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	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

LCM-MVSNet		99.43 1	99.49 1	99.24 1	99.95 1	98.13 1	99.37 1	99.57 1	99.82 1	99.86 1	99.85 1	99.52 1	99.73 1	97.58 1	99.94 1	99.85 1

LTVRE_ROB	93.87 1	97.93 2	98.16 2	97.26 24	98.81 23	93.86 27	99.07 2	98.98 3	97.01 12	98.92 4	98.78 14	95.22 36	98.61 162	96.85 2	99.77 9	99.31 26
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
TDRefinement		97.68 3	97.60 4	97.93 2	99.02 11	95.95 5	98.61 3	98.81 4	97.41 9	97.28 45	98.46 25	94.62 53	98.84 124	94.64 17	99.53 33	98.99 52

UA-Net		97.35 4	97.24 11	97.69 5	98.22 67	93.87 26	98.42 4	98.19 31	96.95 13	95.46 120	99.23 4	93.45 69	99.57 13	95.34 12	99.89 2	99.63 9

abl_6		97.31 5	97.12 13	97.86 3	98.54 41	95.32 7	96.61 24	98.35 16	95.81 30	97.55 34	97.44 62	96.51 9	99.40 36	94.06 30	99.23 73	98.85 75

UniMVSNet_ETH3D		97.13 6	97.72 3	95.35 80	99.51 2	87.38 122	97.70 6	97.54 101	98.16 2	98.94 2	99.33 2	97.84 4	99.08 87	90.73 115	99.73 13	99.59 12

HPM-MVS_fast		97.01 7	96.89 15	97.39 20	99.12 7	93.92 24	97.16 10	98.17 35	93.11 62	96.48 72	97.36 69	96.92 6	99.34 54	94.31 23	99.38 53	98.92 66

mvs_tets		96.83 8	96.71 19	97.17 25	98.83 21	92.51 45	96.58 26	97.61 96	87.57 188	98.80 7	98.90 9	96.50 10	99.59 12	96.15 7	99.47 37	99.40 20

v7n		96.82 9	97.31 10	95.33 82	98.54 41	86.81 135	96.83 18	98.07 48	96.59 19	98.46 17	98.43 27	92.91 86	99.52 17	96.25 6	99.76 10	99.65 8

APD-MVS_3200maxsize		96.82 9	96.65 20	97.32 23	97.95 85	93.82 29	96.31 39	98.25 24	95.51 31	96.99 56	97.05 87	95.63 21	99.39 41	93.31 54	98.88 110	98.75 84

HPM-MVS		96.81 11	96.62 22	97.36 22	98.89 18	93.53 34	97.51 7	98.44 9	92.35 74	95.95 100	96.41 125	96.71 8	99.42 27	93.99 31	99.36 54	99.13 38
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
pmmvs6		96.80 12	97.36 9	95.15 91	99.12 7	87.82 118	96.68 22	97.86 74	96.10 25	98.14 22	99.28 3	97.94 3	98.21 198	91.38 109	99.69 14	99.42 18

OurMVSNet-221017-0		96.80 12	96.75 18	96.96 34	99.03 10	91.85 54	97.98 5	98.01 60	94.15 44	98.93 3	99.07 5	88.07 173	99.57 13	95.86 9	99.69 14	99.46 17

COLMAP_ROB	91.06 5	96.75 14	96.62 22	97.13 26	98.38 57	94.31 12	96.79 20	98.32 17	96.69 16	96.86 58	97.56 54	95.48 25	98.77 141	90.11 135	99.44 43	98.31 119
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
anonymousdsp		96.74 15	96.42 26	97.68 7	98.00 81	94.03 21	96.97 15	97.61 96	87.68 185	98.45 18	98.77 15	94.20 62	99.50 19	96.70 3	99.40 51	99.53 14

DTE-MVSNet		96.74 15	97.43 5	94.67 106	99.13 5	84.68 170	96.51 28	97.94 72	98.14 3	98.67 12	98.32 28	95.04 43	99.69 2	93.27 57	99.82 7	99.62 10

SR-MVS		96.70 17	96.42 26	97.54 9	98.05 76	94.69 8	96.13 45	98.07 48	95.17 32	96.82 60	96.73 107	95.09 42	99.43 26	92.99 69	98.71 129	98.50 106

PS-CasMVS		96.69 18	97.43 5	94.49 119	99.13 5	84.09 180	96.61 24	97.97 66	97.91 5	98.64 13	98.13 31	95.24 35	99.65 3	93.39 52	99.84 3	99.72 2

PEN-MVS		96.69 18	97.39 8	94.61 108	99.16 3	84.50 171	96.54 27	98.05 51	98.06 4	98.64 13	98.25 30	95.01 46	99.65 3	92.95 70	99.83 5	99.68 4

MTAPA		96.65 20	96.38 30	97.47 13	98.95 15	94.05 18	95.88 55	97.62 93	94.46 40	96.29 82	96.94 90	93.56 67	99.37 47	94.29 24	99.42 45	98.99 52

test_djsdf		96.62 21	96.49 25	97.01 31	98.55 39	91.77 56	97.15 11	97.37 110	88.98 156	98.26 20	98.86 10	93.35 74	99.60 8	96.41 4	99.45 41	99.66 6

ACMMP		96.61 22	96.34 31	97.43 17	98.61 32	93.88 25	96.95 16	98.18 32	92.26 77	96.33 78	96.84 99	95.10 41	99.40 36	93.47 46	99.33 57	99.02 49
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
Anonymous20231211		96.60 23	97.13 12	95.00 95	97.46 113	86.35 149	97.11 14	98.24 27	97.58 7	98.72 8	98.97 7	93.15 80	99.15 78	93.18 60	99.74 12	99.50 16

WR-MVS_H		96.60 23	97.05 14	95.24 87	99.02 11	86.44 145	96.78 21	98.08 45	97.42 8	98.48 16	97.86 44	91.76 111	99.63 6	94.23 26	99.84 3	99.66 6

jajsoiax		96.59 25	96.42 26	97.12 27	98.76 26	92.49 46	96.44 33	97.42 108	86.96 197	98.71 10	98.72 17	95.36 30	99.56 16	95.92 8	99.45 41	99.32 25

ACMH	88.36 12	96.59 25	97.43 5	94.07 131	98.56 36	85.33 165	96.33 37	98.30 20	94.66 35	98.72 8	98.30 29	97.51 5	98.00 215	94.87 14	99.59 25	98.86 72
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
XVS		96.49 27	96.18 38	97.44 15	98.56 36	93.99 22	96.50 29	97.95 69	94.58 36	94.38 160	96.49 119	94.56 54	99.39 41	93.57 40	99.05 90	98.93 62

ACMH+	88.43 11	96.48 28	96.82 16	95.47 78	98.54 41	89.06 89	95.65 62	98.61 6	96.10 25	98.16 21	97.52 57	96.90 7	98.62 161	90.30 127	99.60 23	98.72 88

zzz-MVS		96.47 29	96.14 41	97.47 13	98.95 15	94.05 18	93.69 126	97.62 93	94.46 40	96.29 82	96.94 90	93.56 67	99.37 47	94.29 24	99.42 45	98.99 52

APDe-MVS		96.46 30	96.64 21	95.93 58	97.68 100	89.38 86	96.90 17	98.41 13	92.52 69	97.43 40	97.92 40	95.11 40	99.50 19	94.45 19	99.30 61	98.92 66

ACMMPR		96.46 30	96.14 41	97.41 19	98.60 33	93.82 29	96.30 41	97.96 67	92.35 74	95.57 115	96.61 115	94.93 49	99.41 32	93.78 35	99.15 80	99.00 50

mPP-MVS		96.46 30	96.05 47	97.69 5	98.62 30	94.65 9	96.45 31	97.74 87	92.59 68	95.47 118	96.68 110	94.50 56	99.42 27	93.10 64	99.26 69	98.99 52

CP-MVS		96.44 33	96.08 45	97.54 9	98.29 62	94.62 10	96.80 19	98.08 45	92.67 67	95.08 138	96.39 130	94.77 50	99.42 27	93.17 61	99.44 43	98.58 103

ZNCC-MVS		96.42 34	96.20 37	97.07 28	98.80 25	92.79 43	96.08 47	98.16 38	91.74 101	95.34 124	96.36 133	95.68 19	99.44 23	94.41 21	99.28 67	98.97 58

region2R		96.41 35	96.09 44	97.38 21	98.62 30	93.81 31	96.32 38	97.96 67	92.26 77	95.28 128	96.57 117	95.02 45	99.41 32	93.63 39	99.11 85	98.94 61

SteuartSystems-ACMMP		96.40 36	96.30 32	96.71 40	98.63 29	91.96 52	95.70 59	98.01 60	93.34 60	96.64 67	96.57 117	94.99 47	99.36 49	93.48 45	99.34 55	98.82 77
Skip Steuart: Steuart Systems R&D Blog.
HFP-MVS		96.39 37	96.17 40	97.04 29	98.51 45	93.37 35	96.30 41	97.98 63	92.35 74	95.63 112	96.47 120	95.37 27	99.27 67	93.78 35	99.14 81	98.48 108

LPG-MVS_test		96.38 38	96.23 35	96.84 38	98.36 60	92.13 49	95.33 71	98.25 24	91.78 97	97.07 50	97.22 78	96.38 13	99.28 65	92.07 87	99.59 25	99.11 40

nrg030		96.32 39	96.55 24	95.62 73	97.83 89	88.55 102	95.77 58	98.29 23	92.68 65	98.03 24	97.91 42	95.13 39	98.95 109	93.85 33	99.49 36	99.36 23

PGM-MVS		96.32 39	95.94 51	97.43 17	98.59 35	93.84 28	95.33 71	98.30 20	91.40 110	95.76 106	96.87 96	95.26 34	99.45 22	92.77 72	99.21 75	99.00 50

ACMM	88.83 9	96.30 41	96.07 46	96.97 33	98.39 56	92.95 41	94.74 93	98.03 56	90.82 123	97.15 48	96.85 97	96.25 15	99.00 101	93.10 64	99.33 57	98.95 60
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GST-MVS		96.24 42	95.99 50	97.00 32	98.65 28	92.71 44	95.69 61	98.01 60	92.08 82	95.74 108	96.28 138	95.22 36	99.42 27	93.17 61	99.06 87	98.88 70

ACMMP_NAP		96.21 43	96.12 43	96.49 47	98.90 17	91.42 59	94.57 101	98.03 56	90.42 134	96.37 75	97.35 70	95.68 19	99.25 69	94.44 20	99.34 55	98.80 79

CP-MVSNet		96.19 44	96.80 17	94.38 125	98.99 13	83.82 184	96.31 39	97.53 102	97.60 6	98.34 19	97.52 57	91.98 107	99.63 6	93.08 66	99.81 8	99.70 3

