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 26	98.81 26	93.86 32	99.07 2	98.98 6	97.01 13	98.92 4	98.78 14	95.22 37	98.61 183	96.85 2	99.77 10	99.31 29
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 12	95.95 5	98.61 3	98.81 8	97.41 10	97.28 51	98.46 26	94.62 58	98.84 140	94.64 19	99.53 37	98.99 56

UA-Net		97.35 4	97.24 11	97.69 5	98.22 73	93.87 31	98.42 6	98.19 36	96.95 14	95.46 133	99.23 4	93.45 77	99.57 14	95.34 15	99.89 2	99.63 9

abl_6		97.31 5	97.12 13	97.86 3	98.54 45	95.32 7	96.61 31	98.35 20	95.81 31	97.55 38	97.44 71	96.51 9	99.40 47	94.06 32	99.23 84	98.85 79

UniMVSNet_ETH3D		97.13 6	97.72 3	95.35 91	99.51 2	87.38 137	97.70 8	97.54 111	98.16 2	98.94 2	99.33 2	97.84 4	99.08 102	90.73 132	99.73 14	99.59 13

HPM-MVS_fast		97.01 7	96.89 15	97.39 22	99.12 8	93.92 29	97.16 13	98.17 41	93.11 73	96.48 83	97.36 78	96.92 6	99.34 66	94.31 25	99.38 59	98.92 70

SR-MVS-dyc-post		96.84 8	96.60 25	97.56 10	98.07 82	95.27 8	96.37 44	98.12 47	95.66 33	97.00 62	97.03 100	94.85 52	99.42 33	93.49 50	98.84 130	98.00 157

mvs_tets		96.83 9	96.71 19	97.17 27	98.83 24	92.51 50	96.58 33	97.61 106	87.57 210	98.80 7	98.90 9	96.50 10	99.59 13	96.15 7	99.47 42	99.40 22

v7n		96.82 10	97.31 10	95.33 93	98.54 45	86.81 151	96.83 22	98.07 57	96.59 20	98.46 17	98.43 28	92.91 96	99.52 19	96.25 6	99.76 11	99.65 8

APD-MVS_3200maxsize		96.82 10	96.65 21	97.32 25	97.95 95	93.82 34	96.31 50	98.25 28	95.51 35	96.99 64	97.05 99	95.63 21	99.39 52	93.31 65	98.88 125	98.75 89

HPM-MVS		96.81 12	96.62 23	97.36 24	98.89 19	93.53 39	97.51 9	98.44 13	92.35 85	95.95 111	96.41 142	96.71 8	99.42 33	93.99 35	99.36 60	99.13 42
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
pmmvs6		96.80 13	97.36 9	95.15 102	99.12 8	87.82 132	96.68 29	97.86 84	96.10 26	98.14 24	99.28 3	97.94 3	98.21 221	91.38 122	99.69 15	99.42 20

OurMVSNet-221017-0		96.80 13	96.75 18	96.96 36	99.03 11	91.85 59	97.98 7	98.01 70	94.15 51	98.93 3	99.07 5	88.07 186	99.57 14	95.86 9	99.69 15	99.46 19

test1172		96.79 15	96.52 27	97.60 9	98.03 88	94.87 10	96.07 60	98.06 60	95.76 32	96.89 67	96.85 112	94.85 52	99.42 33	93.35 64	98.81 138	98.53 116

COLMAP_ROB	91.06 5	96.75 16	96.62 23	97.13 28	98.38 62	94.31 17	96.79 25	98.32 21	96.69 17	96.86 69	97.56 63	95.48 25	98.77 159	90.11 154	99.44 49	98.31 131
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
anonymousdsp		96.74 17	96.42 29	97.68 7	98.00 91	94.03 26	96.97 19	97.61 106	87.68 207	98.45 18	98.77 15	94.20 67	99.50 21	96.70 3	99.40 57	99.53 15

DTE-MVSNet		96.74 17	97.43 5	94.67 119	99.13 6	84.68 189	96.51 35	97.94 82	98.14 3	98.67 12	98.32 30	95.04 45	99.69 2	93.27 68	99.82 8	99.62 10

SR-MVS		96.70 19	96.42 29	97.54 11	98.05 84	94.69 11	96.13 57	98.07 57	95.17 37	96.82 71	96.73 124	95.09 44	99.43 32	92.99 80	98.71 147	98.50 118

PS-CasMVS		96.69 20	97.43 5	94.49 133	99.13 6	84.09 199	96.61 31	97.97 76	97.91 5	98.64 13	98.13 36	95.24 36	99.65 3	93.39 62	99.84 3	99.72 2

PEN-MVS		96.69 20	97.39 8	94.61 122	99.16 4	84.50 190	96.54 34	98.05 61	98.06 4	98.64 13	98.25 32	95.01 48	99.65 3	92.95 81	99.83 6	99.68 4

MTAPA		96.65 22	96.38 33	97.47 15	98.95 16	94.05 23	95.88 69	97.62 103	94.46 45	96.29 93	96.94 105	93.56 73	99.37 60	94.29 26	99.42 51	98.99 56

test_djsdf		96.62 23	96.49 28	97.01 33	98.55 42	91.77 61	97.15 14	97.37 122	88.98 176	98.26 22	98.86 10	93.35 82	99.60 9	96.41 4	99.45 46	99.66 6

ACMMP		96.61 24	96.34 34	97.43 19	98.61 35	93.88 30	96.95 20	98.18 37	92.26 88	96.33 89	96.84 115	95.10 43	99.40 47	93.47 54	99.33 64	99.02 53
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 25	97.13 12	95.00 106	97.46 127	86.35 166	97.11 18	98.24 31	97.58 8	98.72 8	98.97 7	93.15 88	99.15 90	93.18 71	99.74 13	99.50 17

WR-MVS_H		96.60 25	97.05 14	95.24 98	99.02 12	86.44 162	96.78 26	98.08 54	97.42 9	98.48 16	97.86 52	91.76 122	99.63 6	94.23 28	99.84 3	99.66 6

jajsoiax		96.59 27	96.42 29	97.12 29	98.76 29	92.49 51	96.44 41	97.42 120	86.96 219	98.71 10	98.72 17	95.36 31	99.56 17	95.92 8	99.45 46	99.32 28

ACMH	88.36 12	96.59 27	97.43 5	94.07 146	98.56 39	85.33 183	96.33 47	98.30 24	94.66 40	98.72 8	98.30 31	97.51 5	98.00 239	94.87 16	99.59 29	98.86 76
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
XVS		96.49 29	96.18 42	97.44 17	98.56 39	93.99 27	96.50 36	97.95 79	94.58 41	94.38 176	96.49 136	94.56 59	99.39 52	93.57 46	99.05 104	98.93 66

ACMH+	88.43 11	96.48 30	96.82 16	95.47 88	98.54 45	89.06 103	95.65 78	98.61 11	96.10 26	98.16 23	97.52 66	96.90 7	98.62 182	90.30 145	99.60 27	98.72 95

zzz-MVS		96.47 31	96.14 45	97.47 15	98.95 16	94.05 23	93.69 149	97.62 103	94.46 45	96.29 93	96.94 105	93.56 73	99.37 60	94.29 26	99.42 51	98.99 56

APDe-MVS		96.46 32	96.64 22	95.93 65	97.68 112	89.38 100	96.90 21	98.41 17	92.52 80	97.43 46	97.92 48	95.11 42	99.50 21	94.45 21	99.30 69	98.92 70

ACMMPR		96.46 32	96.14 45	97.41 21	98.60 36	93.82 34	96.30 52	97.96 77	92.35 85	95.57 129	96.61 132	94.93 51	99.41 40	93.78 40	99.15 95	99.00 54

mPP-MVS		96.46 32	96.05 51	97.69 5	98.62 33	94.65 13	96.45 39	97.74 97	92.59 79	95.47 131	96.68 127	94.50 61	99.42 33	93.10 75	99.26 80	98.99 56

CP-MVS		96.44 35	96.08 49	97.54 11	98.29 67	94.62 14	96.80 24	98.08 54	92.67 78	95.08 153	96.39 147	94.77 54	99.42 33	93.17 72	99.44 49	98.58 114

ZNCC-MVS		96.42 36	96.20 41	97.07 30	98.80 28	92.79 48	96.08 59	98.16 44	91.74 112	95.34 138	96.36 150	95.68 19	99.44 28	94.41 23	99.28 77	98.97 62

region2R		96.41 37	96.09 48	97.38 23	98.62 33	93.81 36	96.32 49	97.96 77	92.26 88	95.28 142	96.57 134	95.02 47	99.41 40	93.63 44	99.11 99	98.94 65

SteuartSystems-ACMMP		96.40 38	96.30 36	96.71 42	98.63 32	91.96 57	95.70 75	98.01 70	93.34 69	96.64 78	96.57 134	94.99 49	99.36 62	93.48 53	99.34 62	98.82 81
Skip Steuart: Steuart Systems R&D Blog.
HFP-MVS		96.39 39	96.17 44	97.04 31	98.51 49	93.37 40	96.30 52	97.98 73	92.35 85	95.63 126	96.47 137	95.37 28	99.27 78	93.78 40	99.14 96	98.48 120

LPG-MVS_test		96.38 40	96.23 39	96.84 40	98.36 65	92.13 54	95.33 89	98.25 28	91.78 108	97.07 57	97.22 90	96.38 13	99.28 76	92.07 100	99.59 29	99.11 45

nrg030		96.32 41	96.55 26	95.62 82	97.83 99	88.55 117	95.77 73	98.29 27	92.68 76	98.03 26	97.91 49	95.13 40	98.95 124	93.85 38	99.49 41	99.36 25

PGM-MVS		96.32 41	95.94 55	97.43 19	98.59 38	93.84 33	95.33 89	98.30 24	91.40 121	95.76 120	96.87 111	95.26 35	99.45 26	92.77 83	99.21 87	99.00 54

ACMM	88.83 9	96.30 43	96.07 50	96.97 35	98.39 61	92.95 46	94.74 112	98.03 66	90.82 135	97.15 54	96.85 112	96.25 15	99.00 116	93.10 75	99.33 64	98.95 64
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GST-MVS		96.24 44	95.99 54	97.00 34	98.65 31	92.71 49	95.69 77	98.01 70	92.08 93	95.74 122	96.28 156	95.22 37	99.42 33	93.17 72	99.06 101	98.88 75

ACMMP_NAP		96.21 45	96.12 47	96.49 50	98.90 18	91.42 65	94.57 120	98.03 66	90.42 146	96.37 86	97.35 81	95.68 19	99.25 80	94.44 22	99.34 62	98.80 84

CP-MVSNet		96.19 46	96.80 17	94.38 139	98.99 14	83.82 202	96.31 50	97.53 113	97.60 7	98.34 19	97.52 66	91.98 118	99.63 6	93.08 77	99.81 9	99.70 3

MP-MVS		96.14 47	95.68 67	97.51 13	98.81 26	94.06 21	96.10 58	97.78 96	92.73 75	93.48 202	96.72 125	94.23 66	99.42 33	91.99 102	99.29 72	99.05 51
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
LS3D		96.11 48	95.83 62	96.95 37	94.75 267	94.20 19	97.34 12	97.98 73	97.31 11	95.32 139	96.77 117	93.08 91	99.20 86	91.79 110	98.16 206	97.44 209

