 1	98.85 9	98.93 5	98.56 6	99.41 48	99.40 12

APDe-MVS		98.87 1	98.96 1	98.77 1	99.58 2	99.53 3	99.44 1	97.81 1	98.22 7	97.33 2	98.70 3	99.33 8	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 27	99.55 3	99.34 14	99.32 7	94.51 41	96.21 59	93.07 35	98.05 12	97.95 37	98.82 11	98.22 28	97.89 30	99.48 22	99.09 48

ACMMP_Plus		98.20 15	98.49 10	97.85 23	99.50 4	99.40 8	99.26 10	97.64 8	97.47 29	92.62 44	97.59 18	99.09 17	98.71 15	98.82 10	97.86 31	99.40 51	99.19 38

zzz-MVS		98.43 9	98.31 20	98.57 2	99.48 5	99.40 8	99.32 7	97.62 10	97.70 17	96.67 7	96.59 29	99.09 17	98.86 7	98.65 11	97.56 38	99.45 31	99.17 42

APD-MVS		98.36 12	98.32 19	98.41 6	99.47 6	99.26 20	99.12 13	97.77 5	96.73 44	96.12 13	97.27 25	98.88 20	98.46 23	98.47 16	98.39 12	99.52 14	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 25	99.47 6	99.52 4	98.55 30	95.41 36	97.69 19	95.72 17	94.29 49	95.53 55	98.10 28	96.20 101	97.38 45	99.24 79	99.62 2

v1.0		90.94 128	84.57 214	98.39 7	99.46 8	99.50 5	99.11 14	97.80 2	97.20 35	96.06 14	98.56 4	99.83 1	98.43 24	98.84 8	98.03 24	99.45 31	0.00 242

HPM-MVS++		98.34 13	98.47 12	98.18 14	99.46 8	99.15 28	99.10 15	97.69 6	97.67 20	94.93 24	97.62 17	99.70 5	98.60 18	98.45 17	97.46 41	99.31 68	99.26 27

HSP-MVS		98.59 4	98.65 7	98.52 4	99.44 10	99.57 1	99.34 4	97.65 7	97.36 31	96.62 9	98.49 6	99.65 6	98.67 17	98.60 12	97.44 42	99.40 51	99.46 10

ACMMPR		98.40 10	98.49 10	98.28 11	99.41 11	99.40 8	99.36 3	97.35 18	98.30 4	95.02 23	97.79 15	98.39 32	99.04 2	98.26 25	98.10 19	99.50 20	99.22 33

X-MVS		97.84 21	98.19 24	97.42 28	99.40 12	99.35 11	99.06 16	97.25 22	97.38 30	90.85 55	96.06 33	98.72 25	98.53 22	98.41 20	98.15 18	99.46 27	99.28 22

MCST-MVS		98.20 15	98.36 15	98.01 20	99.40 12	99.05 31	99.00 19	97.62 10	97.59 25	93.70 31	97.42 24	99.30 9	98.77 13	98.39 21	97.48 40	99.59 4	99.31 21

CNVR-MVS		98.47 8	98.46 13	98.48 5	99.40 12	99.05 31	99.02 18	97.54 13	97.73 15	96.65 8	97.20 26	99.13 15	98.85 9	98.91 6	98.10 19	99.41 48	99.08 49

HFP-MVS		98.48 7	98.62 8	98.32 9	99.39 15	99.33 15	99.27 9	97.42 15	98.27 5	95.25 21	98.34 9	98.83 22	99.08 1	98.26 25	98.08 21	99.48 22	99.26 27

NCCC		98.10 18	98.05 27	98.17 16	99.38 16	99.05 31	99.00 19	97.53 14	98.04 10	95.12 22	94.80 45	99.18 13	98.58 20	98.49 15	97.78 33	99.39 53	98.98 65

MP-MVS		98.09 19	98.30 21	97.84 24	99.34 17	99.19 26	99.23 11	97.40 16	97.09 39	93.03 38	97.58 19	98.85 21	98.57 21	98.44 19	97.69 34	99.48 22	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 10	99.34 17	99.30 16	99.15 12	97.35 18	97.49 27	95.58 19	97.72 16	98.62 29	98.82 11	98.29 23	97.67 35	99.51 18	99.28 22

SteuartSystems-ACMMP		98.38 11	98.71 6	97.99 21	99.34 17	99.46 6	99.34 4	97.33 21	97.31 32	94.25 27	98.06 11	99.17 14	98.13 27	98.98 2	98.46 9	99.55 10	99.54 6
Skip Steuart: Steuart Systems R&D Blog.
ESAPD		98.75 2	98.91 2	98.57 2	99.21 20	99.54 2	99.42 2	97.78 4	97.49 27	96.84 5	98.94 1	99.82 3	98.59 19	98.90 7	98.22 15	99.56 9	99.48 8

mPP-MVS					99.21 20							98.29 33

AdaColmap		97.53 27	96.93 40	98.24 12	99.21 20	98.77 63	98.47 32	97.34 20	96.68 46	96.52 11	95.11 42	96.12 51	98.72 14	97.19 56	96.24 74	99.17 92	98.39 109

DeepC-MVS_fast	96.13 1	98.13 17	98.27 22	97.97 22	99.16 23	99.03 36	99.05 17	97.24 23	98.22 7	94.17 29	95.82 34	98.07 34	98.69 16	98.83 9	98.80 2	99.52 14	99.10 46
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 14	99.10 24	99.09 30	98.34 34	96.99 28	97.54 26	96.60 10	94.82 44	98.45 31	98.89 5	97.46 49	98.77 4	99.17 92	99.37 14

3Dnovator	93.79 8	97.08 34	97.20 34	96.95 35	99.09 25	99.03 36	98.20 36	93.33 49	97.99 11	93.82 30	90.61 86	96.80 44	97.82 32	97.90 39	98.78 3	99.47 25	99.26 27

QAPM		96.78 41	97.14 37	96.36 40	99.05 26	99.14 29	98.02 39	93.26 51	97.27 34	90.84 58	91.16 78	97.31 39	97.64 37	97.70 43	98.20 16	99.33 63	99.18 41

OpenMVS	92.33 11	95.50 49	95.22 63	95.82 48	98.98 27	98.97 42	97.67 47	93.04 59	94.64 94	89.18 80	84.44 135	94.79 57	96.79 60	97.23 53	97.61 36	99.24 79	98.88 75

PLC	94.95 3	97.37 30	96.77 43	98.07 18	98.97 28	98.21 90	97.94 42	96.85 31	97.66 21	97.58 1	93.33 54	96.84 43	98.01 31	97.13 58	96.20 77	99.09 104	98.01 127
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 31	98.94 29	98.91 56	98.98 23	97.07 27	96.71 45	90.66 60	97.43 23	99.08 19	98.20 25	97.96 37	97.14 50	99.22 86	99.19 38

CDPH-MVS		96.84 39	97.49 31	96.09 44	98.92 30	98.85 60	98.61 27	95.09 37	96.00 66	87.29 102	95.45 39	97.42 38	97.16 44	97.83 41	97.94 27	99.44 41	98.92 70

CPTT-MVS		97.78 23	97.54 30	98.05 19	98.91 31	99.05 31	99.00 19	96.96 29	97.14 37	95.92 15	95.50 37	98.78 24	98.99 4	97.20 54	96.07 78	98.54 171	99.04 57

3Dnovator+	93.91 7	97.23 32	97.22 33	97.24 30	98.89 32	98.85 60	98.26 35	93.25 53	97.99 11	95.56 20	90.01 92	98.03 36	98.05 29	97.91 38	98.43 10	99.44 41	99.35 16