MP-MVS		96.14 45	95.68 63	97.51 11	98.81 23	94.06 16	96.10 46	97.78 86	92.73 64	93.48 182	96.72 108	94.23 61	99.42 27	91.99 89	99.29 62	99.05 47
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
LS3D		96.11 46	95.83 58	96.95 35	94.75 238	94.20 14	97.34 9	97.98 63	97.31 10	95.32 125	96.77 101	93.08 82	99.20 74	91.79 95	98.16 183	97.44 186

MP-MVS-pluss		96.08 47	95.92 53	96.57 43	99.06 9	91.21 61	93.25 136	98.32 17	87.89 179	96.86 58	97.38 65	95.55 24	99.39 41	95.47 10	99.47 37	99.11 40
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TranMVSNet+NR-MVSNet		96.07 48	96.26 34	95.50 77	98.26 65	87.69 119	93.75 124	97.86 74	95.96 29	97.48 38	97.14 82	95.33 31	99.44 23	90.79 114	99.76 10	99.38 21

PS-MVSNAJss		96.01 49	96.04 48	95.89 61	98.82 22	88.51 104	95.57 65	97.88 73	88.72 162	98.81 6	98.86 10	90.77 136	99.60 8	95.43 11	99.53 33	99.57 13

SED-MVS		96.00 50	96.41 29	94.76 103	98.51 45	86.97 131	95.21 75	98.10 42	91.95 84	97.63 30	97.25 75	96.48 11	99.35 50	93.29 55	99.29 62	97.95 147

DPE-MVS		95.89 51	95.88 54	95.92 60	97.93 86	89.83 77	93.46 132	98.30 20	92.37 72	97.75 27	96.95 89	95.14 38	99.51 18	91.74 97	99.28 67	98.41 114

#test#		95.89 51	95.51 66	97.04 29	98.51 45	93.37 35	95.14 80	97.98 63	89.34 151	95.63 112	96.47 120	95.37 27	99.27 67	91.99 89	99.14 81	98.48 108

SF-MVS		95.88 53	95.88 54	95.87 62	98.12 72	89.65 80	95.58 64	98.56 7	91.84 93	96.36 76	96.68 110	94.37 59	99.32 60	92.41 81	99.05 90	98.64 94

3Dnovator+	92.74 2	95.86 54	95.77 61	96.13 50	96.81 140	90.79 69	96.30 41	97.82 80	96.13 24	94.74 151	97.23 77	91.33 121	99.16 77	93.25 58	98.30 167	98.46 110

MSP-MVS		95.82 55	96.18 38	94.72 105	98.51 45	86.69 138	95.20 77	97.00 139	91.85 90	97.40 43	97.35 70	95.58 22	99.34 54	93.44 49	99.31 59	98.13 132

SMA-MVS		95.77 56	95.54 65	96.47 48	98.27 64	91.19 62	95.09 81	97.79 85	86.48 201	97.42 42	97.51 59	94.47 58	99.29 63	93.55 42	99.29 62	98.93 62

test_0402		95.73 57	96.22 36	94.26 127	98.19 69	85.77 160	93.24 137	97.24 126	96.88 15	97.69 28	97.77 47	94.12 63	99.13 81	91.54 106	99.29 62	97.88 155

ACMP	88.15 13	95.71 58	95.43 70	96.54 44	98.17 70	91.73 57	94.24 111	98.08 45	89.46 149	96.61 69	96.47 120	95.85 17	99.12 83	90.45 119	99.56 31	98.77 83
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
XVG-ACMP-BASELINE		95.68 59	95.34 72	96.69 41	98.40 55	93.04 38	94.54 105	98.05 51	90.45 133	96.31 80	96.76 103	92.91 86	98.72 147	91.19 110	99.42 45	98.32 117

DP-MVS		95.62 60	95.84 57	94.97 96	97.16 124	88.62 99	94.54 105	97.64 92	96.94 14	96.58 70	97.32 73	93.07 83	98.72 147	90.45 119	98.84 115	97.57 179

OPM-MVS		95.61 61	95.45 68	96.08 51	98.49 53	91.00 64	92.65 153	97.33 118	90.05 139	96.77 63	96.85 97	95.04 43	98.56 170	92.77 72	99.06 87	98.70 89

RPSCF		95.58 62	94.89 87	97.62 8	97.58 105	96.30 4	95.97 51	97.53 102	92.42 70	93.41 183	97.78 45	91.21 127	97.77 235	91.06 111	97.06 232	98.80 79

MIMVSNet1		95.52 63	95.45 68	95.72 70	99.14 4	89.02 90	96.23 44	96.87 153	93.73 52	97.87 25	98.49 24	90.73 140	99.05 92	86.43 204	99.60 23	99.10 43

Anonymous20240529		95.50 64	95.83 58	94.50 117	97.33 119	85.93 157	95.19 79	96.77 159	96.64 18	97.61 33	98.05 33	93.23 77	98.79 133	88.60 168	99.04 95	98.78 81

Vis-MVSNet		95.50 64	95.48 67	95.56 76	98.11 73	89.40 85	95.35 69	98.22 29	92.36 73	94.11 164	98.07 32	92.02 103	99.44 23	93.38 53	97.67 215	97.85 159
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
pm-mvs1		95.43 66	95.94 51	93.93 137	98.38 57	85.08 167	95.46 68	97.12 134	91.84 93	97.28 45	98.46 25	95.30 33	97.71 240	90.17 133	99.42 45	98.99 52

DeepC-MVS	91.39 4	95.43 66	95.33 73	95.71 71	97.67 101	90.17 71	93.86 122	98.02 58	87.35 190	96.22 88	97.99 37	94.48 57	99.05 92	92.73 75	99.68 17	97.93 149
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
XVG-OURS-SEG-HR		95.38 68	95.00 84	96.51 45	98.10 74	94.07 15	92.46 160	98.13 41	90.69 126	93.75 175	96.25 141	98.03 2	97.02 269	92.08 86	95.55 265	98.45 111

UniMVSNet_NR-MVSNet		95.35 69	95.21 78	95.76 68	97.69 99	88.59 100	92.26 172	97.84 78	94.91 33	96.80 61	95.78 162	90.42 145	99.41 32	91.60 103	99.58 29	99.29 27

DVP-MVS		95.34 70	94.63 98	97.48 12	98.67 27	94.05 18	96.41 35	98.18 32	91.26 113	95.12 134	95.15 188	86.60 202	99.50 19	93.43 51	96.81 242	98.89 68

FC-MVSNet-test		95.32 71	95.88 54	93.62 146	98.49 53	81.77 204	95.90 54	98.32 17	93.93 49	97.53 36	97.56 54	88.48 166	99.40 36	92.91 71	99.83 5	99.68 4

UniMVSNet (Re)		95.32 71	95.15 80	95.80 65	97.79 90	88.91 92	92.91 144	98.07 48	93.46 58	96.31 80	95.97 152	90.14 149	99.34 54	92.11 84	99.64 21	99.16 35

Gipuma		95.31 73	95.80 60	93.81 143	97.99 84	90.91 66	96.42 34	97.95 69	96.69 16	91.78 232	98.85 12	91.77 110	95.49 304	91.72 98	99.08 86	95.02 270
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
DU-MVS		95.28 74	95.12 82	95.75 69	97.75 92	88.59 100	92.58 154	97.81 81	93.99 46	96.80 61	95.90 153	90.10 153	99.41 32	91.60 103	99.58 29	99.26 28

NR-MVSNet		95.28 74	95.28 76	95.26 86	97.75 92	87.21 126	95.08 82	97.37 110	93.92 50	97.65 29	95.90 153	90.10 153	99.33 59	90.11 135	99.66 19	99.26 28

TransMVSNet (Re)		95.27 76	96.04 48	92.97 168	98.37 59	81.92 203	95.07 83	96.76 160	93.97 48	97.77 26	98.57 19	95.72 18	97.90 220	88.89 162	99.23 73	99.08 44

SD-MVS		95.19 77	95.73 62	93.55 149	96.62 148	88.88 95	94.67 95	98.05 51	91.26 113	97.25 47	96.40 126	95.42 26	94.36 321	92.72 76	99.19 76	97.40 190
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
VPA-MVSNet		95.14 78	95.67 64	93.58 148	97.76 91	83.15 192	94.58 100	97.58 98	93.39 59	97.05 54	98.04 34	93.25 76	98.51 175	89.75 145	99.59 25	99.08 44

xxxxxxxxxxxxxcwj		95.03 79	94.93 85	95.33 82	97.46 113	88.05 112	92.04 180	98.42 12	87.63 186	96.36 76	96.68 110	94.37 59	99.32 60	92.41 81	99.05 90	98.64 94

HPM-MVS++		95.02 80	94.39 104	96.91 36	97.88 87	93.58 33	94.09 115	96.99 141	91.05 118	92.40 216	95.22 187	91.03 134	99.25 69	92.11 84	98.69 132	97.90 153

APD-MVS		95.00 81	94.69 94	95.93 58	97.38 116	90.88 67	94.59 98	97.81 81	89.22 154	95.46 120	96.17 145	93.42 72	99.34 54	89.30 151	98.87 113	97.56 181
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PMVS	87.21 14	94.97 82	95.33 73	93.91 138	98.97 14	97.16 2	95.54 66	95.85 199	96.47 20	93.40 185	97.46 61	95.31 32	95.47 305	86.18 208	98.78 124	89.11 332
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
TSAR-MVS + MP.		94.96 83	94.75 92	95.57 75	98.86 20	88.69 96	96.37 36	96.81 155	85.23 221	94.75 150	97.12 83	91.85 109	99.40 36	93.45 47	98.33 162	98.62 98
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
SixPastTwentyTwo		94.91 84	95.21 78	93.98 133	98.52 44	83.19 191	95.93 52	94.84 228	94.86 34	98.49 15	98.74 16	81.45 243	99.60 8	94.69 16	99.39 52	99.15 36

FIs		94.90 85	95.35 71	93.55 149	98.28 63	81.76 205	95.33 71	98.14 39	93.05 63	97.07 50	97.18 80	87.65 180	99.29 63	91.72 98	99.69 14	99.61 11

Regformer-4		94.90 85	94.67 96	95.59 74	92.78 282	89.02 90	92.39 164	95.91 196	94.50 38	96.41 73	95.56 173	92.10 102	99.01 100	94.23 26	98.14 185	98.74 85

AllTest		94.88 87	94.51 102	96.00 53	98.02 79	92.17 47	95.26 74	98.43 10	90.48 131	95.04 140	96.74 105	92.54 95	97.86 226	85.11 219	98.98 99	97.98 143

ETH3D-3000-0.1		94.86 88	94.55 100	95.81 63	97.61 103	89.72 78	94.05 116	98.37 14	88.09 175	95.06 139	95.85 155	92.58 93	99.10 86	90.33 126	98.99 98	98.62 98