MP-MVS-pluss		96.08 49	95.92 57	96.57 46	99.06 10	91.21 67	93.25 159	98.32 21	87.89 200	96.86 69	97.38 74	95.55 24	99.39 52	95.47 13	99.47 42	99.11 45
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TranMVSNet+NR-MVSNet		96.07 50	96.26 38	95.50 87	98.26 70	87.69 133	93.75 147	97.86 84	95.96 30	97.48 44	97.14 94	95.33 32	99.44 28	90.79 131	99.76 11	99.38 23

PS-MVSNAJss		96.01 51	96.04 52	95.89 70	98.82 25	88.51 119	95.57 82	97.88 83	88.72 182	98.81 6	98.86 10	90.77 147	99.60 9	95.43 14	99.53 37	99.57 14

SED-MVS		96.00 52	96.41 32	94.76 115	98.51 49	86.97 147	95.21 93	98.10 50	91.95 95	97.63 34	97.25 87	96.48 11	99.35 63	93.29 66	99.29 72	97.95 165

DVP-MVS++		95.93 53	96.34 34	94.70 118	96.54 174	86.66 156	98.45 4	98.22 33	93.26 70	97.54 39	97.36 78	93.12 89	99.38 58	93.88 36	98.68 151	98.04 152

DPE-MVS		95.89 54	95.88 58	95.92 67	97.93 96	89.83 89	93.46 155	98.30 24	92.37 83	97.75 31	96.95 104	95.14 39	99.51 20	91.74 111	99.28 77	98.41 125
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
#test#		95.89 54	95.51 72	97.04 31	98.51 49	93.37 40	95.14 98	97.98 73	89.34 167	95.63 126	96.47 137	95.37 28	99.27 78	91.99 102	99.14 96	98.48 120

SF-MVS		95.88 56	95.88 58	95.87 71	98.12 78	89.65 92	95.58 81	98.56 12	91.84 104	96.36 87	96.68 127	94.37 64	99.32 72	92.41 93	99.05 104	98.64 105

3Dnovator+	92.74 2	95.86 57	95.77 65	96.13 55	96.81 160	90.79 75	96.30 52	97.82 90	96.13 25	94.74 167	97.23 89	91.33 132	99.16 89	93.25 69	98.30 191	98.46 122

DVP-MVS		95.82 58	96.18 42	94.72 117	98.51 49	86.69 154	95.20 95	97.00 154	91.85 101	97.40 49	97.35 81	95.58 22	99.34 66	93.44 58	99.31 67	98.13 146
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
CS-MVS		95.77 59	95.58 70	96.37 52	96.84 156	91.72 63	96.73 28	99.06 5	94.23 49	92.48 237	94.79 231	93.56 73	99.49 24	93.47 54	99.05 104	97.89 173

SMA-MVS		95.77 59	95.54 71	96.47 51	98.27 69	91.19 68	95.09 99	97.79 95	86.48 222	97.42 48	97.51 68	94.47 63	99.29 74	93.55 48	99.29 72	98.93 66
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
test_0402		95.73 61	96.22 40	94.26 141	98.19 75	85.77 178	93.24 160	97.24 139	96.88 16	97.69 32	97.77 55	94.12 68	99.13 94	91.54 119	99.29 72	97.88 174

ACMP	88.15 13	95.71 62	95.43 76	96.54 47	98.17 76	91.73 62	94.24 131	98.08 54	89.46 163	96.61 80	96.47 137	95.85 17	99.12 98	90.45 137	99.56 35	98.77 88
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
XVG-ACMP-BASELINE		95.68 63	95.34 79	96.69 43	98.40 60	93.04 43	94.54 124	98.05 61	90.45 145	96.31 91	96.76 119	92.91 96	98.72 165	91.19 123	99.42 51	98.32 129

DP-MVS		95.62 64	95.84 61	94.97 107	97.16 140	88.62 114	94.54 124	97.64 102	96.94 15	96.58 81	97.32 84	93.07 92	98.72 165	90.45 137	98.84 130	97.57 199

OPM-MVS		95.61 65	95.45 74	96.08 56	98.49 57	91.00 70	92.65 176	97.33 131	90.05 151	96.77 74	96.85 112	95.04 45	98.56 191	92.77 83	99.06 101	98.70 98
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
mvsmamba		95.61 65	95.40 77	96.22 53	98.44 59	89.86 88	97.14 16	97.45 119	91.25 126	97.49 43	98.14 34	83.49 235	99.45 26	95.52 11	99.66 22	99.36 25

RPSCF		95.58 67	94.89 97	97.62 8	97.58 119	96.30 4	95.97 65	97.53 113	92.42 81	93.41 203	97.78 53	91.21 138	97.77 260	91.06 124	97.06 257	98.80 84

MIMVSNet1		95.52 68	95.45 74	95.72 79	99.14 5	89.02 104	96.23 55	96.87 167	93.73 61	97.87 28	98.49 25	90.73 151	99.05 107	86.43 230	99.60 27	99.10 48

Anonymous20240529		95.50 69	95.83 62	94.50 131	97.33 133	85.93 175	95.19 97	96.77 175	96.64 19	97.61 37	98.05 41	93.23 85	98.79 151	88.60 190	99.04 110	98.78 86

Vis-MVSNet		95.50 69	95.48 73	95.56 86	98.11 79	89.40 99	95.35 87	98.22 33	92.36 84	94.11 179	98.07 40	92.02 115	99.44 28	93.38 63	97.67 238	97.85 178
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
DROMVSNet		95.44 71	95.62 69	94.89 109	96.93 151	87.69 133	96.48 38	99.14 4	93.93 57	92.77 229	94.52 239	93.95 70	99.49 24	93.62 45	99.22 86	97.51 204

pm-mvs1		95.43 72	95.94 55	93.93 152	98.38 62	85.08 186	95.46 86	97.12 148	91.84 104	97.28 51	98.46 26	95.30 34	97.71 265	90.17 152	99.42 51	98.99 56

DeepC-MVS	91.39 4	95.43 72	95.33 80	95.71 80	97.67 113	90.17 82	93.86 145	98.02 68	87.35 212	96.22 99	97.99 45	94.48 62	99.05 107	92.73 86	99.68 19	97.93 167
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
RRT_MVS		95.41 74	95.20 87	96.05 57	98.86 21	88.92 106	97.49 10	94.48 258	93.12 72	97.94 27	98.54 21	81.19 264	99.63 6	95.48 12	99.69 15	99.60 12

XVG-OURS-SEG-HR		95.38 75	95.00 94	96.51 48	98.10 80	94.07 20	92.46 183	98.13 46	90.69 138	93.75 193	96.25 159	98.03 2	97.02 294	92.08 99	95.55 293	98.45 123

UniMVSNet_NR-MVSNet		95.35 76	95.21 85	95.76 77	97.69 111	88.59 115	92.26 196	97.84 88	94.91 38	96.80 72	95.78 182	90.42 156	99.41 40	91.60 116	99.58 33	99.29 30

MSP-MVS		95.34 77	94.63 110	97.48 14	98.67 30	94.05 23	96.41 43	98.18 37	91.26 124	95.12 149	95.15 210	86.60 215	99.50 21	93.43 60	96.81 267	98.89 73
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
CS-MVS-test		95.32 78	95.10 91	95.96 61	96.86 155	90.75 76	96.33 47	99.20 2	93.99 53	91.03 269	93.73 267	93.52 76	99.55 18	91.81 109	99.45 46	97.58 198

FC-MVSNet-test		95.32 78	95.88 58	93.62 162	98.49 57	81.77 223	95.90 68	98.32 21	93.93 57	97.53 41	97.56 63	88.48 179	99.40 47	92.91 82	99.83 6	99.68 4

UniMVSNet (Re)		95.32 78	95.15 88	95.80 74	97.79 102	88.91 107	92.91 167	98.07 57	93.46 67	96.31 91	95.97 171	90.14 161	99.34 66	92.11 97	99.64 25	99.16 39

Gipuma		95.31 81	95.80 64	93.81 159	97.99 94	90.91 72	96.42 42	97.95 79	96.69 17	91.78 258	98.85 12	91.77 121	95.49 332	91.72 112	99.08 100	95.02 297
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
DU-MVS		95.28 82	95.12 90	95.75 78	97.75 104	88.59 115	92.58 177	97.81 91	93.99 53	96.80 72	95.90 172	90.10 165	99.41 40	91.60 116	99.58 33	99.26 31

NR-MVSNet		95.28 82	95.28 83	95.26 97	97.75 104	87.21 141	95.08 100	97.37 122	93.92 59	97.65 33	95.90 172	90.10 165	99.33 71	90.11 154	99.66 22	99.26 31

TransMVSNet (Re)		95.27 84	96.04 52	92.97 182	98.37 64	81.92 222	95.07 101	96.76 176	93.97 56	97.77 30	98.57 19	95.72 18	97.90 245	88.89 183	99.23 84	99.08 49

SD-MVS		95.19 85	95.73 66	93.55 165	96.62 168	88.88 110	94.67 114	98.05 61	91.26 124	97.25 53	96.40 143	95.42 26	94.36 349	92.72 87	99.19 89	97.40 213
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 86	95.67 68	93.58 164	97.76 103	83.15 210	94.58 119	97.58 108	93.39 68	97.05 60	98.04 42	93.25 84	98.51 196	89.75 164	99.59 29	99.08 49

xxxxxxxxxxxxxcwj		95.03 87	94.93 95	95.33 93	97.46 127	88.05 126	92.04 204	98.42 16	87.63 208	96.36 87	96.68 127	94.37 64	99.32 72	92.41 93	99.05 104	98.64 105

HPM-MVS++		95.02 88	94.39 117	96.91 38	97.88 97	93.58 38	94.09 137	96.99 156	91.05 130	92.40 242	95.22 209	91.03 145	99.25 80	92.11 97	98.69 150	97.90 171

APD-MVS		95.00 89	94.69 105	95.93 65	97.38 130	90.88 73	94.59 117	97.81 91	89.22 172	95.46 133	96.17 164	93.42 80	99.34 66	89.30 170	98.87 128	97.56 201
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PMVS	87.21 14	94.97 90	95.33 80	93.91 154	98.97 15	97.16 2	95.54 83	95.85 216	96.47 21	93.40 205	97.46 70	95.31 33	95.47 333	86.18 234	98.78 142	89.11 360
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
TSAR-MVS + MP.		94.96 91	94.75 102	95.57 85	98.86 21	88.69 111	96.37 44	96.81 171	85.23 242	94.75 166	97.12 95	91.85 120	99.40 47	93.45 56	98.33 186	98.62 109
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 92	95.21 85	93.98 148	98.52 48	83.19 209	95.93 66	94.84 247	94.86 39	98.49 15	98.74 16	81.45 258	99.60 9	94.69 18	99.39 58	99.15 40

FIs		94.90 93	95.35 78	93.55 165	98.28 68	81.76 224	95.33 89	98.14 45	93.05 74	97.07 57	97.18 92	87.65 193	99.29 74	91.72 112	99.69 15	99.61 11

Regformer-4		94.90 93	94.67 108	95.59 83	92.78 312	89.02 104	92.39 188	95.91 213	94.50 43	96.41 84	95.56 193	92.10 114	99.01 115	94.23 28	98.14 208	98.74 92

AllTest		94.88 95	94.51 115	96.00 59	98.02 89	92.17 52	95.26 92	98.43 14	90.48 143	95.04 155	96.74 122	92.54 106	97.86 251	85.11 245	98.98 113	97.98 161

ETH3D-3000-0.1		94.86 96	94.55 112	95.81 72	97.61 116	89.72 90	94.05 138	98.37 18	88.09 196	95.06 154	95.85 174	92.58 104	99.10 101	90.33 144	98.99 112	98.62 109

