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 2	99.95 1	98.13 2	99.37 1	99.57 1	99.82 1	99.86 1	99.85 1	99.52 1	99.73 2	97.58 2	99.94 1	99.85 1

mamv4		98.21 2	97.86 3	99.26 1	98.24 73	99.36 1	96.10 63	99.32 2	98.75 2	99.58 2	98.70 19	91.78 128	99.88 1	98.60 1	99.67 20	98.54 119

LTVRE_ROB	93.87 1	97.93 3	98.16 2	97.26 27	98.81 27	93.86 32	99.07 2	98.98 7	97.01 17	98.92 7	98.78 15	95.22 40	98.61 172	96.85 4	99.77 9	99.31 28
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 4	97.60 5	97.93 3	99.02 12	95.95 9	98.61 3	98.81 10	97.41 11	97.28 58	98.46 33	94.62 64	98.84 130	94.64 36	99.53 39	98.99 56

UA-Net		97.35 5	97.24 12	97.69 5	98.22 74	93.87 31	98.42 6	98.19 44	96.95 18	95.46 145	99.23 4	93.45 84	99.57 17	95.34 31	99.89 2	99.63 9

UniMVSNet_ETH3D		97.13 6	97.72 4	95.35 84	99.51 2	87.38 134	97.70 9	97.54 119	98.16 3	98.94 5	99.33 2	97.84 4	99.08 96	90.73 144	99.73 13	99.59 12

HPM-MVS_fast		97.01 7	96.89 15	97.39 22	99.12 8	93.92 29	97.16 15	98.17 50	93.11 77	96.48 91	97.36 96	96.92 6	99.34 66	94.31 42	99.38 59	98.92 72

SR-MVS-dyc-post		96.84 8	96.60 27	97.56 11	98.07 83	95.27 10	96.37 47	98.12 56	95.66 37	97.00 69	97.03 125	94.85 58	99.42 36	93.49 64	98.84 133	98.00 160

mvs_tets		96.83 9	96.71 21	97.17 28	98.83 24	92.51 49	96.58 34	97.61 113	87.57 211	98.80 10	98.90 9	96.50 9	99.59 16	96.15 14	99.47 43	99.40 21

v7n		96.82 10	97.31 11	95.33 86	98.54 47	86.81 149	96.83 24	98.07 65	96.59 24	98.46 20	98.43 35	92.91 104	99.52 22	96.25 13	99.76 10	99.65 8

APD-MVS_3200maxsize		96.82 10	96.65 23	97.32 26	97.95 95	93.82 34	96.31 53	98.25 34	95.51 39	96.99 71	97.05 124	95.63 23	99.39 52	93.31 76	98.88 128	98.75 91

HPM-MVS		96.81 12	96.62 25	97.36 24	98.89 20	93.53 39	97.51 11	98.44 19	92.35 91	95.95 117	96.41 165	96.71 8	99.42 36	93.99 49	99.36 61	99.13 41
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 10	95.15 97	99.12 8	87.82 129	96.68 31	97.86 91	96.10 31	98.14 27	99.28 3	97.94 3	98.21 212	91.38 133	99.69 14	99.42 19

OurMVSNet-221017-0		96.80 13	96.75 19	96.96 36	99.03 11	91.85 58	97.98 8	98.01 77	94.15 55	98.93 6	99.07 5	88.07 191	99.57 17	95.86 16	99.69 14	99.46 18

testf1		96.77 15	96.49 29	97.60 9	99.01 14	96.70 4	96.31 53	98.33 25	94.96 42	97.30 56	97.93 57	96.05 16	97.90 238	89.32 183	99.23 86	98.19 143

APD_test2		96.77 15	96.49 29	97.60 9	99.01 14	96.70 4	96.31 53	98.33 25	94.96 42	97.30 56	97.93 57	96.05 16	97.90 238	89.32 183	99.23 86	98.19 143

COLMAP_ROB	91.06 5	96.75 17	96.62 25	97.13 29	98.38 62	94.31 18	96.79 27	98.32 27	96.69 21	96.86 76	97.56 78	95.48 27	98.77 147	90.11 167	99.44 50	98.31 134
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
anonymousdsp		96.74 18	96.42 32	97.68 7	98.00 91	94.03 26	96.97 21	97.61 113	87.68 209	98.45 21	98.77 16	94.20 74	99.50 24	96.70 6	99.40 57	99.53 15

DTE-MVSNet		96.74 18	97.43 6	94.67 113	99.13 6	84.68 195	96.51 38	97.94 89	98.14 4	98.67 15	98.32 37	95.04 48	99.69 6	93.27 79	99.82 7	99.62 10

SR-MVS		96.70 20	96.42 32	97.54 12	98.05 85	94.69 12	96.13 62	98.07 65	95.17 41	96.82 78	96.73 149	95.09 47	99.43 35	92.99 90	98.71 152	98.50 122

PS-CasMVS		96.69 21	97.43 6	94.49 127	99.13 6	84.09 206	96.61 33	97.97 83	97.91 6	98.64 16	98.13 44	95.24 38	99.65 7	93.39 74	99.84 3	99.72 2

PEN-MVS		96.69 21	97.39 9	94.61 117	99.16 4	84.50 196	96.54 35	98.05 69	98.06 5	98.64 16	98.25 40	95.01 51	99.65 7	92.95 91	99.83 5	99.68 4