Regformer-2		94.86 88	94.55 100	95.77 67	92.83 280	89.98 73	91.87 192	96.40 177	94.38 42	96.19 92	95.04 195	92.47 98	99.04 95	93.49 44	98.31 165	98.28 121

FMVSNet1		94.84 90	95.13 81	93.97 134	97.60 104	84.29 173	95.99 48	96.56 169	92.38 71	97.03 55	98.53 21	90.12 150	98.98 102	88.78 164	99.16 79	98.65 90

ANet_high		94.83 91	96.28 33	90.47 245	96.65 145	73.16 309	94.33 109	98.74 5	96.39 22	98.09 23	98.93 8	93.37 73	98.70 154	90.38 122	99.68 17	99.53 14

testtj		94.81 92	94.42 103	96.01 52	97.23 121	90.51 70	94.77 92	97.85 77	91.29 112	94.92 145	95.66 166	91.71 112	99.40 36	88.07 176	98.25 173	98.11 134

3Dnovator	92.54 3	94.80 93	94.90 86	94.47 120	95.47 219	87.06 128	96.63 23	97.28 124	91.82 96	94.34 162	97.41 63	90.60 143	98.65 160	92.47 80	98.11 189	97.70 171

CPTT-MVS		94.74 94	94.12 115	96.60 42	98.15 71	93.01 39	95.84 56	97.66 91	89.21 155	93.28 189	95.46 177	88.89 163	98.98 102	89.80 142	98.82 119	97.80 164

XVG-OURS		94.72 95	94.12 115	96.50 46	98.00 81	94.23 13	91.48 205	98.17 35	90.72 125	95.30 126	96.47 120	87.94 177	96.98 270	91.41 108	97.61 218	98.30 120

CSCG		94.69 96	94.75 92	94.52 116	97.55 107	87.87 116	95.01 86	97.57 99	92.68 65	96.20 90	93.44 248	91.92 108	98.78 137	89.11 159	99.24 72	96.92 206

v10		94.68 97	95.27 77	92.90 173	96.57 151	80.15 223	94.65 97	97.57 99	90.68 127	97.43 40	98.00 36	88.18 170	99.15 78	94.84 15	99.55 32	99.41 19

v8		94.65 98	95.29 75	92.74 178	96.65 145	79.77 237	94.59 98	97.17 130	91.86 89	97.47 39	97.93 39	88.16 171	99.08 87	94.32 22	99.47 37	99.38 21

canonicalmvs		94.59 99	94.69 94	94.30 126	95.60 216	87.03 130	95.59 63	98.24 27	91.56 107	95.21 133	92.04 278	94.95 48	98.66 158	91.45 107	97.57 219	97.20 199

CNVR-MVS		94.58 100	94.29 109	95.46 79	96.94 132	89.35 87	91.81 198	96.80 156	89.66 146	93.90 173	95.44 179	92.80 90	98.72 147	92.74 74	98.52 144	98.32 117

Regformer-1		94.55 101	94.33 108	95.19 89	92.83 280	88.54 103	91.87 192	95.84 200	93.99 46	95.95 100	95.04 195	92.00 104	98.79 133	93.14 63	98.31 165	98.23 124

EG-PatchMatch MVS		94.54 102	94.67 96	94.14 129	97.87 88	86.50 141	92.00 183	96.74 161	88.16 174	96.93 57	97.61 52	93.04 84	97.90 220	91.60 103	98.12 188	98.03 139

IS-MVSNet		94.49 103	94.35 107	94.92 97	98.25 66	86.46 144	97.13 13	94.31 242	96.24 23	96.28 85	96.36 133	82.88 227	99.35 50	88.19 172	99.52 35	98.96 59

Baseline_NR-MVSNet		94.47 104	95.09 83	92.60 185	98.50 52	80.82 219	92.08 178	96.68 163	93.82 51	96.29 82	98.56 20	90.10 153	97.75 238	90.10 137	99.66 19	99.24 30

VDD-MVS		94.37 105	94.37 106	94.40 124	97.49 110	86.07 155	93.97 120	93.28 258	94.49 39	96.24 86	97.78 45	87.99 176	98.79 133	88.92 161	99.14 81	98.34 116

EI-MVSNet-Vis-set		94.36 106	94.28 110	94.61 108	92.55 284	85.98 156	92.44 161	94.69 235	93.70 53	96.12 95	95.81 159	91.24 125	98.86 121	93.76 38	98.22 178	98.98 57

EI-MVSNet-UG-set		94.35 107	94.27 112	94.59 113	92.46 285	85.87 158	92.42 163	94.69 235	93.67 57	96.13 94	95.84 158	91.20 128	98.86 121	93.78 35	98.23 176	99.03 48

PHI-MVS		94.34 108	93.80 120	95.95 55	95.65 212	91.67 58	94.82 90	97.86 74	87.86 180	93.04 199	94.16 227	91.58 115	98.78 137	90.27 129	98.96 105	97.41 187

casdiffmvs		94.32 109	94.80 90	92.85 175	96.05 186	81.44 210	92.35 167	98.05 51	91.53 108	95.75 107	96.80 100	93.35 74	98.49 176	91.01 112	98.32 164	98.64 94

Regformer-3		94.28 110	94.23 114	94.46 121	92.78 282	86.28 151	92.39 164	94.70 234	93.69 56	95.97 98	95.56 173	91.34 120	98.48 180	93.45 47	98.14 185	98.62 98

tfpnnormal		94.27 111	94.87 88	92.48 189	97.71 96	80.88 218	94.55 104	95.41 215	93.70 53	96.67 66	97.72 48	91.40 119	98.18 202	87.45 187	99.18 78	98.36 115

HQP_MVS		94.26 112	93.93 117	95.23 88	97.71 96	88.12 110	94.56 102	97.81 81	91.74 101	93.31 186	95.59 168	86.93 194	98.95 109	89.26 155	98.51 146	98.60 101

baseline		94.26 112	94.80 90	92.64 181	96.08 184	80.99 216	93.69 126	98.04 55	90.80 124	94.89 146	96.32 135	93.19 78	98.48 180	91.68 101	98.51 146	98.43 112

OMC-MVS		94.22 114	93.69 125	95.81 63	97.25 120	91.27 60	92.27 171	97.40 109	87.10 196	94.56 155	95.42 180	93.74 65	98.11 207	86.62 199	98.85 114	98.06 135

LCM-MVSNet-Re		94.20 115	94.58 99	93.04 164	95.91 197	83.13 193	93.79 123	99.19 2	92.00 83	98.84 5	98.04 34	93.64 66	99.02 98	81.28 256	98.54 142	96.96 205

DeepPCF-MVS	90.46 6	94.20 115	93.56 130	96.14 49	95.96 193	92.96 40	89.48 259	97.46 106	85.14 224	96.23 87	95.42 180	93.19 78	98.08 208	90.37 123	98.76 126	97.38 193

NCCC		94.08 117	93.54 131	95.70 72	96.49 155	89.90 76	92.39 164	96.91 149	90.64 128	92.33 222	94.60 213	90.58 144	98.96 107	90.21 132	97.70 213	98.23 124

testing_2		94.03 118	94.38 105	93.00 166	96.79 142	81.41 211	92.87 146	96.96 142	85.88 213	97.06 53	97.92 40	91.18 131	98.71 153	91.72 98	99.04 95	98.87 71

VDDNet		94.03 118	94.27 112	93.31 158	98.87 19	82.36 199	95.51 67	91.78 285	97.19 11	96.32 79	98.60 18	84.24 219	98.75 142	87.09 192	98.83 118	98.81 78

ETH3D cwj APD-0.16		93.99 120	93.38 135	95.80 65	96.82 138	89.92 74	92.72 149	98.02 58	84.73 233	93.65 179	95.54 175	91.68 113	99.22 72	88.78 164	98.49 149	98.26 123

EPP-MVSNet		93.91 121	93.68 126	94.59 113	98.08 75	85.55 163	97.44 8	94.03 247	94.22 43	94.94 143	96.19 143	82.07 238	99.57 13	87.28 191	98.89 108	98.65 90

Effi-MVS+-dtu		93.90 122	92.60 154	97.77 4	94.74 239	96.67 3	94.00 118	95.41 215	89.94 140	91.93 230	92.13 276	90.12 150	98.97 106	87.68 183	97.48 221	97.67 174

IterMVS-LS		93.78 123	94.28 110	92.27 192	96.27 171	79.21 248	91.87 192	96.78 157	91.77 99	96.57 71	97.07 85	87.15 189	98.74 145	91.99 89	99.03 97	98.86 72
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DeepC-MVS_fast	89.96 7	93.73 124	93.44 133	94.60 112	96.14 181	87.90 115	93.36 135	97.14 131	85.53 218	93.90 173	95.45 178	91.30 123	98.59 166	89.51 148	98.62 135	97.31 196
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MVS_111021_LR		93.66 125	93.28 138	94.80 101	96.25 174	90.95 65	90.21 237	95.43 214	87.91 177	93.74 177	94.40 218	92.88 88	96.38 290	90.39 121	98.28 168	97.07 200

MVS_111021_HR		93.63 126	93.42 134	94.26 127	96.65 145	86.96 133	89.30 265	96.23 185	88.36 171	93.57 181	94.60 213	93.45 69	97.77 235	90.23 131	98.38 155	98.03 139

v1144		93.50 127	93.81 119	92.57 186	96.28 170	79.61 240	91.86 196	96.96 142	86.95 198	95.91 103	96.32 135	87.65 180	98.96 107	93.51 43	98.88 110	99.13 38

v1192		93.49 128	93.78 121	92.62 184	96.16 180	79.62 239	91.83 197	97.22 128	86.07 209	96.10 96	96.38 131	87.22 187	99.02 98	94.14 29	98.88 110	99.22 31

WR-MVS		93.49 128	93.72 123	92.80 177	97.57 106	80.03 229	90.14 241	95.68 203	93.70 53	96.62 68	95.39 183	87.21 188	99.04 95	87.50 186	99.64 21	99.33 24

V42		93.43 130	93.58 129	92.97 168	95.34 225	81.22 213	92.67 152	96.49 174	87.25 192	96.20 90	96.37 132	87.32 186	98.85 123	92.39 83	98.21 179	98.85 75

K. test v3		93.37 131	93.27 139	93.66 145	98.05 76	82.62 197	94.35 108	86.62 313	96.05 27	97.51 37	98.85 12	76.59 279	99.65 3	93.21 59	98.20 181	98.73 87