Regformer-2		94.86 96	94.55 112	95.77 76	92.83 310	89.98 84	91.87 216	96.40 193	94.38 47	96.19 103	95.04 217	92.47 109	99.04 110	93.49 50	98.31 189	98.28 133

FMVSNet1		94.84 98	95.13 89	93.97 149	97.60 117	84.29 192	95.99 62	96.56 185	92.38 82	97.03 61	98.53 22	90.12 162	98.98 117	88.78 185	99.16 94	98.65 101

ANet_high		94.83 99	96.28 37	90.47 268	96.65 164	73.16 336	94.33 128	98.74 10	96.39 23	98.09 25	98.93 8	93.37 81	98.70 171	90.38 140	99.68 19	99.53 15

testtj		94.81 100	94.42 116	96.01 58	97.23 135	90.51 80	94.77 111	97.85 87	91.29 123	94.92 160	95.66 186	91.71 123	99.40 47	88.07 200	98.25 196	98.11 148

3Dnovator	92.54 3	94.80 101	94.90 96	94.47 134	95.47 245	87.06 144	96.63 30	97.28 137	91.82 107	94.34 178	97.41 72	90.60 154	98.65 180	92.47 92	98.11 212	97.70 190

CPTT-MVS		94.74 102	94.12 128	96.60 45	98.15 77	93.01 44	95.84 70	97.66 101	89.21 173	93.28 209	95.46 198	88.89 176	98.98 117	89.80 161	98.82 136	97.80 183

XVG-OURS		94.72 103	94.12 128	96.50 49	98.00 91	94.23 18	91.48 228	98.17 41	90.72 137	95.30 140	96.47 137	87.94 190	96.98 295	91.41 121	97.61 241	98.30 132

CSCG		94.69 104	94.75 102	94.52 130	97.55 121	87.87 130	95.01 104	97.57 109	92.68 76	96.20 101	93.44 274	91.92 119	98.78 155	89.11 178	99.24 83	96.92 230

v10		94.68 105	95.27 84	92.90 188	96.57 171	80.15 242	94.65 116	97.57 109	90.68 139	97.43 46	98.00 44	88.18 183	99.15 90	94.84 17	99.55 36	99.41 21

v8		94.65 106	95.29 82	92.74 193	96.65 164	79.77 257	94.59 117	97.17 143	91.86 100	97.47 45	97.93 47	88.16 184	99.08 102	94.32 24	99.47 42	99.38 23

canonicalmvs		94.59 107	94.69 105	94.30 140	95.60 242	87.03 146	95.59 79	98.24 31	91.56 118	95.21 148	92.04 306	94.95 50	98.66 178	91.45 120	97.57 242	97.20 222

CNVR-MVS		94.58 108	94.29 121	95.46 89	96.94 149	89.35 101	91.81 222	96.80 172	89.66 159	93.90 191	95.44 200	92.80 100	98.72 165	92.74 85	98.52 166	98.32 129

GeoE		94.55 109	94.68 107	94.15 143	97.23 135	85.11 185	94.14 135	97.34 130	88.71 183	95.26 143	95.50 196	94.65 57	99.12 98	90.94 128	98.40 174	98.23 137

Regformer-1		94.55 109	94.33 120	95.19 100	92.83 310	88.54 118	91.87 216	95.84 217	93.99 53	95.95 111	95.04 217	92.00 116	98.79 151	93.14 74	98.31 189	98.23 137

EG-PatchMatch MVS		94.54 111	94.67 108	94.14 144	97.87 98	86.50 158	92.00 207	96.74 177	88.16 195	96.93 66	97.61 61	93.04 93	97.90 245	91.60 116	98.12 211	98.03 155

IS-MVSNet		94.49 112	94.35 119	94.92 108	98.25 72	86.46 161	97.13 17	94.31 262	96.24 24	96.28 96	96.36 150	82.88 242	99.35 63	88.19 195	99.52 40	98.96 63

Baseline_NR-MVSNet		94.47 113	95.09 92	92.60 200	98.50 56	80.82 238	92.08 202	96.68 179	93.82 60	96.29 93	98.56 20	90.10 165	97.75 263	90.10 156	99.66 22	99.24 33

test_part1		94.39 114	94.55 112	93.92 153	96.14 206	82.86 214	95.54 83	98.09 53	95.36 36	98.27 20	98.36 29	75.91 302	99.44 28	93.41 61	99.84 3	99.47 18

VDD-MVS		94.37 115	94.37 118	94.40 138	97.49 124	86.07 173	93.97 142	93.28 280	94.49 44	96.24 97	97.78 53	87.99 189	98.79 151	88.92 181	99.14 96	98.34 128

EI-MVSNet-Vis-set		94.36 116	94.28 122	94.61 122	92.55 315	85.98 174	92.44 184	94.69 254	93.70 62	96.12 106	95.81 178	91.24 136	98.86 137	93.76 43	98.22 201	98.98 61

EI-MVSNet-UG-set		94.35 117	94.27 124	94.59 127	92.46 316	85.87 176	92.42 186	94.69 254	93.67 66	96.13 105	95.84 177	91.20 139	98.86 137	93.78 40	98.23 199	99.03 52

PHI-MVS		94.34 118	93.80 133	95.95 62	95.65 238	91.67 64	94.82 109	97.86 84	87.86 201	93.04 220	94.16 251	91.58 126	98.78 155	90.27 147	98.96 119	97.41 210

casdiffmvs		94.32 119	94.80 100	92.85 190	96.05 213	81.44 230	92.35 191	98.05 61	91.53 119	95.75 121	96.80 116	93.35 82	98.49 197	91.01 127	98.32 188	98.64 105

Regformer-3		94.28 120	94.23 126	94.46 135	92.78 312	86.28 168	92.39 188	94.70 253	93.69 65	95.97 109	95.56 193	91.34 131	98.48 201	93.45 56	98.14 208	98.62 109

bld_raw_dy_0_64		94.27 121	94.15 127	94.65 121	98.55 42	86.28 168	95.80 72	95.55 229	88.41 190	97.09 56	98.08 39	78.69 277	98.87 136	95.63 10	99.53 37	98.81 82

tfpnnormal		94.27 121	94.87 98	92.48 204	97.71 108	80.88 237	94.55 123	95.41 234	93.70 62	96.67 77	97.72 56	91.40 130	98.18 225	87.45 211	99.18 91	98.36 127

HQP_MVS		94.26 123	93.93 130	95.23 99	97.71 108	88.12 124	94.56 121	97.81 91	91.74 112	93.31 206	95.59 188	86.93 207	98.95 124	89.26 174	98.51 168	98.60 112

baseline		94.26 123	94.80 100	92.64 196	96.08 211	80.99 235	93.69 149	98.04 65	90.80 136	94.89 161	96.32 152	93.19 86	98.48 201	91.68 114	98.51 168	98.43 124

OMC-MVS		94.22 125	93.69 138	95.81 72	97.25 134	91.27 66	92.27 195	97.40 121	87.10 218	94.56 171	95.42 201	93.74 71	98.11 230	86.62 225	98.85 129	98.06 149

LCM-MVSNet-Re		94.20 126	94.58 111	93.04 179	95.91 224	83.13 211	93.79 146	99.19 3	92.00 94	98.84 5	98.04 42	93.64 72	99.02 113	81.28 281	98.54 164	96.96 229

DeepPCF-MVS	90.46 6	94.20 126	93.56 144	96.14 54	95.96 220	92.96 45	89.48 281	97.46 117	85.14 245	96.23 98	95.42 201	93.19 86	98.08 231	90.37 141	98.76 144	97.38 216

KD-MVS_self_test		94.10 128	94.73 104	92.19 210	97.66 114	79.49 263	94.86 108	97.12 148	89.59 162	96.87 68	97.65 59	90.40 159	98.34 211	89.08 179	99.35 61	98.75 89

NCCC		94.08 129	93.54 145	95.70 81	96.49 179	89.90 87	92.39 188	96.91 163	90.64 140	92.33 248	94.60 236	90.58 155	98.96 122	90.21 151	97.70 236	98.23 137

VDDNet		94.03 130	94.27 124	93.31 174	98.87 20	82.36 218	95.51 85	91.78 310	97.19 12	96.32 90	98.60 18	84.24 232	98.75 160	87.09 218	98.83 135	98.81 82

ETH3D cwj APD-0.16		93.99 131	93.38 149	95.80 74	96.82 158	89.92 85	92.72 172	98.02 68	84.73 255	93.65 197	95.54 195	91.68 124	99.22 83	88.78 185	98.49 171	98.26 135

dcpmvs_2		93.96 132	95.01 93	90.82 260	97.60 117	74.04 331	93.68 151	98.85 7	89.80 157	97.82 29	97.01 103	91.14 143	99.21 84	90.56 135	98.59 158	99.19 37

EPP-MVSNet		93.91 133	93.68 139	94.59 127	98.08 81	85.55 181	97.44 11	94.03 267	94.22 50	94.94 158	96.19 161	82.07 253	99.57 14	87.28 215	98.89 123	98.65 101

Effi-MVS+-dtu		93.90 134	92.60 169	97.77 4	94.74 268	96.67 3	94.00 140	95.41 234	89.94 152	91.93 256	92.13 304	90.12 162	98.97 121	87.68 208	97.48 245	97.67 193

IterMVS-LS		93.78 135	94.28 122	92.27 207	96.27 195	79.21 270	91.87 216	96.78 173	91.77 110	96.57 82	97.07 97	87.15 202	98.74 163	91.99 102	99.03 111	98.86 76
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DeepC-MVS_fast	89.96 7	93.73 136	93.44 147	94.60 126	96.14 206	87.90 129	93.36 158	97.14 145	85.53 239	93.90 191	95.45 199	91.30 134	98.59 187	89.51 167	98.62 155	97.31 219
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		93.66 137	93.28 152	94.80 113	96.25 198	90.95 71	90.21 260	95.43 233	87.91 198	93.74 195	94.40 242	92.88 98	96.38 314	90.39 139	98.28 192	97.07 223

MVS_111021_HR		93.63 138	93.42 148	94.26 141	96.65 164	86.96 149	89.30 287	96.23 201	88.36 192	93.57 200	94.60 236	93.45 77	97.77 260	90.23 150	98.38 179	98.03 155

v1144		93.50 139	93.81 132	92.57 201	96.28 194	79.61 260	91.86 220	96.96 157	86.95 220	95.91 115	96.32 152	87.65 193	98.96 122	93.51 49	98.88 125	99.13 42

v1192		93.49 140	93.78 134	92.62 199	96.16 204	79.62 259	91.83 221	97.22 141	86.07 229	96.10 107	96.38 148	87.22 200	99.02 113	94.14 31	98.88 125	99.22 34

WR-MVS		93.49 140	93.72 136	92.80 192	97.57 120	80.03 248	90.14 264	95.68 220	93.70 62	96.62 79	95.39 204	87.21 201	99.04 110	87.50 210	99.64 25	99.33 27

V42		93.43 142	93.58 142	92.97 182	95.34 251	81.22 232	92.67 175	96.49 190	87.25 214	96.20 101	96.37 149	87.32 199	98.85 139	92.39 95	98.21 202	98.85 79

K. test v3		93.37 143	93.27 153	93.66 161	98.05 84	82.62 216	94.35 127	86.62 341	96.05 28	97.51 42	98.85 12	76.59 300	99.65 3	93.21 70	98.20 204	98.73 94