MTAPA		96.65 23	96.38 36	97.47 16	98.95 18	94.05 24	95.88 74	97.62 111	94.46 51	96.29 100	96.94 131	93.56 81	99.37 60	94.29 43	99.42 52	98.99 56

test_djsdf		96.62 24	96.49 29	97.01 33	98.55 45	91.77 60	97.15 16	97.37 130	88.98 179	98.26 25	98.86 10	93.35 89	99.60 12	96.41 10	99.45 47	99.66 6

ACMMP		96.61 25	96.34 37	97.43 19	98.61 37	93.88 30	96.95 22	98.18 46	92.26 94	96.33 96	96.84 139	95.10 46	99.40 49	93.47 67	99.33 67	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 26	97.13 13	95.00 100	97.46 132	86.35 165	97.11 20	98.24 37	97.58 9	98.72 11	98.97 7	93.15 96	99.15 87	93.18 82	99.74 12	99.50 17

WR-MVS_H		96.60 26	97.05 14	95.24 92	99.02 12	86.44 161	96.78 28	98.08 62	97.42 10	98.48 19	97.86 64	91.76 131	99.63 10	94.23 44	99.84 3	99.66 6

jajsoiax		96.59 28	96.42 32	97.12 30	98.76 30	92.49 50	96.44 44	97.42 128	86.96 220	98.71 13	98.72 18	95.36 32	99.56 20	95.92 15	99.45 47	99.32 27

ACMH	88.36 12	96.59 28	97.43 6	94.07 141	98.56 42	85.33 189	96.33 50	98.30 30	94.66 46	98.72 11	98.30 38	97.51 5	98.00 231	94.87 33	99.59 29	98.86 78
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
XVS		96.49 30	96.18 45	97.44 17	98.56 42	93.99 27	96.50 39	97.95 86	94.58 47	94.38 192	96.49 160	94.56 66	99.39 52	93.57 60	99.05 106	98.93 68

ACMH+	88.43 11	96.48 31	96.82 16	95.47 81	98.54 47	89.06 102	95.65 82	98.61 14	96.10 31	98.16 26	97.52 83	96.90 7	98.62 171	90.30 158	99.60 27	98.72 96

APDe-MVS		96.46 32	96.64 24	95.93 62	97.68 118	89.38 96	96.90 23	98.41 22	92.52 86	97.43 50	97.92 60	95.11 45	99.50 24	94.45 38	99.30 72	98.92 72
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
ACMMPR		96.46 32	96.14 48	97.41 21	98.60 38	93.82 34	96.30 57	97.96 84	92.35 91	95.57 138	96.61 156	94.93 56	99.41 42	93.78 54	99.15 98	99.00 54

mPP-MVS		96.46 32	96.05 54	97.69 5	98.62 35	94.65 14	96.45 42	97.74 104	92.59 85	95.47 143	96.68 152	94.50 68	99.42 36	93.10 85	99.26 82	98.99 56

CP-MVS		96.44 35	96.08 52	97.54 12	98.29 67	94.62 15	96.80 26	98.08 62	92.67 84	95.08 169	96.39 170	94.77 60	99.42 36	93.17 83	99.44 50	98.58 118

ZNCC-MVS		96.42 36	96.20 44	97.07 31	98.80 29	92.79 47	96.08 65	98.16 53	91.74 118	95.34 152	96.36 173	95.68 21	99.44 32	94.41 40	99.28 80	98.97 62

region2R		96.41 37	96.09 50	97.38 23	98.62 35	93.81 36	96.32 52	97.96 84	92.26 94	95.28 156	96.57 158	95.02 50	99.41 42	93.63 58	99.11 101	98.94 66

SteuartSystems-ACMMP		96.40 38	96.30 39	96.71 41	98.63 34	91.96 56	95.70 79	98.01 77	93.34 74	96.64 86	96.57 158	94.99 52	99.36 61	93.48 66	99.34 65	98.82 82
Skip Steuart: Steuart Systems R&D Blog.
HFP-MVS		96.39 39	96.17 47	97.04 32	98.51 50	93.37 40	96.30 57	97.98 80	92.35 91	95.63 135	96.47 161	95.37 30	99.27 77	93.78 54	99.14 99	98.48 125

LPG-MVS_test		96.38 40	96.23 42	96.84 39	98.36 65	92.13 53	95.33 94	98.25 34	91.78 114	97.07 64	97.22 110	96.38 12	99.28 75	92.07 110	99.59 29	99.11 44

nrg030		96.32 41	96.55 28	95.62 76	97.83 102	88.55 115	95.77 77	98.29 33	92.68 82	98.03 29	97.91 61	95.13 43	98.95 116	93.85 52	99.49 42	99.36 24

PGM-MVS		96.32 41	95.94 58	97.43 19	98.59 40	93.84 33	95.33 94	98.30 30	91.40 129	95.76 127	96.87 136	95.26 37	99.45 30	92.77 93	99.21 90	99.00 54

ACMM	88.83 9	96.30 43	96.07 53	96.97 35	98.39 61	92.95 45	94.74 116	98.03 74	90.82 142	97.15 61	96.85 137	96.25 14	99.00 108	93.10 85	99.33 67	98.95 65
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GST-MVS		96.24 44	95.99 57	97.00 34	98.65 33	92.71 48	95.69 81	98.01 77	92.08 99	95.74 130	96.28 179	95.22 40	99.42 36	93.17 83	99.06 103	98.88 77

ACMMP_NAP		96.21 45	96.12 49	96.49 49	98.90 19	91.42 64	94.57 124	98.03 74	90.42 153	96.37 94	97.35 99	95.68 21	99.25 78	94.44 39	99.34 65	98.80 85