PM-MVS		93.33 132	92.67 152	95.33 82	96.58 150	94.06 16	92.26 172	92.18 278	85.92 212	96.22 88	96.61 115	85.64 213	95.99 297	90.35 124	98.23 176	95.93 243

v1240		93.29 133	93.71 124	92.06 201	96.01 191	77.89 265	91.81 198	97.37 110	85.12 225	96.69 65	96.40 126	86.67 200	99.07 91	94.51 18	98.76 126	99.22 31

test_prior3		93.29 133	92.85 145	94.61 108	95.95 194	87.23 124	90.21 237	97.36 115	89.33 152	90.77 245	94.81 205	90.41 146	98.68 156	88.21 170	98.55 139	97.93 149

v2v482		93.29 133	93.63 127	92.29 191	96.35 164	78.82 253	91.77 200	96.28 181	88.45 168	95.70 111	96.26 140	86.02 208	98.90 113	93.02 67	98.81 121	99.14 37

alignmvs		93.26 136	92.85 145	94.50 117	95.70 208	87.45 120	93.45 133	95.76 201	91.58 106	95.25 130	92.42 272	81.96 240	98.72 147	91.61 102	97.87 205	97.33 195

v1921920		93.26 136	93.61 128	92.19 195	96.04 190	78.31 259	91.88 191	97.24 126	85.17 223	96.19 92	96.19 143	86.76 199	99.05 92	94.18 28	98.84 115	99.22 31

MSLP-MVS++		93.25 138	93.88 118	91.37 218	96.34 165	82.81 196	93.11 138	97.74 87	89.37 150	94.08 166	95.29 186	90.40 148	96.35 292	90.35 124	98.25 173	94.96 271

GBi-Net		93.21 139	92.96 142	93.97 134	95.40 221	84.29 173	95.99 48	96.56 169	88.63 163	95.10 135	98.53 21	81.31 245	98.98 102	86.74 195	98.38 155	98.65 90

test1		93.21 139	92.96 142	93.97 134	95.40 221	84.29 173	95.99 48	96.56 169	88.63 163	95.10 135	98.53 21	81.31 245	98.98 102	86.74 195	98.38 155	98.65 90

v144192		93.20 141	93.54 131	92.16 198	96.05 186	78.26 260	91.95 184	97.14 131	84.98 229	95.96 99	96.11 146	87.08 191	99.04 95	93.79 34	98.84 115	99.17 34

VPNet		93.08 142	93.76 122	91.03 230	98.60 33	75.83 292	91.51 204	95.62 204	91.84 93	95.74 108	97.10 84	89.31 160	98.32 189	85.07 221	99.06 87	98.93 62

UGNet		93.08 142	92.50 156	94.79 102	93.87 263	87.99 114	95.07 83	94.26 244	90.64 128	87.33 300	97.67 50	86.89 197	98.49 176	88.10 175	98.71 129	97.91 152
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-test1		93.07 144	91.80 171	96.89 37	94.74 239	95.83 6	92.17 175	95.41 215	89.94 140	89.85 263	90.59 301	90.12 150	98.88 116	87.68 183	95.66 263	95.97 241

TSAR-MVS + GP.		93.07 144	92.41 158	95.06 94	95.82 201	90.87 68	90.97 216	92.61 272	88.04 176	94.61 154	93.79 240	88.08 172	97.81 230	89.41 150	98.39 153	96.50 220

ETV-MVS		92.99 146	92.74 149	93.72 144	95.86 200	86.30 150	92.33 168	97.84 78	91.70 104	92.81 204	86.17 333	92.22 99	99.19 75	88.03 177	97.73 209	95.66 256

EI-MVSNet		92.99 146	93.26 140	92.19 195	92.12 292	79.21 248	92.32 169	94.67 237	91.77 99	95.24 131	95.85 155	87.14 190	98.49 176	91.99 89	98.26 170	98.86 72

MCST-MVS		92.91 148	92.51 155	94.10 130	97.52 108	85.72 161	91.36 209	97.13 133	80.33 262	92.91 203	94.24 223	91.23 126	98.72 147	89.99 139	97.93 202	97.86 157

QAPM		92.88 149	92.77 147	93.22 162	95.82 201	83.31 188	96.45 31	97.35 117	83.91 237	93.75 175	96.77 101	89.25 161	98.88 116	84.56 227	97.02 234	97.49 183

v148		92.87 150	93.29 136	91.62 212	96.25 174	77.72 267	91.28 210	95.05 221	89.69 145	95.93 102	96.04 148	87.34 185	98.38 185	90.05 138	97.99 199	98.78 81

Effi-MVS+		92.79 151	92.74 149	92.94 171	95.10 228	83.30 189	94.00 118	97.53 102	91.36 111	89.35 272	90.65 300	94.01 64	98.66 158	87.40 189	95.30 273	96.88 209

FMVSNet2		92.78 152	92.73 151	92.95 170	95.40 221	81.98 202	94.18 113	95.53 212	88.63 163	96.05 97	97.37 66	81.31 245	98.81 131	87.38 190	98.67 133	98.06 135

Fast-Effi-MVS+-dtu		92.77 153	92.16 161	94.58 115	94.66 245	88.25 107	92.05 179	96.65 165	89.62 147	90.08 257	91.23 288	92.56 94	98.60 164	86.30 206	96.27 254	96.90 207

LF4IMVS		92.72 154	92.02 165	94.84 100	95.65 212	91.99 51	92.92 143	96.60 167	85.08 227	92.44 214	93.62 243	86.80 198	96.35 292	86.81 194	98.25 173	96.18 234

train_agg		92.71 155	91.83 169	95.35 80	96.45 157	89.46 81	90.60 225	96.92 147	79.37 271	90.49 250	94.39 219	91.20 128	98.88 116	88.66 167	98.43 151	97.72 170

VNet		92.67 156	92.96 142	91.79 206	96.27 171	80.15 223	91.95 184	94.98 223	92.19 80	94.52 157	96.07 147	87.43 184	97.39 258	84.83 223	98.38 155	97.83 160

CDPH-MVS		92.67 156	91.83 169	95.18 90	96.94 132	88.46 105	90.70 223	97.07 136	77.38 287	92.34 221	95.08 193	92.67 92	98.88 116	85.74 210	98.57 138	98.20 127

agg_prior1		92.60 158	91.76 172	95.10 93	96.20 176	88.89 93	90.37 232	96.88 151	79.67 268	90.21 254	94.41 217	91.30 123	98.78 137	88.46 169	98.37 160	97.64 176

Anonymous202405211		92.58 159	92.50 156	92.83 176	96.55 152	83.22 190	92.43 162	91.64 286	94.10 45	95.59 114	96.64 113	81.88 242	97.50 249	85.12 218	98.52 144	97.77 166

XXY-MVS		92.58 159	93.16 141	90.84 239	97.75 92	79.84 233	91.87 192	96.22 187	85.94 211	95.53 117	97.68 49	92.69 91	94.48 317	83.21 237	97.51 220	98.21 126

MVS_Test		92.57 161	93.29 136	90.40 247	93.53 267	75.85 290	92.52 156	96.96 142	88.73 161	92.35 219	96.70 109	90.77 136	98.37 188	92.53 79	95.49 267	96.99 204

CS-MVS		92.54 162	92.31 159	93.23 161	95.89 199	84.07 181	93.58 129	98.48 8	88.60 166	90.41 253	86.23 332	92.00 104	99.35 50	87.54 185	98.06 193	96.26 230

TAPA-MVS	88.58 10	92.49 163	91.75 173	94.73 104	96.50 154	89.69 79	92.91 144	97.68 90	78.02 285	92.79 205	94.10 228	90.85 135	97.96 219	84.76 225	98.16 183	96.54 215
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
ab-mvs		92.40 164	92.62 153	91.74 208	97.02 129	81.65 206	95.84 56	95.50 213	86.95 198	92.95 202	97.56 54	90.70 141	97.50 249	79.63 273	97.43 223	96.06 238

CANet		92.38 165	91.99 166	93.52 153	93.82 265	83.46 187	91.14 212	97.00 139	89.81 144	86.47 304	94.04 230	87.90 178	99.21 73	89.50 149	98.27 169	97.90 153

EIA-MVS		92.35 166	92.03 164	93.30 159	95.81 203	83.97 182	92.80 148	98.17 35	87.71 183	89.79 266	87.56 322	91.17 132	99.18 76	87.97 178	97.27 227	96.77 212

DP-MVS Recon		92.31 167	91.88 168	93.60 147	97.18 123	86.87 134	91.10 214	97.37 110	84.92 230	92.08 227	94.08 229	88.59 165	98.20 199	83.50 234	98.14 185	95.73 252

F-COLMAP		92.28 168	91.06 189	95.95 55	97.52 108	91.90 53	93.53 130	97.18 129	83.98 236	88.70 284	94.04 230	88.41 168	98.55 172	80.17 267	95.99 257	97.39 191

OpenMVS	89.45 8	92.27 169	92.13 163	92.68 180	94.53 248	84.10 179	95.70 59	97.03 137	82.44 251	91.14 242	96.42 124	88.47 167	98.38 185	85.95 209	97.47 222	95.55 260

MVSFormer		92.18 170	92.23 160	92.04 202	94.74 239	80.06 227	97.15 11	97.37 110	88.98 156	88.83 276	92.79 261	77.02 275	99.60 8	96.41 4	96.75 245	96.46 222

HQP-MVS		92.09 171	91.49 179	93.88 140	96.36 161	84.89 168	91.37 206	97.31 119	87.16 193	88.81 278	93.40 249	84.76 216	98.60 164	86.55 201	97.73 209	98.14 130

DELS-MVS		92.05 172	92.16 161	91.72 209	94.44 249	80.13 225	87.62 286	97.25 125	87.34 191	92.22 224	93.18 255	89.54 159	98.73 146	89.67 146	98.20 181	96.30 228
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
TinyColmap		92.00 173	92.76 148	89.71 262	95.62 215	77.02 275	90.72 222	96.17 190	87.70 184	95.26 129	96.29 137	92.54 95	96.45 287	81.77 251	98.77 125	95.66 256

ETH3 D test6400		91.91 174	91.25 185	93.89 139	96.59 149	84.41 172	92.10 177	97.72 89	78.52 281	91.82 231	93.78 241	88.70 164	99.13 81	83.61 233	98.39 153	98.14 130

CLD-MVS		91.82 175	91.41 181	93.04 164	96.37 159	83.65 186	86.82 305	97.29 122	84.65 234	92.27 223	89.67 310	92.20 100	97.85 228	83.95 231	99.47 37	97.62 177
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
diffmvs		91.74 176	91.93 167	91.15 228	93.06 275	78.17 261	88.77 276	97.51 105	86.28 205	92.42 215	93.96 235	88.04 174	97.46 252	90.69 117	96.67 247	97.82 162

CNLPA		91.72 177	91.20 186	93.26 160	96.17 179	91.02 63	91.14 212	95.55 211	90.16 138	90.87 244	93.56 246	86.31 204	94.40 320	79.92 272	97.12 231	94.37 284