ACMMP		97.37 30	97.48 32	97.25 29	98.88 33	99.28 18	98.47 32	96.86 30	97.04 41	92.15 46	97.57 20	96.05 53	97.67 35	97.27 52	95.99 83	99.46 27	99.14 45
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 34	98.73 34	99.25 22	98.11 37	95.54 35	96.66 47	92.79 41	98.52 5	99.38 7	97.50 39	97.84 40	98.39 12	99.45 31	99.03 58

OMC-MVS		97.00 36	96.92 41	97.09 32	98.69 35	98.66 70	97.85 43	95.02 38	98.09 9	94.47 25	93.15 56	96.90 41	97.38 40	97.16 57	96.82 59	99.13 99	97.65 151

MAR-MVS		95.50 49	95.60 56	95.39 55	98.67 36	98.18 92	95.89 92	89.81 104	94.55 96	91.97 48	92.99 57	90.21 78	97.30 41	96.79 68	97.49 39	98.72 156	98.99 63
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 37	98.64 37	99.05 31	98.85 24	97.23 24	98.45 2	89.40 78	97.51 21	99.27 11	96.88 58	98.53 13	97.81 32	98.96 118	99.59 4

abl_6				96.82 36	98.60 38	98.74 64	97.74 45	93.73 45	96.25 57	94.37 26	94.55 48	98.60 30	97.25 42			99.27 74	98.61 89

CNLPA		96.90 38	96.28 49	97.64 26	98.56 39	98.63 74	96.85 60	96.60 32	97.73 15	97.08 4	89.78 94	96.28 50	97.80 34	96.73 73	96.63 61	98.94 119	98.14 123

EPNet		96.27 45	96.97 39	95.46 53	98.47 40	98.28 85	97.41 50	93.67 46	95.86 71	92.86 40	97.51 21	93.79 61	91.76 136	97.03 59	97.03 51	98.61 167	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 43	98.47 40	98.98 41	96.94 57	93.89 44	97.64 22	91.44 50	98.89 2	96.41 46	97.20 43	98.02 36	97.29 49	99.04 113	98.85 78

MVS_111021_HR		97.04 35	98.20 23	95.69 49	98.44 42	99.29 17	96.59 74	93.20 54	97.70 17	89.94 71	98.46 7	96.89 42	96.71 63	98.11 33	97.95 26	99.27 74	99.01 61

MSDG		94.82 61	93.73 93	96.09 44	98.34 43	97.43 104	97.06 53	96.05 33	95.84 72	90.56 61	86.30 126	89.10 87	95.55 81	96.13 104	95.61 103	99.00 114	95.73 189

TAPA-MVS	94.18 5	96.38 43	96.49 47	96.25 41	98.26 44	98.66 70	98.00 40	94.96 39	97.17 36	89.48 76	92.91 59	96.35 47	97.53 38	96.59 80	95.90 86	99.28 72	97.82 140
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 33	98.21 45	99.28 18	98.67 26	97.38 17	97.31 32	90.36 66	89.19 96	93.58 62	98.19 26	98.31 22	98.50 7	99.51 18	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 5	98.23 13	98.15 46	99.26 20	98.79 25	97.59 12	98.52 1	96.25 12	97.99 13	99.75 4	99.01 3	98.27 24	97.97 25	99.59 4	99.63 1

TSAR-MVS + MP.		98.49 6	98.78 4	98.15 17	98.14 47	99.17 27	99.34 4	97.18 25	98.44 3	95.72 17	97.84 14	99.28 10	98.87 6	99.05 1	98.05 22	99.66 1	99.60 3

EPNet_dtu		92.45 111	95.02 68	89.46 141	98.02 48	95.47 160	94.79 118	92.62 60	94.97 90	70.11 205	94.76 46	92.61 67	84.07 212	95.94 108	95.56 104	97.15 198	95.82 188
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 38	97.92 49	99.24 24	98.60 28	93.51 48	97.11 38	93.07 35	91.16 78	97.24 40	96.21 71	98.24 27	98.05 22	99.22 86	99.35 16

LS3D		95.46 51	95.14 64	95.84 47	97.91 50	98.90 58	98.58 29	97.79 3	97.07 40	83.65 115	88.71 99	88.64 90	97.82 32	97.49 48	97.42 43	99.26 78	97.72 150

DELS-MVS		96.06 46	96.04 53	96.07 46	97.77 51	99.25 22	98.10 38	93.26 51	94.42 97	92.79 41	88.52 103	93.48 63	95.06 88	98.51 14	98.83 1	99.45 31	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	93.27 102	92.71 105	93.93 86	97.75 52	97.44 103	96.07 87	93.17 55	95.40 80	83.86 113	83.76 140	88.72 89	93.87 102	94.25 145	94.11 138	98.87 124	95.28 195
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 64	96.25 41	97.73 53	98.73 66	97.59 48	97.13 26	92.50 132	89.09 82	89.85 93	96.65 45	96.90 57	94.97 133	94.89 119	99.08 105	98.38 110
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PatchMatch-RL		94.69 67	94.41 78	95.02 60	97.63 54	98.15 94	94.50 123	91.99 78	95.32 82	91.31 51	95.47 38	83.44 120	96.02 75	96.56 83	95.23 112	98.69 161	96.67 179

PVSNet_BlendedMVS		95.41 53	95.28 60	95.57 51	97.42 55	99.02 38	95.89 92	93.10 56	96.16 60	93.12 33	91.99 71	85.27 109	94.66 90	98.09 34	97.34 46	99.24 79	99.08 49

PVSNet_Blended		95.41 53	95.28 60	95.57 51	97.42 55	99.02 38	95.89 92	93.10 56	96.16 60	93.12 33	91.99 71	85.27 109	94.66 90	98.09 34	97.34 46	99.24 79	99.08 49

DeepPCF-MVS	95.28 2	97.00 36	98.35 17	95.42 54	97.30 57	98.94 44	94.82 117	96.03 34	98.24 6	92.11 47	95.80 35	98.64 28	95.51 82	98.95 4	98.66 5	96.78 201	99.20 37

CHOSEN 280x420		95.46 51	97.01 38	93.66 92	97.28 58	97.98 98	96.40 81	85.39 158	96.10 64	91.07 52	96.53 30	96.34 49	95.61 79	97.65 44	96.95 55	96.21 205	97.49 153

MVS_0304		96.31 44	96.91 42	95.62 50	97.21 59	99.20 25	98.55 30	93.10 56	97.04 41	89.73 73	90.30 88	96.35 47	95.71 77	98.14 30	97.93 29	99.38 55	99.40 12

CHOSEN 1792x2688		92.66 108	92.49 111	92.85 103	97.13 60	98.89 59	95.90 90	88.50 121	95.32 82	83.31 116	71.99 208	88.96 88	94.10 101	96.69 74	96.49 63	98.15 183	99.10 46

HyFIR lowres test		92.03 112	91.55 131	92.58 107	97.13 60	98.72 67	94.65 120	86.54 143	93.58 112	82.56 119	67.75 223	90.47 76	95.67 78	95.87 110	95.54 105	98.91 122	98.93 69

OPM-MVS		93.61 90	92.43 114	95.00 62	96.94 62	97.34 105	97.78 44	94.23 42	89.64 167	85.53 106	88.70 100	82.81 126	96.28 70	96.28 98	95.00 118	99.24 79	97.22 162

XVS					96.60 63	99.35 11	96.82 61			90.85 55		98.72 25				99.46 27

X-MVStestdata					96.60 63	99.35 11	96.82 61			90.85 55		98.72 25				99.46 27

TSAR-MVS + COLMAP		94.79 63	94.51 76	95.11 57	96.50 65	97.54 100	97.99 41	94.54 40	97.81 13	85.88 105	96.73 28	81.28 133	96.99 56	96.29 97	95.21 113	98.76 153	96.73 178

PVSNet_Blended_VisFu		94.77 65	95.54 58	93.87 87	96.48 66	98.97 42	94.33 125	91.84 82	94.93 91	90.37 65	85.04 131	94.99 56	90.87 152	98.12 32	97.30 48	99.30 70	99.45 11

