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.

WR-MVS		99.22 3	99.15 5	99.30 2	99.54 11	99.62 1	99.63 4	99.45 1	97.75 14	98.47 21	99.71 5	99.05 38	98.88 4	99.54 5	99.49 2	99.81 1	98.87 8

WR-MVS_H		98.97 9	98.82 13	99.14 8	99.56 9	99.56 4	99.54 11	99.42 2	96.07 38	98.37 23	99.34 30	99.09 31	98.43 18	99.45 10	99.41 5	99.53 10	98.86 9

PS-CasMVS		99.08 4	98.90 10	99.28 3	99.65 3	99.56 4	99.59 6	99.39 3	96.36 33	98.83 13	99.46 21	99.09 31	98.62 10	99.51 7	99.36 8	99.63 3	98.97 6

UniMVSNet_ETH3D		98.93 10	99.20 3	98.63 20	99.54 11	99.33 7	98.73 58	99.37 4	98.87 5	97.86 36	99.27 34	99.78 2	96.59 79	99.52 6	99.40 6	99.67 2	98.21 39

CP-MVSNet		98.91 11	98.61 18	99.25 4	99.63 5	99.50 6	99.55 10	99.36 5	95.53 60	98.77 15	99.11 40	98.64 74	98.57 13	99.42 11	99.28 11	99.61 4	98.78 11

PEN-MVS		99.08 4	98.95 8	99.23 5	99.65 3	99.59 2	99.64 2	99.34 6	96.68 26	98.65 16	99.43 23	99.33 14	98.47 17	99.50 8	99.32 9	99.60 5	98.79 10

DTE-MVSNet		99.03 6	98.88 11	99.21 6	99.66 2	99.59 2	99.62 5	99.34 6	96.92 22	98.52 18	99.36 29	98.98 43	98.57 13	99.49 9	99.23 12	99.56 9	98.55 23

UA-Net		98.66 16	98.60 21	98.73 15	99.83 1	99.28 9	98.56 66	99.24 8	96.04 39	97.12 68	98.44 72	98.95 48	98.17 26	99.15 21	99.00 18	99.48 17	99.33 2

gg-mvs-nofinetune		94.13 158	93.93 152	94.37 171	97.99 119	95.86 166	95.45 187	99.22 9	97.61 15	95.10 144	99.50 19	84.50 183	81.73 205	95.31 145	94.12 157	96.71 159	90.59 178

SixPastTwentyTwo		99.25 2	99.20 3	99.32 1	99.53 15	99.32 8	99.64 2	99.19 10	98.05 10	99.19 5	99.74 4	98.96 47	99.03 2	99.69 2	99.58 1	99.32 24	99.06 5

LTVRE_ROB	97.71 1	99.33 1	99.47 1	99.16 7	99.16 41	99.11 11	99.39 12	99.16 11	99.26 2	99.22 4	99.51 18	99.75 3	98.54 15	99.71 1	99.47 3	99.52 12	99.46 1
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
pmmvs6		98.77 13	99.35 2	98.09 40	98.32 93	98.92 20	98.57 64	99.03 12	99.36 1	96.86 81	99.77 3	99.86 1	96.20 94	99.56 4	99.39 7	99.59 6	98.61 20

Fast-Effi-MVS+-dtu		94.34 154	93.26 161	95.62 149	97.82 134	95.97 165	95.86 174	99.01 13	86.88 188	93.39 177	90.83 190	95.46 148	90.61 167	94.46 158	94.68 151	97.01 148	94.51 149

TranMVSNet+NR-MVSNet		98.45 18	98.22 29	98.72 16	99.32 32	99.06 14	98.99 33	98.89 14	95.52 61	97.53 47	99.42 25	98.83 59	98.01 32	98.55 47	98.34 50	99.57 8	97.80 55

DU-MVS		98.23 25	97.74 53	98.81 12	99.23 34	98.77 33	98.76 52	98.88 15	94.10 107	98.50 19	98.87 51	98.32 91	97.99 33	98.40 55	98.08 67	99.49 16	97.64 63

NR-MVSNet		98.00 41	97.88 43	98.13 38	98.33 91	98.77 33	98.83 49	98.88 15	94.10 107	97.46 51	98.87 51	98.58 79	95.78 102	99.13 22	98.16 61	99.52 12	97.53 71

UniMVSNet (Re)		98.23 25	97.85 45	98.67 18	99.15 42	98.87 23	98.74 55	98.84 17	94.27 105	97.94 35	99.01 42	98.39 87	97.82 40	98.35 60	98.29 55	99.51 15	97.78 56

v7n		99.03 6	99.03 7	99.02 9	99.09 54	99.11 11	99.57 9	98.82 18	98.21 9	99.25 2	99.84 2	99.59 5	98.76 6	99.23 16	98.83 27	98.63 66	98.40 32

UniMVSNet_NR-MVSNet		98.12 35	97.56 60	98.78 13	99.13 47	98.89 22	98.76 52	98.78 19	93.81 115	98.50 19	98.81 55	97.64 114	97.99 33	98.18 66	97.92 70	99.53 10	97.64 63

Effi-MVS+		96.46 109	95.28 126	97.85 63	98.64 80	97.16 130	97.15 142	98.75 20	90.27 159	98.03 32	93.93 163	96.21 140	96.55 83	96.34 122	96.69 104	97.97 107	96.33 115

Baseline_NR-MVSNet		98.17 29	97.90 42	98.48 27	99.23 34	98.59 50	98.83 49	98.73 21	93.97 112	96.95 75	99.66 7	98.23 96	97.90 37	98.40 55	99.06 16	99.25 27	97.42 78

DCV-MVSNet		97.56 61	97.63 56	97.47 83	98.41 88	99.12 10	98.63 61	98.57 22	95.71 51	95.60 132	93.79 165	98.01 104	94.25 131	99.16 20	98.88 24	99.35 20	98.74 13

FC-MVSNet-test		97.54 63	98.26 27	96.70 117	98.87 65	97.79 105	98.49 69	98.56 23	96.04 39	90.39 192	99.65 8	98.67 71	95.15 115	99.23 16	99.07 14	98.73 64	97.39 79

MIMVSNet1		98.22 28	98.51 22	97.87 61	99.40 26	98.82 29	99.31 14	98.53 24	97.39 17	96.59 90	99.31 32	99.23 26	94.76 123	98.93 28	98.67 33	98.63 66	97.25 86

FMVSNet1		97.40 76	98.09 34	96.60 121	97.80 137	98.76 36	98.26 80	98.50 25	96.79 24	93.13 180	99.28 33	98.64 74	92.90 150	97.67 78	97.86 73	99.02 36	97.64 63

zzz-MVS		98.14 32	97.78 50	98.55 23	99.58 6	98.58 52	98.98 35	98.48 26	95.98 42	97.39 53	94.73 147	99.27 20	97.98 35	98.81 31	98.64 36	98.90 50	98.46 28

ACMMPR		98.31 22	98.07 36	98.60 21	99.58 6	98.83 27	99.09 24	98.48 26	96.25 35	97.03 72	96.81 107	99.09 31	98.39 20	98.55 47	98.45 42	99.01 38	98.53 26