IterMVS-SCA-FT		91.65 178	91.55 175	91.94 203	93.89 262	79.22 247	87.56 289	93.51 255	91.53 108	95.37 123	96.62 114	78.65 260	98.90 113	91.89 94	94.95 279	97.70 171

PVSNet_Blended_VisFu		91.63 179	91.20 186	92.94 171	97.73 95	83.95 183	92.14 176	97.46 106	78.85 280	92.35 219	94.98 198	84.16 220	99.08 87	86.36 205	96.77 244	95.79 250

AdaColmap		91.63 179	91.36 182	92.47 190	95.56 217	86.36 148	92.24 174	96.27 182	88.88 160	89.90 262	92.69 264	91.65 114	98.32 189	77.38 292	97.64 216	92.72 315

pmmvs-eth3d		91.54 181	90.73 197	93.99 132	95.76 206	87.86 117	90.83 219	93.98 250	78.23 284	94.02 171	96.22 142	82.62 233	96.83 276	86.57 200	98.33 162	97.29 197

API-MVS		91.52 182	91.61 174	91.26 222	94.16 254	86.26 152	94.66 96	94.82 229	91.17 116	92.13 226	91.08 291	90.03 156	97.06 268	79.09 280	97.35 226	90.45 330

xiu_mvs_v1_base_debu		91.47 183	91.52 176	91.33 219	95.69 209	81.56 207	89.92 248	96.05 193	83.22 241	91.26 238	90.74 295	91.55 116	98.82 126	89.29 152	95.91 258	93.62 303

xiu_mvs_v1_base		91.47 183	91.52 176	91.33 219	95.69 209	81.56 207	89.92 248	96.05 193	83.22 241	91.26 238	90.74 295	91.55 116	98.82 126	89.29 152	95.91 258	93.62 303

xiu_mvs_v1_base_debi		91.47 183	91.52 176	91.33 219	95.69 209	81.56 207	89.92 248	96.05 193	83.22 241	91.26 238	90.74 295	91.55 116	98.82 126	89.29 152	95.91 258	93.62 303

RRT_MVS		91.36 186	90.05 209	95.29 85	89.21 329	88.15 109	92.51 159	94.89 226	86.73 200	95.54 116	95.68 165	61.82 331	99.30 62	94.91 13	99.13 84	98.43 112

LFMVS		91.33 187	91.16 188	91.82 205	96.27 171	79.36 243	95.01 86	85.61 324	96.04 28	94.82 148	97.06 86	72.03 293	98.46 182	84.96 222	98.70 131	97.65 175

cl_fuxian		91.32 188	91.42 180	91.00 233	92.29 287	76.79 281	87.52 292	96.42 176	85.76 216	94.72 153	93.89 237	82.73 230	98.16 204	90.93 113	98.55 139	98.04 138

Fast-Effi-MVS+		91.28 189	90.86 192	92.53 188	95.45 220	82.53 198	89.25 268	96.52 173	85.00 228	89.91 261	88.55 318	92.94 85	98.84 124	84.72 226	95.44 269	96.22 232

MDA-MVSNet-bldmvs		91.04 190	90.88 191	91.55 214	94.68 244	80.16 222	85.49 316	92.14 281	90.41 135	94.93 144	95.79 160	85.10 214	96.93 273	85.15 216	94.19 296	97.57 179

PAPM_NR		91.03 191	90.81 194	91.68 211	96.73 143	81.10 215	93.72 125	96.35 180	88.19 173	88.77 282	92.12 277	85.09 215	97.25 262	82.40 246	93.90 297	96.68 214

MVS_0304		90.96 192	90.15 208	93.37 155	93.17 272	87.06 128	93.62 128	92.43 276	89.60 148	82.25 329	95.50 176	82.56 234	97.83 229	84.41 229	97.83 207	95.22 264

MSDG		90.82 193	90.67 198	91.26 222	94.16 254	83.08 194	86.63 310	96.19 188	90.60 130	91.94 229	91.89 279	89.16 162	95.75 299	80.96 262	94.51 289	94.95 272

test20.03		90.80 194	90.85 193	90.63 242	95.63 214	79.24 246	89.81 253	92.87 264	89.90 142	94.39 159	96.40 126	85.77 209	95.27 312	73.86 309	99.05 90	97.39 191

FMVSNet3		90.78 195	90.32 205	92.16 198	93.03 277	79.92 232	92.54 155	94.95 224	86.17 208	95.10 135	96.01 150	69.97 297	98.75 142	86.74 195	98.38 155	97.82 162

eth_miper_zixun_eth		90.72 196	90.61 199	91.05 229	92.04 294	76.84 280	86.91 301	96.67 164	85.21 222	94.41 158	93.92 236	79.53 255	98.26 195	89.76 144	97.02 234	98.06 135

X-MVStestdata		90.70 197	88.45 234	97.44 15	98.56 36	93.99 22	96.50 29	97.95 69	94.58 36	94.38 160	26.89 348	94.56 54	99.39 41	93.57 40	99.05 90	98.93 62

BH-untuned		90.68 198	90.90 190	90.05 259	95.98 192	79.57 241	90.04 244	94.94 225	87.91 177	94.07 167	93.00 257	87.76 179	97.78 234	79.19 279	95.17 276	92.80 313

cl-mvsnet_		90.65 199	90.56 200	90.91 237	91.85 296	76.98 278	86.75 306	95.36 218	85.53 218	94.06 168	94.89 202	77.36 273	97.98 218	90.27 129	98.98 99	97.76 167

cl-mvsnet1		90.65 199	90.56 200	90.91 237	91.85 296	76.99 277	86.75 306	95.36 218	85.52 220	94.06 168	94.89 202	77.37 272	97.99 217	90.28 128	98.97 103	97.76 167

114514_t		90.51 201	89.80 212	92.63 183	98.00 81	82.24 200	93.40 134	97.29 122	65.84 336	89.40 271	94.80 208	86.99 192	98.75 142	83.88 232	98.61 136	96.89 208

miper_ehance_all_eth		90.48 202	90.42 203	90.69 240	91.62 301	76.57 283	86.83 304	96.18 189	83.38 239	94.06 168	92.66 266	82.20 236	98.04 210	89.79 143	97.02 234	97.45 185

BH-RMVSNet		90.47 203	90.44 202	90.56 244	95.21 227	78.65 257	89.15 269	93.94 251	88.21 172	92.74 206	94.22 224	86.38 203	97.88 222	78.67 282	95.39 271	95.14 267

Vis-MVSNet (Re-imp)		90.42 204	90.16 206	91.20 226	97.66 102	77.32 272	94.33 109	87.66 307	91.20 115	92.99 200	95.13 190	75.40 282	98.28 191	77.86 285	99.19 76	97.99 142

PLC	85.34 15	90.40 205	88.92 226	94.85 99	96.53 153	90.02 72	91.58 203	96.48 175	80.16 263	86.14 306	92.18 274	85.73 210	98.25 196	76.87 295	94.61 288	96.30 228
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
testgi		90.38 206	91.34 183	87.50 295	97.49 110	71.54 318	89.43 260	95.16 220	88.38 170	94.54 156	94.68 212	92.88 88	93.09 331	71.60 322	97.85 206	97.88 155

mvs_anonymous		90.37 207	91.30 184	87.58 294	92.17 291	68.00 329	89.84 252	94.73 233	83.82 238	93.22 193	97.40 64	87.54 182	97.40 257	87.94 179	95.05 278	97.34 194

PVSNet_BlendedMVS		90.35 208	89.96 210	91.54 215	94.81 235	78.80 255	90.14 241	96.93 145	79.43 270	88.68 285	95.06 194	86.27 205	98.15 205	80.27 264	98.04 196	97.68 173

UnsupCasMVSNet_eth		90.33 209	90.34 204	90.28 249	94.64 246	80.24 221	89.69 255	95.88 197	85.77 215	93.94 172	95.69 164	81.99 239	92.98 332	84.21 230	91.30 322	97.62 177

MAR-MVS		90.32 210	88.87 229	94.66 107	94.82 234	91.85 54	94.22 112	94.75 232	80.91 257	87.52 298	88.07 321	86.63 201	97.87 225	76.67 296	96.21 255	94.25 287
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
1121		90.26 211	89.23 218	93.34 156	97.15 126	87.40 121	91.94 186	94.39 240	67.88 331	91.02 243	94.91 201	86.91 196	98.59 166	81.17 259	97.71 212	94.02 293

IterMVS		90.18 212	90.16 206	90.21 254	93.15 273	75.98 289	87.56 289	92.97 263	86.43 203	94.09 165	96.40 126	78.32 264	97.43 254	87.87 180	94.69 286	97.23 198
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TAMVS		90.16 213	89.05 222	93.49 154	96.49 155	86.37 147	90.34 234	92.55 273	80.84 260	92.99 200	94.57 215	81.94 241	98.20 199	73.51 310	98.21 179	95.90 246

test_yl		90.11 214	89.73 215	91.26 222	94.09 257	79.82 234	90.44 229	92.65 270	90.90 119	93.19 194	93.30 251	73.90 285	98.03 211	82.23 247	96.87 240	95.93 243

DCV-MVSNet		90.11 214	89.73 215	91.26 222	94.09 257	79.82 234	90.44 229	92.65 270	90.90 119	93.19 194	93.30 251	73.90 285	98.03 211	82.23 247	96.87 240	95.93 243

Patchmtry		90.11 214	89.92 211	90.66 241	90.35 317	77.00 276	92.96 142	92.81 265	90.25 137	94.74 151	96.93 92	67.11 303	97.52 248	85.17 214	98.98 99	97.46 184

MVP-Stereo		90.07 217	88.92 226	93.54 151	96.31 168	86.49 142	90.93 217	95.59 208	79.80 264	91.48 234	95.59 168	80.79 249	97.39 258	78.57 283	91.19 323	96.76 213
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
D2MVS		89.93 218	89.60 217	90.92 235	94.03 259	78.40 258	88.69 278	94.85 227	78.96 278	93.08 196	95.09 192	74.57 283	96.94 271	88.19 172	98.96 105	97.41 187

miper_lstm_enhance		89.90 219	89.80 212	90.19 256	91.37 305	77.50 269	83.82 331	95.00 222	84.84 231	93.05 198	94.96 199	76.53 280	95.20 313	89.96 140	98.67 133	97.86 157

CANet_DTU		89.85 220	89.17 220	91.87 204	92.20 290	80.02 230	90.79 220	95.87 198	86.02 210	82.53 328	91.77 281	80.01 252	98.57 169	85.66 211	97.70 213	97.01 203

tttt0517		89.81 221	88.90 228	92.55 187	97.00 130	79.73 238	95.03 85	83.65 336	89.88 143	95.30 126	94.79 209	53.64 345	99.39 41	91.99 89	98.79 123	98.54 104