LGP-MVS_train		94.12 76	94.62 73	93.53 93	96.44 67	97.54 100	97.40 51	91.84 82	94.66 93	81.09 130	95.70 36	83.36 124	95.10 87	96.36 95	95.71 98	99.32 65	99.03 58

HQP-MVS		94.43 70	94.57 74	94.27 81	96.41 68	97.23 107	96.89 58	93.98 43	95.94 68	83.68 114	95.01 43	84.46 114	95.58 80	95.47 121	94.85 121	99.07 107	99.00 62

ACMM	92.75 10	94.41 72	93.84 90	95.09 58	96.41 68	96.80 117	94.88 116	93.54 47	96.41 50	90.16 67	92.31 68	83.11 125	96.32 68	96.22 100	94.65 123	99.22 86	97.35 158
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
RPSCF		94.05 77	94.00 86	94.12 83	96.20 70	96.41 131	96.61 72	91.54 87	95.83 73	89.73 73	96.94 27	92.80 66	95.35 86	91.63 197	90.44 205	95.27 217	93.94 204

UA-Net		93.96 80	95.95 54	91.64 114	96.06 71	98.59 76	95.29 107	90.00 100	91.06 150	82.87 117	90.64 85	98.06 35	86.06 200	98.14 30	98.20 16	99.58 6	96.96 172

UGNet		94.92 58	96.63 44	92.93 102	96.03 72	98.63 74	94.53 122	91.52 88	96.23 58	90.03 69	92.87 60	96.10 52	86.28 199	96.68 75	96.60 62	99.16 95	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 70	94.38 79	94.50 77	96.01 73	97.69 99	95.85 95	92.09 75	95.74 75	89.12 81	95.14 41	82.62 128	94.77 89	95.73 115	94.67 122	99.14 98	99.06 53
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
IB-MVS	89.56 15	91.71 117	92.50 110	90.79 124	95.94 74	98.44 81	87.05 211	91.38 89	93.15 116	92.98 39	84.78 132	85.14 112	78.27 219	92.47 172	94.44 134	99.10 103	99.08 49
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		91.82 115	92.51 109	91.02 118	95.83 75	96.88 112	95.05 110	84.55 173	93.85 107	82.01 120	82.51 148	91.71 68	90.52 165	95.07 131	93.03 159	98.13 184	94.52 197

CANet_DTU		93.92 81	96.57 45	90.83 122	95.63 76	98.39 82	96.99 55	87.38 135	96.26 56	71.97 192	96.31 31	93.02 64	94.53 93	97.38 50	96.83 58	98.49 174	97.79 142

ACMH	90.77 13	91.51 122	91.63 129	91.38 116	95.62 77	96.87 114	91.76 182	89.66 107	91.58 145	78.67 139	86.73 115	78.12 142	93.77 105	94.59 136	94.54 130	98.78 147	98.98 65
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 39	95.56 78	98.93 50	97.74 45	93.31 50	97.61 23	94.24 28	98.44 8	99.19 12	98.03 30	97.60 45	97.41 44	99.44 41	99.33 18

view800		93.45 99	92.37 118	94.71 74	95.42 79	98.92 54	96.51 77	92.19 73	93.14 117	87.62 96	86.72 116	76.54 155	97.08 54	96.86 61	95.74 97	99.45 31	98.70 85

tfpn		92.91 105	91.44 133	94.63 76	95.42 79	98.92 54	96.41 80	92.10 74	93.19 115	87.34 101	86.85 113	69.20 212	97.01 55	96.88 60	96.28 70	99.47 25	98.75 84

thres600view7		93.49 97	92.37 118	94.79 73	95.42 79	98.93 50	96.58 75	92.31 65	93.04 119	87.88 94	86.62 119	76.94 152	97.09 53	96.82 63	95.63 101	99.45 31	98.63 87

view600		93.50 96	92.39 117	94.80 72	95.41 82	98.93 50	96.60 73	92.30 70	93.09 118	87.96 93	86.67 118	76.97 151	97.12 47	96.83 62	95.64 100	99.43 46	98.62 88

thres400		93.56 92	92.43 114	94.87 69	95.40 83	98.91 56	96.70 70	92.38 64	92.93 121	88.19 92	86.69 117	77.35 149	97.13 45	96.75 72	95.85 90	99.42 47	98.56 92

thres200		93.62 89	92.54 108	94.88 66	95.36 84	98.93 50	96.75 69	92.31 65	92.84 122	88.28 88	86.99 112	77.81 148	97.13 45	96.82 63	95.92 84	99.45 31	98.49 102

tfpn111		94.05 77	93.34 102	94.88 66	95.33 85	98.94 44	96.82 61	92.31 65	92.63 124	88.26 89	92.61 62	78.01 144	97.12 47	96.82 63	95.85 90	99.45 31	98.56 92

conf200view11		93.64 86	92.57 106	94.88 66	95.33 85	98.94 44	96.82 61	92.31 65	92.63 124	88.26 89	87.21 109	78.01 144	97.12 47	96.82 63	95.85 90	99.45 31	98.56 92

thres100view900		93.55 95	92.47 113	94.81 71	95.33 85	98.74 64	96.78 68	92.30 70	92.63 124	88.29 86	87.21 109	78.01 144	96.78 61	96.38 93	95.92 84	99.38 55	98.40 108

tfpn200view9		93.64 86	92.57 106	94.89 65	95.33 85	98.94 44	96.82 61	92.31 65	92.63 124	88.29 86	87.21 109	78.01 144	97.12 47	96.82 63	95.85 90	99.45 31	98.56 92

conf0.01		93.33 100	91.89 126	95.00 62	95.32 89	98.94 44	96.82 61	92.41 63	92.63 124	88.91 84	88.02 107	72.75 185	97.12 47	96.78 69	95.85 90	99.44 41	98.27 116

conf0.002		93.20 103	91.63 129	95.02 60	95.31 90	98.94 44	96.82 61	92.43 62	92.63 124	88.99 83	88.16 106	70.49 204	97.12 47	96.77 70	96.30 66	99.44 41	98.16 122

IS_MVSNet		95.28 55	96.43 48	93.94 85	95.30 91	99.01 40	95.90 90	91.12 91	94.13 103	87.50 99	91.23 77	94.45 59	94.17 99	98.45 17	98.50 7	99.65 2	99.23 31

CMPMVS	65.18 17	84.76 208	83.10 218	86.69 195	95.29 92	95.05 180	88.37 206	85.51 157	80.27 226	71.31 196	68.37 221	73.85 169	85.25 203	87.72 217	87.75 216	94.38 226	88.70 224
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
tfpn1000		94.14 75	94.54 75	93.67 91	95.27 93	98.50 80	95.36 106	91.84 82	96.31 55	87.38 100	92.98 58	84.04 116	92.60 123	96.49 90	95.62 102	99.55 10	97.82 140

canonicalmvs		95.25 57	95.45 59	95.00 62	95.27 93	98.72 67	96.89 58	89.82 103	96.51 48	90.84 58	93.72 50	86.01 104	97.66 36	95.78 114	97.94 27	99.54 12	99.50 7

Vis-MVSNet (Re-imp)		94.46 69	96.24 50	92.40 108	95.23 95	98.64 72	95.56 102	90.99 92	94.42 97	85.02 108	90.88 84	94.65 58	88.01 189	98.17 29	98.37 14	99.57 8	98.53 97

conf0.05thres1000		92.47 110	91.39 134	93.73 90	95.21 96	98.52 77	95.66 98	91.56 86	90.87 153	84.27 110	82.79 146	76.12 156	96.29 69	96.59 80	95.68 99	99.39 53	99.19 38

