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.

USDC		92.17 131	92.17 135	92.18 118	92.93 158	92.22 163	93.66 149	87.41 170	93.49 46	97.99 1	94.10 120	96.68 98	86.46 134	92.04 160	89.18 182	94.61 161	87.47 175

Gipuma		95.86 37	96.17 39	95.50 28	95.92 60	94.59 109	94.77 126	92.50 50	97.82 5	97.90 2	95.56 89	97.88 66	94.71 14	98.02 24	94.81 89	97.23 63	94.48 75
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
LTVRE_ROB	95.06 1	97.73 1	98.39 1	96.95 1	96.33 48	96.94 33	98.30 22	94.90 14	98.61 1	97.73 3	97.97 31	98.57 27	95.74 7	99.24 1	98.70 4	98.72 7	98.70 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
TinyColmap		93.17 105	93.33 121	93.00 106	93.84 121	92.76 151	94.75 128	88.90 146	93.97 41	97.48 4	95.28 99	95.29 133	88.37 121	95.31 92	91.58 152	94.65 156	89.10 159

v52		96.35 16	97.40 13	95.12 40	93.83 122	95.54 69	97.82 40	88.95 144	96.27 13	97.22 5	99.11 2	99.40 4	95.80 5	98.16 18	96.37 50	97.10 67	96.96 23

V4		96.35 16	97.40 13	95.12 40	93.83 122	95.54 69	97.82 40	88.95 144	96.27 13	97.21 6	99.10 3	99.40 4	95.79 6	98.17 17	96.37 50	97.10 67	96.96 23

v7n		96.49 14	97.20 17	95.65 23	95.57 71	96.04 54	97.93 36	92.49 51	96.40 11	97.13 7	98.99 4	99.41 3	93.79 28	97.84 33	96.15 56	97.00 72	95.60 54

SixPastTwentyTwo		97.36 4	97.73 9	96.92 2	97.36 13	96.15 52	98.29 23	94.43 22	96.50 10	96.96 8	98.74 7	98.74 19	96.04 3	99.03 5	97.74 16	98.44 22	97.22 12

v1240		93.89 77	93.72 102	94.09 59	93.98 111	94.31 113	97.12 49	89.37 133	90.74 117	96.92 9	98.05 29	97.89 63	92.15 55	91.53 169	91.60 151	94.99 141	91.93 130

v1921920		93.90 76	93.82 97	94.00 65	93.74 129	94.31 113	97.12 49	89.33 135	91.13 106	96.77 10	97.90 33	98.06 54	91.95 56	91.93 165	91.54 153	95.10 135	91.85 131

v748		96.05 28	97.00 19	94.95 46	94.41 95	94.77 102	96.72 67	91.03 91	96.12 15	96.71 11	98.74 7	99.59 1	93.55 31	97.97 28	95.96 60	97.28 60	95.84 49

v1192		93.98 71	93.94 92	94.01 64	93.91 118	94.63 105	97.00 54	89.75 118	91.01 109	96.50 12	97.93 32	98.26 41	91.74 62	92.06 158	92.05 140	95.18 132	91.66 136

v144192		93.89 77	93.85 95	93.94 70	93.50 137	94.33 112	97.12 49	89.49 129	90.89 112	96.49 13	97.78 41	98.27 40	91.89 58	92.17 157	91.70 148	95.19 131	91.78 134

ambc			94.61 77		98.09 5	95.14 87	91.71 188		94.18 37	96.46 14	96.26 77	96.30 106	91.26 76	94.70 105	92.00 143	93.45 177	93.67 87

TDRefinement		97.59 2	98.32 2	96.73 4	95.90 61	98.10 2	99.08 2	93.92 31	98.24 3	96.44 15	98.12 26	97.86 68	96.06 2	99.24 1	98.93 1	99.00 2	97.77 5

anonymousdsp		95.45 45	96.70 25	93.99 66	88.43 214	92.05 167	99.18 1	85.42 194	94.29 35	96.10 16	98.63 12	99.08 10	96.11 1	97.77 36	97.41 29	98.70 8	97.69 6

COLMAP_ROB	93.74 2	97.09 8	97.98 4	96.05 18	95.97 58	97.78 9	98.56 12	91.72 73	97.53 6	96.01 17	98.14 25	98.76 18	95.28 8	98.76 11	98.23 10	98.77 5	96.67 35
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
PHI-MVS		94.65 58	94.84 69	94.44 52	94.95 82	96.55 39	96.46 81	91.10 89	88.96 139	96.00 18	94.55 112	95.32 132	90.67 97	96.97 53	96.69 44	97.44 54	94.84 65

v1144		93.83 81	93.87 93	93.78 77	93.72 130	94.57 110	96.85 61	89.98 111	91.31 102	95.90 19	97.89 34	98.40 35	91.13 82	92.01 161	92.01 142	95.10 135	90.94 140

v11		94.32 66	94.62 76	93.97 67	93.95 114	95.31 80	96.83 63	91.30 84	91.95 80	95.51 20	98.32 20	98.61 25	91.44 70	92.83 139	92.23 135	94.77 150	93.08 101

MTMP										95.43 21		97.25 78

ACMH	90.17 8	96.61 11	97.69 10	95.35 31	95.29 75	96.94 33	98.43 16	92.05 65	98.04 4	95.38 22	98.07 28	99.25 6	93.23 36	98.35 15	97.16 36	97.72 48	96.00 45
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PMVS	87.16 16	95.88 36	96.47 30	95.19 38	97.00 26	96.02 55	96.70 68	91.57 77	94.43 32	95.33 23	97.16 63	95.37 129	92.39 50	98.89 10	98.72 3	98.17 32	94.71 69
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PS-CasMVS		97.22 5	97.84 6	96.50 5	97.08 23	97.92 6	98.17 29	97.02 2	94.71 26	95.32 24	98.52 14	98.97 11	92.91 43	99.04 4	98.47 5	98.49 18	97.24 11

v2v482		93.42 96	93.49 113	93.32 89	93.44 141	94.05 123	96.36 95	89.76 117	91.41 99	95.24 25	97.63 51	98.34 38	90.44 105	91.65 167	91.76 147	94.69 154	89.62 154

CP-MVSNet		96.97 10	97.42 12	96.44 7	97.06 24	97.82 8	98.12 31	96.98 3	93.50 45	95.21 26	97.98 30	98.44 31	92.83 46	98.93 8	98.37 8	98.46 21	96.91 27

v13		94.54 62	94.93 65	94.09 59	93.81 124	95.44 73	96.99 56	91.67 74	92.43 67	95.20 27	98.33 18	98.73 20	91.87 59	93.67 128	92.26 133	95.00 140	93.63 89

DTE-MVSNet		97.16 6	97.75 8	96.47 6	97.40 12	97.95 5	98.20 28	96.89 4	95.30 19	95.15 28	98.66 10	98.80 17	92.77 47	98.97 7	98.27 9	98.44 22	96.28 40

v12		94.44 63	94.79 71	94.02 63	93.75 128	95.37 79	96.92 57	91.61 75	92.21 73	95.10 29	98.27 22	98.69 22	91.73 63	93.49 130	92.15 138	94.97 144	93.37 94

v1		93.48 93	93.57 108	93.37 85	93.48 138	94.18 120	96.41 88	89.61 123	91.46 94	95.03 30	97.82 38	98.43 32	90.95 91	92.00 162	91.37 162	94.75 151	89.70 151

v7		93.65 84	93.73 101	93.57 82	93.38 142	94.60 108	96.83 63	89.92 114	89.69 132	95.02 31	97.89 34	98.24 42	91.27 74	92.38 149	92.18 136	94.99 141	91.12 138

v10		93.96 72	94.12 90	93.77 78	93.37 143	95.45 72	96.83 63	91.13 88	89.70 131	95.02 31	97.88 36	98.23 43	91.27 74	92.39 148	92.18 136	94.99 141	93.00 103

divwei89l23v2f112		93.47 94	93.56 109	93.37 85	93.48 138	94.17 121	96.42 86	89.62 121	91.46 94	95.00 33	97.81 39	98.42 33	90.94 92	92.00 162	91.38 160	94.75 151	89.70 151