PM-MVS		93.33 144	92.67 167	95.33 93	96.58 170	94.06 21	92.26 196	92.18 301	85.92 232	96.22 99	96.61 132	85.64 226	95.99 325	90.35 142	98.23 199	95.93 269

v1240		93.29 145	93.71 137	92.06 217	96.01 218	77.89 288	91.81 222	97.37 122	85.12 246	96.69 76	96.40 143	86.67 213	99.07 106	94.51 20	98.76 144	99.22 34

test_prior3		93.29 145	92.85 159	94.61 122	95.95 221	87.23 139	90.21 260	97.36 127	89.33 168	90.77 272	94.81 227	90.41 157	98.68 175	88.21 193	98.55 161	97.93 167

v2v482		93.29 145	93.63 140	92.29 206	96.35 188	78.82 276	91.77 224	96.28 197	88.45 188	95.70 125	96.26 158	86.02 221	98.90 128	93.02 78	98.81 138	99.14 41

alignmvs		93.26 148	92.85 159	94.50 131	95.70 234	87.45 135	93.45 156	95.76 218	91.58 117	95.25 145	92.42 300	81.96 255	98.72 165	91.61 115	97.87 228	97.33 218

v1921920		93.26 148	93.61 141	92.19 210	96.04 217	78.31 282	91.88 215	97.24 139	85.17 244	96.19 103	96.19 161	86.76 212	99.05 107	94.18 30	98.84 130	99.22 34

MSLP-MVS++		93.25 150	93.88 131	91.37 237	96.34 189	82.81 215	93.11 161	97.74 97	89.37 166	94.08 181	95.29 208	90.40 159	96.35 316	90.35 142	98.25 196	94.96 298

GBi-Net		93.21 151	92.96 156	93.97 149	95.40 247	84.29 192	95.99 62	96.56 185	88.63 184	95.10 150	98.53 22	81.31 260	98.98 117	86.74 221	98.38 179	98.65 101

test1		93.21 151	92.96 156	93.97 149	95.40 247	84.29 192	95.99 62	96.56 185	88.63 184	95.10 150	98.53 22	81.31 260	98.98 117	86.74 221	98.38 179	98.65 101

v144192		93.20 153	93.54 145	92.16 214	96.05 213	78.26 283	91.95 208	97.14 145	84.98 250	95.96 110	96.11 165	87.08 204	99.04 110	93.79 39	98.84 130	99.17 38

VPNet		93.08 154	93.76 135	91.03 250	98.60 36	75.83 317	91.51 227	95.62 221	91.84 104	95.74 122	97.10 96	89.31 173	98.32 212	85.07 247	99.06 101	98.93 66

UGNet		93.08 154	92.50 171	94.79 114	93.87 293	87.99 128	95.07 101	94.26 264	90.64 140	87.33 329	97.67 58	86.89 210	98.49 197	88.10 198	98.71 147	97.91 170
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		93.07 156	91.80 187	96.89 39	94.74 268	95.83 6	92.17 199	95.41 234	89.94 152	89.85 291	90.59 329	90.12 162	98.88 131	87.68 208	95.66 291	95.97 267

TSAR-MVS + GP.		93.07 156	92.41 173	95.06 105	95.82 227	90.87 74	90.97 239	92.61 295	88.04 197	94.61 170	93.79 265	88.08 185	97.81 255	89.41 169	98.39 177	96.50 247

ETV-MVS		92.99 158	92.74 163	93.72 160	95.86 226	86.30 167	92.33 192	97.84 88	91.70 115	92.81 227	86.17 362	92.22 111	99.19 87	88.03 202	97.73 232	95.66 283

EI-MVSNet		92.99 158	93.26 154	92.19 210	92.12 323	79.21 270	92.32 193	94.67 256	91.77 110	95.24 146	95.85 174	87.14 203	98.49 197	91.99 102	98.26 194	98.86 76

MCST-MVS		92.91 160	92.51 170	94.10 145	97.52 122	85.72 179	91.36 232	97.13 147	80.33 290	92.91 225	94.24 247	91.23 137	98.72 165	89.99 158	97.93 225	97.86 176

h-mvs33		92.89 161	91.99 180	95.58 84	96.97 147	90.55 78	93.94 143	94.01 270	89.23 170	93.95 188	96.19 161	76.88 297	99.14 92	91.02 125	95.71 290	97.04 226

QAPM		92.88 162	92.77 161	93.22 177	95.82 227	83.31 206	96.45 39	97.35 129	83.91 260	93.75 193	96.77 117	89.25 174	98.88 131	84.56 253	97.02 259	97.49 205

v148		92.87 163	93.29 150	91.62 230	96.25 198	77.72 291	91.28 233	95.05 241	89.69 158	95.93 114	96.04 167	87.34 198	98.38 207	90.05 157	97.99 222	98.78 86

Anonymous20240521		92.86 164	93.57 143	90.74 262	96.57 171	75.50 319	94.15 134	95.60 222	89.38 165	95.90 116	97.90 51	80.39 268	97.96 243	92.60 90	99.68 19	98.75 89

Effi-MVS+		92.79 165	92.74 163	92.94 186	95.10 255	83.30 207	94.00 140	97.53 113	91.36 122	89.35 300	90.65 328	94.01 69	98.66 178	87.40 213	95.30 301	96.88 233

FMVSNet2		92.78 166	92.73 165	92.95 184	95.40 247	81.98 221	94.18 133	95.53 231	88.63 184	96.05 108	97.37 75	81.31 260	98.81 147	87.38 214	98.67 153	98.06 149

Fast-Effi-MVS+-dtu		92.77 167	92.16 175	94.58 129	94.66 274	88.25 122	92.05 203	96.65 181	89.62 160	90.08 285	91.23 316	92.56 105	98.60 185	86.30 232	96.27 279	96.90 231

LF4IMVS		92.72 168	92.02 179	94.84 112	95.65 238	91.99 56	92.92 166	96.60 183	85.08 248	92.44 240	93.62 269	86.80 211	96.35 316	86.81 220	98.25 196	96.18 260

train_agg		92.71 169	91.83 185	95.35 91	96.45 181	89.46 95	90.60 248	96.92 161	79.37 299	90.49 277	94.39 243	91.20 139	98.88 131	88.66 189	98.43 173	97.72 189

VNet		92.67 170	92.96 156	91.79 222	96.27 195	80.15 242	91.95 208	94.98 243	92.19 91	94.52 173	96.07 166	87.43 197	97.39 282	84.83 249	98.38 179	97.83 179

CDPH-MVS		92.67 170	91.83 185	95.18 101	96.94 149	88.46 120	90.70 246	97.07 151	77.38 315	92.34 247	95.08 215	92.67 103	98.88 131	85.74 236	98.57 160	98.20 141

agg_prior1		92.60 172	91.76 188	95.10 104	96.20 200	88.89 108	90.37 255	96.88 165	79.67 296	90.21 282	94.41 241	91.30 134	98.78 155	88.46 192	98.37 184	97.64 195

Anonymous202405211		92.58 173	92.50 171	92.83 191	96.55 173	83.22 208	92.43 185	91.64 311	94.10 52	95.59 128	96.64 130	81.88 257	97.50 273	85.12 244	98.52 166	97.77 185

XXY-MVS		92.58 173	93.16 155	90.84 259	97.75 104	79.84 253	91.87 216	96.22 203	85.94 231	95.53 130	97.68 57	92.69 102	94.48 345	83.21 263	97.51 243	98.21 140

MVS_Test		92.57 175	93.29 150	90.40 271	93.53 297	75.85 315	92.52 179	96.96 157	88.73 181	92.35 245	96.70 126	90.77 147	98.37 210	92.53 91	95.49 295	96.99 228

TAPA-MVS	88.58 10	92.49 176	91.75 189	94.73 116	96.50 178	89.69 91	92.91 167	97.68 100	78.02 313	92.79 228	94.10 252	90.85 146	97.96 243	84.76 251	98.16 206	96.54 242
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
patch_mono-2		92.46 177	92.72 166	91.71 226	96.65 164	78.91 274	88.85 296	97.17 143	83.89 261	92.45 239	96.76 119	89.86 169	97.09 291	90.24 149	98.59 158	99.12 44

ab-mvs		92.40 178	92.62 168	91.74 224	97.02 145	81.65 225	95.84 70	95.50 232	86.95 220	92.95 224	97.56 63	90.70 152	97.50 273	79.63 300	97.43 247	96.06 264

CANet		92.38 179	91.99 180	93.52 169	93.82 295	83.46 205	91.14 235	97.00 154	89.81 156	86.47 333	94.04 254	87.90 191	99.21 84	89.50 168	98.27 193	97.90 171

EIA-MVS		92.35 180	92.03 178	93.30 175	95.81 229	83.97 200	92.80 170	98.17 41	87.71 205	89.79 294	87.56 352	91.17 142	99.18 88	87.97 203	97.27 251	96.77 237

DP-MVS Recon		92.31 181	91.88 183	93.60 163	97.18 139	86.87 150	91.10 237	97.37 122	84.92 251	92.08 253	94.08 253	88.59 178	98.20 222	83.50 260	98.14 208	95.73 278

F-COLMAP		92.28 182	91.06 206	95.95 62	97.52 122	91.90 58	93.53 153	97.18 142	83.98 259	88.70 312	94.04 254	88.41 181	98.55 193	80.17 293	95.99 284	97.39 214

OpenMVS	89.45 8	92.27 183	92.13 177	92.68 195	94.53 278	84.10 198	95.70 75	97.03 152	82.44 278	91.14 268	96.42 141	88.47 180	98.38 207	85.95 235	97.47 246	95.55 287

hse-mvs2		92.24 184	91.20 202	95.38 90	96.16 204	90.65 77	92.52 179	92.01 308	89.23 170	93.95 188	92.99 284	76.88 297	98.69 173	91.02 125	96.03 282	96.81 235

MVSFormer		92.18 185	92.23 174	92.04 218	94.74 268	80.06 246	97.15 14	97.37 122	88.98 176	88.83 304	92.79 289	77.02 294	99.60 9	96.41 4	96.75 270	96.46 249

HQP-MVS		92.09 186	91.49 195	93.88 156	96.36 185	84.89 187	91.37 229	97.31 132	87.16 215	88.81 306	93.40 275	84.76 229	98.60 185	86.55 227	97.73 232	98.14 144

DELS-MVS		92.05 187	92.16 175	91.72 225	94.44 279	80.13 244	87.62 309	97.25 138	87.34 213	92.22 250	93.18 281	89.54 172	98.73 164	89.67 165	98.20 204	96.30 255
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		92.00 188	92.76 162	89.71 287	95.62 241	77.02 299	90.72 245	96.17 206	87.70 206	95.26 143	96.29 154	92.54 106	96.45 311	81.77 277	98.77 143	95.66 283

ETH3 D test6400		91.91 189	91.25 201	93.89 155	96.59 169	84.41 191	92.10 201	97.72 99	78.52 309	91.82 257	93.78 266	88.70 177	99.13 94	83.61 259	98.39 177	98.14 144

CLD-MVS		91.82 190	91.41 197	93.04 179	96.37 183	83.65 204	86.82 328	97.29 135	84.65 256	92.27 249	89.67 338	92.20 112	97.85 253	83.95 257	99.47 42	97.62 196
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
FA-MVS(test-final)		91.81 191	91.85 184	91.68 228	94.95 258	79.99 250	96.00 61	93.44 278	87.80 202	94.02 186	97.29 85	77.60 287	98.45 204	88.04 201	97.49 244	96.61 241

diffmvs		91.74 192	91.93 182	91.15 248	93.06 305	78.17 284	88.77 299	97.51 116	86.28 225	92.42 241	93.96 259	88.04 187	97.46 276	90.69 134	96.67 272	97.82 181