CLD-MVS		94.79 63	94.36 80	95.30 56	95.21 96	97.46 102	97.23 52	92.24 72	96.43 49	91.77 49	92.69 61	84.31 115	96.06 72	95.52 119	95.03 115	99.31 68	99.06 53
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 73	94.64 72	94.04 84	95.16 98	98.51 79	95.58 100	92.09 75	95.78 74	88.52 85	92.38 67	85.74 106	93.34 113	96.39 91	95.90 86	99.54 12	97.79 142

casdiffmvs1		95.62 48	96.09 52	95.08 59	95.16 98	98.67 69	96.95 56	89.70 106	97.61 23	92.42 45	94.68 47	90.01 79	96.85 59	95.45 123	97.02 52	99.37 58	99.21 35

TDRefinement		89.07 154	88.15 162	90.14 134	95.16 98	96.88 112	95.55 103	90.20 98	89.68 165	76.42 150	76.67 166	74.30 167	84.85 206	93.11 162	91.91 196	98.64 166	94.47 198

ACMH+	90.88 12	91.41 123	91.13 136	91.74 113	95.11 101	96.95 111	93.13 140	89.48 111	92.42 134	79.93 134	85.13 130	78.02 143	93.82 104	93.49 157	93.88 144	98.94 119	97.99 128

Anonymous20240521		94.76 66	95.12 66	94.35 80	95.10 102	95.81 149	96.46 79	89.49 110	96.33 52	90.16 67	92.55 65	90.26 77	95.83 76	95.52 119	96.03 81	99.06 110	99.33 18

tfpnview11		93.63 88	94.42 77	92.71 104	95.08 103	98.26 88	95.58 100	92.06 77	96.32 53	81.88 121	93.44 51	83.43 121	92.14 128	96.58 82	95.88 88	99.52 14	97.07 169

tfpn_n400		93.56 92	94.36 80	92.63 105	95.07 104	98.28 85	95.50 104	91.98 79	95.48 78	81.88 121	93.44 51	83.43 121	92.01 131	96.60 78	96.27 71	99.34 61	97.04 170

tfpnconf		93.56 92	94.36 80	92.63 105	95.07 104	98.28 85	95.50 104	91.98 79	95.48 78	81.88 121	93.44 51	83.43 121	92.01 131	96.60 78	96.27 71	99.34 61	97.04 170

Anonymous202405211			92.18 121		95.04 106	98.20 91	96.14 85	91.79 85	93.93 104		74.60 181	88.38 93	96.48 66	95.17 129	95.82 96	99.00 114	99.15 44

thresconf0.02		93.57 91	93.84 90	93.25 99	95.03 107	98.16 93	95.80 97	92.46 61	96.12 62	83.88 112	92.61 62	80.39 134	92.83 121	96.11 105	96.21 76	99.49 21	97.28 161

FC-MVSNet-train		93.85 82	93.91 87	93.78 89	94.94 108	96.79 120	94.29 126	91.13 90	93.84 108	88.26 89	90.40 87	85.23 111	94.65 92	96.54 85	95.31 110	99.38 55	99.28 22

EPP-MVSNet		95.27 56	96.18 51	94.20 82	94.88 109	98.64 72	94.97 112	90.70 93	95.34 81	89.67 75	91.66 75	93.84 60	95.42 84	97.32 51	97.00 53	99.58 6	99.47 9

casdiffmvs		94.87 60	95.25 62	94.43 79	94.87 110	98.52 77	95.98 89	89.42 112	96.32 53	91.05 53	93.18 55	87.33 97	96.06 72	96.11 105	96.38 65	99.23 85	98.92 70

MVS_Test		94.82 61	95.66 55	93.84 88	94.79 111	98.35 83	96.49 78	89.10 116	96.12 62	87.09 103	92.58 64	90.61 75	96.48 66	96.51 89	96.89 56	99.11 102	98.54 96

Anonymous20231211		93.49 97	92.33 120	94.84 70	94.78 112	98.00 97	96.11 86	91.85 81	94.86 92	90.91 54	74.69 180	89.18 85	96.73 62	94.82 134	95.51 106	98.67 162	99.24 30

MVSTER		94.89 59	95.07 67	94.68 75	94.71 113	96.68 123	97.00 54	90.57 95	95.18 88	93.05 37	95.21 40	86.41 100	93.72 106	97.59 46	95.88 88	99.00 114	98.50 101

EPMVS		90.88 129	92.12 122	89.44 142	94.71 113	97.24 106	93.55 132	76.81 212	95.89 69	81.77 125	91.49 76	86.47 99	93.87 102	90.21 206	90.07 207	95.92 207	93.49 210

DWT-MVSNet_training		91.30 124	89.73 147	93.13 101	94.64 115	96.87 114	94.93 113	86.17 148	94.22 101	93.18 32	89.11 97	73.28 179	93.59 109	88.00 216	90.73 203	96.26 204	95.87 186

diffmvs		94.16 74	94.72 71	93.52 94	94.55 116	98.32 84	96.06 88	88.85 117	96.39 51	87.54 98	92.31 68	86.19 102	95.40 85	95.39 124	95.85 90	98.71 157	98.59 91

DI_MVS_plusplus_trai		94.01 79	93.63 95	94.44 78	94.54 117	98.26 88	97.51 49	90.63 94	95.88 70	89.34 79	80.54 156	89.36 83	95.48 83	96.33 96	96.27 71	99.17 92	98.78 82

ADS-MVSNet		89.80 143	91.33 135	88.00 172	94.43 118	96.71 122	92.29 161	74.95 224	96.07 65	77.39 143	88.67 101	86.09 103	93.26 115	88.44 214	89.57 209	95.68 210	93.81 207

tpmrst		88.86 158	89.62 148	87.97 173	94.33 119	95.98 139	92.62 147	76.36 217	94.62 95	76.94 146	85.98 127	82.80 127	92.80 122	86.90 220	87.15 221	94.77 222	93.93 205

PMMVS		94.61 68	95.56 57	93.50 95	94.30 120	96.74 121	94.91 115	89.56 109	95.58 77	87.72 95	96.15 32	92.86 65	96.06 72	95.47 121	95.02 116	98.43 179	97.09 165

CostFormer		90.69 130	90.48 144	90.93 120	94.18 121	96.08 137	94.03 128	78.20 208	93.47 113	89.96 70	90.97 83	80.30 135	93.72 106	87.66 219	88.75 211	95.51 213	96.12 183

USDC		90.69 130	90.52 143	90.88 121	94.17 122	96.43 130	95.82 96	86.76 141	93.92 105	76.27 152	86.49 121	74.30 167	93.67 108	95.04 132	93.36 153	98.61 167	94.13 202

Vis-MVSNet		92.77 106	95.00 69	90.16 132	94.10 123	98.79 62	94.76 119	88.26 122	92.37 137	79.95 133	88.19 105	91.58 69	84.38 209	97.59 46	97.58 37	99.52 14	98.91 73
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
Effi-MVS+		92.93 104	93.86 89	91.86 110	94.07 124	98.09 96	95.59 99	85.98 151	94.27 100	79.54 137	91.12 81	81.81 130	96.71 63	96.67 76	96.06 79	99.27 74	98.98 65

tpmp4_e23		89.82 142	89.31 153	90.42 128	94.01 125	95.45 161	94.63 121	78.37 205	93.59 111	87.09 103	86.62 119	76.59 154	93.06 119	88.50 213	88.52 212	95.36 214	95.88 185

IterMVS-LS		92.56 109	93.18 103	91.84 111	93.90 126	94.97 182	94.99 111	86.20 147	94.18 102	82.68 118	85.81 128	87.36 96	94.43 94	95.31 125	96.02 82	98.87 124	98.60 90
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
dps		90.11 140	89.37 152	90.98 119	93.89 127	96.21 135	93.49 134	77.61 210	91.95 143	92.74 43	88.85 98	78.77 141	92.37 126	87.71 218	87.71 217	95.80 208	94.38 200

tpm cat1		88.90 156	87.78 175	90.22 131	93.88 128	95.39 170	93.79 131	78.11 209	92.55 131	89.43 77	81.31 152	79.84 137	91.40 139	84.95 224	86.34 226	94.68 225	94.09 203

