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 6	98.86 7	98.96 3	98.40 10	99.63 3	99.57 5

PGM-MVS		97.81 21	98.11 24	97.46 25	99.55 2	99.34 12	99.32 6	94.51 39	96.21 54	93.07 33	98.05 10	97.95 35	98.82 10	98.22 26	97.89 28	99.48 21	99.09 43

ACMMP_Plus		98.20 14	98.49 9	97.85 21	99.50 3	99.40 6	99.26 9	97.64 7	97.47 26	92.62 42	97.59 16	99.09 15	98.71 14	98.82 8	97.86 29	99.40 48	99.19 34

MPTG		98.43 8	98.31 19	98.57 2	99.48 4	99.40 6	99.32 6	97.62 8	97.70 16	96.67 6	96.59 27	99.09 15	98.86 7	98.65 9	97.56 36	99.45 30	99.17 38

APD-MVS		98.36 11	98.32 18	98.41 5	99.47 5	99.26 18	99.12 12	97.77 4	96.73 42	96.12 12	97.27 23	98.88 18	98.46 21	98.47 14	98.39 11	99.52 13	99.22 30
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CSCG		97.44 28	97.18 35	97.75 23	99.47 5	99.52 3	98.55 28	95.41 34	97.69 18	95.72 15	94.29 46	95.53 53	98.10 26	96.20 98	97.38 43	99.24 75	99.62 2

ESAPD		98.59 2	98.77 3	98.39 6	99.46 7	99.50 4	99.11 13	97.80 2	97.20 32	96.06 13	98.56 3	99.83 1	98.43 22	98.84 6	98.03 22	99.45 30	99.45 10

HPM-MVS++		98.34 12	98.47 11	98.18 12	99.46 7	99.15 26	99.10 14	97.69 5	97.67 19	94.93 22	97.62 15	99.70 3	98.60 17	98.45 15	97.46 39	99.31 64	99.26 25

HSP-MVS		98.59 2	98.65 6	98.52 3	99.44 9	99.57 1	99.34 3	97.65 6	97.36 28	96.62 8	98.49 5	99.65 4	98.67 16	98.60 10	97.44 40	99.40 48	99.46 9

ACMMPR		98.40 9	98.49 9	98.28 9	99.41 10	99.40 6	99.36 2	97.35 16	98.30 4	95.02 21	97.79 13	98.39 30	99.04 2	98.26 23	98.10 17	99.50 19	99.22 30

X-MVS		97.84 20	98.19 23	97.42 26	99.40 11	99.35 9	99.06 15	97.25 20	97.38 27	90.85 50	96.06 31	98.72 23	98.53 20	98.41 18	98.15 16	99.46 26	99.28 20

MCST-MVS		98.20 14	98.36 14	98.01 18	99.40 11	99.05 29	99.00 18	97.62 8	97.59 23	93.70 29	97.42 22	99.30 7	98.77 12	98.39 19	97.48 38	99.59 4	99.31 19

CNVR-MVS		98.47 7	98.46 12	98.48 4	99.40 11	99.05 29	99.02 17	97.54 11	97.73 14	96.65 7	97.20 24	99.13 13	98.85 9	98.91 5	98.10 17	99.41 46	99.08 44

HFP-MVS		98.48 6	98.62 7	98.32 7	99.39 14	99.33 13	99.27 8	97.42 13	98.27 5	95.25 19	98.34 8	98.83 20	99.08 1	98.26 23	98.08 19	99.48 21	99.26 25

NCCC		98.10 17	98.05 26	98.17 14	99.38 15	99.05 29	99.00 18	97.53 12	98.04 10	95.12 20	94.80 43	99.18 11	98.58 18	98.49 13	97.78 31	99.39 50	98.98 60

MP-MVS		98.09 18	98.30 20	97.84 22	99.34 16	99.19 24	99.23 10	97.40 14	97.09 36	93.03 36	97.58 17	98.85 19	98.57 19	98.44 17	97.69 32	99.48 21	99.23 28

CP-MVS		98.32 13	98.34 17	98.29 8	99.34 16	99.30 14	99.15 11	97.35 16	97.49 25	95.58 17	97.72 14	98.62 27	98.82 10	98.29 21	97.67 33	99.51 17	99.28 20

SteuartSystems-ACMMP		98.38 10	98.71 5	97.99 19	99.34 16	99.46 5	99.34 3	97.33 19	97.31 29	94.25 25	98.06 9	99.17 12	98.13 25	98.98 2	98.46 8	99.55 9	99.54 6
Skip Steuart: Steuart Systems R&D Blog.
mPP-MVS					99.21 19							98.29 31

AdaColmap		97.53 26	96.93 39	98.24 10	99.21 19	98.77 60	98.47 30	97.34 18	96.68 44	96.52 10	95.11 40	96.12 49	98.72 13	97.19 54	96.24 70	99.17 87	98.39 102

DeepC-MVS_fast	96.13 1	98.13 16	98.27 21	97.97 20	99.16 21	99.03 34	99.05 16	97.24 21	98.22 7	94.17 27	95.82 32	98.07 32	98.69 15	98.83 7	98.80 2	99.52 13	99.10 41
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 19	97.93 28	98.18 12	99.10 22	99.09 28	98.34 32	96.99 26	97.54 24	96.60 9	94.82 42	98.45 29	98.89 5	97.46 47	98.77 4	99.17 87	99.37 13

3Dnovator	93.79 8	97.08 33	97.20 33	96.95 33	99.09 23	99.03 34	98.20 34	93.33 47	97.99 11	93.82 28	90.61 80	96.80 42	97.82 30	97.90 37	98.78 3	99.47 24	99.26 25

QAPM		96.78 40	97.14 36	96.36 38	99.05 24	99.14 27	98.02 37	93.26 49	97.27 31	90.84 53	91.16 72	97.31 37	97.64 35	97.70 41	98.20 14	99.33 59	99.18 37

OpenMVS	92.33 11	95.50 47	95.22 61	95.82 46	98.98 25	98.97 40	97.67 45	93.04 57	94.64 88	89.18 75	84.44 129	94.79 55	96.79 56	97.23 51	97.61 34	99.24 75	98.88 69

PLC	94.95 3	97.37 29	96.77 42	98.07 16	98.97 26	98.21 84	97.94 40	96.85 29	97.66 20	97.58 1	93.33 51	96.84 41	98.01 29	97.13 56	96.20 73	99.09 99	98.01 120
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
train_agg		97.65 25	98.06 25	97.18 29	98.94 27	98.91 53	98.98 21	97.07 25	96.71 43	90.66 55	97.43 21	99.08 17	98.20 23	97.96 35	97.14 48	99.22 81	99.19 34

CDPH-MVS		96.84 38	97.49 30	96.09 42	98.92 28	98.85 57	98.61 25	95.09 35	96.00 61	87.29 95	95.45 37	97.42 36	97.16 42	97.83 39	97.94 25	99.44 39	98.92 65

CPTT-MVS		97.78 22	97.54 29	98.05 17	98.91 29	99.05 29	99.00 18	96.96 27	97.14 34	95.92 14	95.50 35	98.78 22	98.99 4	97.20 52	96.07 74	98.54 162	99.04 52

3Dnovator+	93.91 7	97.23 31	97.22 32	97.24 28	98.89 30	98.85 57	98.26 33	93.25 51	97.99 11	95.56 18	90.01 86	98.03 34	98.05 27	97.91 36	98.43 9	99.44 39	99.35 15