CNLPA		91.72 193	91.20 202	93.26 176	96.17 203	91.02 69	91.14 235	95.55 229	90.16 150	90.87 271	93.56 272	86.31 217	94.40 348	79.92 299	97.12 255	94.37 311

IterMVS-SCA-FT		91.65 194	91.55 191	91.94 219	93.89 292	79.22 269	87.56 312	93.51 276	91.53 119	95.37 136	96.62 131	78.65 278	98.90 128	91.89 107	94.95 307	97.70 190

PVSNet_Blended_VisFu		91.63 195	91.20 202	92.94 186	97.73 107	83.95 201	92.14 200	97.46 117	78.85 308	92.35 245	94.98 220	84.16 233	99.08 102	86.36 231	96.77 269	95.79 276

AdaColmap		91.63 195	91.36 198	92.47 205	95.56 243	86.36 165	92.24 198	96.27 198	88.88 180	89.90 290	92.69 292	91.65 125	98.32 212	77.38 319	97.64 239	92.72 343

pmmvs-eth3d		91.54 197	90.73 214	93.99 147	95.76 232	87.86 131	90.83 242	93.98 271	78.23 312	94.02 186	96.22 160	82.62 248	96.83 301	86.57 226	98.33 186	97.29 220

API-MVS		91.52 198	91.61 190	91.26 242	94.16 284	86.26 170	94.66 115	94.82 248	91.17 128	92.13 252	91.08 319	90.03 168	97.06 293	79.09 307	97.35 250	90.45 358

xiu_mvs_v1_base_debu		91.47 199	91.52 192	91.33 239	95.69 235	81.56 226	89.92 271	96.05 209	83.22 265	91.26 264	90.74 323	91.55 127	98.82 142	89.29 171	95.91 285	93.62 330

xiu_mvs_v1_base		91.47 199	91.52 192	91.33 239	95.69 235	81.56 226	89.92 271	96.05 209	83.22 265	91.26 264	90.74 323	91.55 127	98.82 142	89.29 171	95.91 285	93.62 330

xiu_mvs_v1_base_debi		91.47 199	91.52 192	91.33 239	95.69 235	81.56 226	89.92 271	96.05 209	83.22 265	91.26 264	90.74 323	91.55 127	98.82 142	89.29 171	95.91 285	93.62 330

LFMVS		91.33 202	91.16 205	91.82 221	96.27 195	79.36 265	95.01 104	85.61 351	96.04 29	94.82 163	97.06 98	72.03 315	98.46 203	84.96 248	98.70 149	97.65 194

c3_l		91.32 203	91.42 196	91.00 253	92.29 318	76.79 306	87.52 315	96.42 192	85.76 235	94.72 169	93.89 262	82.73 245	98.16 227	90.93 129	98.55 161	98.04 152

Fast-Effi-MVS+		91.28 204	90.86 209	92.53 203	95.45 246	82.53 217	89.25 290	96.52 189	85.00 249	89.91 289	88.55 348	92.94 94	98.84 140	84.72 252	95.44 297	96.22 258

MDA-MVSNet-bldmvs		91.04 205	90.88 208	91.55 232	94.68 273	80.16 241	85.49 340	92.14 304	90.41 147	94.93 159	95.79 179	85.10 227	96.93 298	85.15 242	94.19 324	97.57 199

PAPM_NR		91.03 206	90.81 211	91.68 228	96.73 162	81.10 234	93.72 148	96.35 196	88.19 194	88.77 310	92.12 305	85.09 228	97.25 286	82.40 272	93.90 325	96.68 240

MVS_0304		90.96 207	90.15 227	93.37 171	93.17 302	87.06 144	93.62 152	92.43 299	89.60 161	82.25 358	95.50 196	82.56 249	97.83 254	84.41 255	97.83 230	95.22 291

MSDG		90.82 208	90.67 215	91.26 242	94.16 284	83.08 212	86.63 333	96.19 204	90.60 142	91.94 255	91.89 307	89.16 175	95.75 327	80.96 287	94.51 317	94.95 299

test20.03		90.80 209	90.85 210	90.63 265	95.63 240	79.24 268	89.81 275	92.87 286	89.90 154	94.39 175	96.40 143	85.77 222	95.27 340	73.86 338	99.05 104	97.39 214

FMVSNet3		90.78 210	90.32 223	92.16 214	93.03 307	79.92 252	92.54 178	94.95 244	86.17 228	95.10 150	96.01 169	69.97 321	98.75 160	86.74 221	98.38 179	97.82 181

eth_miper_zixun_eth		90.72 211	90.61 216	91.05 249	92.04 325	76.84 305	86.91 324	96.67 180	85.21 243	94.41 174	93.92 260	79.53 272	98.26 218	89.76 163	97.02 259	98.06 149

X-MVStestdata		90.70 212	88.45 254	97.44 17	98.56 39	93.99 27	96.50 36	97.95 79	94.58 41	94.38 176	26.89 377	94.56 59	99.39 52	93.57 46	99.05 104	98.93 66

BH-untuned		90.68 213	90.90 207	90.05 282	95.98 219	79.57 261	90.04 267	94.94 245	87.91 198	94.07 182	93.00 283	87.76 192	97.78 259	79.19 306	95.17 304	92.80 342

cl____		90.65 214	90.56 218	90.91 257	91.85 327	76.98 302	86.75 329	95.36 238	85.53 239	94.06 183	94.89 224	77.36 292	97.98 242	90.27 147	98.98 113	97.76 186

DIV-MVS_self_test		90.65 214	90.56 218	90.91 257	91.85 327	76.99 301	86.75 329	95.36 238	85.52 241	94.06 183	94.89 224	77.37 291	97.99 241	90.28 146	98.97 117	97.76 186

114514_t		90.51 216	89.80 232	92.63 198	98.00 91	82.24 219	93.40 157	97.29 135	65.84 364	89.40 299	94.80 230	86.99 205	98.75 160	83.88 258	98.61 156	96.89 232

miper_ehance_all_eth		90.48 217	90.42 221	90.69 263	91.62 332	76.57 308	86.83 327	96.18 205	83.38 263	94.06 183	92.66 294	82.20 251	98.04 233	89.79 162	97.02 259	97.45 207

BH-RMVSNet		90.47 218	90.44 220	90.56 267	95.21 254	78.65 280	89.15 291	93.94 272	88.21 193	92.74 230	94.22 248	86.38 216	97.88 247	78.67 309	95.39 299	95.14 294

Vis-MVSNet (Re-imp)		90.42 219	90.16 224	91.20 246	97.66 114	77.32 296	94.33 128	87.66 335	91.20 127	92.99 221	95.13 212	75.40 304	98.28 214	77.86 312	99.19 89	97.99 160

PLC	85.34 15	90.40 220	88.92 246	94.85 111	96.53 177	90.02 83	91.58 226	96.48 191	80.16 291	86.14 335	92.18 302	85.73 223	98.25 219	76.87 322	94.61 316	96.30 255
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
test1111		90.39 221	90.61 216	89.74 286	98.04 87	71.50 347	95.59 79	79.72 374	89.41 164	95.94 113	98.14 34	70.79 318	98.81 147	88.52 191	99.32 66	98.90 72

testgi		90.38 222	91.34 199	87.50 320	97.49 124	71.54 346	89.43 282	95.16 240	88.38 191	94.54 172	94.68 235	92.88 98	93.09 359	71.60 351	97.85 229	97.88 174

mvs_anonymous		90.37 223	91.30 200	87.58 319	92.17 322	68.00 360	89.84 274	94.73 252	83.82 262	93.22 214	97.40 73	87.54 195	97.40 281	87.94 204	95.05 306	97.34 217

PVSNet_BlendedMVS		90.35 224	89.96 229	91.54 233	94.81 263	78.80 278	90.14 264	96.93 159	79.43 298	88.68 313	95.06 216	86.27 218	98.15 228	80.27 289	98.04 218	97.68 192

UnsupCasMVSNet_eth		90.33 225	90.34 222	90.28 273	94.64 276	80.24 240	89.69 277	95.88 214	85.77 234	93.94 190	95.69 185	81.99 254	92.98 360	84.21 256	91.30 352	97.62 196

MAR-MVS		90.32 226	88.87 249	94.66 120	94.82 262	91.85 59	94.22 132	94.75 251	80.91 285	87.52 327	88.07 351	86.63 214	97.87 250	76.67 323	96.21 280	94.25 314
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
RPMNet		90.31 227	90.14 228	90.81 261	91.01 338	78.93 272	92.52 179	98.12 47	91.91 98	89.10 301	96.89 110	68.84 322	99.41 40	90.17 152	92.70 341	94.08 315

1121		90.26 228	89.23 239	93.34 172	97.15 142	87.40 136	91.94 210	94.39 260	67.88 359	91.02 270	94.91 223	86.91 209	98.59 187	81.17 284	97.71 235	94.02 320

iter_conf_final		90.23 229	89.32 238	92.95 184	94.65 275	81.46 229	94.32 130	95.40 237	85.61 238	92.84 226	95.37 206	54.58 369	99.13 94	92.16 96	98.94 121	98.25 136

IterMVS		90.18 230	90.16 224	90.21 277	93.15 303	75.98 314	87.56 312	92.97 285	86.43 224	94.09 180	96.40 143	78.32 282	97.43 278	87.87 205	94.69 314	97.23 221
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TAMVS		90.16 231	89.05 243	93.49 170	96.49 179	86.37 164	90.34 257	92.55 296	80.84 288	92.99 221	94.57 238	81.94 256	98.20 222	73.51 339	98.21 202	95.90 272

ECVR-MVS		90.12 232	90.16 224	90.00 283	97.81 100	72.68 341	95.76 74	78.54 375	89.04 174	95.36 137	98.10 37	70.51 319	98.64 181	87.10 217	99.18 91	98.67 99

test_yl		90.11 233	89.73 235	91.26 242	94.09 287	79.82 254	90.44 252	92.65 292	90.90 131	93.19 215	93.30 277	73.90 307	98.03 234	82.23 273	96.87 265	95.93 269

DCV-MVSNet		90.11 233	89.73 235	91.26 242	94.09 287	79.82 254	90.44 252	92.65 292	90.90 131	93.19 215	93.30 277	73.90 307	98.03 234	82.23 273	96.87 265	95.93 269

Patchmtry		90.11 233	89.92 230	90.66 264	90.35 347	77.00 300	92.96 165	92.81 287	90.25 149	94.74 167	96.93 107	67.11 327	97.52 272	85.17 240	98.98 113	97.46 206

MVP-Stereo		90.07 236	88.92 246	93.54 167	96.31 192	86.49 159	90.93 240	95.59 226	79.80 292	91.48 260	95.59 188	80.79 265	97.39 282	78.57 310	91.19 353	96.76 238
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
AUN-MVS		90.05 237	88.30 257	95.32 96	96.09 210	90.52 79	92.42 186	92.05 307	82.08 281	88.45 315	92.86 286	65.76 337	98.69 173	88.91 182	96.07 281	96.75 239

CL-MVSNet_self_test		90.04 238	89.90 231	90.47 268	95.24 253	77.81 289	86.60 335	92.62 294	85.64 237	93.25 213	93.92 260	83.84 234	96.06 323	79.93 297	98.03 219	97.53 203