V9		94.33 65	94.66 74	93.94 70	93.69 132	95.31 80	96.84 62	91.53 78	92.04 79	95.00 33	98.22 23	98.64 23	91.62 65	93.29 132	92.05 140	94.93 145	93.10 99

v1141		93.47 94	93.56 109	93.36 87	93.48 138	94.17 121	96.42 86	89.62 121	91.44 97	94.99 35	97.81 39	98.42 33	90.94 92	92.00 162	91.38 160	94.74 153	89.69 153

PEN-MVS		97.16 6	97.87 5	96.33 12	97.20 20	97.97 4	98.25 25	96.86 5	95.09 24	94.93 36	98.66 10	99.16 7	92.27 54	98.98 6	98.39 7	98.49 18	96.83 30

DeepC-MVS	92.47 4	96.44 15	96.75 22	96.08 17	97.57 7	97.19 29	97.96 35	94.28 23	95.29 20	94.92 37	98.31 21	96.92 88	93.69 29	96.81 61	96.50 47	98.06 37	96.27 41
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
V14		94.21 69	94.52 79	93.85 73	93.62 134	95.25 84	96.76 66	91.42 79	91.83 83	94.91 38	98.15 24	98.57 27	91.49 69	93.06 137	91.93 144	94.90 146	92.82 106

MTAPA										94.88 39		96.88 90

EG-PatchMatch MVS		94.81 55	95.53 53	93.97 67	95.89 63	94.62 106	95.55 115	88.18 155	92.77 61	94.88 39	97.04 66	98.61 25	93.31 33	96.89 57	95.19 80	95.99 111	93.56 91

v15		94.09 70	94.37 83	93.77 78	93.56 136	95.18 85	96.68 72	91.34 83	91.64 88	94.83 41	98.09 27	98.51 30	91.37 72	92.84 138	91.80 146	94.85 147	92.53 118

zzz-MVS		96.18 22	96.01 43	96.38 8	98.30 2	96.18 51	98.51 14	94.48 21	94.56 28	94.81 42	91.73 147	96.96 86	94.30 22	98.09 20	97.83 15	97.91 43	96.73 32

LS3D		95.83 39	96.35 33	95.22 37	96.47 45	97.49 15	97.99 32	92.35 54	94.92 25	94.58 43	94.88 108	95.11 138	91.52 68	98.48 13	98.05 12	98.42 24	95.49 55

Effi-MVS+		92.93 112	92.16 136	93.83 74	94.29 98	93.53 142	95.04 121	92.98 46	85.27 178	94.46 44	90.24 162	95.34 131	89.99 108	93.72 126	94.23 106	96.22 102	92.79 108

WR-MVS		97.53 3	98.20 3	96.76 3	96.93 27	98.17 1	98.60 11	96.67 6	96.39 12	94.46 44	99.14 1	98.92 12	94.57 18	99.06 3	98.80 2	99.32 1	96.92 26

MVS_0304		93.92 74	93.81 99	94.05 62	96.06 56	96.00 56	96.43 83	92.76 47	85.99 173	94.43 46	94.04 122	97.08 82	88.12 124	94.65 107	94.20 107	96.47 81	94.71 69

MP-MVS		96.13 24	95.93 46	96.37 9	98.19 4	97.31 24	98.49 15	94.53 20	91.39 100	94.38 47	94.32 117	96.43 104	94.59 17	97.75 37	97.44 26	98.04 38	96.88 28
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
HPM-MVS++		95.21 50	94.89 67	95.59 24	97.79 6	95.39 78	97.68 44	94.05 29	91.91 82	94.35 48	93.38 129	95.07 139	92.94 42	96.01 76	95.88 64	96.73 75	96.61 36

3Dnovator+	92.82 3	95.22 49	95.16 61	95.29 35	96.17 52	96.55 39	97.64 45	94.02 30	94.16 38	94.29 49	92.09 143	93.71 155	91.90 57	96.68 64	96.51 46	97.70 50	96.40 38

ACMMPR		96.54 13	96.71 24	96.35 11	97.55 9	97.63 11	98.62 10	94.54 17	94.45 30	94.19 50	95.04 105	97.35 76	94.92 13	97.85 31	97.50 23	98.26 28	97.17 14

OMC-MVS		94.74 56	95.46 56	93.91 72	94.62 89	96.26 49	96.64 74	89.36 134	94.20 36	94.15 51	94.02 123	97.73 69	91.34 73	96.15 74	95.04 83	97.37 57	94.80 66

CP-MVS		96.21 21	96.16 41	96.27 13	97.56 8	97.13 32	98.43 16	94.70 16	92.62 63	94.13 52	92.71 137	98.03 57	94.54 19	98.00 26	97.60 20	98.23 29	97.05 20

DeepPCF-MVS	90.68 7	94.56 60	94.92 66	94.15 58	94.11 105	95.71 65	97.03 52	90.65 99	93.39 50	94.08 53	95.29 97	94.15 149	93.21 37	95.22 94	94.92 87	95.82 118	95.75 52

v8		93.60 86	93.82 97	93.34 88	93.13 149	95.06 89	96.39 89	90.75 97	89.90 127	94.03 54	97.70 47	98.21 46	91.08 86	92.36 150	91.47 158	94.63 157	92.07 126

ACMMP_Plus		95.86 37	96.18 38	95.47 29	97.11 22	97.26 26	98.37 21	93.48 42	93.49 46	93.99 55	95.61 86	94.11 150	92.49 48	97.87 30	97.44 26	97.40 55	97.52 8

v1neww		93.27 100	93.40 118	93.12 100	93.13 149	94.20 117	96.39 89	89.56 124	89.87 129	93.95 56	97.71 45	98.21 46	91.09 84	92.36 150	91.49 154	94.62 159	89.96 147

v7new		93.27 100	93.40 118	93.12 100	93.13 149	94.20 117	96.39 89	89.56 124	89.87 129	93.95 56	97.71 45	98.21 46	91.09 84	92.36 150	91.49 154	94.62 159	89.96 147

v6		93.27 100	93.41 116	93.12 100	93.13 149	94.20 117	96.39 89	89.55 126	89.89 128	93.93 58	97.72 43	98.22 45	91.10 83	92.36 150	91.49 154	94.63 157	89.95 149

TAPA-MVS	88.94 13	93.78 83	94.31 86	93.18 99	94.14 103	95.99 57	95.74 106	86.98 177	93.43 48	93.88 59	90.16 163	96.88 90	91.05 87	94.33 111	93.95 108	97.28 60	95.40 56
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
ACMM	90.06 9	96.31 18	96.42 31	96.19 15	97.21 19	97.16 31	98.71 5	93.79 35	94.35 34	93.81 60	92.80 136	98.23 43	95.11 9	98.07 22	97.45 25	98.51 17	96.86 29
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
SMA-MVS		95.99 30	96.48 29	95.41 30	97.43 11	97.36 21	97.55 48	93.70 38	94.05 39	93.79 61	97.02 67	94.53 145	92.28 53	97.53 41	97.19 33	97.73 47	97.67 7

UniMVSNet_NR-MVSNet		95.34 47	95.51 54	95.14 39	95.80 66	96.55 39	96.61 75	94.79 15	90.04 124	93.78 62	97.51 54	97.25 78	91.19 78	96.68 64	96.31 54	98.65 11	94.22 77

DU-MVS		95.51 42	95.68 50	95.33 32	96.45 46	96.44 46	96.61 75	95.32 10	89.97 125	93.78 62	97.46 56	98.07 53	91.19 78	97.03 51	96.53 45	98.61 13	94.22 77