ACMMP		97.37 29	97.48 31	97.25 27	98.88 31	99.28 16	98.47 30	96.86 28	97.04 38	92.15 43	97.57 18	96.05 51	97.67 33	97.27 50	95.99 78	99.46 26	99.14 40
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 22	98.44 13	97.02 32	98.73 32	99.25 20	98.11 35	95.54 33	96.66 45	92.79 39	98.52 4	99.38 5	97.50 37	97.84 38	98.39 11	99.45 30	99.03 53

OMC-MVS		97.00 35	96.92 40	97.09 30	98.69 33	98.66 66	97.85 41	95.02 36	98.09 9	94.47 23	93.15 52	96.90 39	97.38 38	97.16 55	96.82 56	99.13 94	97.65 144

MAR-MVS		95.50 47	95.60 55	95.39 53	98.67 34	98.18 85	95.89 84	89.81 99	94.55 90	91.97 45	92.99 53	90.21 76	97.30 39	96.79 65	97.49 37	98.72 149	98.99 58
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 24	98.60 8	96.66 35	98.64 35	99.05 29	98.85 22	97.23 22	98.45 2	89.40 73	97.51 19	99.27 9	96.88 55	98.53 11	97.81 30	98.96 111	99.59 4

abl_6				96.82 34	98.60 36	98.74 61	97.74 43	93.73 43	96.25 52	94.37 24	94.55 45	98.60 28	97.25 40			99.27 70	98.61 84

CNLPA		96.90 37	96.28 48	97.64 24	98.56 37	98.63 70	96.85 57	96.60 30	97.73 14	97.08 4	89.78 88	96.28 48	97.80 32	96.73 70	96.63 58	98.94 112	98.14 116

EPNet		96.27 44	96.97 38	95.46 51	98.47 38	98.28 79	97.41 48	93.67 44	95.86 66	92.86 38	97.51 19	93.79 59	91.76 128	97.03 57	97.03 49	98.61 158	99.28 20
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MVS_111021_LR		97.16 32	98.01 27	96.16 41	98.47 38	98.98 39	96.94 54	93.89 42	97.64 21	91.44 47	98.89 1	96.41 44	97.20 41	98.02 34	97.29 47	99.04 107	98.85 72

MVS_111021_HR		97.04 34	98.20 22	95.69 47	98.44 40	99.29 15	96.59 70	93.20 52	97.70 16	89.94 65	98.46 6	96.89 40	96.71 58	98.11 31	97.95 24	99.27 70	99.01 56

MSDG		94.82 59	93.73 89	96.09 42	98.34 41	97.43 97	97.06 51	96.05 31	95.84 67	90.56 56	86.30 120	89.10 83	95.55 73	96.13 101	95.61 96	99.00 108	95.73 182

TAPA-MVS	94.18 5	96.38 42	96.49 46	96.25 39	98.26 42	98.66 66	98.00 38	94.96 37	97.17 33	89.48 70	92.91 55	96.35 45	97.53 36	96.59 77	95.90 82	99.28 68	97.82 133
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
DeepC-MVS	94.87 4	96.76 41	96.50 45	97.05 31	98.21 43	99.28 16	98.67 24	97.38 15	97.31 29	90.36 61	89.19 90	93.58 61	98.19 24	98.31 20	98.50 6	99.51 17	99.36 14
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 4	98.77 3	98.23 11	98.15 44	99.26 18	98.79 23	97.59 10	98.52 1	96.25 11	97.99 11	99.75 2	99.01 3	98.27 22	97.97 23	99.59 4	99.63 1

TSAR-MVS + MP.		98.49 5	98.78 2	98.15 15	98.14 45	99.17 25	99.34 3	97.18 23	98.44 3	95.72 15	97.84 12	99.28 8	98.87 6	99.05 1	98.05 20	99.66 1	99.60 3

EPNet_dtu		92.45 105	95.02 65	89.46 134	98.02 46	95.47 152	94.79 110	92.62 58	94.97 85	70.11 198	94.76 44	92.61 66	84.07 204	95.94 104	95.56 97	97.15 190	95.82 181
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CANet		96.84 38	97.20 33	96.42 36	97.92 47	99.24 22	98.60 26	93.51 46	97.11 35	93.07 33	91.16 72	97.24 38	96.21 65	98.24 25	98.05 20	99.22 81	99.35 15

LS3D		95.46 49	95.14 62	95.84 45	97.91 48	98.90 55	98.58 27	97.79 3	97.07 37	83.65 108	88.71 93	88.64 86	97.82 30	97.49 46	97.42 41	99.26 74	97.72 143

DELS-MVS		96.06 45	96.04 51	96.07 44	97.77 49	99.25 20	98.10 36	93.26 49	94.42 91	92.79 39	88.52 97	93.48 62	95.06 80	98.51 12	98.83 1	99.45 30	99.28 20
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 96	92.71 100	93.93 79	97.75 50	97.44 96	96.07 81	93.17 53	95.40 75	83.86 106	83.76 134	88.72 85	93.87 94	94.25 137	94.11 130	98.87 117	95.28 188
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 46	95.14 62	96.25 39	97.73 51	98.73 63	97.59 46	97.13 24	92.50 124	89.09 77	89.85 87	96.65 43	96.90 54	94.97 125	94.89 111	99.08 100	98.38 103
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PatchMatch-RL		94.69 64	94.41 74	95.02 57	97.63 52	98.15 87	94.50 115	91.99 75	95.32 77	91.31 48	95.47 36	83.44 113	96.02 68	96.56 80	95.23 104	98.69 153	96.67 172

PVSNet_BlendedMVS		95.41 51	95.28 59	95.57 49	97.42 53	99.02 36	95.89 84	93.10 54	96.16 55	93.12 31	91.99 64	85.27 102	94.66 82	98.09 32	97.34 44	99.24 75	99.08 44

PVSNet_Blended		95.41 51	95.28 59	95.57 49	97.42 53	99.02 36	95.89 84	93.10 54	96.16 55	93.12 31	91.99 64	85.27 102	94.66 82	98.09 32	97.34 44	99.24 75	99.08 44

DeepPCF-MVS	95.28 2	97.00 35	98.35 16	95.42 52	97.30 55	98.94 42	94.82 109	96.03 32	98.24 6	92.11 44	95.80 33	98.64 26	95.51 74	98.95 4	98.66 5	96.78 193	99.20 33

CHOSEN 280x420		95.46 49	97.01 37	93.66 86	97.28 56	97.98 90	96.40 77	85.39 150	96.10 59	91.07 49	96.53 28	96.34 47	95.61 71	97.65 42	96.95 52	96.21 197	97.49 146

MVS_0304		96.31 43	96.91 41	95.62 48	97.21 57	99.20 23	98.55 28	93.10 54	97.04 38	89.73 67	90.30 82	96.35 45	95.71 69	98.14 28	97.93 27	99.38 52	99.40 12

CHOSEN 1792x2688		92.66 102	92.49 106	92.85 96	97.13 58	98.89 56	95.90 82	88.50 113	95.32 77	83.31 109	71.99 200	88.96 84	94.10 93	96.69 71	96.49 60	98.15 175	99.10 41

HyFIR lowres test		92.03 106	91.55 124	92.58 100	97.13 58	98.72 64	94.65 112	86.54 135	93.58 105	82.56 112	67.75 215	90.47 75	95.67 70	95.87 106	95.54 98	98.91 115	98.93 64

OPM-MVS		93.61 85	92.43 109	95.00 59	96.94 60	97.34 98	97.78 42	94.23 40	89.64 159	85.53 99	88.70 94	82.81 119	96.28 64	96.28 95	95.00 110	99.24 75	97.22 155