D2MVS		89.93 239	89.60 237	90.92 255	94.03 289	78.40 281	88.69 301	94.85 246	78.96 306	93.08 217	95.09 214	74.57 305	96.94 296	88.19 195	98.96 119	97.41 210

miper_lstm_enhance		89.90 240	89.80 232	90.19 279	91.37 335	77.50 293	83.82 355	95.00 242	84.84 253	93.05 219	94.96 221	76.53 301	95.20 341	89.96 159	98.67 153	97.86 176

CANet_DTU		89.85 241	89.17 241	91.87 220	92.20 321	80.02 249	90.79 243	95.87 215	86.02 230	82.53 357	91.77 309	80.01 269	98.57 190	85.66 237	97.70 236	97.01 227

tttt0517		89.81 242	88.90 248	92.55 202	97.00 146	79.73 258	95.03 103	83.65 363	89.88 155	95.30 140	94.79 231	53.64 372	99.39 52	91.99 102	98.79 141	98.54 115

EPNet		89.80 243	88.25 260	94.45 136	83.91 378	86.18 171	93.87 144	87.07 339	91.16 129	80.64 366	94.72 233	78.83 275	98.89 130	85.17 240	98.89 123	98.28 133
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CDS-MVSNet		89.55 244	88.22 263	93.53 168	95.37 250	86.49 159	89.26 288	93.59 274	79.76 294	91.15 267	92.31 301	77.12 293	98.38 207	77.51 317	97.92 226	95.71 279
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MG-MVS		89.54 245	89.80 232	88.76 302	94.88 259	72.47 343	89.60 278	92.44 298	85.82 233	89.48 298	95.98 170	82.85 243	97.74 264	81.87 276	95.27 302	96.08 263

OpenMVS_ROB	85.12 16	89.52 246	89.05 243	90.92 255	94.58 277	81.21 233	91.10 237	93.41 279	77.03 319	93.41 203	93.99 258	83.23 239	97.80 256	79.93 297	94.80 311	93.74 327

DPM-MVS		89.35 247	88.40 255	92.18 213	96.13 209	84.20 196	86.96 323	96.15 207	75.40 326	87.36 328	91.55 314	83.30 238	98.01 238	82.17 275	96.62 273	94.32 313

MVSTER		89.32 248	88.75 250	91.03 250	90.10 349	76.62 307	90.85 241	94.67 256	82.27 279	95.24 146	95.79 179	61.09 358	98.49 197	90.49 136	98.26 194	97.97 164

PatchMatch-RL		89.18 249	88.02 268	92.64 196	95.90 225	92.87 47	88.67 303	91.06 314	80.34 289	90.03 287	91.67 311	83.34 237	94.42 347	76.35 326	94.84 310	90.64 357

jason		89.17 250	88.32 256	91.70 227	95.73 233	80.07 245	88.10 306	93.22 281	71.98 342	90.09 284	92.79 289	78.53 281	98.56 191	87.43 212	97.06 257	96.46 249
jason: jason.
PCF-MVS	84.52 17	89.12 251	87.71 271	93.34 172	96.06 212	85.84 177	86.58 336	97.31 132	68.46 357	93.61 199	93.89 262	87.51 196	98.52 195	67.85 362	98.11 212	95.66 283
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
FE-MVS		89.06 252	88.29 258	91.36 238	94.78 265	79.57 261	96.77 27	90.99 315	84.87 252	92.96 223	96.29 154	60.69 360	98.80 150	80.18 292	97.11 256	95.71 279

cl22		89.02 253	88.50 253	90.59 266	89.76 351	76.45 309	86.62 334	94.03 267	82.98 271	92.65 232	92.49 295	72.05 314	97.53 271	88.93 180	97.02 259	97.78 184

USDC		89.02 253	89.08 242	88.84 301	95.07 256	74.50 326	88.97 293	96.39 194	73.21 336	93.27 210	96.28 156	82.16 252	96.39 313	77.55 316	98.80 140	95.62 286

xiu_mvs_v2_base		89.00 255	89.19 240	88.46 309	94.86 261	74.63 323	86.97 322	95.60 222	80.88 286	87.83 323	88.62 347	91.04 144	98.81 147	82.51 271	94.38 318	91.93 349

new-patchmatchnet		88.97 256	90.79 212	83.50 346	94.28 283	55.83 380	85.34 341	93.56 275	86.18 227	95.47 131	95.73 184	83.10 240	96.51 309	85.40 239	98.06 216	98.16 142

pmmvs4		88.95 257	87.70 272	92.70 194	94.30 282	85.60 180	87.22 318	92.16 303	74.62 329	89.75 296	94.19 249	77.97 285	96.41 312	82.71 267	96.36 278	96.09 262

iter_conf05		88.94 258	88.09 266	91.50 234	92.74 314	76.97 303	92.80 170	95.92 212	82.82 273	93.65 197	95.37 206	49.41 376	99.13 94	90.82 130	99.28 77	98.40 126

N_pmnet		88.90 259	87.25 278	93.83 158	94.40 281	93.81 36	84.73 345	87.09 338	79.36 301	93.26 211	92.43 299	79.29 273	91.68 364	77.50 318	97.22 253	96.00 266

PS-MVSNAJ		88.86 260	88.99 245	88.48 308	94.88 259	74.71 321	86.69 331	95.60 222	80.88 286	87.83 323	87.37 355	90.77 147	98.82 142	82.52 270	94.37 319	91.93 349

Patchmatch-RL test		88.81 261	88.52 252	89.69 288	95.33 252	79.94 251	86.22 337	92.71 291	78.46 310	95.80 119	94.18 250	66.25 335	95.33 338	89.22 176	98.53 165	93.78 325

Anonymous20231206		88.77 262	88.29 258	90.20 278	96.31 192	78.81 277	89.56 280	93.49 277	74.26 331	92.38 243	95.58 191	82.21 250	95.43 335	72.07 347	98.75 146	96.34 253

PVSNet_Blended		88.74 263	88.16 265	90.46 270	94.81 263	78.80 278	86.64 332	96.93 159	74.67 328	88.68 313	89.18 344	86.27 218	98.15 228	80.27 289	96.00 283	94.44 310

thisisatest0530		88.69 264	87.52 274	92.20 209	96.33 190	79.36 265	92.81 169	84.01 362	86.44 223	93.67 196	92.68 293	53.62 373	99.25 80	89.65 166	98.45 172	98.00 157

ppachtmachnet_test		88.61 265	88.64 251	88.50 307	91.76 329	70.99 350	84.59 348	92.98 284	79.30 303	92.38 243	93.53 273	79.57 271	97.45 277	86.50 229	97.17 254	97.07 223

UnsupCasMVSNet_bld		88.50 266	88.03 267	89.90 284	95.52 244	78.88 275	87.39 316	94.02 269	79.32 302	93.06 218	94.02 256	80.72 266	94.27 350	75.16 332	93.08 337	96.54 242

miper_enhance_ethall		88.42 267	87.87 269	90.07 280	88.67 363	75.52 318	85.10 342	95.59 226	75.68 322	92.49 236	89.45 341	78.96 274	97.88 247	87.86 206	97.02 259	96.81 235

1112_ss		88.42 267	87.41 275	91.45 235	96.69 163	80.99 235	89.72 276	96.72 178	73.37 335	87.00 331	90.69 326	77.38 290	98.20 222	81.38 280	93.72 328	95.15 293

lupinMVS		88.34 269	87.31 276	91.45 235	94.74 268	80.06 246	87.23 317	92.27 300	71.10 346	88.83 304	91.15 317	77.02 294	98.53 194	86.67 224	96.75 270	95.76 277

YYNet1		88.17 270	88.24 261	87.93 315	92.21 320	73.62 333	80.75 363	88.77 325	82.51 277	94.99 157	95.11 213	82.70 246	93.70 354	83.33 261	93.83 326	96.48 248

MDA-MVSNet_test_wron		88.16 271	88.23 262	87.93 315	92.22 319	73.71 332	80.71 364	88.84 324	82.52 276	94.88 162	95.14 211	82.70 246	93.61 355	83.28 262	93.80 327	96.46 249

MS-PatchMatch		88.05 272	87.75 270	88.95 298	93.28 299	77.93 286	87.88 308	92.49 297	75.42 325	92.57 235	93.59 271	80.44 267	94.24 352	81.28 281	92.75 340	94.69 306

CR-MVSNet		87.89 273	87.12 282	90.22 276	91.01 338	78.93 272	92.52 179	92.81 287	73.08 337	89.10 301	96.93 107	67.11 327	97.64 268	88.80 184	92.70 341	94.08 315

pmmvs5		87.87 274	87.14 281	90.07 280	93.26 301	76.97 303	88.89 295	92.18 301	73.71 334	88.36 316	93.89 262	76.86 299	96.73 304	80.32 288	96.81 267	96.51 244

wuyk23d		87.83 275	90.79 212	78.96 355	90.46 346	88.63 113	92.72 172	90.67 319	91.65 116	98.68 11	97.64 60	96.06 16	77.53 376	59.84 371	99.41 56	70.73 374

FMVSNet5		87.82 276	86.56 291	91.62 230	92.31 317	79.81 256	93.49 154	94.81 250	83.26 264	91.36 262	96.93 107	52.77 374	97.49 275	76.07 327	98.03 219	97.55 202

GA-MVS		87.70 277	86.82 286	90.31 272	93.27 300	77.22 298	84.72 347	92.79 289	85.11 247	89.82 292	90.07 330	66.80 330	97.76 262	84.56 253	94.27 322	95.96 268

TR-MVS		87.70 277	87.17 280	89.27 295	94.11 286	79.26 267	88.69 301	91.86 309	81.94 282	90.69 275	89.79 335	82.82 244	97.42 279	72.65 345	91.98 349	91.14 354

thres600view7		87.66 279	87.10 283	89.36 293	96.05 213	73.17 335	92.72 172	85.31 354	91.89 99	93.29 208	90.97 320	63.42 349	98.39 205	73.23 341	96.99 264	96.51 244

PAPR		87.65 280	86.77 288	90.27 274	92.85 309	77.38 295	88.56 304	96.23 201	76.82 321	84.98 341	89.75 337	86.08 220	97.16 289	72.33 346	93.35 331	96.26 257

baseline1		87.62 281	87.31 276	88.54 306	94.71 272	74.27 329	93.10 162	88.20 331	86.20 226	92.18 251	93.04 282	73.21 310	95.52 330	79.32 304	85.82 365	95.83 274

our_test_3		87.55 282	87.59 273	87.44 321	91.76 329	70.48 351	83.83 354	90.55 320	79.79 293	92.06 254	92.17 303	78.63 280	95.63 328	84.77 250	94.73 312	96.22 258

PatchT		87.51 283	88.17 264	85.55 333	90.64 341	66.91 362	92.02 206	86.09 345	92.20 90	89.05 303	97.16 93	64.15 345	96.37 315	89.21 177	92.98 339	93.37 334

Test_1112_low_res		87.50 284	86.58 290	90.25 275	96.80 161	77.75 290	87.53 314	96.25 199	69.73 353	86.47 333	93.61 270	75.67 303	97.88 247	79.95 295	93.20 333	95.11 295

SCA		87.43 285	87.21 279	88.10 313	92.01 326	71.98 345	89.43 282	88.11 333	82.26 280	88.71 311	92.83 287	78.65 278	97.59 269	79.61 301	93.30 332	94.75 303

EU-MVSNet		87.39 286	86.71 289	89.44 290	93.40 298	76.11 312	94.93 107	90.00 322	57.17 373	95.71 124	97.37 75	64.77 343	97.68 267	92.67 88	94.37 319	94.52 308

thres100view900		87.35 287	86.89 285	88.72 303	96.14 206	73.09 337	93.00 164	85.31 354	92.13 92	93.26 211	90.96 321	63.42 349	98.28 214	71.27 353	96.54 274	94.79 301