CR-MVSNet		91.94 176	88.50 186	95.94 140	96.14 182	92.08 189	95.23 190	98.47 28	84.30 202	96.44 95	94.58 151	75.57 202	92.92 148	90.22 193	92.22 174	96.43 164	90.56 179

Patchmtry						92.70 184	95.23 190	98.47 28		96.44 95

HFP-MVS		98.17 29	98.02 37	98.35 33	99.36 28	98.62 48	98.79 51	98.46 30	96.24 36	96.53 92	97.13 104	98.98 43	98.02 31	98.20 63	98.42 44	98.95 47	98.54 24

MIMVSNet		93.68 166	93.96 150	93.35 180	97.82 134	96.08 163	96.34 164	98.46 30	91.28 147	86.67 206	94.95 143	94.87 154	84.39 203	94.53 154	94.65 152	96.45 163	91.34 175

SteuartSystems-ACMMP		98.06 38	97.78 50	98.39 31	99.54 11	98.79 31	98.94 40	98.42 32	93.98 111	95.85 118	96.66 112	99.25 24	98.61 11	98.71 38	98.38 47	98.97 43	98.67 18
Skip Steuart: Steuart Systems R&D Blog.
SR-MVS					99.33 31			98.40 33				98.90 51

MP-MVS		97.98 45	97.53 61	98.50 25	99.56 9	98.58 52	98.97 36	98.39 34	93.49 118	97.14 65	96.08 123	99.23 26	98.06 29	98.50 52	98.38 47	98.90 50	98.44 30
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CP-MVS		98.00 41	97.57 59	98.50 25	99.47 21	98.56 55	98.91 42	98.38 35	94.71 86	97.01 73	95.20 137	99.06 35	98.20 24	98.61 44	98.46 40	99.02 36	98.40 32

X-MVS		97.60 59	97.00 83	98.29 34	99.50 18	98.76 36	98.90 43	98.37 36	94.67 89	96.40 99	91.47 185	98.78 65	97.60 50	98.55 47	98.50 38	98.96 45	98.29 35

EPP-MVSNet		97.29 78	96.88 88	97.76 70	98.70 74	99.10 13	98.92 41	98.36 37	95.12 73	93.36 178	97.39 97	91.00 175	97.65 47	98.72 36	98.91 21	99.58 7	97.92 51

PGM-MVS		97.82 52	97.25 68	98.48 27	99.54 11	98.75 40	99.02 28	98.35 38	92.41 134	96.84 82	95.39 134	98.99 42	98.24 23	98.43 54	98.34 50	98.90 50	98.41 31

3Dnovator+	96.20 4	97.58 60	97.14 75	98.10 39	98.98 62	97.85 99	98.60 63	98.33 39	96.41 31	97.23 63	94.66 150	97.26 123	96.91 70	97.91 68	97.87 72	98.53 73	98.03 45

APDe-MVS		98.29 23	98.42 24	98.14 37	99.45 22	98.90 21	99.18 21	98.30 40	95.96 44	95.13 142	98.79 56	99.25 24	97.92 36	98.80 32	98.71 30	98.85 58	98.54 24

ACMMP		97.99 43	97.60 58	98.45 29	99.53 15	98.83 27	99.13 23	98.30 40	94.57 92	96.39 103	95.32 135	98.95 48	98.37 21	98.61 44	98.47 39	99.00 39	98.45 29
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
TDRefinement		99.00 8	99.13 6	98.86 10	98.99 61	99.05 16	99.58 7	98.29 42	98.96 4	97.96 34	99.40 26	98.67 71	98.87 5	99.60 3	99.46 4	99.46 18	98.74 13

LGP-MVS_train		97.96 48	97.53 61	98.45 29	99.45 22	98.64 47	99.09 24	98.27 43	92.99 130	96.04 114	96.57 113	99.29 16	98.66 8	98.73 34	98.42 44	99.19 29	98.09 43

ACMM	94.29 11	98.12 35	97.71 54	98.59 22	99.51 17	98.58 52	99.24 17	98.25 44	96.22 37	96.90 76	95.01 141	98.89 53	98.52 16	98.66 41	98.32 53	99.13 31	98.28 38
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Fast-Effi-MVS+		96.80 98	95.92 118	97.84 64	98.57 81	97.46 119	98.06 89	98.24 45	89.64 167	97.57 46	96.45 115	97.35 121	96.73 74	97.22 95	96.64 106	97.86 110	96.65 105

IS_MVSNet		96.62 107	96.48 102	96.78 115	98.46 85	98.68 46	98.61 62	98.24 45	92.23 136	89.63 196	95.90 128	94.40 158	96.23 92	98.65 42	98.77 28	99.52 12	96.76 103

APD-MVS		97.47 72	97.16 73	97.84 64	99.32 32	98.39 64	98.47 72	98.21 47	92.08 138	95.23 139	96.68 111	98.90 51	96.99 68	98.20 63	98.21 57	98.80 61	97.67 61
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
HPM-MVS++		97.56 61	97.11 79	98.09 40	99.18 39	97.95 94	98.57 64	98.20 48	94.08 109	97.25 62	95.96 127	98.81 62	97.13 63	97.51 86	97.30 89	98.21 93	98.15 42

EPNet		94.33 156	93.52 157	95.27 157	98.81 71	94.71 176	96.77 152	98.20 48	88.12 180	96.53 92	92.53 175	91.19 173	85.25 202	95.22 147	95.26 142	96.09 169	97.63 67
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
pm-mvs1		98.14 32	98.66 17	97.53 79	97.93 123	98.49 60	98.14 86	98.19 50	97.95 11	96.17 110	99.63 10	98.85 56	95.41 111	98.91 29	98.89 23	99.34 21	97.86 53

SCA		91.15 183	87.65 190	95.23 160	96.15 181	95.68 168	96.68 156	98.18 51	90.46 156	97.21 64	92.44 177	80.17 194	93.51 144	86.04 201	83.58 197	89.68 198	85.21 198

v10		97.64 58	97.26 67	98.08 44	98.07 113	98.56 55	98.86 47	98.18 51	94.48 98	98.24 27	99.56 15	98.98 43	97.72 44	96.05 131	96.26 118	97.42 129	96.93 94

DVP-MVS		98.27 24	98.61 18	97.87 61	99.17 40	99.03 17	99.07 26	98.17 53	96.75 25	94.35 158	98.92 46	99.58 6	97.86 39	98.67 40	98.70 31	98.63 66	98.63 19

v8		97.51 67	97.16 73	97.91 58	97.99 119	98.48 61	98.76 52	98.17 53	94.54 96	97.69 43	99.48 20	98.76 68	97.63 49	96.10 130	96.14 120	97.20 139	96.64 106