XVS					96.60 61	99.35 9	96.82 58			90.85 50		98.72 23				99.46 26

X-MVStestdata					96.60 61	99.35 9	96.82 58			90.85 50		98.72 23				99.46 26

TSAR-MVS + COLMAP		94.79 61	94.51 72	95.11 55	96.50 63	97.54 93	97.99 39	94.54 38	97.81 13	85.88 98	96.73 26	81.28 126	96.99 53	96.29 94	95.21 105	98.76 146	96.73 171

PVSNet_Blended_VisFu		94.77 63	95.54 57	93.87 81	96.48 64	98.97 40	94.33 117	91.84 78	94.93 86	90.37 60	85.04 125	94.99 54	90.87 144	98.12 30	97.30 46	99.30 66	99.45 10

LGP-MVS_train		94.12 72	94.62 69	93.53 87	96.44 65	97.54 93	97.40 49	91.84 78	94.66 87	81.09 123	95.70 34	83.36 117	95.10 79	96.36 92	95.71 91	99.32 61	99.03 53

HQP-MVS		94.43 67	94.57 70	94.27 74	96.41 66	97.23 100	96.89 55	93.98 41	95.94 63	83.68 107	95.01 41	84.46 107	95.58 72	95.47 116	94.85 113	99.07 102	99.00 57

ACMM	92.75 10	94.41 69	93.84 86	95.09 56	96.41 66	96.80 110	94.88 108	93.54 45	96.41 48	90.16 62	92.31 62	83.11 118	96.32 62	96.22 97	94.65 115	99.22 81	97.35 151
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
RPSCF		94.05 73	94.00 82	94.12 76	96.20 68	96.41 124	96.61 68	91.54 82	95.83 68	89.73 67	96.94 25	92.80 65	95.35 77	91.63 189	90.44 197	95.27 209	93.94 198

UA-Net		93.96 75	95.95 52	91.64 107	96.06 69	98.59 72	95.29 99	90.00 95	91.06 142	82.87 110	90.64 79	98.06 33	86.06 192	98.14 28	98.20 14	99.58 6	96.96 165

UGNet		94.92 56	96.63 43	92.93 95	96.03 70	98.63 70	94.53 114	91.52 83	96.23 53	90.03 63	92.87 56	96.10 50	86.28 191	96.68 72	96.60 59	99.16 90	99.32 18
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 67	94.38 75	94.50 72	96.01 71	97.69 92	95.85 87	92.09 72	95.74 70	89.12 76	95.14 39	82.62 121	94.77 81	95.73 111	94.67 114	99.14 93	99.06 48
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
IB-MVS	89.56 15	91.71 111	92.50 105	90.79 117	95.94 72	98.44 76	87.05 203	91.38 84	93.15 109	92.98 37	84.78 126	85.14 105	78.27 211	92.47 164	94.44 126	99.10 98	99.08 44
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		91.82 109	92.51 104	91.02 111	95.83 73	96.88 105	95.05 102	84.55 165	93.85 100	82.01 113	82.51 142	91.71 67	90.52 157	95.07 123	93.03 151	98.13 176	94.52 191

CANet_DTU		93.92 76	96.57 44	90.83 115	95.63 74	98.39 77	96.99 53	87.38 127	96.26 51	71.97 185	96.31 29	93.02 63	94.53 85	97.38 48	96.83 55	98.49 165	97.79 135

ACMH	90.77 13	91.51 116	91.63 122	91.38 109	95.62 75	96.87 107	91.76 174	89.66 101	91.58 137	78.67 132	86.73 109	78.12 135	93.77 97	94.59 128	94.54 122	98.78 140	98.98 60
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
TSAR-MVS + GP.		97.45 27	98.36 14	96.39 37	95.56 76	98.93 47	97.74 43	93.31 48	97.61 22	94.24 26	98.44 7	99.19 10	98.03 28	97.60 43	97.41 42	99.44 39	99.33 17

view800		93.45 93	92.37 113	94.71 69	95.42 77	98.92 51	96.51 73	92.19 70	93.14 110	87.62 90	86.72 110	76.54 147	97.08 51	96.86 59	95.74 90	99.45 30	98.70 79

tfpn		92.91 99	91.44 126	94.63 71	95.42 77	98.92 51	96.41 76	92.10 71	93.19 108	87.34 94	86.85 107	69.20 204	97.01 52	96.88 58	96.28 66	99.47 24	98.75 78

thres600view7		93.49 92	92.37 113	94.79 68	95.42 77	98.93 47	96.58 71	92.31 63	93.04 112	87.88 88	86.62 113	76.94 144	97.09 50	96.82 61	95.63 94	99.45 30	98.63 82

view600		93.50 91	92.39 112	94.80 67	95.41 80	98.93 47	96.60 69	92.30 67	93.09 111	87.96 87	86.67 112	76.97 143	97.12 45	96.83 60	95.64 93	99.43 44	98.62 83

thres400		93.56 87	92.43 109	94.87 65	95.40 81	98.91 53	96.70 66	92.38 62	92.93 114	88.19 86	86.69 111	77.35 141	97.13 43	96.75 69	95.85 86	99.42 45	98.56 86

thres200		93.62 84	92.54 103	94.88 63	95.36 82	98.93 47	96.75 65	92.31 63	92.84 115	88.28 83	86.99 106	77.81 140	97.13 43	96.82 61	95.92 80	99.45 30	98.49 95

conf200view11		93.64 81	92.57 101	94.88 63	95.33 83	98.94 42	96.82 58	92.31 63	92.63 117	88.26 84	87.21 103	78.01 137	97.12 45	96.82 61	95.85 86	99.45 30	98.56 86

thres100view900		93.55 90	92.47 108	94.81 66	95.33 83	98.74 61	96.78 64	92.30 67	92.63 117	88.29 81	87.21 103	78.01 137	96.78 57	96.38 90	95.92 80	99.38 52	98.40 101

tfpn200view9		93.64 81	92.57 101	94.89 62	95.33 83	98.94 42	96.82 58	92.31 63	92.63 117	88.29 81	87.21 103	78.01 137	97.12 45	96.82 61	95.85 86	99.45 30	98.56 86

conf0.01		93.33 94	91.89 119	95.00 59	95.32 86	98.94 42	96.82 58	92.41 61	92.63 117	88.91 79	88.02 101	72.75 177	97.12 45	96.78 66	95.85 86	99.44 39	98.27 109

conf0.002		93.20 97	91.63 122	95.02 57	95.31 87	98.94 42	96.82 58	92.43 60	92.63 117	88.99 78	88.16 100	70.49 196	97.12 45	96.77 67	96.30 62	99.44 39	98.16 115

IS_MVSNet		95.28 53	96.43 47	93.94 78	95.30 88	99.01 38	95.90 82	91.12 86	94.13 97	87.50 92	91.23 71	94.45 57	94.17 91	98.45 15	98.50 6	99.65 2	99.23 28

CMPMVS	65.18 17	84.76 201	83.10 210	86.69 188	95.29 89	95.05 172	88.37 198	85.51 149	80.27 218	71.31 189	68.37 213	73.85 161	85.25 195	87.72 209	87.75 208	94.38 218	88.70 218
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
tfpn1000		94.14 71	94.54 71	93.67 85	95.27 90	98.50 75	95.36 98	91.84 78	96.31 50	87.38 93	92.98 54	84.04 109	92.60 115	96.49 87	95.62 95	99.55 9	97.82 133

canonicalmvs		95.25 55	95.45 58	95.00 59	95.27 90	98.72 64	96.89 55	89.82 98	96.51 46	90.84 53	93.72 47	86.01 97	97.66 34	95.78 110	97.94 25	99.54 11	99.50 7