CMPMVS	68.83 22	87.28 288	85.67 301	92.09 216	88.77 362	85.42 182	90.31 258	94.38 261	70.02 352	88.00 321	93.30 277	73.78 309	94.03 353	75.96 329	96.54 274	96.83 234
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
sss		87.23 289	86.82 286	88.46 309	93.96 290	77.94 285	86.84 326	92.78 290	77.59 314	87.61 326	91.83 308	78.75 276	91.92 363	77.84 313	94.20 323	95.52 288

BH-w/o		87.21 290	87.02 284	87.79 318	94.77 266	77.27 297	87.90 307	93.21 283	81.74 283	89.99 288	88.39 350	83.47 236	96.93 298	71.29 352	92.43 345	89.15 359

thres400		87.20 291	86.52 293	89.24 297	95.77 230	72.94 338	91.89 213	86.00 346	90.84 133	92.61 233	89.80 333	63.93 346	98.28 214	71.27 353	96.54 274	96.51 244

CHOSEN 1792x2688		87.19 292	85.92 300	91.00 253	97.13 143	79.41 264	84.51 349	95.60 222	64.14 367	90.07 286	94.81 227	78.26 283	97.14 290	73.34 340	95.38 300	96.46 249

HyFIR lowres test		87.19 292	85.51 302	92.24 208	97.12 144	80.51 239	85.03 343	96.06 208	66.11 363	91.66 259	92.98 285	70.12 320	99.14 92	75.29 331	95.23 303	97.07 223

MIMVSNet		87.13 294	86.54 292	88.89 300	96.05 213	76.11 312	94.39 126	88.51 327	81.37 284	88.27 318	96.75 121	72.38 312	95.52 330	65.71 367	95.47 296	95.03 296

tfpn200view9		87.05 295	86.52 293	88.67 304	95.77 230	72.94 338	91.89 213	86.00 346	90.84 133	92.61 233	89.80 333	63.93 346	98.28 214	71.27 353	96.54 274	94.79 301

cascas		87.02 296	86.28 297	89.25 296	91.56 333	76.45 309	84.33 351	96.78 173	71.01 347	86.89 332	85.91 363	81.35 259	96.94 296	83.09 264	95.60 292	94.35 312

WTY-MVS		86.93 297	86.50 295	88.24 311	94.96 257	74.64 322	87.19 319	92.07 306	78.29 311	88.32 317	91.59 313	78.06 284	94.27 350	74.88 333	93.15 335	95.80 275

HY-MVS	82.50 18	86.81 298	85.93 299	89.47 289	93.63 296	77.93 286	94.02 139	91.58 312	75.68 322	83.64 350	93.64 268	77.40 289	97.42 279	71.70 350	92.07 348	93.05 339

1314		86.46 299	86.33 296	86.87 325	91.65 331	74.54 324	91.94 210	94.10 266	74.28 330	84.78 343	87.33 356	83.03 241	95.00 342	78.72 308	91.16 354	91.06 355

ET-MVSNet_ETH3D		86.15 300	84.27 309	91.79 222	93.04 306	81.28 231	87.17 320	86.14 344	79.57 297	83.65 349	88.66 346	57.10 364	98.18 225	87.74 207	95.40 298	95.90 272

Patchmatch-test		86.10 301	86.01 298	86.38 330	90.63 342	74.22 330	89.57 279	86.69 340	85.73 236	89.81 293	92.83 287	65.24 341	91.04 366	77.82 315	95.78 289	93.88 324

thres200		85.85 302	85.18 303	87.88 317	94.44 279	72.52 342	89.08 292	86.21 343	88.57 187	91.44 261	88.40 349	64.22 344	98.00 239	68.35 361	95.88 288	93.12 336

EPNet_dtu		85.63 303	84.37 307	89.40 292	86.30 372	74.33 328	91.64 225	88.26 329	84.84 253	72.96 375	89.85 331	71.27 317	97.69 266	76.60 324	97.62 240	96.18 260
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test2506		85.42 304	84.57 306	87.96 314	97.81 100	66.53 365	96.14 56	56.35 382	89.04 174	93.55 201	98.10 37	42.88 384	98.68 175	88.09 199	99.18 91	98.67 99

PatchmatchNet		85.22 305	84.64 305	86.98 324	89.51 356	69.83 357	90.52 250	87.34 337	78.87 307	87.22 330	92.74 291	66.91 329	96.53 307	81.77 277	86.88 364	94.58 307
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CVMVSNet		85.16 306	84.72 304	86.48 326	92.12 323	70.19 352	92.32 193	88.17 332	56.15 374	90.64 276	95.85 174	67.97 325	96.69 305	88.78 185	90.52 356	92.56 344

JIA-IIPM		85.08 307	83.04 316	91.19 247	87.56 365	86.14 172	89.40 284	84.44 361	88.98 176	82.20 359	97.95 46	56.82 366	96.15 319	76.55 325	83.45 369	91.30 353

MVS		84.98 308	84.30 308	87.01 323	91.03 337	77.69 292	91.94 210	94.16 265	59.36 372	84.23 347	87.50 354	85.66 224	96.80 302	71.79 348	93.05 338	86.54 365

thisisatest0515		84.72 309	82.99 317	89.90 284	92.96 308	75.33 320	84.36 350	83.42 364	77.37 316	88.27 318	86.65 357	53.94 371	98.72 165	82.56 269	97.40 248	95.67 282

FPMVS		84.50 310	83.28 314	88.16 312	96.32 191	94.49 16	85.76 338	85.47 352	83.09 268	85.20 339	94.26 246	63.79 348	86.58 373	63.72 369	91.88 351	83.40 368

tpm		84.38 311	84.08 310	85.30 336	90.47 345	63.43 375	89.34 285	85.63 350	77.24 318	87.62 325	95.03 219	61.00 359	97.30 285	79.26 305	91.09 355	95.16 292

tpmvs		84.22 312	83.97 311	84.94 337	87.09 369	65.18 368	91.21 234	88.35 328	82.87 272	85.21 338	90.96 321	65.24 341	96.75 303	79.60 303	85.25 366	92.90 341

ADS-MVSNet2		84.01 313	82.20 321	89.41 291	89.04 359	76.37 311	87.57 310	90.98 316	72.71 340	84.46 344	92.45 296	68.08 323	96.48 310	70.58 357	83.97 367	95.38 289

test-LLR		83.58 314	83.17 315	84.79 339	89.68 353	66.86 363	83.08 356	84.52 359	83.07 269	82.85 355	84.78 366	62.86 352	93.49 356	82.85 265	94.86 308	94.03 318

baseline2		83.38 315	81.54 324	88.90 299	91.38 334	72.84 340	88.78 298	81.22 369	78.97 305	79.82 368	87.56 352	61.73 356	97.80 256	74.30 336	90.05 358	96.05 265

IB-MVS	77.21 19	83.11 316	81.05 327	89.29 294	91.15 336	75.85 315	85.66 339	86.00 346	79.70 295	82.02 362	86.61 358	48.26 377	98.39 205	77.84 313	92.22 346	93.63 329
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		83.09 317	82.21 320	85.73 332	89.27 358	67.01 361	90.35 256	86.47 342	70.42 350	83.52 352	93.23 280	61.18 357	96.85 300	77.21 320	88.26 362	93.34 335

PMMVS		83.00 318	81.11 326	88.66 305	83.81 379	86.44 162	82.24 360	85.65 349	61.75 371	82.07 360	85.64 364	79.75 270	91.59 365	75.99 328	93.09 336	87.94 364

PVSNet	76.22 20	82.89 319	82.37 319	84.48 341	93.96 290	64.38 373	78.60 366	88.61 326	71.50 344	84.43 346	86.36 361	74.27 306	94.60 344	69.87 359	93.69 329	94.46 309

tpmrst		82.85 320	82.93 318	82.64 348	87.65 364	58.99 378	90.14 264	87.90 334	75.54 324	83.93 348	91.63 312	66.79 332	95.36 336	81.21 283	81.54 372	93.57 333

test0.0.03 1		82.48 321	81.47 325	85.48 334	89.70 352	73.57 334	84.73 345	81.64 368	83.07 269	88.13 320	86.61 358	62.86 352	89.10 372	66.24 366	90.29 357	93.77 326

ADS-MVSNet		82.25 322	81.55 323	84.34 342	89.04 359	65.30 367	87.57 310	85.13 358	72.71 340	84.46 344	92.45 296	68.08 323	92.33 362	70.58 357	83.97 367	95.38 289

DSMNet-mixed		82.21 323	81.56 322	84.16 343	89.57 355	70.00 356	90.65 247	77.66 377	54.99 375	83.30 353	97.57 62	77.89 286	90.50 368	66.86 365	95.54 294	91.97 348

KD-MVS_2432*1600		82.17 324	80.75 331	86.42 328	82.04 380	70.09 354	81.75 361	90.80 317	82.56 274	90.37 280	89.30 342	42.90 382	96.11 321	74.47 334	92.55 343	93.06 337

miper_refine_blended		82.17 324	80.75 331	86.42 328	82.04 380	70.09 354	81.75 361	90.80 317	82.56 274	90.37 280	89.30 342	42.90 382	96.11 321	74.47 334	92.55 343	93.06 337

gg-mvs-nofinetune		82.10 326	81.02 328	85.34 335	87.46 367	71.04 348	94.74 112	67.56 379	96.44 22	79.43 369	98.99 6	45.24 378	96.15 319	67.18 364	92.17 347	88.85 361

PAPM		81.91 327	80.11 337	87.31 322	93.87 293	72.32 344	84.02 353	93.22 281	69.47 354	76.13 373	89.84 332	72.15 313	97.23 287	53.27 375	89.02 359	92.37 346

tpm2		81.46 328	80.35 335	84.80 338	89.90 350	65.14 369	90.44 252	85.36 353	65.82 365	82.05 361	92.44 298	57.94 363	96.69 305	70.71 356	88.49 361	92.56 344

PMMVS2		81.31 329	83.44 313	74.92 357	90.52 344	46.49 382	69.19 371	85.23 357	84.30 258	87.95 322	94.71 234	76.95 296	84.36 375	64.07 368	98.09 214	93.89 323

new_pmnet		81.22 330	81.01 329	81.86 350	90.92 340	70.15 353	84.03 352	80.25 373	70.83 348	85.97 336	89.78 336	67.93 326	84.65 374	67.44 363	91.90 350	90.78 356

test-mter		81.21 331	80.01 338	84.79 339	89.68 353	66.86 363	83.08 356	84.52 359	73.85 333	82.85 355	84.78 366	43.66 381	93.49 356	82.85 265	94.86 308	94.03 318

EPMVS		81.17 332	80.37 334	83.58 345	85.58 374	65.08 370	90.31 258	71.34 378	77.31 317	85.80 337	91.30 315	59.38 361	92.70 361	79.99 294	82.34 371	92.96 340

EGC-MVSNET		80.97 333	75.73 344	96.67 44	98.85 23	94.55 15	96.83 22	96.60 183	2.44 379	5.32 380	98.25 32	92.24 110	98.02 237	91.85 108	99.21 87	97.45 207

pmmvs3		80.83 334	78.96 340	86.45 327	87.23 368	77.48 294	84.87 344	82.31 366	63.83 368	85.03 340	89.50 340	49.66 375	93.10 358	73.12 343	95.10 305	88.78 363