DeepC-MVS_fast	91.38 6	94.73 57	94.98 64	94.44 52	96.83 34	96.12 53	96.69 70	92.17 60	92.98 56	93.72 64	94.14 119	95.45 127	90.49 104	95.73 82	95.30 76	96.71 76	95.13 63
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PGM-MVS		95.90 35	95.72 49	96.10 16	97.53 10	97.45 20	98.55 13	94.12 28	90.25 119	93.71 65	93.20 131	97.18 80	94.63 16	97.68 38	97.34 32	98.08 35	96.97 22

WR-MVS_H		97.06 9	97.78 7	96.23 14	96.74 36	98.04 3	98.25 25	97.32 1	94.40 33	93.71 65	98.55 13	98.89 13	92.97 40	98.91 9	98.45 6	98.38 27	97.19 13

v17		93.60 86	93.85 95	93.30 92	93.15 148	94.99 94	96.46 81	90.81 95	89.58 134	93.61 67	97.66 49	98.15 50	91.19 78	92.60 145	91.61 150	94.61 161	92.37 120

pmmvs-eth3d		92.34 127	92.33 132	92.34 114	92.67 163	90.67 179	96.37 93	89.06 138	90.98 110	93.60 68	97.13 64	97.02 84	88.29 122	90.20 179	91.42 159	94.07 171	88.89 162

CSCG		96.07 27	97.15 18	94.81 48	96.06 56	97.58 13	96.52 78	90.98 92	96.51 9	93.60 68	97.13 64	98.55 29	93.01 38	97.17 49	95.36 75	98.68 9	97.78 4

OPM-MVS		95.96 32	96.59 26	95.23 36	96.67 39	96.52 43	97.86 38	93.28 43	95.27 22	93.46 70	96.26 77	98.85 16	92.89 44	97.09 50	96.37 50	97.22 64	95.78 51

CANet		93.07 109	93.05 125	93.10 103	95.90 61	95.41 76	95.88 102	91.94 68	84.77 181	93.36 71	94.05 121	95.25 136	86.25 136	94.33 111	93.94 109	95.30 126	93.58 90

MAR-MVS		91.86 135	91.14 147	92.71 108	94.29 98	94.24 116	94.91 123	91.82 71	81.66 195	93.32 72	84.51 208	93.42 158	86.86 132	95.16 96	94.44 104	95.05 139	94.53 74
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
XVS					96.86 30	97.48 16	98.73 3			93.28 73		96.82 93				98.17 32

X-MVStestdata					96.86 30	97.48 16	98.73 3			93.28 73		96.82 93				98.17 32

X-MVS		95.33 48	95.13 63	95.57 26	97.35 14	97.48 16	98.43 16	94.28 23	92.30 72	93.28 73	86.89 194	96.82 93	91.87 59	97.85 31	97.59 21	98.19 30	96.95 25

V42		92.67 119	93.50 112	91.71 123	91.41 187	92.96 149	95.71 108	85.00 195	89.67 133	93.22 76	97.67 48	98.01 58	91.02 89	92.65 142	92.12 139	93.86 173	91.42 137

MDTV_nov1_ep13_2view		88.22 172	87.85 180	88.65 166	91.40 188	86.75 198	94.07 143	84.97 196	88.86 143	93.20 77	96.11 81	96.21 112	83.70 150	87.29 206	80.29 215	84.56 214	79.46 211

PVSNet_Blended_VisFu		93.60 86	93.41 116	93.83 74	96.31 49	95.65 67	95.71 108	90.58 102	88.08 152	93.17 78	95.29 97	92.20 164	90.72 96	94.69 106	93.41 120	96.51 80	94.54 73

gm-plane-assit		86.15 188	82.51 204	90.40 138	95.81 65	92.29 161	97.99 32	84.66 199	92.15 76	93.15 79	97.84 37	44.65 243	78.60 190	88.02 202	85.95 198	92.20 188	76.69 218

ACMMP		96.12 25	96.27 37	95.93 19	97.20 20	97.60 12	98.64 8	93.74 36	92.47 65	93.13 80	93.23 130	98.06 54	94.51 20	97.99 27	97.57 22	98.39 26	96.99 21
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
v16		93.53 91	93.80 100	93.20 97	93.10 154	94.98 95	96.43 83	90.81 95	89.39 137	93.12 81	97.63 51	98.01 58	91.19 78	92.60 145	91.65 149	94.58 163	92.36 121

v148		92.38 126	92.78 128	91.91 120	92.86 160	92.13 166	94.84 124	87.03 176	91.47 93	93.07 82	96.92 68	98.89 13	90.10 107	92.05 159	89.69 175	93.56 176	88.27 172

MVS_111021_LR		93.15 106	93.65 104	92.56 110	93.89 120	92.28 162	95.09 120	86.92 179	91.26 104	92.99 83	94.46 115	96.22 110	90.64 99	95.11 97	93.45 118	95.85 116	92.74 111

v18		93.33 99	93.59 107	93.04 105	92.94 157	94.87 99	96.31 96	90.59 101	88.96 139	92.89 84	97.51 54	97.90 62	91.01 90	92.33 154	91.48 157	94.50 164	92.05 127

TSAR-MVS + MP.		95.99 30	96.57 27	95.31 33	96.87 28	96.50 44	98.71 5	91.58 76	93.25 51	92.71 85	96.86 69	96.57 100	93.92 24	98.09 20	97.91 13	98.08 35	96.81 31

Fast-Effi-MVS+		92.93 112	92.64 130	93.27 93	93.81 124	93.88 130	95.90 101	90.61 100	83.98 186	92.71 85	92.81 135	96.22 110	90.67 97	94.90 103	93.92 110	95.92 113	92.77 109

SD-MVS		95.77 40	96.17 39	95.30 34	96.72 37	96.19 50	97.01 53	93.04 45	94.03 40	92.71 85	96.45 75	96.78 97	93.91 25	96.79 62	95.89 63	98.42 24	97.09 18

casdiffmvs1		93.02 111	93.07 124	92.96 107	94.93 83	95.42 74	96.24 97	90.96 93	91.68 87	92.69 88	93.74 126	96.88 90	87.86 125	90.19 180	89.56 178	95.09 137	94.03 81

LGP-MVS_train		96.10 26	96.29 35	95.87 20	96.72 37	97.35 23	98.43 16	93.83 34	90.81 116	92.67 89	95.05 103	98.86 15	95.01 10	98.11 19	97.37 31	98.52 16	96.50 37

UA-Net		96.56 12	96.73 23	96.36 10	98.99 1	97.90 7	97.79 42	95.64 9	92.78 60	92.54 90	96.23 80	95.02 140	94.31 21	98.43 14	98.12 11	98.89 3	98.58 2

HFP-MVS		96.18 22	96.53 28	95.77 21	97.34 16	97.26 26	98.16 30	94.54 17	94.45 30	92.52 91	95.05 103	96.95 87	93.89 26	97.28 45	97.46 24	98.19 30	97.25 9

MVS_111021_HR		93.82 82	94.26 89	93.31 90	95.01 80	93.97 128	95.73 107	89.75 118	92.06 78	92.49 92	94.01 124	96.05 116	90.61 102	95.95 77	94.78 92	96.28 96	93.04 102

CNLPA		93.14 107	93.67 103	92.53 111	94.62 89	94.73 103	95.00 122	86.57 183	92.85 59	92.43 93	90.94 152	94.67 143	90.35 106	95.41 85	93.70 112	96.23 101	93.37 94

PM-MVS		92.65 120	93.20 123	92.00 119	92.11 179	90.16 183	95.99 100	84.81 198	91.31 102	92.41 94	95.87 82	96.64 99	92.35 51	93.65 129	92.91 125	94.34 168	91.85 131

3Dnovator	91.81 5	93.36 98	94.27 88	92.29 115	92.99 156	95.03 90	95.76 105	87.79 164	93.82 43	92.38 95	92.19 142	93.37 159	88.14 123	95.26 93	94.85 88	96.69 77	95.40 56