CP-MVSNet		96.19 46	96.80 18	94.38 132	98.99 16	83.82 209	96.31 53	97.53 121	97.60 8	98.34 22	97.52 83	91.98 124	99.63 10	93.08 87	99.81 8	99.70 3

MP-MVS		96.14 47	95.68 73	97.51 14	98.81 27	94.06 22	96.10 63	97.78 102	92.73 81	93.48 218	96.72 150	94.23 73	99.42 36	91.99 112	99.29 75	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 67	96.95 37	94.75 282	94.20 20	97.34 13	97.98 80	97.31 12	95.32 153	96.77 141	93.08 99	99.20 83	91.79 119	98.16 207	97.44 213

MP-MVS-pluss		96.08 49	95.92 61	96.57 45	99.06 10	91.21 66	93.25 169	98.32 27	87.89 202	96.86 76	97.38 92	95.55 26	99.39 52	95.47 24	99.47 43	99.11 44
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TranMVSNet+NR-MVSNet		96.07 50	96.26 41	95.50 80	98.26 70	87.69 131	93.75 154	97.86 91	95.96 36	97.48 48	97.14 116	95.33 34	99.44 32	90.79 142	99.76 10	99.38 22

PS-MVSNAJss		96.01 51	96.04 55	95.89 67	98.82 25	88.51 116	95.57 88	97.88 90	88.72 185	98.81 9	98.86 10	90.77 154	99.60 12	95.43 26	99.53 39	99.57 13

SED-MVS		96.00 52	96.41 35	94.76 109	98.51 50	86.97 145	95.21 99	98.10 59	91.95 101	97.63 37	97.25 106	96.48 10	99.35 63	93.29 77	99.29 75	97.95 168

DVP-MVS++		95.93 53	96.34 37	94.70 112	96.54 181	86.66 155	98.45 4	98.22 41	93.26 75	97.54 42	97.36 96	93.12 97	99.38 58	93.88 50	98.68 156	98.04 155

APD_test1		95.91 54	95.42 84	97.36 24	98.82 25	96.62 7	95.64 83	97.64 109	93.38 73	95.89 122	97.23 108	93.35 89	97.66 266	88.20 209	98.66 160	97.79 187

test_fmvsmconf0.01_n		95.90 55	96.09 50	95.31 89	97.30 139	89.21 98	94.24 136	98.76 12	86.25 228	97.56 41	98.66 20	95.73 19	98.44 193	97.35 3	98.99 114	98.27 137

DPE-MVS		95.89 56	95.88 63	95.92 64	97.93 96	89.83 86	93.46 163	98.30 30	92.37 89	97.75 34	96.95 130	95.14 42	99.51 23	91.74 120	99.28 80	98.41 129
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
SF-MVS		95.88 57	95.88 63	95.87 68	98.12 79	89.65 88	95.58 87	98.56 16	91.84 110	96.36 95	96.68 152	94.37 72	99.32 72	92.41 103	99.05 106	98.64 111

3Dnovator+	92.74 2	95.86 58	95.77 70	96.13 54	96.81 165	90.79 74	96.30 57	97.82 96	96.13 30	94.74 183	97.23 108	91.33 138	99.16 86	93.25 80	98.30 193	98.46 126

DVP-MVS		95.82 59	96.18 45	94.72 111	98.51 50	86.69 153	95.20 101	97.00 162	91.85 107	97.40 54	97.35 99	95.58 24	99.34 66	93.44 70	99.31 70	98.13 149
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
bld_raw_dy_0_64		95.78 60	96.81 17	92.68 196	98.57 41	78.77 295	98.00 7	98.53 17	97.16 14	99.19 4	98.85 12	86.45 223	99.72 3	94.96 32	99.61 26	99.56 14

CS-MVS		95.77 61	95.58 77	96.37 51	96.84 162	91.72 62	96.73 30	99.06 6	94.23 53	92.48 255	94.79 246	93.56 81	99.49 27	93.47 67	99.05 106	97.89 175

SMA-MVS		95.77 61	95.54 78	96.47 50	98.27 69	91.19 67	95.09 104	97.79 101	86.48 224	97.42 52	97.51 86	94.47 71	99.29 73	93.55 62	99.29 75	98.93 68
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 63	96.22 43	94.26 135	98.19 76	85.77 179	93.24 170	97.24 146	96.88 20	97.69 35	97.77 67	94.12 75	99.13 92	91.54 129	99.29 75	97.88 176

ACMP	88.15 13	95.71 64	95.43 83	96.54 46	98.17 77	91.73 61	94.24 136	98.08 62	89.46 168	96.61 88	96.47 161	95.85 18	99.12 93	90.45 150	99.56 37	98.77 90
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
XVG-ACMP-BASELINE		95.68 65	95.34 88	96.69 42	98.40 60	93.04 42	94.54 128	98.05 69	90.45 152	96.31 98	96.76 143	92.91 104	98.72 153	91.19 134	99.42 52	98.32 132

DP-MVS		95.62 66	95.84 66	94.97 101	97.16 146	88.62 111	94.54 128	97.64 109	96.94 19	96.58 89	97.32 103	93.07 100	98.72 153	90.45 150	98.84 133	97.57 203

test_fmvsmconf0.1_n		95.61 67	95.72 72	95.26 90	96.85 161	89.20 99	93.51 161	98.60 15	85.68 241	97.42 52	98.30 38	95.34 33	98.39 194	96.85 4	98.98 115	98.19 143