PatchmatchNet		90.56 132	92.49 111	88.31 160	93.83 129	96.86 116	92.42 151	76.50 216	95.96 67	78.31 140	91.96 73	89.66 82	93.48 111	90.04 208	89.20 210	95.32 215	93.73 208
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
TinyColmap		89.42 146	88.58 157	90.40 129	93.80 130	95.45 161	93.96 130	86.54 143	92.24 140	76.49 149	80.83 154	70.44 205	93.37 112	94.45 140	93.30 156	98.26 182	93.37 212

RPMNet		90.19 138	92.03 124	88.05 169	93.46 131	95.95 142	93.41 135	74.59 225	92.40 135	75.91 154	84.22 136	86.41 100	92.49 124	94.42 141	93.85 146	98.44 177	96.96 172

gg-mvs-nofinetune		86.17 202	88.57 158	83.36 211	93.44 132	98.15 94	96.58 75	72.05 230	74.12 231	49.23 236	64.81 226	90.85 73	89.90 180	97.83 41	96.84 57	98.97 117	97.41 156

MDTV_nov1_ep13		91.57 120	93.18 103	89.70 138	93.39 133	96.97 110	93.53 133	80.91 200	95.70 76	81.86 124	92.40 66	89.93 80	93.25 116	91.97 195	90.80 202	95.25 218	94.46 199

CR-MVSNet		90.16 139	91.96 125	88.06 168	93.32 134	95.95 142	93.36 136	75.99 219	92.40 135	75.19 161	83.18 143	85.37 108	92.05 129	95.21 127	94.56 128	98.47 176	97.08 167

test-LLR		91.62 119	93.56 98	89.35 144	93.31 135	96.57 126	92.02 177	87.06 139	92.34 138	75.05 164	90.20 89	88.64 90	90.93 148	96.19 102	94.07 139	97.75 193	96.90 175

test0.0.03 1		91.97 113	93.91 87	89.72 137	93.31 135	96.40 132	91.34 187	87.06 139	93.86 106	81.67 126	91.15 80	89.16 86	86.02 201	95.08 130	95.09 114	98.91 122	96.64 181

CVMVSNet		89.77 144	91.66 128	87.56 186	93.21 137	95.45 161	91.94 181	89.22 114	89.62 168	69.34 210	83.99 138	85.90 105	84.81 207	94.30 144	95.28 111	96.85 200	97.09 165

PatchT		89.13 153	91.71 127	86.11 203	92.92 138	95.59 156	83.64 219	75.09 223	91.87 144	75.19 161	82.63 147	85.06 113	92.05 129	95.21 127	94.56 128	97.76 192	97.08 167

Fast-Effi-MVS+		91.87 114	92.08 123	91.62 115	92.91 139	97.21 108	94.93 113	84.60 170	93.61 110	81.49 128	83.50 141	78.95 139	96.62 65	96.55 84	96.22 75	99.16 95	98.51 100

IterMVS		90.20 137	92.43 114	87.61 184	92.82 140	94.31 200	94.11 127	81.54 198	92.97 120	69.90 206	84.71 133	88.16 95	89.96 179	95.25 126	94.17 137	97.31 196	97.46 154
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CDS-MVSNet		92.77 106	93.60 96	91.80 112	92.63 141	96.80 117	95.24 108	89.14 115	90.30 161	84.58 109	86.76 114	90.65 74	90.42 168	95.89 109	96.49 63	98.79 140	98.32 114
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
tpm		87.95 172	89.44 151	86.21 201	92.53 142	94.62 195	91.40 185	76.36 217	91.46 146	69.80 208	87.43 108	75.14 162	91.55 138	89.85 211	90.60 204	95.61 211	96.96 172

Effi-MVS+-dtu		91.78 116	93.59 97	89.68 140	92.44 143	97.11 109	94.40 124	84.94 166	92.43 133	75.48 156	91.09 82	83.75 119	93.55 110	96.61 77	95.47 107	97.24 197	98.67 86

testgi		89.42 146	91.50 132	87.00 193	92.40 144	95.59 156	89.15 205	85.27 163	92.78 123	72.42 190	91.75 74	76.00 159	84.09 211	94.38 142	93.82 148	98.65 165	96.15 182

LP		84.43 210	85.10 211	83.66 209	92.31 145	93.89 202	87.13 209	72.88 227	90.81 154	67.08 214	70.65 216	75.76 160	86.87 195	86.43 223	87.15 221	95.70 209	90.98 218

Fast-Effi-MVS+-dtu		91.19 125	93.64 94	88.33 159	92.19 146	96.46 129	93.99 129	81.52 199	92.59 130	71.82 193	92.17 70	85.54 107	91.68 137	95.73 115	94.64 124	98.80 134	98.34 111

FC-MVSNet-test		91.63 118	93.82 92	89.08 145	92.02 147	96.40 132	93.26 138	87.26 136	93.72 109	77.26 144	88.61 102	89.86 81	85.50 202	95.72 117	95.02 116	99.16 95	97.44 155

GA-MVS		89.28 149	90.75 142	87.57 185	91.77 148	96.48 128	92.29 161	87.58 134	90.61 158	65.77 216	84.48 134	76.84 153	89.46 181	95.84 111	93.68 149	98.52 172	97.34 159

TAMVS		90.54 134	90.87 141	90.16 132	91.48 149	96.61 125	93.26 138	86.08 149	87.71 196	81.66 127	83.11 145	84.04 116	90.42 168	94.54 137	94.60 125	98.04 188	95.48 193

tfpnnormal		88.50 159	87.01 195	90.23 130	91.36 150	95.78 151	92.74 144	90.09 99	83.65 219	76.33 151	71.46 213	69.58 210	91.84 134	95.54 118	94.02 141	99.06 110	99.03 58

GBi-Net		93.81 83	94.18 83	93.38 96	91.34 151	95.86 145	96.22 82	88.68 118	95.23 85	90.40 62	86.39 122	91.16 70	94.40 96	96.52 86	96.30 66	99.21 89	97.79 142

test1		93.81 83	94.18 83	93.38 96	91.34 151	95.86 145	96.22 82	88.68 118	95.23 85	90.40 62	86.39 122	91.16 70	94.40 96	96.52 86	96.30 66	99.21 89	97.79 142

FMVSNet2		93.30 101	93.36 101	93.22 100	91.34 151	95.86 145	96.22 82	88.24 123	95.15 89	89.92 72	81.64 150	89.36 83	94.40 96	96.77 70	96.98 54	99.21 89	97.79 142

FMVSNet3		93.79 85	94.17 85	93.35 98	91.21 154	95.99 138	96.62 71	88.68 118	95.23 85	90.40 62	86.39 122	91.16 70	94.11 100	95.96 107	96.67 60	99.07 107	97.79 142

testpf		83.57 213	85.70 206	81.08 214	90.99 155	88.96 226	82.71 222	65.32 238	90.22 163	73.86 176	81.58 151	76.10 157	81.19 216	84.14 228	85.41 228	92.43 231	93.45 211

TransMVSNet (Re)		87.73 181	86.79 197	88.83 150	90.76 156	94.40 198	91.33 188	89.62 108	84.73 215	75.41 158	72.73 204	71.41 200	86.80 196	94.53 138	93.93 143	99.06 110	95.83 187