CPTT-MVS		95.00 53	94.52 79	95.57 26	96.84 32	96.78 36	97.88 37	93.67 40	92.20 74	92.35 96	85.87 201	97.56 73	94.98 12	96.96 54	96.07 59	97.70 50	96.18 42

ACMP	89.62 11	95.96 32	96.28 36	95.59 24	96.58 42	97.23 28	98.26 24	93.22 44	92.33 71	92.31 97	94.29 118	98.73 20	94.68 15	98.04 23	97.14 37	98.47 20	96.17 43
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PCF-MVS	87.46 14	92.44 124	91.80 139	93.19 98	94.66 87	95.80 63	96.37 93	90.19 104	87.57 157	92.23 98	89.26 171	93.97 151	89.24 112	91.32 171	90.82 167	96.46 83	93.86 85
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PLC	87.27 15	93.08 108	92.92 126	93.26 94	94.67 86	95.03 90	94.38 132	90.10 105	91.69 85	92.14 99	87.24 190	93.91 152	91.61 66	95.05 98	94.73 98	96.67 78	92.80 107
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
UniMVSNet (Re)		95.46 43	95.86 47	95.00 45	96.09 53	96.60 38	96.68 72	94.99 12	90.36 118	92.13 100	97.64 50	98.13 51	91.38 71	96.90 56	96.74 42	98.73 6	94.63 72

NCCC		93.87 80	93.42 115	94.40 54	96.84 32	95.42 74	96.47 80	92.62 48	92.36 70	92.05 101	83.83 210	95.55 122	91.84 61	95.89 78	95.23 79	96.56 79	95.63 53

tpm		81.58 208	78.84 217	84.79 203	91.11 194	79.50 221	89.79 208	83.75 202	79.30 214	92.05 101	90.98 151	64.78 231	74.54 211	80.50 227	76.67 224	77.49 231	80.15 207

CLD-MVS		92.81 116	94.32 85	91.05 129	95.39 73	95.31 80	95.82 104	81.44 216	89.40 135	91.94 103	95.86 83	97.36 75	85.83 138	95.35 87	94.59 101	95.85 116	92.34 123
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
TranMVSNet+NR-MVSNet		95.72 41	96.42 31	94.91 47	96.21 51	96.77 37	96.90 60	94.99 12	92.62 63	91.92 104	98.51 15	98.63 24	90.82 94	97.27 46	96.83 41	98.63 12	94.31 76

SteuartSystems-ACMMP		95.96 32	96.13 42	95.76 22	97.06 24	97.36 21	98.40 20	94.24 25	91.49 91	91.91 105	94.50 113	96.89 89	94.99 11	98.01 25	97.44 26	97.97 41	97.25 9
Skip Steuart: Steuart Systems R&D Blog.
EPNet		90.17 147	89.07 165	91.45 126	97.25 18	90.62 181	94.84 124	93.54 41	80.96 197	91.85 106	86.98 193	85.88 188	77.79 198	92.30 155	92.58 129	93.41 178	94.20 79
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MSDG		92.09 133	92.84 127	91.22 128	92.55 164	92.97 148	93.42 153	85.43 193	90.24 120	91.83 107	94.70 109	94.59 144	88.48 120	94.91 102	93.31 122	95.59 122	89.15 158

EU-MVSNet		91.63 137	92.73 129	90.35 139	88.36 215	87.89 193	96.53 77	81.51 215	92.45 66	91.82 108	96.44 76	97.05 83	93.26 35	94.10 122	88.94 186	90.61 193	92.24 124

NR-MVSNet		94.55 61	95.66 52	93.25 96	94.26 100	96.44 46	96.69 70	95.32 10	89.97 125	91.79 109	97.46 56	98.39 36	82.85 156	96.87 59	96.48 48	98.57 14	93.98 83

MCST-MVS		93.60 86	93.40 118	93.83 74	95.30 74	95.40 77	96.49 79	90.87 94	90.08 122	91.72 110	90.28 161	95.99 117	91.69 64	93.94 124	92.99 123	96.93 74	95.13 63

TSAR-MVS + COLMAP		93.06 110	93.65 104	92.36 112	94.62 89	94.28 115	95.36 118	89.46 131	92.18 75	91.64 111	95.55 90	95.27 135	88.60 119	93.24 133	92.50 130	94.46 165	92.55 117

HQP-MVS		92.87 114	92.49 131	93.31 90	95.75 67	95.01 93	95.64 111	91.06 90	88.54 146	91.62 112	88.16 180	96.25 108	89.47 111	92.26 156	91.81 145	96.34 91	95.40 56

ESAPD		96.00 29	96.80 21	95.06 43	95.87 64	97.47 19	98.25 25	93.73 37	92.38 68	91.57 113	97.55 53	97.97 60	92.98 39	97.49 42	97.61 19	97.96 42	97.16 15

HSP-MVS		95.04 52	95.45 57	94.57 51	96.87 28	97.77 10	98.71 5	93.88 33	91.21 105	91.48 114	95.36 94	98.37 37	90.73 95	94.37 110	92.98 124	95.77 119	98.08 3

casdiffmvs		91.65 136	91.03 148	92.36 112	93.69 132	94.95 98	95.60 113	91.36 81	85.32 176	91.43 115	91.77 144	95.47 126	87.29 128	88.58 196	88.39 189	94.81 149	92.75 110

MSLP-MVS++		93.91 75	94.30 87	93.45 84	95.51 72	95.83 62	93.12 164	91.93 69	91.45 96	91.40 116	87.42 189	96.12 114	93.27 34	96.57 68	96.40 49	95.49 123	96.29 39

RPSCF		95.46 43	96.95 20	93.73 80	95.72 68	95.94 58	95.58 114	88.08 159	95.31 18	91.34 117	96.26 77	98.04 56	93.63 30	98.28 16	97.67 17	98.01 39	97.13 16

EPNet_dtu		87.40 184	86.27 190	88.72 165	95.68 69	83.37 211	92.09 180	90.08 106	78.11 223	91.29 118	86.33 197	89.74 176	75.39 210	89.07 190	87.89 191	87.81 203	89.38 156
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CNVR-MVS		94.24 68	94.47 81	93.96 69	96.56 43	95.67 66	96.43 83	91.95 67	92.08 77	91.28 119	90.51 159	95.35 130	91.20 77	96.34 73	95.50 73	96.34 91	95.88 48

AdaColmap		92.41 125	91.49 144	93.48 83	95.96 59	95.02 92	95.37 117	91.73 72	87.97 155	91.28 119	82.82 215	91.04 170	90.62 101	95.82 80	95.07 82	95.95 112	92.67 112

CDPH-MVS		93.96 72	93.86 94	94.08 61	96.31 49	95.84 61	96.92 57	91.85 70	87.21 163	91.25 121	92.83 134	96.06 115	91.05 87	95.57 83	94.81 89	97.12 65	94.72 68

QAPM		92.57 121	93.51 111	91.47 125	92.91 159	94.82 100	93.01 166	87.51 168	91.49 91	91.21 122	92.24 140	91.70 166	88.74 117	94.54 108	94.39 105	95.41 124	95.37 59

MDA-MVSNet-bldmvs		89.75 151	91.67 140	87.50 183	74.25 240	90.88 177	94.68 129	85.89 188	91.64 88	91.03 123	95.86 83	94.35 148	89.10 114	96.87 59	86.37 197	90.04 194	85.72 186

pmmvs4		89.95 150	89.32 163	90.69 134	91.60 186	89.17 187	94.37 133	87.63 167	88.07 153	91.02 124	94.50 113	90.50 174	86.13 137	86.33 209	89.40 179	93.39 179	87.29 177

TSAR-MVS + ACMM		95.17 51	95.95 44	94.26 56	96.07 55	96.46 45	95.67 110	94.21 26	93.84 42	90.99 125	97.18 62	95.24 137	93.55 31	96.60 67	95.61 71	95.06 138	96.69 34