Vis-MVSNet (Re-imp)		94.46 66	96.24 49	92.40 101	95.23 92	98.64 68	95.56 94	90.99 87	94.42 91	85.02 101	90.88 78	94.65 56	88.01 181	98.17 27	98.37 13	99.57 8	98.53 90

conf0.05thres1000		92.47 104	91.39 127	93.73 84	95.21 93	98.52 73	95.66 90	91.56 81	90.87 145	84.27 103	82.79 140	76.12 148	96.29 63	96.59 77	95.68 92	99.39 50	99.19 34

CLD-MVS		94.79 61	94.36 76	95.30 54	95.21 93	97.46 95	97.23 50	92.24 69	96.43 47	91.77 46	92.69 57	84.31 108	96.06 66	95.52 115	95.03 107	99.31 64	99.06 48
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 70	94.64 68	94.04 77	95.16 95	98.51 74	95.58 92	92.09 72	95.78 69	88.52 80	92.38 61	85.74 99	93.34 105	96.39 88	95.90 82	99.54 11	97.79 135

TDRefinement		89.07 147	88.15 155	90.14 127	95.16 95	96.88 105	95.55 95	90.20 93	89.68 157	76.42 143	76.67 160	74.30 159	84.85 198	93.11 154	91.91 188	98.64 157	94.47 192

ACMH+	90.88 12	91.41 117	91.13 129	91.74 106	95.11 97	96.95 104	93.13 132	89.48 104	92.42 126	79.93 127	85.13 124	78.02 136	93.82 96	93.49 149	93.88 136	98.94 112	97.99 121

tfpnview11		93.63 83	94.42 73	92.71 97	95.08 98	98.26 82	95.58 92	92.06 74	96.32 49	81.88 114	93.44 48	83.43 114	92.14 120	96.58 79	95.88 84	99.52 13	97.07 162

tfpn_n400		93.56 87	94.36 76	92.63 98	95.07 99	98.28 79	95.50 96	91.98 76	95.48 73	81.88 114	93.44 48	83.43 114	92.01 123	96.60 75	96.27 67	99.34 57	97.04 163

tfpnconf		93.56 87	94.36 76	92.63 98	95.07 99	98.28 79	95.50 96	91.98 76	95.48 73	81.88 114	93.44 48	83.43 114	92.01 123	96.60 75	96.27 67	99.34 57	97.04 163

thresconf0.02		93.57 86	93.84 86	93.25 92	95.03 101	98.16 86	95.80 89	92.46 59	96.12 57	83.88 105	92.61 58	80.39 127	92.83 113	96.11 102	96.21 72	99.49 20	97.28 154

FC-MVSNet-train		93.85 77	93.91 83	93.78 83	94.94 102	96.79 113	94.29 118	91.13 85	93.84 101	88.26 84	90.40 81	85.23 104	94.65 84	96.54 82	95.31 102	99.38 52	99.28 20

EPP-MVSNet		95.27 54	96.18 50	94.20 75	94.88 103	98.64 68	94.97 104	90.70 88	95.34 76	89.67 69	91.66 68	93.84 58	95.42 76	97.32 49	97.00 50	99.58 6	99.47 8

MVS_Test		94.82 59	95.66 53	93.84 82	94.79 104	98.35 78	96.49 74	89.10 109	96.12 57	87.09 96	92.58 59	90.61 74	96.48 61	96.51 86	96.89 53	99.11 97	98.54 89

diffmvs		94.83 58	95.64 54	93.89 80	94.73 105	97.96 91	96.49 74	89.13 108	96.82 41	89.47 71	91.66 68	93.63 60	95.15 78	94.76 126	95.93 79	98.36 172	98.69 80

MVSTER		94.89 57	95.07 64	94.68 70	94.71 106	96.68 116	97.00 52	90.57 90	95.18 83	93.05 35	95.21 38	86.41 94	93.72 98	97.59 44	95.88 84	99.00 108	98.50 94

EPMVS		90.88 122	92.12 115	89.44 135	94.71 106	97.24 99	93.55 124	76.81 205	95.89 64	81.77 118	91.49 70	86.47 93	93.87 94	90.21 198	90.07 199	95.92 199	93.49 204

DWT-MVSNet_training		91.30 118	89.73 140	93.13 94	94.64 108	96.87 107	94.93 105	86.17 140	94.22 95	93.18 30	89.11 91	73.28 171	93.59 101	88.00 208	90.73 195	96.26 196	95.87 179

DI_MVS_plusplus_trai		94.01 74	93.63 91	94.44 73	94.54 109	98.26 82	97.51 47	90.63 89	95.88 65	89.34 74	80.54 150	89.36 80	95.48 75	96.33 93	96.27 67	99.17 87	98.78 76

ADS-MVSNet		89.80 136	91.33 128	88.00 165	94.43 110	96.71 115	92.29 153	74.95 217	96.07 60	77.39 136	88.67 95	86.09 96	93.26 107	88.44 206	89.57 201	95.68 202	93.81 201

tpmrst		88.86 151	89.62 141	87.97 166	94.33 111	95.98 132	92.62 139	76.36 210	94.62 89	76.94 139	85.98 121	82.80 120	92.80 114	86.90 212	87.15 213	94.77 214	93.93 199

PMMVS		94.61 65	95.56 56	93.50 88	94.30 112	96.74 114	94.91 107	89.56 103	95.58 72	87.72 89	96.15 30	92.86 64	96.06 66	95.47 116	95.02 108	98.43 170	97.09 158

CostFormer		90.69 123	90.48 137	90.93 113	94.18 113	96.08 130	94.03 120	78.20 201	93.47 106	89.96 64	90.97 77	80.30 128	93.72 98	87.66 211	88.75 203	95.51 205	96.12 176

USDC		90.69 123	90.52 136	90.88 114	94.17 114	96.43 123	95.82 88	86.76 133	93.92 98	76.27 145	86.49 115	74.30 159	93.67 100	95.04 124	93.36 145	98.61 158	94.13 196

Vis-MVSNet		92.77 100	95.00 66	90.16 125	94.10 115	98.79 59	94.76 111	88.26 114	92.37 129	79.95 126	88.19 99	91.58 68	84.38 201	97.59 44	97.58 35	99.52 13	98.91 67
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
Effi-MVS+		92.93 98	93.86 85	91.86 103	94.07 116	98.09 89	95.59 91	85.98 143	94.27 94	79.54 130	91.12 75	81.81 123	96.71 58	96.67 73	96.06 75	99.27 70	98.98 60

tpmp4_e23		89.82 135	89.31 146	90.42 121	94.01 117	95.45 153	94.63 113	78.37 198	93.59 104	87.09 96	86.62 113	76.59 146	93.06 111	88.50 205	88.52 204	95.36 206	95.88 178

IterMVS-LS		92.56 103	93.18 98	91.84 104	93.90 118	94.97 174	94.99 103	86.20 139	94.18 96	82.68 111	85.81 122	87.36 91	94.43 86	95.31 118	96.02 77	98.87 117	98.60 85
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
dps		90.11 133	89.37 145	90.98 112	93.89 119	96.21 128	93.49 126	77.61 203	91.95 135	92.74 41	88.85 92	78.77 134	92.37 118	87.71 210	87.71 209	95.80 200	94.38 194

tpm cat1		88.90 149	87.78 168	90.22 124	93.88 120	95.39 162	93.79 123	78.11 202	92.55 123	89.43 72	81.31 146	79.84 130	91.40 131	84.95 217	86.34 219	94.68 217	94.09 197

PatchmatchNet		90.56 125	92.49 106	88.31 153	93.83 121	96.86 109	92.42 143	76.50 209	95.96 62	78.31 133	91.96 66	89.66 79	93.48 103	90.04 200	89.20 202	95.32 207	93.73 202
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
TinyColmap		89.42 139	88.58 150	90.40 122	93.80 122	95.45 153	93.96 122	86.54 135	92.24 132	76.49 142	80.83 148	70.44 197	93.37 104	94.45 132	93.30 148	98.26 174	93.37 206