EPNet		89.80 222	88.25 238	94.45 122	83.91 350	86.18 153	93.87 121	87.07 311	91.16 117	80.64 338	94.72 210	78.83 258	98.89 115	85.17 214	98.89 108	98.28 121
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CDS-MVSNet		89.55 223	88.22 241	93.53 152	95.37 224	86.49 142	89.26 266	93.59 253	79.76 266	91.15 241	92.31 273	77.12 274	98.38 185	77.51 290	97.92 203	95.71 253
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MG-MVS		89.54 224	89.80 212	88.76 277	94.88 231	72.47 315	89.60 256	92.44 275	85.82 214	89.48 270	95.98 151	82.85 228	97.74 239	81.87 250	95.27 274	96.08 237

OpenMVS_ROB	85.12 16	89.52 225	89.05 222	90.92 235	94.58 247	81.21 214	91.10 214	93.41 257	77.03 291	93.41 183	93.99 234	83.23 224	97.80 231	79.93 271	94.80 283	93.74 300

DPM-MVS		89.35 226	88.40 235	92.18 197	96.13 183	84.20 177	86.96 300	96.15 191	75.40 298	87.36 299	91.55 286	83.30 223	98.01 214	82.17 249	96.62 248	94.32 286

MVSTER		89.32 227	88.75 230	91.03 230	90.10 319	76.62 282	90.85 218	94.67 237	82.27 252	95.24 131	95.79 160	61.09 334	98.49 176	90.49 118	98.26 170	97.97 146

RPMNet		89.30 228	89.00 224	90.22 252	91.01 308	78.93 250	92.52 156	87.85 306	91.91 87	89.10 273	96.89 95	68.84 298	97.64 243	90.17 133	92.70 313	94.08 288

PatchMatch-RL		89.18 229	88.02 246	92.64 181	95.90 198	92.87 42	88.67 280	91.06 289	80.34 261	90.03 259	91.67 283	83.34 222	94.42 319	76.35 299	94.84 282	90.64 329

jason		89.17 230	88.32 236	91.70 210	95.73 207	80.07 226	88.10 283	93.22 259	71.98 314	90.09 256	92.79 261	78.53 263	98.56 170	87.43 188	97.06 232	96.46 222
jason: jason.
PCF-MVS	84.52 17	89.12 231	87.71 249	93.34 156	96.06 185	85.84 159	86.58 312	97.31 119	68.46 329	93.61 180	93.89 237	87.51 183	98.52 174	67.85 333	98.11 189	95.66 256
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
cl-mvsnet2		89.02 232	88.50 233	90.59 243	89.76 321	76.45 284	86.62 311	94.03 247	82.98 247	92.65 208	92.49 267	72.05 292	97.53 247	88.93 160	97.02 234	97.78 165

USDC		89.02 232	89.08 221	88.84 276	95.07 229	74.50 300	88.97 271	96.39 178	73.21 308	93.27 190	96.28 138	82.16 237	96.39 289	77.55 289	98.80 122	95.62 259

xiu_mvs_v2_base		89.00 234	89.19 219	88.46 284	94.86 233	74.63 297	86.97 299	95.60 205	80.88 258	87.83 294	88.62 317	91.04 133	98.81 131	82.51 245	94.38 290	91.93 321

new-patchmatchnet		88.97 235	90.79 195	83.50 320	94.28 253	55.83 350	85.34 317	93.56 254	86.18 207	95.47 118	95.73 163	83.10 225	96.51 285	85.40 213	98.06 193	98.16 128

pmmvs4		88.95 236	87.70 250	92.70 179	94.30 252	85.60 162	87.22 295	92.16 280	74.62 300	89.75 268	94.19 225	77.97 267	96.41 288	82.71 241	96.36 253	96.09 236

N_pmnet		88.90 237	87.25 256	93.83 142	94.40 251	93.81 31	84.73 321	87.09 310	79.36 273	93.26 191	92.43 271	79.29 256	91.68 336	77.50 291	97.22 229	96.00 240

PS-MVSNAJ		88.86 238	88.99 225	88.48 283	94.88 231	74.71 295	86.69 308	95.60 205	80.88 258	87.83 294	87.37 325	90.77 136	98.82 126	82.52 244	94.37 291	91.93 321

Patchmatch-RL test		88.81 239	88.52 232	89.69 263	95.33 226	79.94 231	86.22 313	92.71 269	78.46 282	95.80 105	94.18 226	66.25 311	95.33 310	89.22 157	98.53 143	93.78 298

Anonymous20231206		88.77 240	88.29 237	90.20 255	96.31 168	78.81 254	89.56 258	93.49 256	74.26 302	92.38 217	95.58 171	82.21 235	95.43 307	72.07 318	98.75 128	96.34 226

PVSNet_Blended		88.74 241	88.16 244	90.46 246	94.81 235	78.80 255	86.64 309	96.93 145	74.67 299	88.68 285	89.18 314	86.27 205	98.15 205	80.27 264	96.00 256	94.44 283

thisisatest0530		88.69 242	87.52 252	92.20 194	96.33 166	79.36 243	92.81 147	84.01 335	86.44 202	93.67 178	92.68 265	53.62 346	99.25 69	89.65 147	98.45 150	98.00 141

ppachtmachnet_test		88.61 243	88.64 231	88.50 282	91.76 298	70.99 321	84.59 324	92.98 262	79.30 275	92.38 217	93.53 247	79.57 254	97.45 253	86.50 203	97.17 230	97.07 200

UnsupCasMVSNet_bld		88.50 244	88.03 245	89.90 260	95.52 218	78.88 252	87.39 293	94.02 249	79.32 274	93.06 197	94.02 232	80.72 250	94.27 322	75.16 305	93.08 309	96.54 215

miper_enhance_ethall		88.42 245	87.87 247	90.07 257	88.67 334	75.52 293	85.10 318	95.59 208	75.68 294	92.49 212	89.45 313	78.96 257	97.88 222	87.86 181	97.02 234	96.81 211

1112_ss		88.42 245	87.41 253	91.45 216	96.69 144	80.99 216	89.72 254	96.72 162	73.37 307	87.00 302	90.69 298	77.38 271	98.20 199	81.38 255	93.72 300	95.15 266

lupinMVS		88.34 247	87.31 254	91.45 216	94.74 239	80.06 227	87.23 294	92.27 277	71.10 318	88.83 276	91.15 289	77.02 275	98.53 173	86.67 198	96.75 245	95.76 251

RRT_test8_iter05		88.21 248	88.17 242	88.33 286	91.62 301	66.82 335	91.73 201	96.60 167	86.34 204	94.14 163	95.38 185	47.72 351	99.11 84	91.78 96	98.26 170	99.06 46

YYNet1		88.17 249	88.24 239	87.93 290	92.21 289	73.62 306	80.75 337	88.77 296	82.51 250	94.99 142	95.11 191	82.70 231	93.70 326	83.33 235	93.83 298	96.48 221

MDA-MVSNet_test_wron		88.16 250	88.23 240	87.93 290	92.22 288	73.71 305	80.71 338	88.84 295	82.52 249	94.88 147	95.14 189	82.70 231	93.61 327	83.28 236	93.80 299	96.46 222

MS-PatchMatch		88.05 251	87.75 248	88.95 273	93.28 269	77.93 263	87.88 285	92.49 274	75.42 297	92.57 211	93.59 245	80.44 251	94.24 324	81.28 256	92.75 312	94.69 279

CR-MVSNet		87.89 252	87.12 260	90.22 252	91.01 308	78.93 250	92.52 156	92.81 265	73.08 309	89.10 273	96.93 92	67.11 303	97.64 243	88.80 163	92.70 313	94.08 288

pmmvs5		87.87 253	87.14 259	90.07 257	93.26 271	76.97 279	88.89 273	92.18 278	73.71 306	88.36 287	93.89 237	76.86 278	96.73 279	80.32 263	96.81 242	96.51 217

wuyk23d		87.83 254	90.79 195	78.96 329	90.46 316	88.63 98	92.72 149	90.67 291	91.65 105	98.68 11	97.64 51	96.06 16	77.53 348	59.84 342	99.41 50	70.73 345

FMVSNet5		87.82 255	86.56 269	91.62 212	92.31 286	79.81 236	93.49 131	94.81 231	83.26 240	91.36 236	96.93 92	52.77 347	97.49 251	76.07 300	98.03 197	97.55 182

GA-MVS		87.70 256	86.82 264	90.31 248	93.27 270	77.22 274	84.72 323	92.79 267	85.11 226	89.82 264	90.07 302	66.80 306	97.76 237	84.56 227	94.27 294	95.96 242

TR-MVS		87.70 256	87.17 258	89.27 270	94.11 256	79.26 245	88.69 278	91.86 284	81.94 254	90.69 248	89.79 307	82.82 229	97.42 255	72.65 316	91.98 319	91.14 326

thres600view7		87.66 258	87.10 261	89.36 268	96.05 186	73.17 308	92.72 149	85.31 327	91.89 88	93.29 188	90.97 292	63.42 324	98.39 183	73.23 312	96.99 239	96.51 217

PAPR		87.65 259	86.77 266	90.27 250	92.85 279	77.38 271	88.56 281	96.23 185	76.82 293	84.98 312	89.75 309	86.08 207	97.16 265	72.33 317	93.35 303	96.26 230

baseline1		87.62 260	87.31 254	88.54 281	94.71 243	74.27 303	93.10 139	88.20 302	86.20 206	92.18 225	93.04 256	73.21 288	95.52 302	79.32 277	85.82 335	95.83 248

our_test_3		87.55 261	87.59 251	87.44 296	91.76 298	70.48 322	83.83 330	90.55 292	79.79 265	92.06 228	92.17 275	78.63 262	95.63 300	84.77 224	94.73 284	96.22 232

PatchT		87.51 262	88.17 242	85.55 306	90.64 311	66.91 331	92.02 182	86.09 317	92.20 79	89.05 275	97.16 81	64.15 320	96.37 291	89.21 158	92.98 311	93.37 307

Test_1112_low_res		87.50 263	86.58 268	90.25 251	96.80 141	77.75 266	87.53 291	96.25 183	69.73 325	86.47 304	93.61 244	75.67 281	97.88 222	79.95 269	93.20 305	95.11 268

SCA		87.43 264	87.21 257	88.10 289	92.01 295	71.98 317	89.43 260	88.11 304	82.26 253	88.71 283	92.83 259	78.65 260	97.59 245	79.61 274	93.30 304	94.75 276

EU-MVSNet		87.39 265	86.71 267	89.44 265	93.40 268	76.11 287	94.93 89	90.00 293	57.17 345	95.71 110	97.37 66	64.77 318	97.68 242	92.67 77	94.37 291	94.52 281

thres100view900		87.35 266	86.89 263	88.72 278	96.14 181	73.09 310	93.00 141	85.31 327	92.13 81	93.26 191	90.96 293	63.42 324	98.28 191	71.27 324	96.54 249	94.79 274