CR-MVSNet		85.32 192	81.58 209	89.69 152	90.36 201	84.79 205	86.72 224	92.22 56	75.38 228	90.73 126	90.41 160	67.88 226	84.86 141	83.76 218	85.74 199	93.24 181	83.14 194

Patchmtry						83.74 210	86.72 224	92.22 56		90.73 126

PatchT		83.44 199	81.10 211	86.18 194	77.92 237	82.58 215	89.87 207	87.39 171	75.88 227	90.73 126	89.86 165	66.71 229	84.86 141	83.76 218	85.74 199	86.33 210	83.14 194

OpenMVS	89.22 12	91.09 140	91.42 145	90.71 133	92.79 162	93.61 139	92.74 173	85.47 192	86.10 172	90.73 126	85.71 202	93.07 162	86.69 133	94.07 123	93.34 121	95.86 114	94.02 82

CANet_DTU		88.95 162	89.51 160	88.29 176	93.12 153	91.22 174	93.61 150	83.47 207	80.07 209	90.71 130	89.19 172	93.68 156	76.27 209	91.44 170	91.17 164	92.59 186	89.83 150

IterMVS-LS		92.10 132	92.33 132	91.82 122	93.18 146	93.66 134	92.80 172	92.27 55	90.82 114	90.59 131	97.19 61	90.97 171	87.76 126	89.60 186	90.94 166	94.34 168	93.16 98
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
train_agg		93.89 77	93.46 114	94.40 54	97.35 14	93.78 132	97.63 46	92.19 59	88.12 149	90.52 132	93.57 128	95.78 120	92.31 52	94.78 104	93.46 117	96.36 86	94.70 71

ACMH+	89.90 10	96.27 19	97.52 11	94.81 48	95.19 77	97.18 30	97.97 34	92.52 49	96.72 8	90.50 133	97.31 59	99.11 8	94.10 23	98.67 12	97.90 14	98.56 15	95.79 50

APD-MVS		95.38 46	95.68 50	95.03 44	97.30 17	96.90 35	97.83 39	93.92 31	89.40 135	90.35 134	95.41 93	97.69 70	92.97 40	97.24 48	97.17 35	97.83 45	95.96 46
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
Effi-MVS+-dtu		92.32 129	91.66 141	93.09 104	95.13 79	94.73 103	94.57 131	92.14 61	81.74 194	90.33 135	88.13 181	95.91 118	89.24 112	94.23 120	93.65 116	97.12 65	93.23 96

PVSNet_BlendedMVS		90.09 148	90.12 154	90.05 144	92.40 168	92.74 153	91.74 185	85.89 188	80.54 204	90.30 136	88.54 176	95.51 123	84.69 143	92.64 143	90.25 171	95.28 128	90.61 142

PVSNet_Blended		90.09 148	90.12 154	90.05 144	92.40 168	92.74 153	91.74 185	85.89 188	80.54 204	90.30 136	88.54 176	95.51 123	84.69 143	92.64 143	90.25 171	95.28 128	90.61 142

Baseline_NR-MVSNet		94.85 54	95.35 59	94.26 56	96.45 46	93.86 131	96.70 68	94.54 17	90.07 123	90.17 138	98.77 6	97.89 63	90.64 99	97.03 51	96.16 55	97.04 71	93.67 87

v1.0		88.18 175	82.50 205	94.80 50	96.76 35	97.29 25	97.74 43	94.15 27	91.69 85	90.01 139	96.65 72	97.29 77	92.45 49	97.41 43	97.18 34	97.67 52	0.00 242

abl_6				91.88 121	93.76 127	94.98 95	95.64 111	88.97 141	86.20 171	90.00 140	86.31 198	94.50 146	87.31 127			95.60 121	92.48 119

GA-MVS		88.76 163	88.04 178	89.59 154	92.32 171	91.46 172	92.28 179	86.62 181	83.82 188	89.84 141	92.51 139	81.94 199	83.53 152	89.41 188	89.27 181	92.95 184	87.90 173

APDe-MVS		96.23 20	97.22 16	95.08 42	96.66 40	97.56 14	98.63 9	93.69 39	94.62 27	89.80 142	97.73 42	98.13 51	93.84 27	97.79 35	97.63 18	97.87 44	97.08 19

no-one		92.05 134	94.57 78	89.12 159	85.55 226	87.65 196	94.21 140	77.34 222	93.43 48	89.64 143	95.11 102	99.11 8	95.86 4	95.38 86	95.24 78	92.08 190	96.11 44

Vis-MVSNet		94.39 64	95.85 48	92.68 109	90.91 196	95.88 60	97.62 47	91.41 80	91.95 80	89.20 144	97.29 60	96.26 107	90.60 103	96.95 55	95.91 61	96.32 94	96.71 33
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
TSAR-MVS + GP.		94.25 67	94.81 70	93.60 81	96.52 44	95.80 63	94.37 133	92.47 52	90.89 112	88.92 145	95.34 95	94.38 147	92.85 45	96.36 72	95.62 70	96.47 81	95.28 60

PatchmatchNet		82.44 202	78.69 219	86.83 189	89.81 204	81.55 218	90.78 198	87.27 174	82.39 193	88.85 146	88.31 179	70.96 222	81.90 166	78.58 231	74.33 231	82.35 226	74.69 222
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MDTV_nov1_ep13		82.33 203	79.66 214	85.45 197	88.83 211	83.88 209	90.09 206	81.98 212	79.07 217	88.82 147	88.70 174	73.77 218	78.41 195	80.29 228	76.08 225	84.56 214	75.83 219

CMPMVS	66.55 18	85.55 191	87.46 184	83.32 207	84.99 227	81.97 216	79.19 238	75.93 224	79.32 213	88.82 147	85.09 203	91.07 169	82.12 164	92.56 147	89.63 177	88.84 199	92.56 116
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
DI_MVS_plusplus_trai		90.68 142	90.40 152	91.00 130	92.43 167	92.61 156	94.17 142	88.98 140	88.32 148	88.76 149	93.67 127	87.58 183	86.44 135	89.74 184	90.33 169	95.24 130	90.56 144

IterMVS		88.32 168	88.25 176	88.41 171	90.83 198	91.24 173	93.07 165	81.69 213	86.77 165	88.55 150	95.61 86	86.91 187	87.01 129	87.38 204	83.77 204	89.29 196	86.06 185
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
canonicalmvs		93.38 97	94.36 84	92.24 116	93.94 116	96.41 48	94.18 141	90.47 103	93.07 55	88.47 151	88.66 175	93.78 154	88.80 116	95.74 81	95.75 67	97.57 53	97.13 16

FPMVS		90.81 141	91.60 142	89.88 147	92.52 165	88.18 189	93.31 160	83.62 204	91.59 90	88.45 152	88.96 173	89.73 177	86.96 130	96.42 71	95.69 69	94.43 166	90.65 141

DELS-MVS		92.33 128	93.61 106	90.83 132	92.84 161	95.13 88	94.76 127	87.22 175	87.78 156	88.42 153	95.78 85	95.28 134	85.71 139	94.44 109	93.91 111	96.01 110	92.97 104
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
PatchMatch-RL		89.59 152	88.80 169	90.51 136	92.20 177	88.00 192	91.72 187	86.64 180	84.75 182	88.25 154	87.10 192	90.66 173	89.85 110	93.23 134	92.28 132	94.41 167	85.60 188

pmmvs5		88.63 165	89.70 158	87.39 185	89.24 208	90.64 180	91.87 182	82.13 211	83.34 189	87.86 155	94.58 111	96.15 113	79.87 180	87.33 205	89.07 185	93.39 179	86.76 181

Fast-Effi-MVS+-dtu		89.57 153	88.42 175	90.92 131	93.35 144	91.57 171	93.01 166	95.71 8	78.94 218	87.65 156	84.68 207	93.14 161	82.00 165	90.84 174	91.01 165	93.78 175	88.77 163