OPM-MVS		95.61 67	95.45 81	96.08 55	98.49 57	91.00 69	92.65 189	97.33 138	90.05 158	96.77 81	96.85 137	95.04 48	98.56 179	92.77 93	99.06 103	98.70 100
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
mvsmamba		95.61 67	95.40 85	96.22 52	98.44 59	89.86 85	97.14 18	97.45 127	91.25 133	97.49 46	98.14 42	83.49 249	99.45 30	95.52 22	99.66 21	99.36 24

RPSCF		95.58 70	94.89 105	97.62 8	97.58 124	96.30 8	95.97 70	97.53 121	92.42 87	93.41 219	97.78 65	91.21 143	97.77 256	91.06 136	97.06 264	98.80 85

iter_conf05		95.52 71	96.74 20	91.88 224	97.82 103	77.68 309	97.26 14	98.91 8	97.14 15	99.22 3	98.48 31	87.01 210	99.71 4	95.43 26	99.38 59	98.25 138

MIMVSNet1		95.52 71	95.45 81	95.72 73	99.14 5	89.02 103	96.23 60	96.87 174	93.73 64	97.87 30	98.49 30	90.73 158	99.05 101	86.43 246	99.60 27	99.10 47

Anonymous20240529		95.50 73	95.83 67	94.50 125	97.33 138	85.93 174	95.19 103	96.77 182	96.64 23	97.61 40	98.05 48	93.23 93	98.79 141	88.60 206	99.04 111	98.78 87

Vis-MVSNet		95.50 73	95.48 80	95.56 79	98.11 80	89.40 95	95.35 92	98.22 41	92.36 90	94.11 197	98.07 47	92.02 122	99.44 32	93.38 75	97.67 240	97.85 180
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
EC-MVSNet		95.44 75	95.62 75	94.89 103	96.93 156	87.69 131	96.48 41	99.14 5	93.93 60	92.77 246	94.52 256	93.95 78	99.49 27	93.62 59	99.22 89	97.51 208

test_fmvsmconf_n		95.43 76	95.50 79	95.22 94	96.48 188	89.19 100	93.23 171	98.36 24	85.61 244	96.92 74	98.02 52	95.23 39	98.38 197	96.69 7	98.95 124	98.09 151

pm-mvs1		95.43 76	95.94 58	93.93 148	98.38 62	85.08 192	95.46 91	97.12 155	91.84 110	97.28 58	98.46 33	95.30 36	97.71 263	90.17 165	99.42 52	98.99 56

DeepC-MVS	91.39 4	95.43 76	95.33 89	95.71 74	97.67 119	90.17 81	93.86 151	98.02 76	87.35 213	96.22 106	97.99 55	94.48 70	99.05 101	92.73 96	99.68 17	97.93 170
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
tt0805		95.42 79	95.93 60	93.86 152	98.75 31	88.47 117	97.68 10	94.29 273	96.48 25	95.38 148	93.63 284	94.89 57	97.94 237	95.38 29	96.92 272	95.17 310

XVG-OURS-SEG-HR		95.38 80	95.00 103	96.51 47	98.10 81	94.07 21	92.46 197	98.13 55	90.69 145	93.75 211	96.25 182	98.03 2	97.02 300	92.08 109	95.55 304	98.45 127

UniMVSNet_NR-MVSNet		95.35 81	95.21 94	95.76 71	97.69 117	88.59 113	92.26 211	97.84 94	94.91 44	96.80 79	95.78 205	90.42 163	99.41 42	91.60 125	99.58 34	99.29 29

MSP-MVS		95.34 82	94.63 120	97.48 15	98.67 32	94.05 24	96.41 46	98.18 46	91.26 131	95.12 165	95.15 229	86.60 222	99.50 24	93.43 73	96.81 276	98.89 75
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 83	95.10 99	95.96 58	96.86 160	90.75 75	96.33 50	99.20 3	93.99 57	91.03 288	93.73 282	93.52 83	99.55 21	91.81 118	99.45 47	97.58 202

FC-MVSNet-test		95.32 83	95.88 63	93.62 160	98.49 57	81.77 236	95.90 73	98.32 27	93.93 60	97.53 44	97.56 78	88.48 184	99.40 49	92.91 92	99.83 5	99.68 4

UniMVSNet (Re)		95.32 83	95.15 96	95.80 70	97.79 107	88.91 105	92.91 180	98.07 65	93.46 71	96.31 98	95.97 195	90.14 168	99.34 66	92.11 107	99.64 24	99.16 38

Gipuma		95.31 86	95.80 69	93.81 155	97.99 94	90.91 71	96.42 45	97.95 86	96.69 21	91.78 275	98.85 12	91.77 129	95.49 344	91.72 121	99.08 102	95.02 318
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
DU-MVS		95.28 87	95.12 98	95.75 72	97.75 109	88.59 113	92.58 191	97.81 97	93.99 57	96.80 79	95.90 196	90.10 171	99.41 42	91.60 125	99.58 34	99.26 30

NR-MVSNet		95.28 87	95.28 92	95.26 90	97.75 109	87.21 138	95.08 105	97.37 130	93.92 62	97.65 36	95.90 196	90.10 171	99.33 71	90.11 167	99.66 21	99.26 30

TransMVSNet (Re)		95.27 89	96.04 55	92.97 183	98.37 64	81.92 235	95.07 106	96.76 183	93.97 59	97.77 33	98.57 25	95.72 20	97.90 238	88.89 200	99.23 86	99.08 48