LTVRE_ROB	87.32 16	87.55 183	88.25 161	86.73 194	90.66 157	95.80 150	93.05 141	84.77 167	83.35 220	60.32 225	83.12 144	67.39 217	93.32 114	94.36 143	94.86 120	98.28 181	98.87 77
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		86.68 196	87.24 187	86.02 204	90.58 158	96.26 134	91.08 191	81.59 197	84.96 214	69.80 208	71.35 214	75.08 164	84.23 210	94.24 146	93.35 154	98.82 127	95.46 194

TESTMET0.1,1		91.07 126	93.56 98	88.17 163	90.43 159	96.57 126	92.02 177	82.83 184	92.34 138	75.05 164	90.20 89	88.64 90	90.93 148	96.19 102	94.07 139	97.75 193	96.90 175

pm-mvs1		89.19 152	89.02 154	89.38 143	90.40 160	95.74 152	92.05 174	88.10 125	86.13 210	77.70 141	73.72 199	79.44 138	88.97 184	95.81 113	94.51 132	99.08 105	97.78 148

NR-MVSNet		89.34 148	88.66 156	90.13 135	90.40 160	95.61 154	93.04 142	89.91 101	91.22 148	78.96 138	77.72 164	68.90 214	89.16 183	94.24 146	93.95 142	99.32 65	98.99 63

FMVSNet1		91.54 121	90.93 139	92.26 109	90.35 162	95.27 174	95.22 109	87.16 138	91.37 147	87.62 96	75.45 169	83.84 118	94.43 94	96.52 86	96.30 66	98.82 127	97.74 149

test-mter		90.95 127	93.54 100	87.93 174	90.28 163	96.80 117	91.44 184	82.68 186	92.15 142	74.37 172	89.57 95	88.23 94	90.88 151	96.37 94	94.31 135	97.93 190	97.37 157

pmmvs4		90.55 133	89.91 146	91.30 117	90.26 164	94.95 183	92.73 145	87.94 129	93.44 114	85.35 107	82.28 149	76.09 158	93.02 120	93.56 155	92.26 194	98.51 173	96.77 177

MVS-HIRNet		85.36 206	86.89 196	83.57 210	90.13 165	94.51 196	83.57 220	72.61 228	88.27 192	71.22 197	68.97 219	81.81 130	88.91 185	93.08 163	91.94 195	94.97 221	89.64 223

SixPastTwentyTwo		88.37 163	89.47 150	87.08 191	90.01 166	95.93 144	87.41 208	85.32 160	90.26 162	70.26 203	86.34 125	71.95 195	90.93 148	92.89 167	91.72 198	98.55 170	97.22 162

UniMVSNet (Re)		90.03 141	89.61 149	90.51 127	89.97 167	96.12 136	92.32 157	89.26 113	90.99 151	80.95 131	78.25 163	75.08 164	91.14 143	93.78 150	93.87 145	99.41 48	99.21 35

our_test_3					89.78 168	93.84 203	85.59 215

v18		87.93 173	87.61 181	88.31 160	89.74 169	92.04 210	92.59 148	82.71 185	89.70 164	75.32 159	75.23 171	73.55 173	90.74 155	92.11 186	92.77 174	98.78 147	97.87 136

v16		87.87 178	87.60 182	88.19 162	89.70 170	92.01 212	92.37 152	82.54 188	89.67 166	75.00 166	75.02 175	73.65 171	90.73 157	92.14 182	92.80 168	98.77 151	97.90 133

UniMVSNet_NR-MVSNet		90.35 136	89.96 145	90.80 123	89.66 171	95.83 148	92.48 149	90.53 96	90.96 152	79.57 135	79.33 160	77.14 150	93.21 117	92.91 166	94.50 133	99.37 58	99.05 55

v17		87.83 179	87.56 183	88.13 164	89.65 172	92.02 211	92.34 156	82.55 187	89.38 169	74.76 167	75.14 172	73.59 172	90.70 158	92.15 181	92.78 172	98.78 147	97.89 134

v8		88.21 166	87.94 172	88.51 156	89.62 173	95.01 181	92.31 158	84.99 165	88.94 176	74.70 168	75.03 174	73.51 174	90.67 161	92.11 186	92.74 180	98.80 134	98.24 117

WR-MVS_H		87.93 173	87.85 173	88.03 171	89.62 173	95.58 158	90.47 196	85.55 156	87.20 202	76.83 147	74.42 186	72.67 191	86.37 198	93.22 161	93.04 158	99.33 63	98.83 79

v1neww		88.41 161	88.00 168	88.89 147	89.61 175	95.44 164	92.31 158	87.65 132	89.09 172	74.30 173	75.02 175	73.42 177	90.68 159	92.12 183	92.77 174	98.79 140	98.18 119

v7new		88.41 161	88.00 168	88.89 147	89.61 175	95.44 164	92.31 158	87.65 132	89.09 172	74.30 173	75.02 175	73.42 177	90.68 159	92.12 183	92.77 174	98.79 140	98.18 119

v6		88.43 160	88.01 165	88.92 146	89.60 177	95.43 166	92.36 153	87.66 131	89.07 174	74.50 170	75.06 173	73.47 175	90.59 164	92.11 186	92.76 178	98.79 140	98.18 119

pmmvs5		87.83 179	88.09 163	87.51 188	89.59 178	95.48 159	89.75 203	84.73 168	86.07 212	71.44 195	80.57 155	70.09 208	90.74 155	94.47 139	92.87 164	98.82 127	97.10 164

gm-plane-assit		83.26 214	85.29 209	80.89 215	89.52 179	89.89 224	70.26 232	78.24 207	77.11 229	58.01 229	74.16 191	66.90 219	90.63 163	97.20 54	96.05 80	98.66 164	95.68 190

v7		88.18 167	88.01 165	88.39 157	89.45 180	95.14 178	92.36 153	85.37 159	89.29 171	72.94 189	73.98 195	72.77 183	91.38 140	93.59 151	92.87 164	98.82 127	98.42 105

v1141		88.17 168	87.69 177	88.74 152	89.44 181	95.41 167	92.25 166	87.98 126	88.38 186	73.54 184	74.43 185	72.71 189	90.45 166	92.08 190	92.72 182	98.79 140	98.09 124

divwei89l23v2f112		88.17 168	87.69 177	88.74 152	89.44 181	95.41 167	92.26 164	87.97 128	88.29 190	73.57 183	74.45 184	72.75 185	90.42 168	92.08 190	92.72 182	98.81 131	98.09 124

v10		88.00 171	87.96 170	88.05 169	89.44 181	94.68 191	92.36 153	83.35 180	89.37 170	72.96 187	73.98 195	72.79 182	91.35 141	93.59 151	92.88 163	98.81 131	98.42 105

v1		88.17 168	87.66 179	88.77 151	89.44 181	95.40 169	92.29 161	87.98 126	88.21 193	73.75 178	74.41 187	72.75 185	90.36 174	92.07 193	92.71 185	98.80 134	98.09 124

V42		88.31 164	87.95 171	88.73 154	89.44 181	95.34 171	92.23 168	87.21 137	88.83 178	74.49 171	74.89 179	73.43 176	90.41 171	92.08 190	92.77 174	98.60 169	98.33 112

v15		87.46 187	87.16 190	87.81 175	89.41 186	91.96 213	92.26 164	82.28 191	88.42 184	73.72 179	74.29 190	72.73 188	90.41 171	92.17 180	92.76 178	98.79 140	97.83 139

v148		87.51 184	86.79 197	88.36 158	89.39 187	95.21 176	89.84 202	88.20 124	87.61 198	77.56 142	73.38 202	70.32 207	86.80 196	90.70 203	92.31 191	98.37 180	97.98 130

V14		87.47 186	87.19 189	87.80 176	89.37 188	91.95 214	92.25 166	82.12 192	88.39 185	73.83 177	74.31 188	72.84 181	90.44 167	92.20 178	92.78 172	98.80 134	97.84 138

v11		87.58 182	87.50 184	87.67 181	89.34 189	91.91 217	92.22 170	81.63 196	89.01 175	72.95 188	74.11 193	72.51 193	91.08 145	94.01 149	93.00 160	98.77 151	97.93 131