CMPMVS	68.83 22	87.28 267	85.67 279	92.09 200	88.77 333	85.42 164	90.31 235	94.38 241	70.02 324	88.00 292	93.30 251	73.78 287	94.03 325	75.96 302	96.54 249	96.83 210
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
sss		87.23 268	86.82 264	88.46 284	93.96 260	77.94 262	86.84 303	92.78 268	77.59 286	87.61 297	91.83 280	78.75 259	91.92 335	77.84 286	94.20 295	95.52 261

BH-w/o		87.21 269	87.02 262	87.79 293	94.77 237	77.27 273	87.90 284	93.21 261	81.74 255	89.99 260	88.39 320	83.47 221	96.93 273	71.29 323	92.43 315	89.15 331

thres400		87.20 270	86.52 271	89.24 272	95.77 204	72.94 311	91.89 189	86.00 319	90.84 121	92.61 209	89.80 305	63.93 321	98.28 191	71.27 324	96.54 249	96.51 217

CHOSEN 1792x2688		87.19 271	85.92 278	91.00 233	97.13 127	79.41 242	84.51 325	95.60 205	64.14 339	90.07 258	94.81 205	78.26 265	97.14 266	73.34 311	95.38 272	96.46 222

HyFIR lowres test		87.19 271	85.51 280	92.24 193	97.12 128	80.51 220	85.03 319	96.06 192	66.11 335	91.66 233	92.98 258	70.12 296	99.14 80	75.29 304	95.23 275	97.07 200

MIMVSNet		87.13 273	86.54 270	88.89 275	96.05 186	76.11 287	94.39 107	88.51 298	81.37 256	88.27 289	96.75 104	72.38 290	95.52 302	65.71 338	95.47 268	95.03 269

tfpn200view9		87.05 274	86.52 271	88.67 279	95.77 204	72.94 311	91.89 189	86.00 319	90.84 121	92.61 209	89.80 305	63.93 321	98.28 191	71.27 324	96.54 249	94.79 274

cascas		87.02 275	86.28 275	89.25 271	91.56 303	76.45 284	84.33 327	96.78 157	71.01 319	86.89 303	85.91 334	81.35 244	96.94 271	83.09 238	95.60 264	94.35 285

WTY-MVS		86.93 276	86.50 273	88.24 287	94.96 230	74.64 296	87.19 296	92.07 283	78.29 283	88.32 288	91.59 285	78.06 266	94.27 322	74.88 306	93.15 307	95.80 249

HY-MVS	82.50 18	86.81 277	85.93 277	89.47 264	93.63 266	77.93 263	94.02 117	91.58 287	75.68 294	83.64 321	93.64 242	77.40 270	97.42 255	71.70 321	92.07 318	93.05 310

1314		86.46 278	86.33 274	86.87 300	91.65 300	74.54 298	91.94 186	94.10 246	74.28 301	84.78 314	87.33 326	83.03 226	95.00 314	78.72 281	91.16 324	91.06 327

ET-MVSNet_ETH3D		86.15 279	84.27 286	91.79 206	93.04 276	81.28 212	87.17 297	86.14 316	79.57 269	83.65 320	88.66 316	57.10 339	98.18 202	87.74 182	95.40 270	95.90 246

Patchmatch-test		86.10 280	86.01 276	86.38 303	90.63 312	74.22 304	89.57 257	86.69 312	85.73 217	89.81 265	92.83 259	65.24 316	91.04 338	77.82 288	95.78 262	93.88 297

thres200		85.85 281	85.18 281	87.88 292	94.44 249	72.52 314	89.08 270	86.21 315	88.57 167	91.44 235	88.40 319	64.22 319	98.00 215	68.35 332	95.88 261	93.12 309

EPNet_dtu		85.63 282	84.37 284	89.40 267	86.30 344	74.33 302	91.64 202	88.26 300	84.84 231	72.96 347	89.85 303	71.27 295	97.69 241	76.60 297	97.62 217	96.18 234
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PatchmatchNet		85.22 283	84.64 283	86.98 299	89.51 326	69.83 326	90.52 227	87.34 309	78.87 279	87.22 301	92.74 263	66.91 305	96.53 283	81.77 251	86.88 334	94.58 280
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CVMVSNet		85.16 284	84.72 282	86.48 301	92.12 292	70.19 323	92.32 169	88.17 303	56.15 346	90.64 249	95.85 155	67.97 301	96.69 280	88.78 164	90.52 326	92.56 316

JIA-IIPM		85.08 285	83.04 293	91.19 227	87.56 336	86.14 154	89.40 262	84.44 334	88.98 156	82.20 330	97.95 38	56.82 341	96.15 294	76.55 298	83.45 339	91.30 325

MVS		84.98 286	84.30 285	87.01 298	91.03 307	77.69 268	91.94 186	94.16 245	59.36 344	84.23 318	87.50 324	85.66 211	96.80 277	71.79 319	93.05 310	86.54 337

thisisatest0515		84.72 287	82.99 294	89.90 260	92.96 278	75.33 294	84.36 326	83.42 337	77.37 288	88.27 289	86.65 327	53.94 344	98.72 147	82.56 243	97.40 224	95.67 255

FPMVS		84.50 288	83.28 291	88.16 288	96.32 167	94.49 11	85.76 314	85.47 325	83.09 244	85.20 310	94.26 222	63.79 323	86.58 345	63.72 340	91.88 321	83.40 340

tpm		84.38 289	84.08 287	85.30 310	90.47 315	63.43 345	89.34 263	85.63 323	77.24 290	87.62 296	95.03 197	61.00 335	97.30 261	79.26 278	91.09 325	95.16 265

tpmvs		84.22 290	83.97 288	84.94 311	87.09 341	65.18 338	91.21 211	88.35 299	82.87 248	85.21 309	90.96 293	65.24 316	96.75 278	79.60 276	85.25 336	92.90 312

ADS-MVSNet2		84.01 291	82.20 298	89.41 266	89.04 330	76.37 286	87.57 287	90.98 290	72.71 312	84.46 315	92.45 268	68.08 299	96.48 286	70.58 328	83.97 337	95.38 262

test-LLR		83.58 292	83.17 292	84.79 313	89.68 323	66.86 333	83.08 332	84.52 332	83.07 245	82.85 326	84.78 337	62.86 327	93.49 328	82.85 239	94.86 280	94.03 291

baseline2		83.38 293	81.54 301	88.90 274	91.38 304	72.84 313	88.78 275	81.22 342	78.97 277	79.82 340	87.56 322	61.73 332	97.80 231	74.30 307	90.05 328	96.05 239

IB-MVS	77.21 19	83.11 294	81.05 304	89.29 269	91.15 306	75.85 290	85.66 315	86.00 319	79.70 267	82.02 333	86.61 328	48.26 350	98.39 183	77.84 286	92.22 316	93.63 302
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
CostFormer		83.09 295	82.21 297	85.73 305	89.27 328	67.01 330	90.35 233	86.47 314	70.42 322	83.52 323	93.23 254	61.18 333	96.85 275	77.21 293	88.26 332	93.34 308

PMMVS		83.00 296	81.11 303	88.66 280	83.81 351	86.44 145	82.24 336	85.65 322	61.75 343	82.07 331	85.64 335	79.75 253	91.59 337	75.99 301	93.09 308	87.94 336

PVSNet	76.22 20	82.89 297	82.37 296	84.48 315	93.96 260	64.38 343	78.60 340	88.61 297	71.50 316	84.43 317	86.36 331	74.27 284	94.60 316	69.87 330	93.69 301	94.46 282

tpmrst		82.85 298	82.93 295	82.64 322	87.65 335	58.99 348	90.14 241	87.90 305	75.54 296	83.93 319	91.63 284	66.79 308	95.36 308	81.21 258	81.54 343	93.57 306

test0.0.03 1		82.48 299	81.47 302	85.48 307	89.70 322	73.57 307	84.73 321	81.64 341	83.07 245	88.13 291	86.61 328	62.86 327	89.10 344	66.24 337	90.29 327	93.77 299

ADS-MVSNet		82.25 300	81.55 300	84.34 316	89.04 330	65.30 337	87.57 287	85.13 331	72.71 312	84.46 315	92.45 268	68.08 299	92.33 334	70.58 328	83.97 337	95.38 262

DSMNet-mixed		82.21 301	81.56 299	84.16 317	89.57 325	70.00 325	90.65 224	77.66 348	54.99 347	83.30 324	97.57 53	77.89 268	90.50 340	66.86 336	95.54 266	91.97 320

gg-mvs-nofinetune		82.10 302	81.02 305	85.34 309	87.46 339	71.04 319	94.74 93	67.56 350	96.44 21	79.43 341	98.99 6	45.24 352	96.15 294	67.18 335	92.17 317	88.85 333

PAPM		81.91 303	80.11 312	87.31 297	93.87 263	72.32 316	84.02 329	93.22 259	69.47 326	76.13 345	89.84 304	72.15 291	97.23 263	53.27 346	89.02 329	92.37 318

tpm2		81.46 304	80.35 310	84.80 312	89.90 320	65.14 339	90.44 229	85.36 326	65.82 337	82.05 332	92.44 270	57.94 338	96.69 280	70.71 327	88.49 331	92.56 316

PMMVS2		81.31 305	83.44 290	74.92 331	90.52 314	46.49 352	69.19 345	85.23 330	84.30 235	87.95 293	94.71 211	76.95 277	84.36 347	64.07 339	98.09 191	93.89 296

new_pmnet		81.22 306	81.01 306	81.86 324	90.92 310	70.15 324	84.03 328	80.25 346	70.83 320	85.97 307	89.78 308	67.93 302	84.65 346	67.44 334	91.90 320	90.78 328

test-mter		81.21 307	80.01 313	84.79 313	89.68 323	66.86 333	83.08 332	84.52 332	73.85 305	82.85 326	84.78 337	43.66 355	93.49 328	82.85 239	94.86 280	94.03 291

EPMVS		81.17 308	80.37 309	83.58 319	85.58 346	65.08 340	90.31 235	71.34 349	77.31 289	85.80 308	91.30 287	59.38 336	92.70 333	79.99 268	82.34 342	92.96 311

pmmvs3		80.83 309	78.96 316	86.45 302	87.23 340	77.48 270	84.87 320	82.31 339	63.83 340	85.03 311	89.50 312	49.66 348	93.10 330	73.12 314	95.10 277	88.78 335

DWT-MVSNet_test		80.74 310	79.18 315	85.43 308	87.51 338	66.87 332	89.87 251	86.01 318	74.20 303	80.86 337	80.62 343	48.84 349	96.68 282	81.54 253	83.14 341	92.75 314