DeepC-MVS	96.08 5	98.58 17	98.49 23	98.68 17	99.37 27	98.52 58	99.01 32	98.17 53	97.17 20	98.25 26	99.56 15	99.62 4	98.29 22	98.40 55	98.09 64	98.97 43	98.08 44
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CS-MVS		96.24 114	94.67 141	98.08 44	99.10 52	98.62 48	98.25 81	98.12 56	87.70 182	97.76 39	88.13 197	96.08 143	96.39 89	97.64 82	98.10 63	98.84 60	96.39 113

MSP-MVS		97.67 55	97.88 43	97.43 85	99.34 29	98.99 19	98.87 46	98.12 56	95.63 52	94.16 164	97.45 95	99.50 7	96.44 88	96.35 121	98.70 31	97.65 120	98.57 22

RPMNet		90.52 185	86.27 199	95.48 152	95.95 187	92.08 189	95.55 183	98.12 56	84.30 202	95.60 132	87.49 199	72.78 207	91.24 158	87.93 196	89.34 186	96.41 165	89.98 182

ACMH	95.26 7	98.75 14	98.93 9	98.54 24	98.86 66	99.01 18	99.58 7	98.10 59	98.67 6	97.30 58	99.18 38	99.42 9	98.40 19	99.19 18	98.86 25	98.99 41	98.19 40
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
FC-MVSNet-train		97.65 57	98.16 31	97.05 102	98.85 67	98.85 25	99.34 13	98.08 60	94.50 97	94.41 156	99.21 36	98.80 63	92.66 152	98.98 26	98.85 26	98.96 45	97.94 49

thisisatest0515		97.82 52	97.67 55	97.99 57	98.49 83	98.07 83	98.48 70	98.06 61	95.35 66	97.74 41	98.83 54	97.61 115	96.74 73	97.53 85	98.30 54	98.43 82	98.01 47

ACMP	94.03 12	97.97 47	97.61 57	98.39 31	99.43 25	98.51 59	98.97 36	98.06 61	94.63 90	96.10 112	96.12 122	99.20 28	98.63 9	98.68 39	98.20 60	99.14 30	97.93 50
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
Gipuma		98.43 20	98.15 32	98.76 14	99.00 60	98.29 67	97.91 99	98.06 61	99.02 3	99.50 1	96.33 117	98.67 71	99.22 1	99.02 24	98.02 69	98.88 56	97.66 62
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
DPE-MVS		97.99 43	98.12 33	97.84 64	98.65 79	98.86 24	98.86 47	98.05 64	94.18 106	95.49 135	98.90 47	99.33 14	97.11 64	98.53 50	98.65 35	98.86 57	98.39 34

MAR-MVS		95.51 130	94.49 146	96.71 116	97.92 124	96.40 154	96.72 154	98.04 65	86.74 190	96.72 84	92.52 176	95.14 152	94.02 136	96.81 109	96.54 109	96.85 151	97.25 86
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
EIA-MVS		96.23 116	94.85 138	97.84 64	99.08 55	98.21 69	97.69 109	98.03 66	85.68 196	98.09 30	91.75 183	97.07 128	95.66 108	97.58 84	97.72 77	98.47 77	95.91 126

TransMVSNet (Re)		98.23 25	98.72 15	97.66 72	98.22 102	98.73 42	98.66 60	98.03 66	98.60 7	96.40 99	99.60 12	98.24 94	95.26 113	99.19 18	99.05 17	99.36 19	97.64 63

ACMMP_NAP		98.12 35	98.08 35	98.18 36	99.34 29	98.74 41	98.97 36	98.00 68	95.13 72	96.90 76	97.54 94	99.27 20	97.18 62	98.72 36	98.45 42	98.68 65	98.69 15

CPTT-MVS		97.08 87	96.25 105	98.05 50	99.21 36	98.30 66	98.54 68	97.98 69	94.28 103	95.89 117	89.57 194	98.54 81	98.18 25	97.82 71	97.32 87	98.54 71	97.91 52

IterMVS-LS		96.35 110	95.85 119	96.93 108	97.53 149	98.00 90	97.37 128	97.97 70	95.49 63	96.71 87	98.94 45	93.23 164	94.82 122	93.15 175	95.05 144	97.17 141	97.12 90
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
canonicalmvs		97.11 84	96.88 88	97.38 86	98.34 90	98.72 44	97.52 120	97.94 71	95.60 53	95.01 147	94.58 151	94.50 157	96.59 79	97.84 70	98.03 68	98.90 50	98.91 7

Anonymous202405211			97.39 63		98.85 67	98.59 50	97.89 102	97.93 72	94.41 100		97.37 98	96.99 130	93.09 147	98.61 44	98.46 40	99.11 33	97.27 84

LS3D		97.93 49	97.80 47	98.08 44	99.20 37	98.77 33	98.89 44	97.92 73	96.59 28	96.99 74	96.71 110	97.14 127	96.39 89	99.04 23	98.96 19	99.10 35	97.39 79

ETV-MVS		97.11 84	96.30 104	98.05 50	99.13 47	97.45 120	98.56 66	97.90 74	91.91 141	97.30 58	95.59 132	95.27 150	96.52 85	98.45 53	98.53 37	98.90 50	96.88 99

FMVSNet2		95.77 126	96.20 109	95.27 157	96.77 173	98.18 72	97.28 133	97.90 74	93.12 125	91.37 189	98.25 78	96.05 144	90.04 173	94.96 151	95.94 127	98.28 86	96.90 95

Effi-MVS+-dtu		95.94 123	95.08 132	96.94 107	98.54 82	97.38 121	96.66 157	97.89 76	88.68 172	95.92 115	92.90 173	97.28 122	94.18 134	96.68 115	96.13 122	98.45 78	96.51 111

TSAR-MVS + ACMM		97.54 63	97.79 48	97.26 91	98.23 100	98.10 82	97.71 108	97.88 77	95.97 43	95.57 134	98.71 63	98.57 80	97.36 56	97.74 74	96.81 100	96.83 154	98.59 21

SMA-MVS		98.13 34	98.22 29	98.02 55	99.44 24	98.73 42	98.24 82	97.87 78	95.22 68	96.76 83	98.66 65	99.35 13	97.03 67	98.53 50	98.39 46	98.80 61	98.69 15

Vis-MVSNet (Re-imp)		96.29 112	96.50 100	96.05 136	97.96 122	97.83 100	97.30 132	97.86 79	93.14 124	88.90 199	96.80 108	95.28 149	95.15 115	98.37 59	98.25 56	99.12 32	95.84 127

ACMH+	94.90 8	98.40 21	98.71 16	98.04 52	98.93 63	98.84 26	99.30 15	97.86 79	97.78 13	94.19 163	98.77 59	99.39 11	98.61 11	99.33 13	99.07 14	99.33 22	97.81 54

Anonymous20231211		97.49 70	97.91 41	97.00 104	98.31 96	98.72 44	98.27 79	97.84 81	94.76 85	94.77 150	98.14 81	98.38 89	93.60 141	98.96 27	98.66 34	99.22 28	97.77 57