E-PMN		80.72 335	80.86 330	80.29 353	85.11 375	68.77 359	72.96 368	81.97 367	87.76 204	83.25 354	83.01 370	62.22 355	89.17 371	77.15 321	94.31 321	82.93 369

tpm cat1		80.61 336	79.46 339	84.07 344	88.78 361	65.06 371	89.26 288	88.23 330	62.27 370	81.90 363	89.66 339	62.70 354	95.29 339	71.72 349	80.60 373	91.86 351

EMVS		80.35 337	80.28 336	80.54 352	84.73 377	69.07 358	72.54 370	80.73 370	87.80 202	81.66 364	81.73 371	62.89 351	89.84 369	75.79 330	94.65 315	82.71 370

CHOSEN 280x420		80.04 338	77.97 343	86.23 331	90.13 348	74.53 325	72.87 369	89.59 323	66.38 362	76.29 372	85.32 365	56.96 365	95.36 336	69.49 360	94.72 313	88.79 362

dp		79.28 339	78.62 341	81.24 351	85.97 373	56.45 379	86.91 324	85.26 356	72.97 338	81.45 365	89.17 345	56.01 368	95.45 334	73.19 342	76.68 374	91.82 352

TESTMET0.1,1		79.09 340	78.04 342	82.25 349	87.52 366	64.03 374	83.08 356	80.62 371	70.28 351	80.16 367	83.22 369	44.13 380	90.56 367	79.95 295	93.36 330	92.15 347

MVS-HIRNet		78.83 341	80.60 333	73.51 358	93.07 304	47.37 381	87.10 321	78.00 376	68.94 355	77.53 371	97.26 86	71.45 316	94.62 343	63.28 370	88.74 360	78.55 373

PVSNet_0	70.34 21	74.58 342	72.96 345	79.47 354	90.63 342	66.24 366	73.26 367	83.40 365	63.67 369	78.02 370	78.35 373	72.53 311	89.59 370	56.68 373	60.05 377	82.57 371

MVE	59.87 23	73.86 343	72.65 346	77.47 356	87.00 371	74.35 327	61.37 373	60.93 381	67.27 360	69.69 376	86.49 360	81.24 263	72.33 377	56.45 374	83.45 369	85.74 366
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		50.44 344	48.94 347	54.93 359	39.68 383	12.38 385	28.59 374	90.09 321	6.82 377	41.10 379	78.41 372	54.41 370	70.69 378	50.12 376	51.26 378	81.72 372

tmp_tt		37.97 345	44.33 348	18.88 361	11.80 384	21.54 384	63.51 372	45.66 385	4.23 378	51.34 378	50.48 376	59.08 362	22.11 380	44.50 377	68.35 376	13.00 376

cdsmvs_eth3d_5k		23.35 346	31.13 349	0.00 364	0.00 387	0.00 388	0.00 375	95.58 228	0.00 382	0.00 383	91.15 317	93.43 79	0.00 383	0.00 381	0.00 381	0.00 379

test123		9.49 347	12.01 350	1.91 362	2.87 385	1.30 386	82.38 359	1.34 387	1.36 380	2.84 381	6.56 379	2.45 385	0.97 381	2.73 379	5.56 379	3.47 377

testmvs		9.02 348	11.42 351	1.81 363	2.77 386	1.13 387	79.44 365	1.90 386	1.18 381	2.65 382	6.80 378	1.95 386	0.87 382	2.62 380	3.45 380	3.44 378

pcd_1.5k_mvsjas		7.56 349	10.09 352	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	90.77 147	0.00 383	0.00 381	0.00 381	0.00 379

ab-mvs-re		7.56 349	10.08 353	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	90.69 326	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

test_blank		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

uanet_test		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

DCPMVS		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

sosnet-low-res		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

sosnet		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

uncertanet		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

Regformer		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

uanet		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

FOURS1					99.21 3	94.68 12	98.45 4	98.81 8	97.73 6	98.27 20

MSC_two_6792asdad				95.90 68	96.54 174	89.57 93		96.87 167					99.41 40	94.06 32	99.30 69	98.72 95

PC_three_1452									75.31 327	95.87 117	95.75 183	92.93 95	96.34 318	87.18 216	98.68 151	98.04 152

No_MVS				95.90 68	96.54 174	89.57 93		96.87 167					99.41 40	94.06 32	99.30 69	98.72 95

test_one_0601					98.26 70	87.14 142		98.18 37	94.25 48	96.99 64	97.36 78	95.13 40

eth-test2					0.00 387

eth-test					0.00 387

ZD-MVS					97.23 135	90.32 81		97.54 111	84.40 257	94.78 165	95.79 179	92.76 101	99.39 52	88.72 188	98.40 174

RE-MVS-def			96.66 20		98.07 82	95.27 8	96.37 44	98.12 47	95.66 33	97.00 62	97.03 100	95.40 27		93.49 50	98.84 130	98.00 157

IU-MVS					98.51 49	86.66 156		96.83 170	72.74 339	95.83 118				93.00 79	99.29 72	98.64 105

OPU-MVS				95.15 102	96.84 156	89.43 97	95.21 93				95.66 186	93.12 89	98.06 232	86.28 233	98.61 156	97.95 165

test_241102_TWO								98.10 50	91.95 95	97.54 39	97.25 87	95.37 28	99.35 63	93.29 66	99.25 81	98.49 119

test_241102_ONE					98.51 49	86.97 147		98.10 50	91.85 101	97.63 34	97.03 100	96.48 11	98.95 124

9.14			94.81 99		97.49 124		94.11 136	98.37 18	87.56 211	95.38 135	96.03 168	94.66 56	99.08 102	90.70 133	98.97 117

save fliter					97.46 127	88.05 126	92.04 204	97.08 150	87.63 208

test_0728_THIRD									93.26 70	97.40 49	97.35 81	94.69 55	99.34 66	93.88 36	99.42 51	98.89 73

test_0728_SECOND				94.88 110	98.55 42	86.72 153	95.20 95	98.22 33					99.38 58	93.44 58	99.31 67	98.53 116

test0726					98.51 49	86.69 154	95.34 88	98.18 37	91.85 101	97.63 34	97.37 75	95.58 22

GSMVS																94.75 303

test_part2					98.21 74	89.41 98				96.72 75

sam_mvs1												66.64 333				94.75 303

sam_mvs												66.41 334

ambc				92.98 181	96.88 153	83.01 213	95.92 67	96.38 195		96.41 84	97.48 69	88.26 182	97.80 256	89.96 159	98.93 122	98.12 147

MTGPA								97.62 103

test_post1							90.21 260				5.85 381	65.36 339	96.00 324	79.61 301

test_post											6.07 380	65.74 338	95.84 326

patchmatchnet-post											91.71 310	66.22 336	97.59 269

GG-mvs-BLEND				83.24 347	85.06 376	71.03 349	94.99 106	65.55 380		74.09 374	75.51 374	44.57 379	94.46 346	59.57 372	87.54 363	84.24 367

MTMP							94.82 109	54.62 383

gm-plane-assit					87.08 370	59.33 377			71.22 345		83.58 368		97.20 288	73.95 337

test9_res														88.16 197	98.40 174	97.83 179

TEST9					96.45 181	89.46 95	90.60 248	96.92 161	79.09 304	90.49 277	94.39 243	91.31 133	98.88 131

test_8					96.37 183	89.14 102	90.51 251	96.89 164	79.37 299	90.42 279	94.36 245	91.20 139	98.82 142

agg_prior2														87.06 219	98.36 185	97.98 161

agg_prior					96.20 200	88.89 108		96.88 165		90.21 282			98.78 155

TestCases				96.00 59	98.02 89	92.17 52		98.43 14	90.48 143	95.04 155	96.74 122	92.54 106	97.86 251	85.11 245	98.98 113	97.98 161

test_prior4						89.91 86	90.74 244

test_prior2							90.21 260		89.33 168	90.77 272	94.81 227	90.41 157		88.21 193	98.55 161

test_prior				94.61 122	95.95 221	87.23 139		97.36 127					98.68 175			97.93 167

旧先验2							90.00 269		68.65 356	92.71 231			96.52 308	85.15 242

新几何2							90.02 268

新几何1				93.17 178	97.16 140	87.29 138		94.43 259	67.95 358	91.29 263	94.94 222	86.97 206	98.23 220	81.06 286	97.75 231	93.98 321

旧先验1					96.20 200	84.17 197		94.82 248			95.57 192	89.57 171			97.89 227	96.32 254

无先验							89.94 270	95.75 219	70.81 349				98.59 187	81.17 284		94.81 300

原ACMM2							89.34 285

原ACMM1				92.87 189	96.91 152	84.22 195		97.01 153	76.84 320	89.64 297	94.46 240	88.00 188	98.70 171	81.53 279	98.01 221	95.70 281

test222					96.95 148	85.27 184	88.83 297	93.61 273	65.09 366	90.74 274	94.85 226	84.62 231			97.36 249	93.91 322

testdata2													98.03 234	80.24 291

segment_acmp												92.14 113

testdata				91.03 250	96.87 154	82.01 220		94.28 263	71.55 343	92.46 238	95.42 201	85.65 225	97.38 284	82.64 268	97.27 251	93.70 328

testdata1							88.96 294		88.44 189

test12				94.43 137	95.95 221	86.75 152		96.24 200		89.76 295		89.79 170	98.79 151		97.95 224	97.75 188

plane_prior7					97.71 108	88.68 112

plane_prior6					97.21 138	88.23 123						86.93 207

plane_prior5								97.81 91					98.95 124	89.26 174	98.51 168	98.60 112

plane_prior4											95.59 188

plane_prior3						88.43 121			90.35 148	93.31 206

plane_prior2							94.56 121		91.74 112

plane_prior1					97.38 130

plane_prior						88.12 124	93.01 163		88.98 176						98.06 216

n2								0.00 388

nn								0.00 388

door-mid								92.13 305

lessismore_v0				93.87 157	98.05 84	83.77 203		80.32 372		97.13 55	97.91 49	77.49 288	99.11 100	92.62 89	98.08 215	98.74 92

LGP-MVS_train				96.84 40	98.36 65	92.13 54		98.25 28	91.78 108	97.07 57	97.22 90	96.38 13	99.28 76	92.07 100	99.59 29	99.11 45

test11								96.65 181

door								91.26 313

HQP5-MVS						84.89 187

HQP-NCC					96.36 185		91.37 229		87.16 215	88.81 306

ACMP_Plane					96.36 185		91.37 229		87.16 215	88.81 306

BP-MVS														86.55 227

HQP4-MVS										88.81 306			98.61 183			98.15 143

HQP3-MVS								97.31 132							97.73 232

HQP2-MVS												84.76 229

NP-MVS					96.82 158	87.10 143					93.40 275

MDTV_nov1_ep13_2view						42.48 383	88.45 305		67.22 361	83.56 351		66.80 330		72.86 344		94.06 317

MDTV_nov1_ep13			83.88 312		89.42 357	61.52 376	88.74 300	87.41 336	73.99 332	84.96 342	94.01 257	65.25 340	95.53 329	78.02 311	93.16 334

ACMMP++_ref															98.82 136

ACMMP++															99.25 81

Test By Simon												90.61 153

ITE_SJBPF				95.95 62	97.34 132	93.36 42		96.55 188	91.93 97	94.82 163	95.39 204	91.99 117	97.08 292	85.53 238	97.96 223	97.41 210

DeepMVS_CX				53.83 360	70.38 382	64.56 372		48.52 384	33.01 376	65.50 377	74.21 375	56.19 367	46.64 379	38.45 378	70.07 375	50.30 375

High-resolution multi-view benchmark