RPMNet		90.19 131	92.03 117	88.05 162	93.46 123	95.95 135	93.41 127	74.59 218	92.40 127	75.91 147	84.22 130	86.41 94	92.49 116	94.42 133	93.85 138	98.44 168	96.96 165

gg-mvs-nofinetune		86.17 195	88.57 151	83.36 204	93.44 124	98.15 87	96.58 71	72.05 223	74.12 223	49.23 230	64.81 218	90.85 72	89.90 172	97.83 39	96.84 54	98.97 110	97.41 149

MDTV_nov1_ep13		91.57 114	93.18 98	89.70 131	93.39 125	96.97 103	93.53 125	80.91 193	95.70 71	81.86 117	92.40 60	89.93 77	93.25 108	91.97 187	90.80 194	95.25 210	94.46 193

CR-MVSNet		90.16 132	91.96 118	88.06 161	93.32 126	95.95 135	93.36 128	75.99 212	92.40 127	75.19 154	83.18 137	85.37 101	92.05 121	95.21 120	94.56 120	98.47 167	97.08 160

test-LLR		91.62 113	93.56 94	89.35 137	93.31 127	96.57 119	92.02 169	87.06 131	92.34 130	75.05 157	90.20 83	88.64 86	90.93 140	96.19 99	94.07 131	97.75 185	96.90 168

test0.0.03 1		91.97 107	93.91 83	89.72 130	93.31 127	96.40 125	91.34 179	87.06 131	93.86 99	81.67 119	91.15 74	89.16 82	86.02 193	95.08 122	95.09 106	98.91 115	96.64 174

CVMVSNet		89.77 137	91.66 121	87.56 179	93.21 129	95.45 153	91.94 173	89.22 106	89.62 160	69.34 203	83.99 132	85.90 98	84.81 199	94.30 136	95.28 103	96.85 192	97.09 158

PatchT		89.13 146	91.71 120	86.11 196	92.92 130	95.59 148	83.64 210	75.09 216	91.87 136	75.19 154	82.63 141	85.06 106	92.05 121	95.21 120	94.56 120	97.76 184	97.08 160

Fast-Effi-MVS+		91.87 108	92.08 116	91.62 108	92.91 131	97.21 101	94.93 105	84.60 162	93.61 103	81.49 121	83.50 135	78.95 132	96.62 60	96.55 81	96.22 71	99.16 90	98.51 93

IterMVS		90.20 130	92.43 109	87.61 177	92.82 132	94.31 192	94.11 119	81.54 190	92.97 113	69.90 199	84.71 127	88.16 90	89.96 171	95.25 119	94.17 129	97.31 188	97.46 147
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CDS-MVSNet		92.77 100	93.60 92	91.80 105	92.63 133	96.80 110	95.24 100	89.14 107	90.30 153	84.58 102	86.76 108	90.65 73	90.42 160	95.89 105	96.49 60	98.79 133	98.32 107
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
tpm		87.95 165	89.44 144	86.21 194	92.53 134	94.62 187	91.40 177	76.36 210	91.46 138	69.80 201	87.43 102	75.14 154	91.55 130	89.85 203	90.60 196	95.61 203	96.96 165

Effi-MVS+-dtu		91.78 110	93.59 93	89.68 133	92.44 135	97.11 102	94.40 116	84.94 158	92.43 125	75.48 149	91.09 76	83.75 112	93.55 102	96.61 74	95.47 99	97.24 189	98.67 81

testgi		89.42 139	91.50 125	87.00 186	92.40 136	95.59 148	89.15 197	85.27 155	92.78 116	72.42 183	91.75 67	76.00 151	84.09 203	94.38 134	93.82 140	98.65 156	96.15 175

LP		84.43 203	85.10 204	83.66 202	92.31 137	93.89 194	87.13 201	72.88 220	90.81 146	67.08 207	70.65 208	75.76 152	86.87 187	86.43 215	87.15 213	95.70 201	90.98 212

Fast-Effi-MVS+-dtu		91.19 119	93.64 90	88.33 152	92.19 138	96.46 122	93.99 121	81.52 191	92.59 122	71.82 186	92.17 63	85.54 100	91.68 129	95.73 111	94.64 116	98.80 127	98.34 104

FC-MVSNet-test		91.63 112	93.82 88	89.08 138	92.02 139	96.40 125	93.26 130	87.26 128	93.72 102	77.26 137	88.61 96	89.86 78	85.50 194	95.72 113	95.02 108	99.16 90	97.44 148

GA-MVS		89.28 142	90.75 135	87.57 178	91.77 140	96.48 121	92.29 153	87.58 126	90.61 150	65.77 209	84.48 128	76.84 145	89.46 173	95.84 107	93.68 141	98.52 163	97.34 152

TAMVS		90.54 127	90.87 134	90.16 125	91.48 141	96.61 118	93.26 130	86.08 141	87.71 188	81.66 120	83.11 139	84.04 109	90.42 160	94.54 129	94.60 117	98.04 180	95.48 186

tfpnnormal		88.50 152	87.01 188	90.23 123	91.36 142	95.78 143	92.74 136	90.09 94	83.65 211	76.33 144	71.46 205	69.58 202	91.84 126	95.54 114	94.02 133	99.06 105	99.03 53

GBi-Net		93.81 78	94.18 79	93.38 89	91.34 143	95.86 138	96.22 78	88.68 110	95.23 80	90.40 57	86.39 116	91.16 69	94.40 88	96.52 83	96.30 62	99.21 84	97.79 135

test1		93.81 78	94.18 79	93.38 89	91.34 143	95.86 138	96.22 78	88.68 110	95.23 80	90.40 57	86.39 116	91.16 69	94.40 88	96.52 83	96.30 62	99.21 84	97.79 135

FMVSNet2		93.30 95	93.36 97	93.22 93	91.34 143	95.86 138	96.22 78	88.24 115	95.15 84	89.92 66	81.64 144	89.36 80	94.40 88	96.77 67	96.98 51	99.21 84	97.79 135

FMVSNet3		93.79 80	94.17 81	93.35 91	91.21 146	95.99 131	96.62 67	88.68 110	95.23 80	90.40 57	86.39 116	91.16 69	94.11 92	95.96 103	96.67 57	99.07 102	97.79 135

testpf		83.57 206	85.70 199	81.08 207	90.99 147	88.96 217	82.71 213	65.32 231	90.22 155	73.86 169	81.58 145	76.10 149	81.19 208	84.14 221	85.41 221	92.43 224	93.45 205

TransMVSNet (Re)		87.73 174	86.79 190	88.83 143	90.76 148	94.40 190	91.33 180	89.62 102	84.73 207	75.41 151	72.73 196	71.41 192	86.80 188	94.53 130	93.93 135	99.06 105	95.83 180

LTVRE_ROB	87.32 16	87.55 176	88.25 154	86.73 187	90.66 149	95.80 142	93.05 133	84.77 159	83.35 212	60.32 218	83.12 138	67.39 209	93.32 106	94.36 135	94.86 112	98.28 173	98.87 71
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		86.68 189	87.24 180	86.02 197	90.58 150	96.26 127	91.08 183	81.59 189	84.96 206	69.80 201	71.35 206	75.08 156	84.23 202	94.24 138	93.35 146	98.82 120	95.46 187

TESTMET0.1,1		91.07 120	93.56 94	88.17 156	90.43 151	96.57 119	92.02 169	82.83 176	92.34 130	75.05 157	90.20 83	88.64 86	90.93 140	96.19 99	94.07 131	97.75 185	96.90 168

pm-mvs1		89.19 145	89.02 147	89.38 136	90.40 152	95.74 144	92.05 166	88.10 117	86.13 202	77.70 134	73.72 191	79.44 131	88.97 176	95.81 109	94.51 124	99.08 100	97.78 141