UGNet		96.79 99	97.82 46	95.58 150	97.57 148	98.39 64	98.48 70	97.84 81	95.85 47	94.68 151	97.91 85	99.07 34	87.12 192	97.71 75	97.51 79	97.80 111	98.29 35
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
MVS_0304		97.18 83	96.84 91	97.58 75	99.15 42	98.19 71	98.11 87	97.81 83	92.36 135	98.06 31	97.43 96	99.06 35	94.24 132	96.80 110	96.54 109	98.12 99	97.52 72

SD-MVS		97.84 50	97.78 50	97.90 59	98.33 91	98.06 84	97.95 96	97.80 84	96.03 41	96.72 84	97.57 92	99.18 29	97.50 51	97.88 69	97.08 92	99.11 33	98.68 17
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
OPM-MVS		98.01 39	98.01 38	98.00 56	99.11 50	98.12 79	98.68 59	97.72 85	96.65 27	96.68 88	98.40 74	99.28 19	97.44 53	98.20 63	97.82 76	98.40 83	97.58 68

tfpnnormal		97.66 56	97.79 48	97.52 81	98.32 93	98.53 57	98.45 73	97.69 86	97.59 16	96.12 111	97.79 88	96.70 132	95.69 105	98.35 60	98.34 50	98.85 58	97.22 89

COLMAP_ROB	96.84 2	98.75 14	98.82 13	98.66 19	99.14 45	98.79 31	99.30 15	97.67 87	98.33 8	97.82 38	99.20 37	99.18 29	98.76 6	99.27 15	98.96 19	99.29 26	98.03 45
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
TSAR-MVS + GP.		97.26 80	97.33 65	97.18 96	98.21 103	98.06 84	96.38 163	97.66 88	93.92 114	95.23 139	98.48 70	98.33 90	97.41 54	97.63 83	97.35 83	98.18 95	97.57 69

PVSNet_Blended_VisFu		97.44 73	97.14 75	97.79 68	99.15 42	98.44 62	98.32 77	97.66 88	93.74 117	97.73 42	98.79 56	96.93 131	95.64 110	97.69 76	96.91 97	98.25 91	97.50 74

Vis-MVSNet		98.01 39	98.42 24	97.54 78	96.89 170	98.82 29	99.14 22	97.59 90	96.30 34	97.04 71	99.26 35	98.83 59	96.01 99	98.73 34	98.21 57	98.58 70	98.75 12
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
GBi-Net		95.21 137	95.35 124	95.04 162	96.77 173	98.18 72	97.28 133	97.58 91	88.43 177	90.28 193	96.01 124	92.43 167	90.04 173	97.67 78	97.86 73	98.28 86	96.90 95

test1		95.21 137	95.35 124	95.04 162	96.77 173	98.18 72	97.28 133	97.58 91	88.43 177	90.28 193	96.01 124	92.43 167	90.04 173	97.67 78	97.86 73	98.28 86	96.90 95

FMVSNet3		94.06 160	93.85 154	94.31 174	95.46 197	97.80 104	96.34 164	97.58 91	88.43 177	90.28 193	96.01 124	92.43 167	88.67 185	91.82 184	93.96 160	97.53 122	96.50 112

test20.03		96.08 118	96.80 93	95.25 159	99.19 38	97.58 111	97.24 137	97.56 94	94.95 79	91.91 187	98.58 67	98.03 102	87.88 188	97.43 88	96.94 96	97.69 117	94.05 157

NCCC		96.56 108	95.68 120	97.59 74	99.04 58	97.54 116	97.67 110	97.56 94	94.84 82	96.10 112	87.91 198	98.09 99	96.98 69	97.20 96	96.80 101	98.21 93	97.38 82

CANet		96.81 97	96.50 100	97.17 97	99.10 52	97.96 93	97.86 104	97.51 96	91.30 145	97.75 40	97.64 90	97.89 107	93.39 145	96.98 106	96.73 102	97.40 130	96.99 92

PatchT		91.40 180	88.54 185	94.74 166	91.48 210	92.18 188	97.42 126	97.51 96	84.96 198	96.44 95	94.16 158	75.47 203	92.92 148	90.22 193	92.22 174	92.66 189	90.56 179

EPNet_dtu		93.45 167	92.51 168	94.55 170	98.39 89	91.67 195	95.46 185	97.50 98	86.56 191	97.38 54	93.52 166	94.20 161	85.82 197	93.31 172	92.53 173	92.72 186	95.76 132
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PHI-MVS		97.44 73	97.17 72	97.74 71	98.14 109	98.41 63	98.03 92	97.50 98	92.07 139	98.01 33	97.33 100	98.62 77	96.02 98	98.34 62	98.21 57	98.76 63	97.24 88

CSCG		98.45 18	98.61 18	98.26 35	99.11 50	99.06 14	98.17 85	97.49 100	97.93 12	97.37 55	98.88 49	99.29 16	98.10 27	98.40 55	97.51 79	99.32 24	99.16 3

tfpn200view9		93.80 165	91.75 175	96.20 134	97.52 150	98.15 77	97.48 123	97.47 101	87.65 183	93.56 174	83.03 205	84.12 184	92.62 153	97.04 101	98.09 64	98.52 74	94.17 154

CDPH-MVS		96.68 103	95.99 114	97.48 82	99.13 47	97.64 108	98.08 88	97.46 102	90.56 155	95.13 142	94.87 145	98.27 93	96.56 82	97.09 100	96.45 112	98.54 71	97.08 91

HyFIR lowres test		95.05 140	93.54 156	96.81 114	97.81 136	96.88 141	98.18 83	97.46 102	94.28 103	94.98 148	96.57 113	92.89 166	96.15 96	90.90 192	91.87 177	96.28 166	91.35 174

thres600view7		94.34 154	92.31 170	96.70 117	98.19 105	98.12 79	97.85 105	97.45 104	91.49 143	93.98 167	84.27 201	82.02 190	94.24 132	97.04 101	98.76 29	98.49 75	94.47 151

thres200		93.98 163	91.90 174	96.40 131	97.66 141	98.12 79	97.20 138	97.45 104	90.16 161	93.82 168	83.08 204	83.74 186	93.80 138	97.04 101	97.48 81	98.49 75	93.70 161

v2v482		97.33 77	96.84 91	97.90 59	98.19 105	97.83 100	98.74 55	97.44 106	95.42 64	98.23 28	99.46 21	98.84 58	97.46 52	95.51 142	96.10 123	97.36 133	94.72 146

v1144		97.51 67	97.05 81	98.04 52	98.26 98	97.98 91	98.88 45	97.42 107	95.38 65	98.56 17	99.59 14	99.01 41	97.65 47	95.77 135	96.06 125	97.47 125	95.56 137

tpm		89.84 189	86.81 196	93.36 179	96.60 176	91.92 193	95.02 192	97.39 108	86.79 189	96.54 91	95.03 139	69.70 211	87.66 189	88.79 195	86.19 192	86.95 206	89.27 186

gm-plane-assit		91.85 177	87.91 188	96.44 130	99.14 45	98.25 68	99.02 28	97.38 109	95.57 56	98.31 24	99.34 30	51.00 216	88.93 182	93.16 174	91.57 178	95.85 170	86.50 196

train_agg		96.68 103	95.93 117	97.56 76	99.08 55	97.16 130	98.44 75	97.37 110	91.12 149	95.18 141	95.43 133	98.48 85	97.36 56	96.48 118	95.52 137	97.95 108	97.34 83

thisisatest0530		94.81 146	93.06 162	96.85 113	98.01 116	97.18 129	96.93 148	97.36 111	89.73 166	95.80 121	94.98 142	77.88 199	94.89 119	96.73 112	97.35 83	98.13 98	97.54 70

tttt0517		94.81 146	93.04 163	96.88 112	98.15 108	97.37 122	96.99 145	97.36 111	89.51 168	95.74 124	94.89 144	77.53 201	94.89 119	96.94 107	97.35 83	98.17 96	97.70 60

anonymousdsp		98.85 12	98.88 11	98.83 11	98.69 77	98.20 70	99.68 1	97.35 113	97.09 21	98.98 9	99.86 1	99.43 8	98.94 3	99.28 14	99.19 13	99.33 22	99.08 4