SD-MVS		95.19 90	95.73 71	93.55 163	96.62 176	88.88 107	94.67 118	98.05 69	91.26 131	97.25 60	96.40 166	95.42 28	94.36 364	92.72 97	99.19 92	97.40 217
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 91	95.67 74	93.58 162	97.76 108	83.15 220	94.58 123	97.58 116	93.39 72	97.05 67	98.04 50	93.25 92	98.51 185	89.75 177	99.59 29	99.08 48

casdiffmvs_mvg		95.10 92	95.62 75	93.53 166	96.25 207	83.23 216	92.66 188	98.19 44	93.06 78	97.49 46	97.15 115	94.78 59	98.71 159	92.27 106	98.72 150	98.65 106
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
test_fmvsmvis_n_1920		95.08 93	95.40 85	94.13 139	96.66 171	87.75 130	93.44 165	98.49 18	85.57 245	98.27 23	97.11 119	94.11 76	97.75 259	96.26 12	98.72 150	96.89 243

HPM-MVS++		95.02 94	94.39 124	96.91 38	97.88 99	93.58 38	94.09 144	96.99 164	91.05 137	92.40 260	95.22 228	91.03 150	99.25 78	92.11 107	98.69 155	97.90 173

APD-MVS		95.00 95	94.69 114	95.93 62	97.38 134	90.88 72	94.59 121	97.81 97	89.22 175	95.46 145	96.17 187	93.42 87	99.34 66	89.30 185	98.87 131	97.56 205
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PMVS	87.21 14	94.97 96	95.33 89	93.91 149	98.97 17	97.16 3	95.54 89	95.85 226	96.47 26	93.40 221	97.46 89	95.31 35	95.47 345	86.18 250	98.78 145	89.11 387
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
TSAR-MVS + MP.		94.96 97	94.75 110	95.57 78	98.86 22	88.69 108	96.37 47	96.81 178	85.23 250	94.75 182	97.12 118	91.85 126	99.40 49	93.45 69	98.33 190	98.62 115
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 98	95.21 94	93.98 143	98.52 49	83.19 218	95.93 71	94.84 260	94.86 45	98.49 18	98.74 17	81.45 273	99.60 12	94.69 35	99.39 58	99.15 39

FIs		94.90 99	95.35 87	93.55 163	98.28 68	81.76 237	95.33 94	98.14 54	93.05 79	97.07 64	97.18 113	87.65 198	99.29 73	91.72 121	99.69 14	99.61 11

AllTest		94.88 100	94.51 122	96.00 56	98.02 89	92.17 51	95.26 97	98.43 20	90.48 150	95.04 170	96.74 147	92.54 113	97.86 246	85.11 263	98.98 115	97.98 164

FMVSNet1		94.84 101	95.13 97	93.97 144	97.60 122	84.29 199	95.99 67	96.56 195	92.38 88	97.03 68	98.53 27	90.12 169	98.98 109	88.78 202	99.16 97	98.65 106

ANet_high		94.83 102	96.28 40	90.47 276	96.65 172	73.16 353	94.33 133	98.74 13	96.39 28	98.09 28	98.93 8	93.37 88	98.70 160	90.38 153	99.68 17	99.53 15

MVSMamba_PlusPlus		94.82 103	95.89 62	91.62 236	97.82 103	78.88 289	96.52 36	97.60 115	97.14 15	94.23 195	98.48 31	87.01 210	99.71 4	95.43 26	98.80 142	96.28 269

3Dnovator	92.54 3	94.80 104	94.90 104	94.47 128	95.47 260	87.06 142	96.63 32	97.28 144	91.82 113	94.34 194	97.41 90	90.60 161	98.65 169	92.47 102	98.11 211	97.70 195

CPTT-MVS		94.74 105	94.12 136	96.60 44	98.15 78	93.01 43	95.84 75	97.66 108	89.21 176	93.28 225	95.46 218	88.89 182	98.98 109	89.80 174	98.82 139	97.80 186

test_fmvsm_n_1920		94.72 106	94.74 112	94.67 113	96.30 202	88.62 111	93.19 172	98.07 65	85.63 243	97.08 63	97.35 99	90.86 151	97.66 266	95.70 17	98.48 177	97.74 193

XVG-OURS		94.72 106	94.12 136	96.50 48	98.00 91	94.23 19	91.48 238	98.17 50	90.72 144	95.30 154	96.47 161	87.94 195	96.98 301	91.41 132	97.61 244	98.30 135

CSCG		94.69 108	94.75 110	94.52 124	97.55 126	87.87 127	95.01 109	97.57 117	92.68 82	96.20 108	93.44 290	91.92 125	98.78 144	89.11 194	99.24 85	96.92 241

v10		94.68 109	95.27 93	92.90 188	96.57 178	80.15 255	94.65 120	97.57 117	90.68 146	97.43 50	98.00 53	88.18 188	99.15 87	94.84 34	99.55 38	99.41 20

v8		94.65 110	95.29 91	92.74 193	96.65 172	79.77 270	94.59 121	97.17 150	91.86 106	97.47 49	97.93 57	88.16 189	99.08 96	94.32 41	99.47 43	99.38 22

sasdasda		94.59 111	94.69 114	94.30 133	95.60 255	87.03 143	95.59 84	98.24 37	91.56 124	95.21 162	92.04 323	94.95 53	98.66 166	91.45 130	97.57 245	97.20 228

canonicalmvs		94.59 111	94.69 114	94.30 133	95.60 255	87.03 143	95.59 84	98.24 37	91.56 124	95.21 162	92.04 323	94.95 53	98.66 166	91.45 130	97.57 245	97.20 228

CNVR-MVS		94.58 113	94.29 129	95.46 82	96.94 154	89.35 97	91.81 232	96.80 179	89.66 165	93.90 209	95.44 220	92.80 108	98.72 153	92.74 95	98.52 172	98.32 132