V9		87.41 188	87.15 191	87.72 179	89.33 190	91.93 215	92.23 168	82.02 193	88.35 187	73.59 182	74.13 192	72.77 183	90.37 173	92.21 177	92.80 168	98.79 140	97.86 137

v13		87.34 191	87.11 194	87.62 183	89.30 191	91.91 217	92.04 175	81.86 195	88.35 187	73.36 185	73.88 197	72.69 190	90.34 175	92.23 175	92.82 166	98.80 134	97.88 135

v12		87.38 190	87.13 192	87.68 180	89.30 191	91.92 216	92.01 179	81.94 194	88.35 187	73.69 180	74.10 194	72.57 192	90.33 176	92.23 175	92.82 166	98.80 134	97.91 132

CP-MVSNet		87.89 177	87.27 186	88.62 155	89.30 191	95.06 179	90.60 195	85.78 153	87.43 200	75.98 153	74.60 181	68.14 216	90.76 153	93.07 164	93.60 150	99.30 70	98.98 65

v1144		87.92 176	87.79 174	88.07 166	89.27 194	95.15 177	92.17 171	85.62 155	88.52 182	71.52 194	73.80 198	72.40 194	91.06 146	93.54 156	92.80 168	98.81 131	98.33 112

DU-MVS		89.67 145	88.84 155	90.63 126	89.26 195	95.61 154	92.48 149	89.91 101	91.22 148	79.57 135	77.72 164	71.18 201	93.21 117	92.53 170	94.57 127	99.35 60	99.05 55

WR-MVS		87.93 173	88.09 163	87.75 177	89.26 195	95.28 172	90.81 193	86.69 142	88.90 177	75.29 160	74.31 188	73.72 170	85.19 205	92.26 173	93.32 155	99.27 74	98.81 80

Baseline_NR-MVSNet		89.27 150	88.01 165	90.73 125	89.26 195	93.71 204	92.71 146	89.78 105	90.73 155	81.28 129	73.53 200	72.85 180	92.30 127	92.53 170	93.84 147	99.07 107	98.88 75

N_pmnet		84.80 207	85.10 211	84.45 207	89.25 198	92.86 207	84.04 218	86.21 145	88.78 179	66.73 215	72.41 207	74.87 166	85.21 204	88.32 215	86.45 224	95.30 216	92.04 214

v2v482		88.25 165	87.71 176	88.88 149	89.23 199	95.28 172	92.10 172	87.89 130	88.69 181	73.31 186	75.32 170	71.64 197	91.89 133	92.10 189	92.92 162	98.86 126	97.99 128

PS-CasMVS		87.33 192	86.68 200	88.10 165	89.22 200	94.93 184	90.35 198	85.70 154	86.44 206	74.01 175	73.43 201	66.59 222	90.04 178	92.92 165	93.52 151	99.28 72	98.91 73

TranMVSNet+NR-MVSNet		89.23 151	88.48 159	90.11 136	89.07 201	95.25 175	92.91 143	90.43 97	90.31 160	77.10 145	76.62 167	71.57 199	91.83 135	92.12 183	94.59 126	99.32 65	98.92 70

v1192		87.51 184	87.31 185	87.74 178	89.04 202	94.87 189	92.07 173	85.03 164	88.49 183	70.32 202	72.65 205	70.35 206	91.21 142	93.59 151	92.80 168	98.78 147	98.42 105

v144192		87.40 189	87.20 188	87.64 182	88.89 203	94.88 188	91.65 183	84.70 169	87.80 195	71.17 199	73.20 203	70.91 202	90.75 154	92.69 168	92.49 187	98.71 157	98.43 104

PEN-MVS		87.22 194	86.50 204	88.07 166	88.88 204	94.44 197	90.99 192	86.21 145	86.53 205	73.66 181	74.97 178	66.56 223	89.42 182	91.20 199	93.48 152	99.24 79	98.31 115

v1921920		87.31 193	87.13 192	87.52 187	88.87 205	94.72 190	91.96 180	84.59 171	88.28 191	69.86 207	72.50 206	70.03 209	91.10 144	93.33 159	92.61 186	98.71 157	98.44 103

pmmvs6		85.98 203	84.89 213	87.25 190	88.83 206	94.35 199	89.36 204	85.30 162	78.51 228	75.44 157	62.71 229	75.41 161	87.65 191	93.58 154	92.40 189	96.89 199	97.29 160

v1240		86.89 195	86.75 199	87.06 192	88.75 207	94.65 193	91.30 189	84.05 174	87.49 199	68.94 211	71.96 209	68.86 215	90.65 162	93.33 159	92.72 182	98.67 162	98.24 117

anonymousdsp		88.90 156	91.00 138	86.44 199	88.74 208	95.97 140	90.40 197	82.86 183	88.77 180	67.33 213	81.18 153	81.44 132	90.22 177	96.23 99	94.27 136	99.12 101	99.16 43

EU-MVSNet		85.62 205	87.65 180	83.24 212	88.54 209	92.77 208	87.12 210	85.32 160	86.71 203	64.54 218	78.52 162	75.11 163	78.35 218	92.25 174	92.28 193	95.58 212	95.93 184

DTE-MVSNet		86.67 197	86.09 205	87.35 189	88.45 210	94.08 201	90.65 194	86.05 150	86.13 210	72.19 191	74.58 183	66.77 221	87.61 192	90.31 205	93.12 157	99.13 99	97.62 152

v748		85.88 204	85.66 207	86.14 202	88.03 211	94.63 194	87.02 212	84.59 171	84.30 216	74.56 169	70.94 215	67.27 218	83.94 213	90.96 202	92.74 180	98.71 157	98.81 80

FMVSNet5		90.36 135	90.93 139	89.70 138	87.99 212	92.25 209	92.03 176	83.51 177	92.20 141	84.13 111	85.59 129	86.48 98	92.43 125	94.61 135	94.52 131	98.13 184	90.85 219

v7n		86.43 200	86.52 203	86.33 200	87.91 213	94.93 184	90.15 199	83.05 181	86.57 204	70.21 204	71.48 212	66.78 220	87.72 190	94.19 148	92.96 161	98.92 121	98.76 83

test20.03		82.92 215	85.52 208	79.90 218	87.75 214	91.84 219	82.80 221	82.99 182	82.65 224	60.32 225	78.90 161	70.50 203	67.10 229	92.05 194	90.89 201	98.44 177	91.80 215

MDTV_nov1_ep13_2view		86.30 201	88.27 160	84.01 208	87.71 215	94.67 192	88.08 207	76.78 213	90.59 159	68.66 212	80.46 157	80.12 136	87.58 193	89.95 210	88.20 214	95.25 218	93.90 206

V4		86.56 199	86.61 202	86.50 197	87.49 216	94.90 186	89.87 201	83.39 178	86.25 208	71.20 198	71.57 210	71.58 198	88.30 188	91.14 200	92.31 191	98.75 154	98.52 98

v52		86.57 198	86.63 201	86.50 197	87.47 217	94.89 187	89.90 200	83.39 178	86.36 207	71.17 199	71.53 211	71.65 196	88.34 187	91.14 200	92.32 190	98.74 155	98.52 98

Anonymous20231206		83.84 212	85.19 210	82.26 213	87.38 218	92.87 206	85.49 216	83.65 176	86.07 212	63.44 221	68.42 220	69.01 213	75.45 222	93.34 158	92.44 188	98.12 186	94.20 201

FPMVS		75.84 223	74.59 225	77.29 224	86.92 219	83.89 232	85.01 217	80.05 203	82.91 222	60.61 224	65.25 225	60.41 227	63.86 230	75.60 233	73.60 235	87.29 236	80.47 231