MCST-MVS		96.79 99	96.08 111	97.62 73	98.78 72	97.52 117	98.01 94	97.32 114	93.20 122	95.84 119	93.97 162	98.12 98	97.34 58	96.34 122	95.88 130	98.45 78	97.51 73

AdaColmap		95.85 125	94.65 142	97.26 91	98.70 74	97.20 127	97.33 131	97.30 115	91.28 147	95.90 116	88.16 196	96.17 141	96.60 78	97.34 91	96.82 99	97.71 114	95.60 136

v1192		97.52 66	97.03 82	98.09 40	98.31 96	98.01 89	98.96 39	97.25 116	95.22 68	98.89 11	99.64 9	98.83 59	97.68 46	95.63 138	95.91 128	97.47 125	95.97 125

v144192		97.49 70	96.99 85	98.07 47	98.11 112	97.95 94	99.02 28	97.21 117	94.90 81	98.88 12	99.53 17	98.89 53	97.75 42	95.59 139	95.90 129	97.43 128	96.16 119

CNVR-MVS		97.03 90	96.77 95	97.34 87	98.89 64	97.67 107	97.64 113	97.17 118	94.40 101	95.70 128	94.02 160	98.76 68	96.49 87	97.78 73	97.29 90	98.12 99	97.47 75

DeepC-MVS_fast	95.38 6	97.53 65	97.30 66	97.79 68	98.83 70	97.64 108	98.18 83	97.14 119	95.57 56	97.83 37	97.10 105	98.80 63	96.53 84	97.41 89	97.32 87	98.24 92	97.26 85
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MVS_Test		95.34 136	94.88 137	95.89 141	96.93 169	96.84 145	96.66 157	97.08 120	90.06 163	94.02 165	97.61 91	96.64 133	93.59 142	92.73 179	94.02 159	97.03 147	96.24 116

v1921920		97.50 69	97.00 83	98.07 47	98.20 104	97.94 97	99.03 27	97.06 121	95.29 67	99.01 8	99.62 11	98.73 70	97.74 43	95.52 141	95.78 133	97.39 131	96.12 121

v1240		97.43 75	96.87 90	98.09 40	98.25 99	97.92 98	99.02 28	97.06 121	94.77 84	99.09 7	99.68 6	98.51 83	97.78 41	95.25 146	95.81 131	97.32 135	96.13 120

PMVS	90.51 17	97.77 54	97.98 39	97.53 79	98.68 78	98.14 78	97.67 110	97.03 123	96.43 29	98.38 22	98.72 62	97.03 129	94.44 128	99.37 12	99.30 10	98.98 42	96.86 100
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
v148		96.99 92	96.70 97	97.34 87	97.89 126	97.23 126	98.33 76	96.96 124	95.57 56	97.12 68	98.99 43	99.40 10	97.23 61	96.22 127	95.45 138	96.50 161	94.02 158

MSLP-MVS++		96.66 105	96.46 103	96.89 111	98.02 115	97.71 106	95.57 179	96.96 124	94.36 102	96.19 109	91.37 186	98.24 94	97.07 65	97.69 76	97.89 71	97.52 123	97.95 48

thres400		94.04 161	91.94 173	96.50 127	97.98 121	97.82 102	97.66 112	96.96 124	90.96 150	94.20 161	83.24 203	82.82 188	93.80 138	96.50 117	98.09 64	98.38 84	94.15 155

pmmvs-eth3d		96.84 96	96.22 107	97.56 76	97.63 145	96.38 157	98.74 55	96.91 127	94.63 90	98.26 25	99.43 23	98.28 92	96.58 81	94.52 156	95.54 136	97.24 137	94.75 145

TSAR-MVS + MP.		98.15 31	98.23 28	98.06 49	98.47 84	98.16 76	99.23 18	96.87 128	95.58 55	96.72 84	98.41 73	99.06 35	98.05 30	98.99 25	98.90 22	99.00 39	98.51 27
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
MVS_111021_HR		97.27 79	97.11 79	97.46 84	98.46 85	97.82 102	97.50 121	96.86 129	94.97 77	97.13 67	96.99 106	98.39 87	96.82 72	97.65 81	97.38 82	98.02 103	96.56 109

MDTV_nov1_ep13_2view		94.39 153	93.34 159	95.63 148	97.23 164	95.33 171	97.76 106	96.84 130	94.55 93	97.47 49	98.96 44	97.70 111	93.88 137	92.27 181	86.81 191	90.56 193	87.73 192

EG-PatchMatch MVS		97.98 45	97.92 40	98.04 52	98.84 69	98.04 87	97.90 100	96.83 131	95.07 74	98.79 14	99.07 41	99.37 12	97.88 38	98.74 33	98.16 61	98.01 104	96.96 93

casdiffmvs		97.00 91	97.36 64	96.59 122	97.65 142	97.98 91	98.06 89	96.81 132	95.78 49	92.77 186	99.40 26	99.26 23	95.65 109	96.70 113	96.39 114	98.59 69	95.99 124

DI_MVS_plusplus_trai		95.48 131	94.51 144	96.61 120	97.13 165	97.30 123	98.05 91	96.79 133	93.75 116	95.08 145	96.38 116	89.76 178	94.95 118	93.97 166	94.82 150	97.64 121	95.63 135

3Dnovator	96.31 3	97.22 82	97.19 71	97.25 94	98.14 109	97.95 94	98.03 92	96.77 134	96.42 30	97.14 65	95.11 138	97.59 116	95.14 117	97.79 72	97.72 77	98.26 89	97.76 59

QAPM		97.04 89	97.14 75	96.93 108	97.78 140	98.02 88	97.36 130	96.72 135	94.68 88	96.23 105	97.21 102	97.68 112	95.70 104	97.37 90	97.24 91	97.78 113	97.77 57

PatchmatchNet		89.98 187	86.23 200	94.36 173	96.56 177	91.90 194	96.07 171	96.72 135	90.18 160	96.87 78	93.36 170	78.06 198	91.46 156	84.71 205	81.40 203	88.45 201	83.97 204
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
testgi		94.81 146	96.05 113	93.35 180	99.06 57	96.87 143	97.57 118	96.70 137	95.77 50	88.60 201	93.19 171	98.87 55	81.21 206	97.03 104	96.64 106	96.97 150	93.99 159