GeoE		94.55 114	94.68 117	94.15 137	97.23 141	85.11 191	94.14 142	97.34 137	88.71 186	95.26 157	95.50 217	94.65 63	99.12 93	90.94 140	98.40 180	98.23 139

EG-PatchMatch MVS		94.54 115	94.67 118	94.14 138	97.87 101	86.50 157	92.00 219	96.74 184	88.16 198	96.93 73	97.61 75	93.04 101	97.90 238	91.60 125	98.12 210	98.03 158

IS-MVSNet		94.49 116	94.35 128	94.92 102	98.25 72	86.46 160	97.13 19	94.31 272	96.24 29	96.28 102	96.36 173	82.88 257	99.35 63	88.19 210	99.52 41	98.96 64

Baseline_NR-MVSNet		94.47 117	95.09 100	92.60 203	98.50 56	80.82 251	92.08 215	96.68 187	93.82 63	96.29 100	98.56 26	90.10 171	97.75 259	90.10 169	99.66 21	99.24 32

MGCFI-Net		94.44 118	94.67 118	93.75 156	95.56 257	85.47 186	95.25 98	98.24 37	91.53 126	95.04 170	92.21 318	94.94 55	98.54 182	91.56 128	97.66 241	97.24 226

SDMVSNet		94.43 119	95.02 101	92.69 195	97.93 96	82.88 225	91.92 224	95.99 223	93.65 69	95.51 140	98.63 22	94.60 65	96.48 319	87.57 224	99.35 62	98.70 100

MM		94.41 120	94.14 135	95.22 94	95.84 238	87.21 138	94.31 135	90.92 329	94.48 50	92.80 244	97.52 83	85.27 236	99.49 27	96.58 9	99.57 36	98.97 62

VDD-MVS		94.37 121	94.37 126	94.40 131	97.49 129	86.07 172	93.97 148	93.28 292	94.49 49	96.24 104	97.78 65	87.99 194	98.79 141	88.92 198	99.14 99	98.34 131

EI-MVSNet-Vis-set		94.36 122	94.28 130	94.61 117	92.55 329	85.98 173	92.44 199	94.69 266	93.70 65	96.12 112	95.81 201	91.24 141	98.86 127	93.76 57	98.22 202	98.98 60

EI-MVSNet-UG-set		94.35 123	94.27 132	94.59 121	92.46 332	85.87 176	92.42 201	94.69 266	93.67 68	96.13 111	95.84 200	91.20 144	98.86 127	93.78 54	98.23 200	99.03 52

PHI-MVS		94.34 124	93.80 143	95.95 59	95.65 251	91.67 63	94.82 114	97.86 91	87.86 203	93.04 237	94.16 267	91.58 133	98.78 144	90.27 160	98.96 122	97.41 214

casdiffmvs		94.32 125	94.80 108	92.85 190	96.05 223	81.44 242	92.35 204	98.05 69	91.53 126	95.75 129	96.80 140	93.35 89	98.49 186	91.01 139	98.32 192	98.64 111
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
tfpnnormal		94.27 126	94.87 106	92.48 207	97.71 114	80.88 250	94.55 127	95.41 245	93.70 65	96.67 85	97.72 68	91.40 137	98.18 216	87.45 226	99.18 94	98.36 130

fmvsm_s_conf0.1_n_a		94.26 127	94.37 126	93.95 147	97.36 136	85.72 181	94.15 140	95.44 242	83.25 277	95.51 140	98.05 48	92.54 113	97.19 291	95.55 21	97.46 251	98.94 66

HQP_MVS		94.26 127	93.93 139	95.23 93	97.71 114	88.12 122	94.56 125	97.81 97	91.74 118	93.31 222	95.59 212	86.93 214	98.95 116	89.26 189	98.51 174	98.60 116

baseline		94.26 127	94.80 108	92.64 198	96.08 221	80.99 248	93.69 157	98.04 73	90.80 143	94.89 177	96.32 175	93.19 94	98.48 190	91.68 123	98.51 174	98.43 128

OMC-MVS		94.22 130	93.69 149	95.81 69	97.25 140	91.27 65	92.27 210	97.40 129	87.10 219	94.56 187	95.42 221	93.74 79	98.11 221	86.62 240	98.85 132	98.06 152

LCM-MVSNet-Re		94.20 131	94.58 121	93.04 180	95.91 234	83.13 221	93.79 153	99.19 4	92.00 100	98.84 8	98.04 50	93.64 80	99.02 106	81.28 301	98.54 170	96.96 240

DeepPCF-MVS	90.46 6	94.20 131	93.56 157	96.14 53	95.96 230	92.96 44	89.48 296	97.46 125	85.14 253	96.23 105	95.42 221	93.19 94	98.08 223	90.37 154	98.76 147	97.38 220

fmvsm_s_conf0.1_n		94.19 133	94.41 123	93.52 168	97.22 143	84.37 197	93.73 155	95.26 249	84.45 265	95.76 127	98.00 53	91.85 126	97.21 288	95.62 18	97.82 232	98.98 60

KD-MVS_self_test		94.10 134	94.73 113	92.19 214	97.66 120	79.49 276	94.86 113	97.12 155	89.59 167	96.87 75	97.65 72	90.40 165	98.34 202	89.08 195	99.35 62	98.75 91

NCCC		94.08 135	93.54 158	95.70 75	96.49 186	89.90 84	92.39 203	96.91 171	90.64 147	92.33 266	94.60 253	90.58 162	98.96 114	90.21 164	97.70 238	98.23 139