NR-MVSNet		89.34 141	88.66 149	90.13 128	90.40 152	95.61 146	93.04 134	89.91 96	91.22 140	78.96 131	77.72 158	68.90 206	89.16 175	94.24 138	93.95 134	99.32 61	98.99 58

FMVSNet1		91.54 115	90.93 132	92.26 102	90.35 154	95.27 166	95.22 101	87.16 130	91.37 139	87.62 90	75.45 163	83.84 111	94.43 86	96.52 83	96.30 62	98.82 120	97.74 142

test-mter		90.95 121	93.54 96	87.93 167	90.28 155	96.80 110	91.44 176	82.68 178	92.15 134	74.37 165	89.57 89	88.23 89	90.88 143	96.37 91	94.31 127	97.93 182	97.37 150

pmmvs4		90.55 126	89.91 139	91.30 110	90.26 156	94.95 175	92.73 137	87.94 121	93.44 107	85.35 100	82.28 143	76.09 150	93.02 112	93.56 147	92.26 186	98.51 164	96.77 170

MVS-HIRNet		85.36 199	86.89 189	83.57 203	90.13 157	94.51 188	83.57 211	72.61 221	88.27 184	71.22 190	68.97 211	81.81 123	88.91 177	93.08 155	91.94 187	94.97 213	89.64 217

SixPastTwentyTwo		88.37 156	89.47 143	87.08 184	90.01 158	95.93 137	87.41 200	85.32 152	90.26 154	70.26 196	86.34 119	71.95 187	90.93 140	92.89 159	91.72 190	98.55 161	97.22 155

UniMVSNet (Re)		90.03 134	89.61 142	90.51 120	89.97 159	96.12 129	92.32 149	89.26 105	90.99 143	80.95 124	78.25 157	75.08 156	91.14 135	93.78 142	93.87 137	99.41 46	99.21 32

v18		87.93 166	87.61 174	88.31 153	89.74 160	92.04 201	92.59 140	82.71 177	89.70 156	75.32 152	75.23 165	73.55 165	90.74 147	92.11 178	92.77 166	98.78 140	97.87 129

v16		87.87 171	87.60 175	88.19 155	89.70 161	92.01 203	92.37 144	82.54 180	89.67 158	75.00 159	75.02 169	73.65 163	90.73 149	92.14 174	92.80 160	98.77 144	97.90 126

UniMVSNet_NR-MVSNet		90.35 129	89.96 138	90.80 116	89.66 162	95.83 141	92.48 141	90.53 91	90.96 144	79.57 128	79.33 154	77.14 142	93.21 109	92.91 158	94.50 125	99.37 55	99.05 50

v17		87.83 172	87.56 176	88.13 157	89.65 163	92.02 202	92.34 148	82.55 179	89.38 161	74.76 160	75.14 166	73.59 164	90.70 150	92.15 173	92.78 164	98.78 140	97.89 127

v8		88.21 159	87.94 165	88.51 149	89.62 164	95.01 173	92.31 150	84.99 157	88.94 168	74.70 161	75.03 168	73.51 166	90.67 153	92.11 178	92.74 172	98.80 127	98.24 110

WR-MVS_H		87.93 166	87.85 166	88.03 164	89.62 164	95.58 150	90.47 188	85.55 148	87.20 194	76.83 140	74.42 178	72.67 183	86.37 190	93.22 153	93.04 150	99.33 59	98.83 73

v1neww		88.41 154	88.00 161	88.89 140	89.61 166	95.44 156	92.31 150	87.65 124	89.09 164	74.30 166	75.02 169	73.42 169	90.68 151	92.12 175	92.77 166	98.79 133	98.18 112

v7new		88.41 154	88.00 161	88.89 140	89.61 166	95.44 156	92.31 150	87.65 124	89.09 164	74.30 166	75.02 169	73.42 169	90.68 151	92.12 175	92.77 166	98.79 133	98.18 112

v6		88.43 153	88.01 158	88.92 139	89.60 168	95.43 158	92.36 145	87.66 123	89.07 166	74.50 163	75.06 167	73.47 167	90.59 156	92.11 178	92.76 170	98.79 133	98.18 112

pmmvs5		87.83 172	88.09 156	87.51 181	89.59 169	95.48 151	89.75 195	84.73 160	86.07 204	71.44 188	80.57 149	70.09 200	90.74 147	94.47 131	92.87 156	98.82 120	97.10 157

gm-plane-assit		83.26 207	85.29 202	80.89 208	89.52 170	89.89 215	70.26 224	78.24 200	77.11 221	58.01 223	74.16 183	66.90 211	90.63 155	97.20 52	96.05 76	98.66 155	95.68 183

v7		88.18 160	88.01 158	88.39 150	89.45 171	95.14 170	92.36 145	85.37 151	89.29 163	72.94 182	73.98 187	72.77 175	91.38 132	93.59 143	92.87 156	98.82 120	98.42 98

v1141		88.17 161	87.69 170	88.74 145	89.44 172	95.41 159	92.25 158	87.98 118	88.38 178	73.54 177	74.43 177	72.71 181	90.45 158	92.08 182	92.72 174	98.79 133	98.09 117

divwei89l23v2f112		88.17 161	87.69 170	88.74 145	89.44 172	95.41 159	92.26 156	87.97 120	88.29 182	73.57 176	74.45 176	72.75 177	90.42 160	92.08 182	92.72 174	98.81 124	98.09 117

v10		88.00 164	87.96 163	88.05 162	89.44 172	94.68 183	92.36 145	83.35 172	89.37 162	72.96 180	73.98 187	72.79 174	91.35 133	93.59 143	92.88 155	98.81 124	98.42 98

v1		88.17 161	87.66 172	88.77 144	89.44 172	95.40 161	92.29 153	87.98 118	88.21 185	73.75 171	74.41 179	72.75 177	90.36 166	92.07 185	92.71 177	98.80 127	98.09 117

V42		88.31 157	87.95 164	88.73 147	89.44 172	95.34 163	92.23 160	87.21 129	88.83 170	74.49 164	74.89 173	73.43 168	90.41 163	92.08 182	92.77 166	98.60 160	98.33 105

v15		87.46 180	87.16 183	87.81 168	89.41 177	91.96 204	92.26 156	82.28 183	88.42 176	73.72 172	74.29 182	72.73 180	90.41 163	92.17 172	92.76 170	98.79 133	97.83 132

v148		87.51 177	86.79 190	88.36 151	89.39 178	95.21 168	89.84 194	88.20 116	87.61 190	77.56 135	73.38 194	70.32 199	86.80 188	90.70 195	92.31 183	98.37 171	97.98 123

V14		87.47 179	87.19 182	87.80 169	89.37 179	91.95 205	92.25 158	82.12 184	88.39 177	73.83 170	74.31 180	72.84 173	90.44 159	92.20 170	92.78 164	98.80 127	97.84 131

v11		87.58 175	87.50 177	87.67 174	89.34 180	91.91 208	92.22 162	81.63 188	89.01 167	72.95 181	74.11 185	72.51 185	91.08 137	94.01 141	93.00 152	98.77 144	97.93 124