HQP-MVS		95.97 122	95.01 135	97.08 99	98.72 73	97.19 128	97.07 143	96.69 138	91.49 143	95.77 123	92.19 179	97.93 105	96.15 96	94.66 153	94.16 155	98.10 101	97.45 76

PLC	92.55 15	96.10 117	95.36 123	96.96 105	98.13 111	96.88 141	96.49 161	96.67 139	94.07 110	95.71 127	91.14 187	96.09 142	96.84 71	96.70 113	96.58 108	97.92 109	96.03 122
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
Anonymous20231206		95.69 129	95.68 120	95.70 146	98.32 93	96.95 139	97.37 128	96.65 140	93.33 120	93.61 172	98.70 64	98.03 102	91.04 161	95.07 149	94.59 153	97.20 139	93.09 168

PCF-MVS	92.69 14	95.98 121	95.05 133	97.06 101	98.43 87	97.56 114	97.76 106	96.65 140	89.95 164	95.70 128	96.18 121	98.48 85	95.74 103	93.64 167	93.35 168	98.09 102	96.18 118
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
ET-MVSNet_ETH3D		93.18 169	90.80 180	95.95 139	96.05 184	96.07 164	96.92 149	96.51 142	89.34 169	95.63 130	94.08 159	72.31 210	93.13 146	94.33 160	94.83 148	97.44 127	94.65 147

CDS-MVSNet		94.91 143	95.17 129	94.60 169	97.85 128	96.21 161	96.90 151	96.39 143	90.81 152	93.40 176	97.24 101	94.54 156	85.78 198	96.25 125	96.15 119	97.26 136	95.01 143
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
baseline1		93.89 164	92.82 166	95.14 161	97.62 146	96.97 138	96.12 170	96.36 144	91.30 145	91.53 188	94.68 148	80.72 192	90.80 165	95.71 136	96.29 116	98.44 81	94.09 156

MVS-HIRNet		88.72 195	86.49 197	91.33 196	91.81 208	85.66 208	87.02 211	96.25 145	81.48 209	94.82 149	96.31 119	92.14 170	90.32 170	87.60 197	83.82 195	87.74 203	78.42 208

TinyColmap		96.64 106	96.07 112	97.32 89	97.84 133	96.40 154	97.63 115	96.25 145	95.86 46	98.98 9	97.94 84	96.34 139	96.17 95	97.30 93	95.38 141	97.04 146	93.24 165

abl_6				96.45 129	97.79 139	97.28 124	97.16 141	96.16 147	89.92 165	95.72 126	91.59 184	97.16 126	94.37 130			97.51 124	95.49 138

pmmvs4		95.37 135	94.25 147	96.67 119	97.01 168	95.28 172	97.60 116	96.07 148	93.11 126	97.29 60	98.09 83	94.23 160	95.21 114	91.56 187	93.91 161	96.82 156	93.59 163

OpenMVS	94.63 9	95.75 127	95.04 134	96.58 123	97.85 128	97.55 115	96.71 155	96.07 148	90.15 162	96.47 94	90.77 192	95.95 145	94.41 129	97.01 105	96.95 95	98.00 105	96.90 95

IterMVS-SCA-FT		95.16 139	93.95 151	96.56 125	97.89 126	96.69 147	96.94 147	96.05 150	93.06 129	97.35 56	98.79 56	91.45 172	95.93 101	92.78 177	91.00 181	95.22 174	93.91 160

V42		97.10 86	96.97 86	97.26 91	97.64 143	97.60 110	98.45 73	95.99 151	94.44 99	97.35 56	99.40 26	98.63 76	97.34 58	96.33 124	96.38 115	96.82 156	96.00 123

DPM-MVS		94.86 144	93.90 153	95.99 138	98.19 105	96.52 149	96.29 168	95.95 152	93.11 126	94.61 153	88.17 195	96.44 137	93.77 140	93.33 170	93.54 166	97.11 143	96.22 117

dps		88.36 197	84.32 204	93.07 182	93.86 204	92.29 187	94.89 195	95.93 153	83.50 204	93.13 180	91.87 182	67.79 213	90.32 170	85.99 202	83.22 199	90.28 196	85.56 197

DELS-MVS		96.90 93	97.24 69	96.50 127	97.85 128	98.18 72	97.88 103	95.92 154	93.48 119	95.34 137	98.86 53	98.94 50	94.03 135	97.33 92	97.04 93	98.00 105	96.85 101
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
GA-MVS		94.18 157	92.98 164	95.58 150	97.36 157	96.42 152	96.21 169	95.86 155	90.29 158	95.08 145	96.19 120	85.37 182	92.82 151	94.01 165	94.14 156	96.16 168	94.41 153

CostFormer		89.06 194	85.65 201	93.03 185	95.88 188	92.40 186	95.30 189	95.86 155	86.49 193	93.12 182	93.40 169	74.18 205	88.25 186	82.99 206	81.46 202	89.77 197	88.66 189

EPMVS		89.28 192	86.28 198	92.79 186	96.01 185	92.00 192	95.83 175	95.85 157	90.78 153	91.00 191	94.58 151	74.65 204	88.93 182	85.00 203	82.88 201	89.09 200	84.09 203

TSAR-MVS + COLMAP		96.05 119	95.94 116	96.18 135	97.46 155	96.41 153	97.26 136	95.83 158	94.69 87	95.30 138	98.31 75	96.52 135	94.71 124	95.48 143	94.87 146	96.54 160	95.33 140

test-LLR		89.77 190	87.47 192	92.45 188	98.01 116	89.77 202	93.25 204	95.80 159	81.56 207	89.19 197	92.08 180	79.59 195	85.77 200	91.47 189	89.04 189	92.69 187	88.75 187

test0.0.03 1		91.17 182	91.50 176	90.80 198	98.01 116	95.46 170	94.22 198	95.80 159	86.55 192	81.75 209	90.83 190	87.93 179	78.48 207	94.51 157	94.11 158	96.50 161	91.08 176

RPSCF		97.83 51	98.27 26	97.31 90	98.23 100	98.06 84	97.44 125	95.79 161	96.90 23	95.81 120	98.76 60	98.61 78	97.70 45	98.90 30	98.36 49	98.90 50	98.29 35

OMC-MVS		97.23 81	97.21 70	97.25 94	97.85 128	97.52 117	97.92 98	95.77 162	95.83 48	97.09 70	97.86 86	98.52 82	96.62 77	97.51 86	96.65 105	98.26 89	96.57 107

USDC		96.30 111	95.64 122	97.07 100	97.62 146	96.35 159	97.17 140	95.71 163	95.52 61	99.17 6	98.11 82	97.46 118	95.67 106	95.44 144	93.60 164	97.09 144	92.99 169