VDDNet		94.03 136	94.27 132	93.31 174	98.87 21	82.36 231	95.51 90	91.78 321	97.19 13	96.32 97	98.60 24	84.24 245	98.75 148	87.09 233	98.83 138	98.81 84

fmvsm_s_conf0.5_n_a		94.02 137	94.08 138	93.84 153	96.72 168	85.73 180	93.65 159	95.23 250	83.30 275	95.13 164	97.56 78	92.22 118	97.17 292	95.51 23	97.41 253	98.64 111

fmvsm_s_conf0.5_n		94.00 138	94.20 134	93.42 172	96.69 169	84.37 197	93.38 167	95.13 252	84.50 264	95.40 147	97.55 82	91.77 129	97.20 289	95.59 19	97.79 233	98.69 103

dcpmvs_2		93.96 139	95.01 102	90.82 268	97.60 122	74.04 348	93.68 158	98.85 9	89.80 163	97.82 31	97.01 128	91.14 148	99.21 81	90.56 148	98.59 165	99.19 36

sd_testset		93.94 140	94.39 124	92.61 202	97.93 96	83.24 215	93.17 173	95.04 254	93.65 69	95.51 140	98.63 22	94.49 69	95.89 337	81.72 297	99.35 62	98.70 100

MVS_0304		93.92 141	93.68 150	94.64 116	95.94 233	85.83 178	94.34 132	88.14 346	92.98 80	91.09 287	97.68 69	86.73 219	99.36 61	96.64 8	99.59 29	98.72 96

EPP-MVSNet		93.91 142	93.68 150	94.59 121	98.08 82	85.55 185	97.44 12	94.03 278	94.22 54	94.94 174	96.19 184	82.07 268	99.57 17	87.28 230	98.89 126	98.65 106

Effi-MVS+-dtu		93.90 143	92.60 181	97.77 4	94.74 283	96.67 6	94.00 146	95.41 245	89.94 159	91.93 274	92.13 321	90.12 169	98.97 113	87.68 223	97.48 249	97.67 198

fmvsm_l_conf0.5_n		93.79 144	93.81 141	93.73 157	96.16 213	86.26 167	92.46 197	96.72 185	81.69 298	95.77 126	97.11 119	90.83 153	97.82 249	95.58 20	97.99 222	97.11 231

IterMVS-LS		93.78 145	94.28 130	92.27 211	96.27 204	79.21 283	91.87 228	96.78 180	91.77 116	96.57 90	97.07 122	87.15 207	98.74 151	91.99 112	99.03 112	98.86 78
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DeepC-MVS_fast	89.96 7	93.73 146	93.44 160	94.60 120	96.14 216	87.90 126	93.36 168	97.14 152	85.53 246	93.90 209	95.45 219	91.30 140	98.59 176	89.51 180	98.62 161	97.31 223
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		93.66 147	93.28 164	94.80 107	96.25 207	90.95 70	90.21 273	95.43 244	87.91 200	93.74 213	94.40 258	92.88 106	96.38 324	90.39 152	98.28 194	97.07 233

iter_conf05_11		93.64 148	93.80 143	93.13 179	95.85 237	83.17 219	96.52 36	97.98 80	86.92 223	94.23 195	96.75 145	84.59 244	99.15 87	92.39 104	99.02 113	97.08 232

MVS_111021_HR		93.63 149	93.42 161	94.26 135	96.65 172	86.96 147	89.30 303	96.23 211	88.36 194	93.57 216	94.60 253	93.45 84	97.77 256	90.23 163	98.38 184	98.03 158

fmvsm_l_conf0.5_n_a		93.59 150	93.63 152	93.49 170	96.10 219	85.66 183	92.32 206	96.57 194	81.32 301	95.63 135	97.14 116	90.19 167	97.73 262	95.37 30	98.03 218	97.07 233

v1144		93.50 151	93.81 141	92.57 204	96.28 203	79.61 273	91.86 230	96.96 165	86.95 221	95.91 120	96.32 175	87.65 198	98.96 114	93.51 63	98.88 128	99.13 41

v1192		93.49 152	93.78 145	92.62 201	96.16 213	79.62 272	91.83 231	97.22 148	86.07 233	96.10 113	96.38 171	87.22 205	99.02 106	94.14 46	98.88 128	99.22 33

WR-MVS		93.49 152	93.72 147	92.80 192	97.57 125	80.03 261	90.14 276	95.68 230	93.70 65	96.62 87	95.39 225	87.21 206	99.04 104	87.50 225	99.64 24	99.33 26

V42		93.43 154	93.58 155	92.97 183	95.34 266	81.22 245	92.67 187	96.49 200	87.25 215	96.20 108	96.37 172	87.32 204	98.85 129	92.39 104	98.21 203	98.85 81

K. test v3		93.37 155	93.27 165	93.66 159	98.05 85	82.62 227	94.35 131	86.62 359	96.05 33	97.51 45	98.85 12	76.59 317	99.65 7	93.21 81	98.20 205	98.73 95