MVS_Test		90.19 146	90.58 149	89.74 150	92.12 178	91.74 170	92.51 175	88.54 151	82.80 191	87.50 157	94.62 110	95.02 140	83.97 147	88.69 193	89.32 180	93.79 174	91.85 131

gg-mvs-nofinetune		88.32 168	88.81 168	87.75 182	93.07 155	89.37 186	89.06 213	95.94 7	95.29 20	87.15 158	97.38 58	76.38 215	68.05 222	91.04 173	89.10 184	93.24 181	83.10 196

tpmp4_e23		82.16 204	78.26 222	86.70 191	89.92 203	84.82 204	91.17 193	89.95 113	81.21 196	87.10 159	81.91 217	64.01 232	77.88 197	79.89 229	74.99 229	84.18 218	81.00 203

RPMNet		83.42 200	78.40 220	89.28 158	89.79 205	84.79 205	90.64 201	92.11 63	75.38 228	87.10 159	79.80 222	61.99 235	82.79 158	81.88 224	82.07 209	93.23 183	82.87 197

Anonymous20240521		93.49 92	95.15 62	91.55 124	94.05 106	95.92 59	95.15 119	91.21 85	92.76 62	87.01 161	89.71 166	97.16 81	83.90 149	97.65 39	96.87 40	97.99 40	95.95 47

CVMVSNet		88.97 161	89.73 157	88.10 178	87.33 222	85.22 202	94.68 129	78.68 218	88.94 141	86.98 162	95.55 90	85.71 189	89.87 109	91.19 172	89.69 175	91.05 191	91.78 134

MVS-HIRNet		78.28 223	75.28 234	81.79 211	80.33 233	69.38 240	76.83 239	86.59 182	70.76 235	86.66 163	89.57 168	81.04 203	77.74 199	77.81 233	71.65 232	82.62 223	66.73 234

diffmvs		90.35 144	91.54 143	88.96 161	90.90 197	92.20 164	91.93 181	88.45 154	86.34 170	86.36 164	93.13 133	96.99 85	83.54 151	90.32 178	88.69 188	92.59 186	93.09 100

tpm cat1		80.03 214	75.93 233	84.81 202	89.31 207	83.26 213	88.86 214	86.55 184	79.24 215	86.10 165	84.22 209	63.62 233	77.37 203	73.43 236	70.88 234	80.67 227	76.87 217

UGNet		92.31 130	94.70 73	89.53 155	90.99 195	95.53 71	96.19 98	92.10 64	91.35 101	85.76 166	95.31 96	95.48 125	76.84 205	95.22 94	94.79 91	95.32 125	95.19 61
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
EPP-MVSNet		93.63 85	93.95 91	93.26 94	95.15 78	96.54 42	96.18 99	91.97 66	91.74 84	85.76 166	94.95 107	84.27 192	91.60 67	97.61 40	97.38 30	98.87 4	95.18 62

DWT-MVSNet_training		79.22 218	73.99 235	85.33 198	88.57 212	84.41 207	90.56 202	80.96 217	73.90 233	85.72 168	75.62 229	50.09 242	81.30 174	76.91 235	77.02 223	84.88 212	79.97 209

pmmvs6		94.58 59	97.30 15	91.40 127	94.84 85	94.61 107	93.40 154	92.43 53	98.51 2	85.61 169	98.73 9	99.53 2	84.40 145	97.88 29	97.03 38	97.72 48	94.79 67

HyFIR lowres test		88.19 174	86.56 188	90.09 142	91.24 190	92.17 165	94.30 139	88.79 148	84.06 184	85.45 170	89.52 169	85.64 190	88.64 118	85.40 216	87.28 193	92.14 189	81.87 199

TransMVSNet (Re)		93.55 90	96.32 34	90.32 140	94.38 96	94.05 123	93.30 161	89.53 127	97.15 7	85.12 171	98.83 5	97.89 63	82.21 163	96.75 63	96.14 57	97.35 58	93.46 92

IB-MVS	86.01 17	88.24 171	87.63 181	88.94 162	92.03 182	91.77 169	92.40 178	85.58 191	78.24 220	84.85 172	71.99 236	93.45 157	83.96 148	93.48 131	92.33 131	94.84 148	92.15 125
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
LP		84.09 198	84.31 196	83.85 206	79.40 235	84.34 208	90.26 203	84.02 201	87.99 154	84.66 173	91.61 148	79.13 208	80.58 177	85.90 214	81.59 210	84.16 219	79.59 210

Anonymous20231211		93.19 104	95.50 55	90.49 137	93.77 126	95.29 83	94.36 137	90.04 110	91.44 97	84.59 174	96.72 71	97.65 71	82.45 162	97.25 47	96.32 53	97.74 46	93.79 86

dps		81.42 212	77.88 227	85.56 196	87.67 218	85.17 203	88.37 217	87.46 169	74.37 231	84.55 175	86.80 195	62.18 234	80.20 178	81.13 226	77.52 221	85.10 211	77.98 216

MIMVSNet1		92.52 122	94.88 68	89.77 149	96.09 53	91.99 168	96.92 57	89.68 120	95.92 16	84.55 175	96.64 73	98.21 46	78.44 194	96.08 75	95.10 81	92.91 185	90.22 145

pmmvs3		81.69 207	83.83 198	79.19 220	78.33 236	78.57 224	89.53 210	58.71 237	78.88 219	84.34 177	88.36 178	91.96 165	77.69 200	87.48 203	82.42 208	86.54 209	79.18 213

pm-mvs1		93.27 100	95.94 45	90.16 141	94.13 104	93.66 134	92.61 174	89.91 115	95.73 17	84.28 178	98.51 15	98.29 39	82.80 157	96.44 70	95.76 66	97.25 62	93.21 97

CostFormer		82.15 205	79.54 215	85.20 200	88.92 210	85.70 201	90.87 197	86.26 187	79.19 216	83.87 179	87.89 185	69.20 224	76.62 207	77.50 234	75.28 227	84.69 213	82.02 198

conf0.05thres1000		91.24 139	91.85 138	90.53 135	94.59 92	94.56 111	94.33 138	89.52 128	93.67 44	83.77 180	91.04 150	79.10 209	83.98 146	96.66 66	95.56 72	96.98 73	92.36 121

tpmrst		78.81 221	76.18 232	81.87 210	88.56 213	77.45 227	86.74 223	81.52 214	80.08 208	83.48 181	90.84 155	66.88 227	74.54 211	73.04 237	71.02 233	76.38 233	73.95 227

tfpnnormal		92.45 123	94.77 72	89.74 150	93.95 114	93.44 144	93.25 162	88.49 152	95.27 22	83.20 182	96.51 74	96.23 109	83.17 154	95.47 84	94.52 103	96.38 85	91.97 129

view800		89.42 154	89.11 164	89.78 148	94.00 107	93.71 133	93.96 144	88.47 153	88.10 150	82.91 183	82.61 216	79.85 207	83.10 155	94.92 101	95.38 74	96.26 100	89.19 157

view600		89.09 158	88.78 170	89.46 157	93.59 135	93.33 146	93.92 147	87.76 165	87.40 158	82.79 184	81.29 218	80.71 204	82.59 161	94.28 114	95.72 68	96.12 108	88.70 164

tfpn		87.65 181	85.66 193	89.96 146	94.36 97	93.94 129	93.85 148	89.02 139	88.71 145	82.78 185	83.79 211	53.79 238	83.43 153	95.35 87	94.54 102	96.35 90	89.51 155

MVSTER		84.79 194	83.79 199	85.96 195	89.14 209	89.80 184	89.39 211	82.99 209	74.16 232	82.78 185	85.97 200	66.81 228	76.84 205	90.77 175	88.83 187	94.66 155	90.19 146