MVSTER		91.97 175	90.31 181	93.91 175	96.81 171	96.91 140	94.22 198	95.64 164	84.98 197	92.98 184	93.42 167	72.56 208	86.64 196	95.11 148	93.89 162	97.16 142	95.31 141

CANet_DTU		94.96 142	94.62 143	95.35 154	98.03 114	96.11 162	96.92 149	95.60 165	88.59 174	97.27 61	95.27 136	96.50 136	88.77 184	95.53 140	95.59 135	95.54 172	94.78 144

CHOSEN 1792x2688		94.98 141	94.69 140	95.31 155	97.27 162	95.58 169	97.90 100	95.56 166	95.03 75	93.77 171	95.65 130	99.29 16	95.30 112	91.51 188	91.28 180	92.05 191	94.50 150

MVS_111021_LR		96.86 94	96.72 96	97.03 103	97.80 137	97.06 137	97.04 144	95.51 167	94.55 93	97.47 49	97.35 99	97.68 112	96.66 75	97.11 99	96.73 102	97.69 117	96.57 107

DeepPCF-MVS	94.55 10	97.05 88	97.13 78	96.95 106	96.06 183	97.12 134	98.01 94	95.44 168	95.18 70	97.50 48	97.86 86	98.08 100	97.31 60	97.23 94	97.00 94	97.36 133	97.45 76

MDTV_nov1_ep13		90.30 186	87.32 194	93.78 176	96.00 186	92.97 183	95.46 185	95.39 169	88.61 173	95.41 136	94.45 156	80.39 193	89.87 176	86.58 199	83.54 198	90.56 193	84.71 200

ADS-MVSNet		89.89 188	87.70 189	92.43 189	95.52 194	90.91 198	95.57 179	95.33 170	93.19 123	91.21 190	93.41 168	82.12 189	89.05 180	86.21 200	83.77 196	87.92 202	84.31 201

tpm cat1		87.19 199	82.78 206	92.33 190	95.66 191	90.61 199	94.19 200	95.27 171	86.97 187	94.38 157	90.91 189	69.40 212	87.21 191	79.57 209	77.82 206	87.25 205	84.18 202

PatchMatch-RL		94.79 149	93.75 155	96.00 137	96.80 172	95.00 174	95.47 184	95.25 172	90.68 154	95.80 121	92.97 172	93.64 162	95.67 106	96.13 129	95.81 131	96.99 149	92.01 172

diffmvs		95.86 124	96.21 108	95.44 153	97.25 163	96.85 144	96.99 145	95.23 173	94.96 78	92.82 185	98.89 48	98.85 56	93.52 143	94.21 162	94.25 154	96.84 153	95.49 138

thres100view900		92.93 170	90.89 179	95.31 155	97.52 150	96.82 146	96.41 162	95.08 174	87.65 183	93.56 174	83.03 205	84.12 184	91.12 160	94.53 154	96.91 97	98.17 96	93.21 166

baseline2		92.06 174	89.82 183	94.68 168	97.32 158	95.72 167	94.97 194	95.08 174	84.75 199	94.34 160	90.68 193	77.75 200	90.13 172	93.38 168	93.58 165	96.25 167	92.90 170

PVSNet_BlendedMVS		95.44 133	95.09 130	95.86 142	97.31 160	97.13 132	96.31 166	95.01 176	88.55 175	96.23 105	94.55 154	97.75 109	92.56 154	96.42 119	95.44 139	97.71 114	95.81 128

PVSNet_Blended		95.44 133	95.09 130	95.86 142	97.31 160	97.13 132	96.31 166	95.01 176	88.55 175	96.23 105	94.55 154	97.75 109	92.56 154	96.42 119	95.44 139	97.71 114	95.81 128

pmmvs5		95.70 128	95.22 127	96.26 133	96.55 178	97.24 125	97.50 121	94.99 178	90.95 151	96.87 78	98.47 71	97.40 119	94.45 127	92.86 176	94.98 145	97.23 138	94.64 148

CNLPA		96.24 114	95.97 115	96.57 124	97.48 154	97.10 136	96.75 153	94.95 179	94.92 80	96.20 108	94.81 146	96.61 134	96.25 91	96.94 107	95.64 134	97.79 112	95.74 133

MDA-MVSNet-bldmvs		95.45 132	95.20 128	95.74 145	94.24 202	96.38 157	97.93 97	94.80 180	95.56 59	96.87 78	98.29 76	95.24 151	96.50 86	98.65 42	90.38 183	94.09 178	91.93 173

FMVSNet5		89.65 191	87.60 191	92.04 191	95.63 193	96.61 148	94.82 196	94.75 181	80.11 210	87.72 204	77.73 209	73.81 206	83.81 204	95.64 137	96.08 124	95.49 173	93.21 166

tpmrst		87.60 198	84.13 205	91.66 194	95.65 192	89.73 204	93.77 201	94.74 182	88.85 171	93.35 179	95.60 131	72.37 209	87.40 190	81.24 208	78.19 205	85.02 209	82.90 207

IterMVS		94.48 151	93.46 158	95.66 147	97.52 150	96.43 151	97.20 138	94.73 183	92.91 132	96.44 95	98.75 61	91.10 174	94.53 126	92.10 183	90.10 185	93.51 181	92.84 171
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IB-MVS	92.44 16	93.33 168	92.15 172	94.70 167	97.42 156	96.39 156	95.57 179	94.67 184	86.40 194	93.59 173	78.28 208	95.76 147	89.59 178	95.88 134	95.98 126	97.39 131	96.34 114
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
PM-MVS		96.85 95	96.62 99	97.11 98	97.13 165	96.51 150	98.29 78	94.65 185	94.84 82	98.12 29	98.59 66	97.20 124	97.41 54	96.24 126	96.41 113	97.09 144	96.56 109

CHOSEN 280x420		91.55 179	90.27 182	93.05 183	94.61 200	88.01 207	96.56 159	94.62 186	88.04 181	94.20 161	92.66 174	86.60 180	90.82 163	95.06 150	91.89 176	87.49 204	89.61 184

TAPA-MVS	93.96 13	96.79 99	96.70 97	96.90 110	97.64 143	97.58 111	97.54 119	94.50 187	95.14 71	96.64 89	96.76 109	97.90 106	96.63 76	95.98 132	96.14 120	98.45 78	97.39 79
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MSDG		96.27 113	96.17 110	96.38 132	97.85 128	96.27 160	96.55 160	94.41 188	94.55 93	95.62 131	97.56 93	97.80 108	96.22 93	97.17 98	96.27 117	97.67 119	93.60 162

new-patchmatchnet		94.48 151	94.02 149	95.02 164	97.51 153	95.00 174	95.68 178	94.26 189	97.32 18	95.73 125	99.60 12	98.22 97	91.30 157	94.13 163	84.41 193	95.65 171	89.45 185

MS-PatchMatch		94.84 145	94.76 139	94.94 165	96.38 179	94.69 177	95.90 173	94.03 190	92.49 133	93.81 169	95.79 129	96.38 138	94.54 125	94.70 152	94.85 147	94.97 176	94.43 152