MIMVSNet		88.99 155	91.07 137	86.57 196	86.78 220	95.62 153	91.20 190	75.40 222	90.65 157	76.57 148	84.05 137	82.44 129	91.01 147	95.84 111	95.38 109	98.48 175	93.50 209

tmp_tt				66.88 231	86.07 221	73.86 239	68.22 233	33.38 240	96.88 43	80.67 132	88.23 104	78.82 140	49.78 236	82.68 230	77.47 232	83.19 239

PM-MVS		84.72 209	84.47 215	85.03 206	84.67 222	91.57 220	86.27 214	82.31 190	87.65 197	70.62 201	76.54 168	56.41 234	88.75 186	92.59 169	89.85 208	97.54 195	96.66 180

testus		81.33 217	84.13 216	78.06 221	84.54 223	87.72 227	79.66 226	80.42 201	87.36 201	54.13 235	83.83 139	56.63 232	73.21 227	90.51 204	91.74 197	96.40 202	91.11 217

test2356		81.26 218	84.10 217	77.95 223	84.35 224	87.38 228	79.56 227	79.53 204	86.17 209	54.14 234	83.24 142	60.71 226	73.77 223	90.01 209	91.18 200	96.33 203	90.01 221

pmmvs-eth3d		84.33 211	82.94 219	85.96 205	84.16 225	90.94 221	86.55 213	83.79 175	84.25 217	75.85 155	70.64 217	56.43 233	87.44 194	92.20 178	90.41 206	97.97 189	95.68 190

new-patchmatchnet		78.49 222	78.19 224	78.84 220	84.13 226	90.06 223	77.11 231	80.39 202	79.57 227	59.64 228	66.01 224	55.65 235	75.62 221	84.55 227	80.70 230	96.14 206	90.77 220

new_pmnet		81.53 216	82.68 220	80.20 216	83.47 227	89.47 225	82.21 224	78.36 206	87.86 194	60.14 227	67.90 222	69.43 211	82.03 215	89.22 212	87.47 218	94.99 220	87.39 225

testmv		72.66 225	74.40 226	70.62 226	80.64 228	81.51 235	64.99 237	76.60 214	68.76 234	44.81 237	63.78 227	48.00 237	62.52 231	84.74 225	87.17 219	94.19 227	86.86 226

test1235678		72.65 226	74.40 226	70.62 226	80.64 228	81.50 236	64.99 237	76.59 215	68.74 235	44.81 237	63.78 227	47.99 238	62.51 232	84.73 226	87.17 219	94.19 227	86.85 227

pmmvs3		79.16 221	80.12 223	78.05 222	79.36 230	86.59 230	78.13 230	73.87 226	76.42 230	57.51 230	70.59 218	57.02 231	84.66 208	90.10 207	88.32 213	94.75 223	91.77 216

1111		73.35 224	74.40 226	72.12 225	78.22 231	82.24 233	65.06 235	65.61 236	70.28 232	55.42 231	56.30 232	57.35 229	73.66 224	86.73 221	88.16 215	94.75 223	79.76 233

.test1245		56.65 232	56.09 234	57.30 233	78.22 231	82.24 233	65.06 235	65.61 236	70.28 232	55.42 231	56.30 232	57.35 229	73.66 224	86.73 221	15.01 239	5.84 243	24.75 239

PMVS	63.12 18	67.27 229	66.39 232	68.30 229	77.98 233	60.24 242	59.53 241	76.82 211	66.65 237	60.74 223	54.39 234	59.82 228	51.24 235	73.92 236	70.52 236	83.48 238	79.17 234
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test12356		69.55 227	71.53 230	67.24 230	77.70 234	78.48 237	65.92 234	75.55 221	68.39 236	44.26 239	61.80 230	40.70 240	47.92 239	81.45 231	87.01 223	92.09 232	82.89 229

MDA-MVSNet-bldmvs		80.11 219	80.24 222	79.94 217	77.01 235	93.21 205	78.86 229	85.94 152	82.71 223	60.86 222	79.71 159	51.77 236	83.71 214	75.60 233	86.37 225	93.28 229	92.35 213

ambc			73.83 229		76.23 236	85.13 231	82.27 223		84.16 218	65.58 217	52.82 235	23.31 245	73.55 226	91.41 198	85.26 229	92.97 230	94.70 196

Gipuma		68.35 228	66.71 231	70.27 228	74.16 237	68.78 241	63.93 240	71.77 231	83.34 221	54.57 233	34.37 237	31.88 241	68.69 228	83.30 229	85.53 227	88.48 235	79.78 232
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MIMVSNet1		80.03 220	80.93 221	78.97 219	72.46 238	90.73 222	80.81 225	82.44 189	80.39 225	63.64 220	57.57 231	64.93 224	76.37 220	91.66 196	91.55 199	98.07 187	89.70 222

no-one		55.96 233	55.63 235	56.35 234	68.48 239	73.29 240	43.03 242	72.52 229	44.01 241	34.80 240	32.83 238	29.11 242	35.21 240	56.63 238	75.72 233	84.04 237	77.79 235

PMMVS2		64.36 231	65.94 233	62.52 232	67.37 240	77.44 238	64.39 239	69.32 235	61.47 238	34.59 241	46.09 236	41.03 239	48.02 238	74.56 235	78.23 231	91.43 233	82.76 230

EMVS		49.98 235	46.76 238	53.74 236	64.96 241	51.29 244	37.81 244	69.35 234	51.83 239	22.69 244	29.57 240	25.06 243	57.28 233	44.81 240	56.11 238	70.32 241	68.64 238

E-PMN		50.67 234	47.85 237	53.96 235	64.13 242	50.98 245	38.06 243	69.51 233	51.40 240	24.60 243	29.46 241	24.39 244	56.07 234	48.17 239	59.70 237	71.40 240	70.84 237

MVE	50.86 19	49.54 236	51.43 236	47.33 237	44.14 243	59.20 243	36.45 245	60.59 239	41.47 242	31.14 242	29.58 239	17.06 246	48.52 237	62.22 237	74.63 234	63.12 242	75.87 236
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		12.09 237	16.94 239	6.42 239	3.15 244	6.08 246	9.51 247	3.84 241	21.46 243	5.31 245	27.49 242	6.76 247	10.89 241	17.06 241	15.01 239	5.84 243	24.75 239

GG-mvs-BLEND		66.17 230	94.91 70	32.63 238	1.32 245	96.64 124	91.40 185	0.85 243	94.39 99	2.20 246	90.15 91	95.70 54	2.27 243	96.39 91	95.44 108	97.78 191	95.68 190

test123		9.58 238	13.53 240	4.97 240	1.31 246	5.47 247	8.32 248	2.95 242	18.14 244	2.03 247	20.82 243	2.34 248	10.60 242	10.00 242	14.16 241	4.60 245	23.77 241

sosnet-low-res		0.00 239	0.00 241	0.00 241	0.00 247	0.00 248	0.00 249	0.00 244	0.00 245	0.00 248	0.00 244	0.00 249	0.00 244	0.00 243	0.00 242	0.00 246	0.00 242

sosnet		0.00 239	0.00 241	0.00 241	0.00 247	0.00 248	0.00 249	0.00 244	0.00 245	0.00 248	0.00 244	0.00 249	0.00 244	0.00 243	0.00 242	0.00 246	0.00 242

MTAPA										96.83 6		99.12 16

MTMP										97.18 3		98.83 22

Patchmatch-RL test							34.61 246

NP-MVS									95.32 82

Patchmtry						95.96 141	93.36 136	75.99 219		75.19 161

DeepMVS_CX						86.86 229	79.50 228	70.43 232	90.73 155	63.66 219	80.36 158	60.83 225	79.68 217	76.23 232		89.46 234	86.53 228

High-resolution multi-view benchmark