IS_MVSNet		92.76 117	93.25 122	92.19 117	94.91 84	95.56 68	95.86 103	92.12 62	88.10 150	82.71 187	93.15 132	88.30 181	88.86 115	97.29 44	96.95 39	98.66 10	93.38 93

thres600view7		89.14 157	88.83 167	89.51 156	93.71 131	93.55 140	93.93 146	88.02 160	87.30 160	82.40 188	81.18 219	80.63 205	82.69 160	94.27 115	95.90 62	96.27 98	88.94 161

thres400		88.54 166	88.15 177	88.98 160	93.17 147	92.84 150	93.56 151	86.93 178	86.45 168	82.37 189	79.96 221	81.46 202	81.83 168	93.21 135	94.76 93	96.04 109	88.39 170

CHOSEN 1792x2688		86.64 185	86.62 187	86.65 193	90.33 202	87.86 194	93.19 163	83.30 208	83.95 187	82.32 190	87.93 183	89.34 178	86.92 131	85.64 215	84.95 201	83.85 220	86.68 182

PMMVS		81.93 206	83.48 202	80.12 214	72.35 241	75.05 234	88.54 215	64.01 233	77.02 225	82.22 191	87.51 188	91.12 168	79.70 181	86.59 207	86.64 196	93.88 172	80.41 204

FMVSNet1		92.86 115	95.26 60	90.06 143	92.40 168	95.16 86	94.37 133	92.22 56	93.18 54	82.16 192	96.76 70	97.48 74	81.85 167	95.32 89	94.98 84	97.34 59	93.93 84

FC-MVSNet-train		92.75 118	95.40 58	89.66 153	95.21 76	94.82 100	97.00 54	89.40 132	91.13 106	81.71 193	97.72 43	96.43 104	77.57 201	96.89 57	96.72 43	97.05 70	94.09 80

thres200		88.29 170	87.88 179	88.76 164	92.50 166	93.55 140	92.47 177	88.02 160	84.80 179	81.44 194	79.28 223	82.20 198	81.83 168	94.27 115	93.67 113	96.27 98	87.40 176

thres100view900		86.46 187	86.00 192	86.99 188	92.28 172	91.03 175	91.09 194	84.49 200	80.90 198	80.89 195	78.25 224	82.25 194	77.57 201	90.17 181	92.84 126	95.63 120	86.57 183

tfpn200view9		87.94 177	87.51 182	88.44 170	92.28 172	93.63 138	93.35 159	88.11 158	80.90 198	80.89 195	78.25 224	82.25 194	79.65 183	94.27 115	94.76 93	96.36 86	88.48 167

tfpn111		87.59 182	86.89 185	88.41 171	92.28 172	93.64 136	93.36 155	88.12 156	80.90 198	80.71 197	73.93 233	82.25 194	79.65 183	94.27 115	94.76 93	96.36 86	88.48 167

conf200view11		87.93 178	87.51 182	88.41 171	92.28 172	93.64 136	93.36 155	88.12 156	80.90 198	80.71 197	78.25 224	82.25 194	79.65 183	94.27 115	94.76 93	96.36 86	88.48 167

CDS-MVSNet		88.41 167	89.79 156	86.79 190	94.55 93	90.82 178	92.50 176	89.85 116	83.26 190	80.52 199	91.05 149	89.93 175	69.11 219	93.17 136	92.71 128	94.21 170	87.63 174
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
conf0.01		85.72 190	83.49 201	88.32 174	92.11 179	93.35 145	93.36 155	88.02 160	80.90 198	80.51 200	74.83 231	59.86 236	79.65 183	93.80 125	94.76 93	96.29 95	86.94 180

conf0.002		84.82 193	81.84 208	88.30 175	92.05 181	93.28 147	93.36 155	88.00 163	80.90 198	80.48 201	73.43 235	52.48 241	79.65 183	93.72 126	92.82 127	96.28 96	86.22 184

MS-PatchMatch		87.72 180	88.62 174	86.66 192	90.81 199	88.18 189	90.92 196	82.25 210	85.86 174	80.40 202	90.14 164	89.29 179	84.93 140	89.39 189	89.12 183	90.67 192	88.34 171

FMVSNet2		90.28 145	92.04 137	88.23 177	91.22 191	94.05 123	92.88 169	90.69 98	86.53 167	79.89 203	94.38 116	92.73 163	78.54 191	91.64 168	92.26 133	96.17 105	92.67 112

testpf		72.68 233	66.81 237	79.53 215	86.52 224	73.89 235	83.56 232	88.74 149	58.70 241	79.68 204	71.31 237	53.64 239	62.23 226	68.68 238	66.64 237	76.46 232	74.82 221

tfpn_n400		89.03 159	89.39 161	88.61 167	93.98 111	92.33 159	91.83 183	88.97 141	92.97 57	78.90 205	84.93 204	78.24 211	81.77 170	95.00 99	93.67 113	96.22 102	88.59 165

tfpnconf		89.03 159	89.39 161	88.61 167	93.98 111	92.33 159	91.83 183	88.97 141	92.97 57	78.90 205	84.93 204	78.24 211	81.77 170	95.00 99	93.67 113	96.22 102	88.59 165

tfpnview11		88.74 164	88.95 166	88.50 169	93.91 118	92.43 158	91.70 189	88.90 146	90.93 111	78.90 205	84.93 204	78.24 211	81.71 173	94.32 113	94.60 100	95.86 114	87.23 178

test-mter		78.71 222	78.35 221	79.12 221	84.03 229	76.58 228	88.51 216	59.06 236	71.06 234	78.87 208	83.73 212	71.83 219	76.44 208	83.41 221	80.61 213	87.79 204	81.24 200

GBi-Net		89.35 155	90.58 149	87.91 180	91.22 191	94.05 123	92.88 169	90.05 107	79.40 210	78.60 209	90.58 156	87.05 184	78.54 191	95.32 89	94.98 84	96.17 105	92.67 112

test1		89.35 155	90.58 149	87.91 180	91.22 191	94.05 123	92.88 169	90.05 107	79.40 210	78.60 209	90.58 156	87.05 184	78.54 191	95.32 89	94.98 84	96.17 105	92.67 112

FMVSNet3		87.90 179	88.63 173	87.04 187	89.78 206	93.46 143	91.62 190	90.05 107	79.40 210	78.60 209	90.58 156	87.05 184	77.07 204	88.03 201	89.86 174	95.12 134	92.04 128

test-LLR		80.62 213	77.20 230	84.62 205	93.99 109	75.11 232	87.04 220	87.32 172	70.11 236	78.59 212	83.17 213	71.60 220	73.88 214	82.32 222	79.20 218	86.91 207	78.87 214

TESTMET0.1,1		77.47 227	77.20 230	77.78 224	81.94 231	75.11 232	87.04 220	58.33 238	70.11 236	78.59 212	83.17 213	71.60 220	73.88 214	82.32 222	79.20 218	86.91 207	78.87 214

CHOSEN 280x420		79.24 217	78.26 222	80.38 213	79.60 234	68.80 241	89.32 212	75.38 225	77.25 224	78.02 214	75.57 230	76.17 216	81.19 175	88.61 194	81.39 211	78.79 229	80.03 208

thresconf0.02		84.34 197	82.02 207	87.06 186	92.23 176	90.93 176	91.05 195	86.43 185	88.83 144	77.65 215	73.93 233	55.81 237	79.68 182	90.62 176	90.28 170	95.30 126	83.73 192

ADS-MVSNet		79.11 219	79.38 216	78.80 222	81.90 232	75.59 230	84.36 231	83.69 203	87.31 159	76.76 216	87.58 187	76.90 214	68.55 221	78.70 230	75.56 226	77.53 230	74.07 226

Anonymous20231206		87.45 183	89.66 159	84.87 201	94.00 107	87.73 195	91.36 192	86.41 186	88.89 142	75.03 217	92.59 138	96.82 93	72.48 216	89.72 185	88.06 190	89.93 195	83.81 191