E-PMN		80.72 311	80.86 307	80.29 327	85.11 347	68.77 328	72.96 342	81.97 340	87.76 182	83.25 325	83.01 341	62.22 330	89.17 343	77.15 294	94.31 293	82.93 341

tpm cat1		80.61 312	79.46 314	84.07 318	88.78 332	65.06 341	89.26 266	88.23 301	62.27 342	81.90 334	89.66 311	62.70 329	95.29 311	71.72 320	80.60 344	91.86 323

EMVS		80.35 313	80.28 311	80.54 326	84.73 349	69.07 327	72.54 344	80.73 343	87.80 181	81.66 335	81.73 342	62.89 326	89.84 341	75.79 303	94.65 287	82.71 342

CHOSEN 280x420		80.04 314	77.97 319	86.23 304	90.13 318	74.53 299	72.87 343	89.59 294	66.38 334	76.29 344	85.32 336	56.96 340	95.36 308	69.49 331	94.72 285	88.79 334

dp		79.28 315	78.62 317	81.24 325	85.97 345	56.45 349	86.91 301	85.26 329	72.97 310	81.45 336	89.17 315	56.01 343	95.45 306	73.19 313	76.68 345	91.82 324

TESTMET0.1,1		79.09 316	78.04 318	82.25 323	87.52 337	64.03 344	83.08 332	80.62 344	70.28 323	80.16 339	83.22 340	44.13 354	90.56 339	79.95 269	93.36 302	92.15 319

MVS-HIRNet		78.83 317	80.60 308	73.51 332	93.07 274	47.37 351	87.10 298	78.00 347	68.94 327	77.53 343	97.26 74	71.45 294	94.62 315	63.28 341	88.74 330	78.55 344

PVSNet_0	70.34 21	74.58 318	72.96 320	79.47 328	90.63 312	66.24 336	73.26 341	83.40 338	63.67 341	78.02 342	78.35 344	72.53 289	89.59 342	56.68 344	60.05 348	82.57 343

MVE	59.87 23	73.86 319	72.65 321	77.47 330	87.00 343	74.35 301	61.37 347	60.93 352	67.27 332	69.69 348	86.49 330	81.24 248	72.33 349	56.45 345	83.45 339	85.74 338
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt		37.97 320	44.33 322	18.88 334	11.80 353	21.54 354	63.51 346	45.66 355	4.23 349	51.34 350	50.48 347	59.08 337	22.11 351	44.50 347	68.35 347	13.00 347

cdsmvs_eth3d_5k		23.35 321	31.13 323	0.00 337	0.00 356	0.00 357	0.00 348	95.58 210	0.00 352	0.00 353	91.15 289	93.43 71	0.00 354	0.00 351	0.00 351	0.00 350

test123		9.49 322	12.01 324	1.91 335	2.87 354	1.30 355	82.38 335	1.34 357	1.36 350	2.84 351	6.56 350	2.45 356	0.97 352	2.73 349	5.56 349	3.47 348

testmvs		9.02 323	11.42 325	1.81 336	2.77 355	1.13 356	79.44 339	1.90 356	1.18 351	2.65 352	6.80 349	1.95 357	0.87 353	2.62 350	3.45 350	3.44 349

pcd_1.5k_mvsjas		7.56 324	10.09 326	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	0.00 353	90.77 136	0.00 354	0.00 351	0.00 351	0.00 350

ab-mvs-re		7.56 324	10.08 327	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	90.69 298	0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

uanet_test		0.00 326	0.00 328	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	0.00 353	0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

sosnet-low-res		0.00 326	0.00 328	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	0.00 353	0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

sosnet		0.00 326	0.00 328	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	0.00 353	0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

uncertanet		0.00 326	0.00 328	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	0.00 353	0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

Regformer		0.00 326	0.00 328	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	0.00 353	0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

uanet		0.00 326	0.00 328	0.00 337	0.00 356	0.00 357	0.00 348	0.00 358	0.00 352	0.00 353	0.00 353	0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

IU-MVS					98.51 45	86.66 140		96.83 154	72.74 311	95.83 104				93.00 68	99.29 62	98.64 94

OPU-MVS				95.15 91	96.84 137	89.43 83	95.21 75				95.66 166	93.12 81	98.06 209	86.28 207	98.61 136	97.95 147

test_241102_TWO								98.10 42	91.95 84	97.54 35	97.25 75	95.37 27	99.35 50	93.29 55	99.25 70	98.49 107

test_241102_ONE					98.51 45	86.97 131		98.10 42	91.85 90	97.63 30	97.03 88	96.48 11	98.95 109

9.14			94.81 89		97.49 110		94.11 114	98.37 14	87.56 189	95.38 122	96.03 149	94.66 52	99.08 87	90.70 116	98.97 103

save fliter					97.46 113	88.05 112	92.04 180	97.08 135	87.63 186

test_0728_THIRD									93.26 61	97.40 43	97.35 70	94.69 51	99.34 54	93.88 32	99.42 45	98.89 68

test_0728_SECOND				94.88 98	98.55 39	86.72 137	95.20 77	98.22 29					99.38 46	93.44 49	99.31 59	98.53 105

test0726					98.51 45	86.69 138	95.34 70	98.18 32	91.85 90	97.63 30	97.37 66	95.58 22

GSMVS																94.75 276

test_part2					98.21 68	89.41 84				96.72 64

test_part1				0.00 337		0.00 357	0.00 348	98.14 39				0.00 358	0.00 354	0.00 351	0.00 351	0.00 350

sam_mvs1												66.64 309				94.75 276

sam_mvs												66.41 310

ambc				92.98 167	96.88 135	83.01 195	95.92 53	96.38 179		96.41 73	97.48 60	88.26 169	97.80 231	89.96 140	98.93 107	98.12 133

MTGPA								97.62 93

test_post1							90.21 237				5.85 352	65.36 314	96.00 296	79.61 274

test_post											6.07 351	65.74 313	95.84 298

patchmatchnet-post											91.71 282	66.22 312	97.59 245

GG-mvs-BLEND				83.24 321	85.06 348	71.03 320	94.99 88	65.55 351		74.09 346	75.51 345	44.57 353	94.46 318	59.57 343	87.54 333	84.24 339

MTMP							94.82 90	54.62 353

gm-plane-assit					87.08 342	59.33 347			71.22 317		83.58 339		97.20 264	73.95 308

test9_res														88.16 174	98.40 152	97.83 160

TEST9					96.45 157	89.46 81	90.60 225	96.92 147	79.09 276	90.49 250	94.39 219	91.31 122	98.88 116

test_8					96.37 159	89.14 88	90.51 228	96.89 150	79.37 271	90.42 252	94.36 221	91.20 128	98.82 126

agg_prior2														87.06 193	98.36 161	97.98 143

agg_prior					96.20 176	88.89 93		96.88 151		90.21 254			98.78 137

TestCases				96.00 53	98.02 79	92.17 47		98.43 10	90.48 131	95.04 140	96.74 105	92.54 95	97.86 226	85.11 219	98.98 99	97.98 143

test_prior4						89.91 75	90.74 221

test_prior2							90.21 237		89.33 152	90.77 245	94.81 205	90.41 146		88.21 170	98.55 139

test_prior				94.61 108	95.95 194	87.23 124		97.36 115					98.68 156			97.93 149

旧先验2							90.00 246		68.65 328	92.71 207			96.52 284	85.15 216

新几何2							90.02 245

新几何1				93.17 163	97.16 124	87.29 123		94.43 239	67.95 330	91.29 237	94.94 200	86.97 193	98.23 197	81.06 261	97.75 208	93.98 294

旧先验1					96.20 176	84.17 178		94.82 229			95.57 172	89.57 158			97.89 204	96.32 227

无先验							89.94 247	95.75 202	70.81 321				98.59 166	81.17 259		94.81 273

原ACMM2							89.34 263

原ACMM1				92.87 174	96.91 134	84.22 176		97.01 138	76.84 292	89.64 269	94.46 216	88.00 175	98.70 154	81.53 254	98.01 198	95.70 254

test222					96.95 131	85.27 166	88.83 274	93.61 252	65.09 338	90.74 247	94.85 204	84.62 218			97.36 225	93.91 295

testdata2													98.03 211	80.24 266

segment_acmp												92.14 101

testdata				91.03 230	96.87 136	82.01 201		94.28 243	71.55 315	92.46 213	95.42 180	85.65 212	97.38 260	82.64 242	97.27 227	93.70 301

testdata1							88.96 272		88.44 169

test12				94.43 123	95.95 194	86.75 136		96.24 184		89.76 267		89.79 157	98.79 133		97.95 201	97.75 169

plane_prior7					97.71 96	88.68 97

plane_prior6					97.21 122	88.23 108						86.93 194

plane_prior5								97.81 81					98.95 109	89.26 155	98.51 146	98.60 101

plane_prior4											95.59 168

plane_prior3						88.43 106			90.35 136	93.31 186

plane_prior2							94.56 102		91.74 101

plane_prior1					97.38 116

plane_prior						88.12 110	93.01 140		88.98 156						98.06 193

n2								0.00 358

nn								0.00 358

door-mid								92.13 282

lessismore_v0				93.87 141	98.05 76	83.77 185		80.32 345		97.13 49	97.91 42	77.49 269	99.11 84	92.62 78	98.08 192	98.74 85

LGP-MVS_train				96.84 38	98.36 60	92.13 49		98.25 24	91.78 97	97.07 50	97.22 78	96.38 13	99.28 65	92.07 87	99.59 25	99.11 40

test11								96.65 165

door								91.26 288

HQP5-MVS						84.89 168

HQP-NCC					96.36 161		91.37 206		87.16 193	88.81 278

ACMP_Plane					96.36 161		91.37 206		87.16 193	88.81 278

BP-MVS														86.55 201

HQP4-MVS										88.81 278			98.61 162			98.15 129

HQP3-MVS								97.31 119							97.73 209

HQP2-MVS												84.76 216

NP-MVS					96.82 138	87.10 127					93.40 249

MDTV_nov1_ep13_2view						42.48 353	88.45 282		67.22 333	83.56 322		66.80 306		72.86 315		94.06 290

MDTV_nov1_ep13			83.88 289		89.42 327	61.52 346	88.74 277	87.41 308	73.99 304	84.96 313	94.01 233	65.25 315	95.53 301	78.02 284	93.16 306

ACMMP++_ref															98.82 119

ACMMP++															99.25 70

Test By Simon												90.61 142

ITE_SJBPF				95.95 55	97.34 118	93.36 37		96.55 172	91.93 86	94.82 148	95.39 183	91.99 106	97.08 267	85.53 212	97.96 200	97.41 187

DeepMVS_CX				53.83 333	70.38 352	64.56 342		48.52 354	33.01 348	65.50 349	74.21 346	56.19 342	46.64 350	38.45 348	70.07 346	50.30 346

High-resolution multi-view benchmark