N_pmnet		92.46 171	92.38 169	92.55 187	97.91 125	93.47 180	97.42 126	94.01 191	96.40 32	88.48 202	98.50 69	98.07 101	88.14 187	91.04 191	84.30 194	89.35 199	84.85 199

baseline		94.07 159	94.50 145	93.57 178	96.34 180	93.40 181	95.56 182	92.39 192	92.07 139	94.00 166	98.24 79	97.51 117	89.19 179	91.75 185	92.72 172	93.96 180	95.79 130

CLD-MVS		96.73 102	96.92 87	96.51 126	98.70 74	97.57 113	97.64 113	92.07 193	93.10 128	96.31 104	98.29 76	99.02 40	95.99 100	97.20 96	96.47 111	98.37 85	96.81 102
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
FPMVS		94.70 150	94.99 136	94.37 171	95.84 189	93.20 182	96.00 172	91.93 194	95.03 75	94.64 152	94.68 148	93.29 163	90.95 162	98.07 67	97.34 86	96.85 151	93.29 164

EU-MVSNet		96.03 120	96.23 106	95.80 144	95.48 196	94.18 178	98.99 33	91.51 195	97.22 19	97.66 44	99.15 39	98.51 83	98.08 28	95.92 133	92.88 171	93.09 184	95.72 134

DWT-MVSNet_training		86.69 201	81.24 207	93.05 183	95.31 198	92.06 191	95.75 176	91.51 195	84.32 201	94.49 155	83.46 202	55.37 215	90.81 164	82.76 207	83.19 200	90.45 195	87.52 193

TAMVS		92.46 171	93.34 159	91.44 195	97.03 167	93.84 179	94.68 197	90.60 197	90.44 157	85.31 207	97.14 103	93.03 165	85.78 198	94.34 159	93.67 163	95.22 174	90.93 177

MVE	72.99 18	85.37 204	89.43 184	80.63 204	74.43 212	71.94 213	88.25 210	89.81 198	93.27 121	67.32 211	96.32 118	91.83 171	90.40 169	93.36 169	90.79 182	73.55 211	88.49 190
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
CVMVSNet		94.01 162	94.25 147	93.73 177	94.36 201	92.44 185	97.45 124	88.56 199	95.59 54	93.06 183	98.88 49	90.03 177	94.84 121	94.08 164	93.45 167	94.09 178	95.31 141

CMPMVS	71.81 19	92.34 173	92.85 165	91.75 193	92.70 206	90.43 200	88.84 209	88.56 199	85.87 195	94.35 158	90.98 188	95.89 146	91.14 159	96.14 128	94.83 148	94.93 177	95.78 131
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
E-PMN		86.94 200	85.10 202	89.09 203	95.77 190	83.54 211	89.89 208	86.55 201	92.18 137	87.34 205	94.02 160	83.42 187	89.63 177	93.32 171	77.11 207	85.33 207	72.09 209

PMMVS2		86.47 203	92.62 167	79.29 205	92.01 207	85.63 209	93.74 202	86.37 202	93.95 113	54.18 213	98.19 80	97.39 120	58.46 208	96.57 116	93.07 169	90.99 192	83.55 206

EMVS		86.63 202	84.48 203	89.15 202	95.51 195	83.66 210	90.19 207	86.14 203	91.78 142	88.68 200	93.83 164	81.97 191	89.05 180	92.76 178	76.09 208	85.31 208	71.28 210

PMMVS		91.67 178	91.47 177	91.91 192	89.43 211	88.61 206	94.99 193	85.67 204	87.50 185	93.80 170	94.42 157	94.88 153	90.71 166	92.26 182	92.96 170	96.83 154	89.65 183

test-mter		89.16 193	88.14 187	90.37 199	94.79 199	91.05 197	93.60 203	85.26 205	81.65 206	88.32 203	92.22 178	79.35 197	87.03 193	92.28 180	90.12 184	93.19 183	90.29 181

TESTMET0.1,1		88.60 196	87.47 192	89.93 200	94.23 203	89.77 202	93.25 204	84.47 206	81.56 207	89.19 197	92.08 180	79.59 195	85.77 200	91.47 189	89.04 189	92.69 187	88.75 187

new_pmnet		90.85 184	92.26 171	89.21 201	93.68 205	89.05 205	93.20 206	84.16 207	92.99 130	84.25 208	97.72 89	94.60 155	86.80 195	93.20 173	91.30 179	93.21 182	86.94 195

pmmvs3		91.20 181	91.40 178	90.96 197	91.71 209	91.08 196	95.41 188	81.34 208	87.36 186	94.57 154	95.02 140	94.30 159	90.42 168	94.28 161	89.26 187	92.30 190	88.49 190

DeepMVS_CX						72.99 212	80.14 212	37.34 209	83.46 205	60.13 212	84.40 200	85.48 181	86.93 194	87.22 198		79.61 210	87.32 194

tmp_tt				45.72 206	60.00 213	38.74 214	45.50 214	12.18 210	79.58 211	68.42 210	67.62 210	65.04 214	22.12 209	84.83 204	78.72 204	66.08 212

testmvs		4.99 206	6.88 208	2.78 209	1.73 214	2.04 216	3.10 216	1.71 211	7.27 212	3.92 216	12.18 211	6.71 217	3.31 211	6.94 210	5.51 210	2.94 213	7.51 211

test123		4.41 207	5.71 209	2.88 208	1.28 215	2.21 215	3.09 217	1.65 212	6.35 213	4.98 215	8.53 212	3.88 218	3.46 210	5.79 211	5.71 209	2.85 214	7.50 212

GG-mvs-BLEND		61.03 205	87.02 195	30.71 207	0.74 216	90.01 201	78.90 213	0.74 213	84.56 200	9.46 214	79.17 207	90.69 176	1.37 212	91.74 186	89.13 188	93.04 185	83.83 205

sosnet-low-res		0.00 208	0.00 210	0.00 210	0.00 217	0.00 217	0.00 218	0.00 214	0.00 214	0.00 217	0.00 213	0.00 219	0.00 213	0.00 212	0.00 211	0.00 215	0.00 213

sosnet		0.00 208	0.00 210	0.00 210	0.00 217	0.00 217	0.00 218	0.00 214	0.00 214	0.00 217	0.00 213	0.00 219	0.00 213	0.00 212	0.00 211	0.00 215	0.00 213

our_test_3					97.32 158	95.13 173	97.59 117

test_part1																	98.16 41

ambc			96.78 94		99.01 59	97.11 135	95.73 177		95.91 45	99.25 2	98.56 68	97.17 125	97.04 66	96.76 111	95.22 143	96.72 158	96.73 104

MTAPA										97.43 52		99.27 20

MTMP										97.63 45		99.03 39

Patchmatch-RL test							17.42 215

XVS					99.48 19	98.76 36	99.22 19			96.40 99		98.78 65				98.94 48

X-MVStestdata					99.48 19	98.76 36	99.22 19			96.40 99		98.78 65				98.94 48

mPP-MVS					99.58 6							98.98 43

NP-MVS									89.27 170

High-resolution multi-view benchmark