Vis-MVSNet (Re-imp)		90.68 142	92.18 134	88.92 163	94.63 88	92.75 152	92.91 168	91.20 86	89.21 138	75.01 218	93.96 125	89.07 180	82.72 159	95.88 79	95.30 76	97.08 69	89.08 160

EMVS		77.65 226	77.49 229	77.83 223	87.75 217	71.02 238	81.13 236	70.54 230	86.38 169	74.52 219	89.38 170	80.19 206	78.22 196	89.48 187	67.13 236	74.83 237	58.84 238

tfpn1000		88.13 176	88.68 172	87.49 184	93.94 116	92.64 155	91.50 191	88.70 150	90.12 121	74.35 220	86.74 196	75.27 217	80.14 179	94.16 121	94.66 99	96.33 93	87.16 179

EPMVS		79.26 216	78.20 224	80.49 212	87.04 223	78.86 223	86.08 229	83.51 205	82.63 192	73.94 221	89.59 167	68.67 225	72.03 217	78.17 232	75.08 228	80.37 228	74.37 224

testus		78.20 224	81.50 210	74.36 230	85.59 225	79.36 222	86.99 222	65.76 231	76.01 226	73.00 222	77.98 227	93.35 160	51.30 238	86.33 209	82.79 207	83.50 222	74.68 223

FC-MVSNet-test		91.49 138	94.43 82	88.07 179	94.97 81	90.53 182	95.42 116	91.18 87	93.24 52	72.94 223	98.37 17	93.86 153	78.78 188	97.82 34	96.13 58	95.13 133	91.05 139

E-PMN		77.81 225	77.88 227	77.73 225	88.26 216	70.48 239	80.19 237	71.20 229	86.66 166	72.89 224	88.09 182	81.74 201	78.75 189	90.02 183	68.30 235	75.10 235	59.85 237

test20.03		88.20 173	91.26 146	84.63 204	96.64 41	89.39 185	90.73 200	89.97 112	91.07 108	72.02 225	94.98 106	95.45 127	69.35 218	92.70 141	91.19 163	89.06 198	84.02 189

new-patchmatchnet		84.45 196	88.75 171	79.43 216	93.28 145	81.87 217	81.68 235	83.48 206	94.47 29	71.53 226	98.33 18	97.88 66	58.61 230	90.35 177	77.33 222	87.99 201	81.05 202

tfpn_ndepth		85.89 189	86.40 189	85.30 199	91.31 189	92.47 157	90.78 198	87.75 166	84.79 180	71.04 227	76.95 228	78.80 210	74.52 213	92.72 140	93.43 119	96.39 84	85.65 187

FMVSNet5		79.08 220	78.83 218	79.38 218	87.52 221	86.78 197	87.64 218	78.15 219	69.54 238	70.64 228	65.97 240	65.44 230	63.87 225	90.17 181	90.46 168	88.48 200	83.45 193

testmv		81.49 211	84.76 195	77.67 226	87.67 218	80.25 220	90.12 204	77.62 220	80.34 207	69.71 229	90.92 154	96.47 102	56.57 232	88.58 196	84.92 203	84.33 217	71.86 232

test1235678		81.50 210	84.78 194	77.67 226	87.67 218	80.27 219	90.12 204	77.62 220	80.36 206	69.71 229	90.93 153	96.51 101	56.57 232	88.60 195	84.93 202	84.34 216	71.87 231

MIMVSNet		84.76 195	86.75 186	82.44 208	91.71 185	85.95 200	89.74 209	89.49 129	85.28 177	69.69 231	87.93 183	90.88 172	64.85 224	88.26 199	87.74 192	89.18 197	81.24 200

testgi		86.49 186	90.31 153	82.03 209	95.63 70	88.18 189	93.47 152	84.89 197	93.23 53	69.54 232	87.16 191	97.96 61	60.66 227	91.90 166	89.90 173	87.99 201	83.84 190

test2356		72.95 232	71.24 236	74.95 229	84.89 228	75.49 231	82.67 234	75.38 225	68.02 239	68.65 233	74.40 232	52.81 240	55.61 235	81.50 225	79.80 216	82.50 224	66.70 235

TAMVS		82.96 201	86.15 191	79.24 219	90.57 200	83.12 214	87.29 219	75.12 227	84.06 184	65.81 234	92.22 141	88.27 182	69.11 219	88.72 191	87.26 195	87.56 206	79.38 212

test0.0.03 1		81.51 209	83.30 203	79.42 217	93.99 109	86.50 199	85.93 230	87.32 172	78.16 221	61.62 235	80.78 220	81.78 200	59.87 228	88.40 198	87.27 194	87.78 205	80.19 206

1111		76.85 228	78.03 225	75.46 228	94.16 101	78.29 225	86.40 226	89.12 136	87.23 161	61.26 236	95.15 100	44.14 244	51.46 236	86.04 212	81.00 212	70.40 239	74.37 224

.test1245		60.07 235	56.75 238	63.93 235	94.16 101	78.29 225	86.40 226	89.12 136	87.23 161	61.26 236	95.15 100	44.14 244	51.46 236	86.04 212	2.51 240	1.21 244	3.92 240

test12356		75.40 230	80.89 212	69.01 232	77.43 238	75.75 229	83.03 233	61.48 234	78.13 222	59.08 238	87.69 186	94.95 142	57.37 231	88.18 200	80.59 214	75.65 234	60.93 236

N_pmnet		79.33 215	84.22 197	73.62 231	91.72 184	73.72 236	86.11 228	76.36 223	92.38 68	53.38 239	95.54 92	95.62 121	59.14 229	84.23 217	74.84 230	75.03 236	73.25 228

tmp_tt				28.44 237	36.05 243	15.86 245	21.29 245	6.40 240	54.52 242	51.96 240	50.37 241	38.68 246	9.55 240	61.75 240	59.66 238	45.36 242

new_pmnet		76.65 229	83.52 200	68.63 233	82.60 230	72.08 237	76.76 240	64.17 232	84.41 183	49.73 241	91.77 144	91.53 167	56.16 234	86.59 207	83.26 206	82.37 225	75.02 220

MVE	60.41 19	73.21 231	80.84 213	64.30 234	56.34 242	57.24 243	75.28 242	72.76 228	87.14 164	41.39 242	86.31 198	85.30 191	80.66 176	86.17 211	83.36 205	59.35 240	80.38 205
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX						47.68 244	53.20 244	19.21 239	63.24 240	26.96 243	66.50 239	69.82 223	66.91 223	64.27 239		54.91 241	72.72 229

PMMVS2		69.86 234	82.14 206	55.52 236	75.19 239	63.08 242	75.52 241	60.97 235	88.50 147	25.11 244	91.77 144	96.44 103	25.43 239	88.70 192	79.34 217	70.93 238	67.17 233

GG-mvs-BLEND		54.28 236	77.89 226	26.72 238	0.37 246	83.31 212	70.04 243	0.39 243	74.71 230	5.36 245	68.78 238	83.06 193	0.62 243	83.73 220	78.99 220	83.55 221	72.68 230

test123		2.16 238	2.82 240	1.41 239	0.62 245	1.18 246	1.53 247	0.82 242	2.78 244	2.27 246	4.18 243	1.98 248	1.64 241	2.58 242	3.01 239	1.56 243	4.00 239

testmvs		2.38 237	3.35 239	1.26 240	0.83 244	0.96 247	1.53 247	0.83 241	3.59 243	1.63 247	6.03 242	2.93 247	1.55 242	3.49 241	2.51 240	1.21 244	3.92 240

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

Anonymous202405211			94.63 75		94.51 94	94.96 97	93.94 145	91.35 82	90.82 114		95.60 88	95.85 119	81.74 172	96.47 69	95.84 65	97.39 56	92.85 105

our_test_3					91.78 183	88.87 188	94.37 133

Patchmatch-RL test							8.96 246

mPP-MVS					98.24 3							97.65 71

NP-MVS									85.48 175

High-resolution multi-view benchmark