V9		87.41 181	87.15 184	87.72 172	89.33 181	91.93 206	92.23 160	82.02 185	88.35 179	73.59 175	74.13 184	72.77 175	90.37 165	92.21 169	92.80 160	98.79 133	97.86 130

v13		87.34 184	87.11 187	87.62 176	89.30 182	91.91 208	92.04 167	81.86 187	88.35 179	73.36 178	73.88 189	72.69 182	90.34 167	92.23 167	92.82 158	98.80 127	97.88 128

v12		87.38 183	87.13 185	87.68 173	89.30 182	91.92 207	92.01 171	81.94 186	88.35 179	73.69 173	74.10 186	72.57 184	90.33 168	92.23 167	92.82 158	98.80 127	97.91 125

CP-MVSNet		87.89 170	87.27 179	88.62 148	89.30 182	95.06 171	90.60 187	85.78 145	87.43 192	75.98 146	74.60 174	68.14 208	90.76 145	93.07 156	93.60 142	99.30 66	98.98 60

v1144		87.92 169	87.79 167	88.07 159	89.27 185	95.15 169	92.17 163	85.62 147	88.52 174	71.52 187	73.80 190	72.40 186	91.06 138	93.54 148	92.80 160	98.81 124	98.33 105

DU-MVS		89.67 138	88.84 148	90.63 119	89.26 186	95.61 146	92.48 141	89.91 96	91.22 140	79.57 128	77.72 158	71.18 193	93.21 109	92.53 162	94.57 119	99.35 56	99.05 50

WR-MVS		87.93 166	88.09 156	87.75 170	89.26 186	95.28 164	90.81 185	86.69 134	88.90 169	75.29 153	74.31 180	73.72 162	85.19 197	92.26 165	93.32 147	99.27 70	98.81 74

Baseline_NR-MVSNet		89.27 143	88.01 158	90.73 118	89.26 186	93.71 195	92.71 138	89.78 100	90.73 147	81.28 122	73.53 192	72.85 172	92.30 119	92.53 162	93.84 139	99.07 102	98.88 69

N_pmnet		84.80 200	85.10 204	84.45 200	89.25 189	92.86 198	84.04 209	86.21 137	88.78 171	66.73 208	72.41 199	74.87 158	85.21 196	88.32 207	86.45 217	95.30 208	92.04 208

v2v482		88.25 158	87.71 169	88.88 142	89.23 190	95.28 164	92.10 164	87.89 122	88.69 173	73.31 179	75.32 164	71.64 189	91.89 125	92.10 181	92.92 154	98.86 119	97.99 121

PS-CasMVS		87.33 185	86.68 193	88.10 158	89.22 191	94.93 176	90.35 190	85.70 146	86.44 198	74.01 168	73.43 193	66.59 214	90.04 170	92.92 157	93.52 143	99.28 68	98.91 67

TranMVSNet+NR-MVSNet		89.23 144	88.48 152	90.11 129	89.07 192	95.25 167	92.91 135	90.43 92	90.31 152	77.10 138	76.62 161	71.57 191	91.83 127	92.12 175	94.59 118	99.32 61	98.92 65

v1192		87.51 177	87.31 178	87.74 171	89.04 193	94.87 181	92.07 165	85.03 156	88.49 175	70.32 195	72.65 197	70.35 198	91.21 134	93.59 143	92.80 160	98.78 140	98.42 98

v144192		87.40 182	87.20 181	87.64 175	88.89 194	94.88 180	91.65 175	84.70 161	87.80 187	71.17 192	73.20 195	70.91 194	90.75 146	92.69 160	92.49 179	98.71 150	98.43 97

PEN-MVS		87.22 187	86.50 197	88.07 159	88.88 195	94.44 189	90.99 184	86.21 137	86.53 197	73.66 174	74.97 172	66.56 215	89.42 174	91.20 191	93.48 144	99.24 75	98.31 108

v1921920		87.31 186	87.13 185	87.52 180	88.87 196	94.72 182	91.96 172	84.59 163	88.28 183	69.86 200	72.50 198	70.03 201	91.10 136	93.33 151	92.61 178	98.71 150	98.44 96

pmmvs6		85.98 196	84.89 206	87.25 183	88.83 197	94.35 191	89.36 196	85.30 154	78.51 220	75.44 150	62.71 221	75.41 153	87.65 183	93.58 146	92.40 181	96.89 191	97.29 153

v1240		86.89 188	86.75 192	87.06 185	88.75 198	94.65 185	91.30 181	84.05 166	87.49 191	68.94 204	71.96 201	68.86 207	90.65 154	93.33 151	92.72 174	98.67 154	98.24 110

anonymousdsp		88.90 149	91.00 131	86.44 192	88.74 199	95.97 133	90.40 189	82.86 175	88.77 172	67.33 206	81.18 147	81.44 125	90.22 169	96.23 96	94.27 128	99.12 96	99.16 39

EU-MVSNet		85.62 198	87.65 173	83.24 205	88.54 200	92.77 199	87.12 202	85.32 152	86.71 195	64.54 211	78.52 156	75.11 155	78.35 210	92.25 166	92.28 185	95.58 204	95.93 177

DTE-MVSNet		86.67 190	86.09 198	87.35 182	88.45 201	94.08 193	90.65 186	86.05 142	86.13 202	72.19 184	74.58 175	66.77 213	87.61 184	90.31 197	93.12 149	99.13 94	97.62 145

v748		85.88 197	85.66 200	86.14 195	88.03 202	94.63 186	87.02 204	84.59 163	84.30 208	74.56 162	70.94 207	67.27 210	83.94 205	90.96 194	92.74 172	98.71 150	98.81 74

FMVSNet5		90.36 128	90.93 132	89.70 131	87.99 203	92.25 200	92.03 168	83.51 169	92.20 133	84.13 104	85.59 123	86.48 92	92.43 117	94.61 127	94.52 123	98.13 176	90.85 213

v7n		86.43 193	86.52 196	86.33 193	87.91 204	94.93 176	90.15 191	83.05 173	86.57 196	70.21 197	71.48 204	66.78 212	87.72 182	94.19 140	92.96 153	98.92 114	98.76 77

test20.03		82.92 208	85.52 201	79.90 211	87.75 205	91.84 210	82.80 212	82.99 174	82.65 216	60.32 218	78.90 155	70.50 195	67.10 222	92.05 186	90.89 193	98.44 168	91.80 209

MDTV_nov1_ep13_2view		86.30 194	88.27 153	84.01 201	87.71 206	94.67 184	88.08 199	76.78 206	90.59 151	68.66 205	80.46 151	80.12 129	87.58 185	89.95 202	88.20 206	95.25 210	93.90 200

V4		86.56 192	86.61 195	86.50 190	87.49 207	94.90 178	89.87 193	83.39 170	86.25 200	71.20 191	71.57 202	71.58 190	88.30 180	91.14 192	92.31 183	98.75 147	98.52 91

v52		86.57 191	86.63 194	86.50 190	87.47 208	94.89 179	89.90 192	83.39 170	86.36 199	71.17 192	71.53 203	71.65 188	88.34 179	91.14 192	92.32 182	98.74 148	98.52 91

Anonymous20231206		83.84 205	85.19 203	82.26 206	87.38 209	92.87 197	85.49 207	83.65 168	86.07 204	63.44 214	68.42 212	69.01 205	75.45 214	93.34 150	92.44 180	98.12 178	94.20 195

FPMVS		75.84 217	74.59 217	77.29 218	86.92 210	83.89 224	85.01 208	80.05 196	82.91 214	60.61 217	65.25 217	60.41 219	63.86 223	75.60 226	73.60 228	87.29 229	80.47 225

MIMVSNet		88.99 148	91.07 130	86.57 189	86.78 211	95.62 145	91.20 182	75.40 215	90.65 149	76.57 141	84.05 131	82.44 122	91.01 139	95.84 107	95.38 101	98.48 166	93.50 203

tmp_tt				66.88 225	86.07 212	73.86 231	68.22 225	33.38 233	96.88 40	80.67 125	88.23 98	78.82 133	49.78 229	82.68 223	77.47 225	83.19 232

PM-MVS		84.72 202	84.47 207	85.03 199	84.67 213	91.57 211	86.27 206	82.31 182	87.65 189	70.62 194	76.54 162	56.41 226	88.75 178	92.59 161	89.85 200	97.54 187	96.66 173

testus		81.33 210	84.13 208	78.06 214	84.54 214	87.72 218	79.66 217	80.42 194	87.36 193	54.13 229	83.83 133	56.63 224	73.21 219	90.51 196	91.74 189	96.40 194	91.11 211

test2356		81.26 211	84.10 209	77.95 216	84.35 215	87.38 220	79.56 218	79.53 197	86.17 201	54.14 228	83.24 136	60.71 218	73.77 215	90.01 201	91.18 192	96.33 195	90.01 215

pmmvs-eth3d		84.33 204	82.94 211	85.96 198	84.16 216	90.94 212	86.55 205	83.79 167	84.25 209	75.85 148	70.64 209	56.43 225	87.44 186	92.20 170	90.41 198	97.97 181	95.68 183

new-patchmatchnet		78.49 215	78.19 216	78.84 213	84.13 217	90.06 214	77.11 223	80.39 195	79.57 219	59.64 222	66.01 216	55.65 227	75.62 213	84.55 220	80.70 223	96.14 198	90.77 214

new_pmnet		81.53 209	82.68 212	80.20 209	83.47 218	89.47 216	82.21 215	78.36 199	87.86 186	60.14 220	67.90 214	69.43 203	82.03 207	89.22 204	87.47 210	94.99 212	87.39 219

Anonymous20231211		75.89 216	74.18 221	77.88 217	81.42 219	87.72 218	79.33 220	81.05 192	66.49 230	60.00 221	45.74 229	51.46 229	71.22 220	85.70 216	86.91 216	94.25 219	95.25 189

testmv		72.66 219	74.40 218	70.62 220	80.64 220	81.51 227	64.99 229	76.60 207	68.76 226	44.81 231	63.78 219	48.00 230	62.52 224	84.74 218	87.17 211	94.19 220	86.86 220

test1235678		72.65 220	74.40 218	70.62 220	80.64 220	81.50 228	64.99 229	76.59 208	68.74 227	44.81 231	63.78 219	47.99 231	62.51 225	84.73 219	87.17 211	94.19 220	86.85 221

pmmvs3		79.16 214	80.12 215	78.05 215	79.36 222	86.59 222	78.13 222	73.87 219	76.42 222	57.51 224	70.59 210	57.02 223	84.66 200	90.10 199	88.32 205	94.75 215	91.77 210

1111		73.35 218	74.40 218	72.12 219	78.22 223	82.24 225	65.06 227	65.61 229	70.28 224	55.42 225	56.30 224	57.35 221	73.66 216	86.73 213	88.16 207	94.75 215	79.76 227

.test1245		56.65 226	56.09 227	57.30 227	78.22 223	82.24 225	65.06 227	65.61 229	70.28 224	55.42 225	56.30 224	57.35 221	73.66 216	86.73 213	15.01 232	5.84 236	24.75 233

PMVS	63.12 18	67.27 223	66.39 225	68.30 223	77.98 225	60.24 234	59.53 233	76.82 204	66.65 229	60.74 216	54.39 226	59.82 220	51.24 228	73.92 229	70.52 229	83.48 231	79.17 228
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test12356		69.55 221	71.53 223	67.24 224	77.70 226	78.48 229	65.92 226	75.55 214	68.39 228	44.26 233	61.80 222	40.70 233	47.92 232	81.45 224	87.01 215	92.09 225	82.89 223

MDA-MVSNet-bldmvs		80.11 212	80.24 214	79.94 210	77.01 227	93.21 196	78.86 221	85.94 144	82.71 215	60.86 215	79.71 153	51.77 228	83.71 206	75.60 226	86.37 218	93.28 222	92.35 207

ambc			73.83 222		76.23 228	85.13 223	82.27 214		84.16 210	65.58 210	52.82 227	23.31 238	73.55 218	91.41 190	85.26 222	92.97 223	94.70 190

Gipuma		68.35 222	66.71 224	70.27 222	74.16 229	68.78 233	63.93 232	71.77 224	83.34 213	54.57 227	34.37 230	31.88 234	68.69 221	83.30 222	85.53 220	88.48 228	79.78 226
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MIMVSNet1		80.03 213	80.93 213	78.97 212	72.46 230	90.73 213	80.81 216	82.44 181	80.39 217	63.64 213	57.57 223	64.93 216	76.37 212	91.66 188	91.55 191	98.07 179	89.70 216

no-one		55.96 227	55.63 228	56.35 228	68.48 231	73.29 232	43.03 234	72.52 222	44.01 234	34.80 234	32.83 231	29.11 235	35.21 233	56.63 231	75.72 226	84.04 230	77.79 229

PMMVS2		64.36 225	65.94 226	62.52 226	67.37 232	77.44 230	64.39 231	69.32 228	61.47 231	34.59 235	46.09 228	41.03 232	48.02 231	74.56 228	78.23 224	91.43 226	82.76 224

EMVS		49.98 229	46.76 231	53.74 230	64.96 233	51.29 236	37.81 236	69.35 227	51.83 232	22.69 238	29.57 233	25.06 236	57.28 226	44.81 233	56.11 231	70.32 234	68.64 232

E-PMN		50.67 228	47.85 230	53.96 229	64.13 234	50.98 237	38.06 235	69.51 226	51.40 233	24.60 237	29.46 234	24.39 237	56.07 227	48.17 232	59.70 230	71.40 233	70.84 231

MVE	50.86 19	49.54 230	51.43 229	47.33 231	44.14 235	59.20 235	36.45 237	60.59 232	41.47 235	31.14 236	29.58 232	17.06 239	48.52 230	62.22 230	74.63 227	63.12 235	75.87 230
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		12.09 231	16.94 232	6.42 233	3.15 236	6.08 238	9.51 239	3.84 234	21.46 236	5.31 239	27.49 235	6.76 240	10.89 234	17.06 234	15.01 232	5.84 236	24.75 233

GG-mvs-BLEND		66.17 224	94.91 67	32.63 232	1.32 237	96.64 117	91.40 177	0.85 236	94.39 93	2.20 240	90.15 85	95.70 52	2.27 236	96.39 88	95.44 100	97.78 183	95.68 183

test123		9.58 232	13.53 233	4.97 234	1.31 238	5.47 239	8.32 240	2.95 235	18.14 237	2.03 241	20.82 236	2.34 241	10.60 235	10.00 235	14.16 234	4.60 238	23.77 235

sosnet-low-res		0.00 233	0.00 234	0.00 235	0.00 239	0.00 240	0.00 241	0.00 237	0.00 238	0.00 242	0.00 237	0.00 242	0.00 237	0.00 236	0.00 235	0.00 239	0.00 236

sosnet		0.00 233	0.00 234	0.00 235	0.00 239	0.00 240	0.00 241	0.00 237	0.00 238	0.00 242	0.00 237	0.00 242	0.00 237	0.00 236	0.00 235	0.00 239	0.00 236

MTAPA										96.83 5		99.12 14

MTMP										97.18 3		98.83 20

Patchmatch-RL test							34.61 238

NP-MVS									95.32 77

Patchmtry						95.96 134	93.36 128	75.99 212		75.19 154

DeepMVS_CX						86.86 221	79.50 219	70.43 225	90.73 147	63.66 212	80.36 152	60.83 217	79.68 209	76.23 225		89.46 227	86.53 222

High-resolution multi-view benchmark