PM-MVS		93.33 156	92.67 179	95.33 86	96.58 177	94.06 22	92.26 211	92.18 312	85.92 236	96.22 106	96.61 156	85.64 234	95.99 335	90.35 155	98.23 200	95.93 285

v1240		93.29 157	93.71 148	92.06 221	96.01 228	77.89 304	91.81 232	97.37 130	85.12 254	96.69 84	96.40 166	86.67 220	99.07 100	94.51 37	98.76 147	99.22 33

v2v482		93.29 157	93.63 152	92.29 210	96.35 196	78.82 291	91.77 234	96.28 207	88.45 191	95.70 134	96.26 181	86.02 229	98.90 120	93.02 88	98.81 141	99.14 40

alignmvs		93.26 159	92.85 172	94.50 125	95.70 247	87.45 133	93.45 164	95.76 227	91.58 123	95.25 159	92.42 316	81.96 270	98.72 153	91.61 124	97.87 230	97.33 222

v1921920		93.26 159	93.61 154	92.19 214	96.04 227	78.31 298	91.88 227	97.24 146	85.17 252	96.19 110	96.19 184	86.76 218	99.05 101	94.18 45	98.84 133	99.22 33

MSLP-MVS++		93.25 161	93.88 140	91.37 244	96.34 197	82.81 226	93.11 174	97.74 104	89.37 171	94.08 199	95.29 227	90.40 165	96.35 326	90.35 155	98.25 198	94.96 319

GBi-Net		93.21 162	92.96 168	93.97 144	95.40 262	84.29 199	95.99 67	96.56 195	88.63 187	95.10 166	98.53 27	81.31 275	98.98 109	86.74 236	98.38 184	98.65 106

test1		93.21 162	92.96 168	93.97 144	95.40 262	84.29 199	95.99 67	96.56 195	88.63 187	95.10 166	98.53 27	81.31 275	98.98 109	86.74 236	98.38 184	98.65 106

v144192		93.20 164	93.54 158	92.16 218	96.05 223	78.26 299	91.95 220	97.14 152	84.98 258	95.96 116	96.11 188	87.08 209	99.04 104	93.79 53	98.84 133	99.17 37

VPNet		93.08 165	93.76 146	91.03 258	98.60 38	75.83 334	91.51 237	95.62 231	91.84 110	95.74 130	97.10 121	89.31 179	98.32 203	85.07 265	99.06 103	98.93 68

UGNet		93.08 165	92.50 183	94.79 108	93.87 306	87.99 125	95.07 106	94.26 275	90.64 147	87.33 351	97.67 71	86.89 216	98.49 186	88.10 213	98.71 152	97.91 172
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
TSAR-MVS + GP.		93.07 167	92.41 185	95.06 99	95.82 240	90.87 73	90.97 249	92.61 307	88.04 199	94.61 186	93.79 281	88.08 190	97.81 250	89.41 182	98.39 183	96.50 259

ETV-MVS		92.99 168	92.74 175	93.72 158	95.86 236	86.30 166	92.33 205	97.84 94	91.70 121	92.81 243	86.17 383	92.22 118	99.19 84	88.03 217	97.73 235	95.66 299

EI-MVSNet		92.99 168	93.26 166	92.19 214	92.12 341	79.21 283	92.32 206	94.67 268	91.77 116	95.24 160	95.85 198	87.14 208	98.49 186	91.99 112	98.26 196	98.86 78

MCST-MVS		92.91 170	92.51 182	94.10 140	97.52 127	85.72 181	91.36 242	97.13 154	80.33 309	92.91 242	94.24 263	91.23 142	98.72 153	89.99 171	97.93 227	97.86 178

h-mvs33		92.89 171	91.99 194	95.58 77	96.97 152	90.55 77	93.94 149	94.01 281	89.23 173	93.95 206	96.19 184	76.88 313	99.14 90	91.02 137	95.71 301	97.04 237

QAPM		92.88 172	92.77 173	93.22 177	95.82 240	83.31 213	96.45 42	97.35 136	83.91 270	93.75 211	96.77 141	89.25 180	98.88 123	84.56 271	97.02 266	97.49 209

v148		92.87 173	93.29 162	91.62 236	96.25 207	77.72 307	91.28 243	95.05 253	89.69 164	95.93 119	96.04 191	87.34 203	98.38 197	90.05 170	97.99 222	98.78 87

Anonymous20240521		92.86 174	93.57 156	90.74 270	96.57 178	75.50 336	94.15 140	95.60 232	89.38 170	95.90 121	97.90 63	80.39 282	97.96 235	92.60 100	99.68 17	98.75 91

Effi-MVS+		92.79 175	92.74 175	92.94 186	95.10 270	83.30 214	94.00 146	97.53 121	91.36 130	89.35 318	90.65 346	94.01 77	98.66 166	87.40 228	95.30 313	96.88 245

FMVSNet2		92.78 176	92.73 177	92.95 185	95.40 262	81.98 234	94.18 139	95.53 240	88.63 187	96.05 114	97.37 93	81.31 275	98.81 137	87.38 229	98.67 158	98.06 152

Fast-Effi-MVS+-dtu		92.77 177	92.16 188	94.58 123	94.66 288	88.25 120	92.05 216	96.65 189	89.62 166	90.08 304	91.23 334	92.56 112	98.60 174	86.30 248	96.27 290	96.90 242

LF4IMVS		92.72 178	92.02 193	94.84 106	95.65 251	91.99 55	92.92 179	96.60 191	85.08 256	92.44 258	93.62 285	86.80 217	96.35 326	86.81 235	98.25 198	96.18 274

train_agg		92.71 179	91.83 199	95.35 84	96.45 189	89.46 91	90.60 260	96.92 169	79.37 318	90.49 295	94.39 259	91.20 144	98.88 123	88.66 205	98.43 179	97.72 194

VNet		92.67 180	92.96 168	91.79 228	96.27 204	80.15 255	91.95 220	94.98 256	92.19 97	94.52 189	96.07 190	87.43 202	97.39 282	84.83 267	98.38 184	97.83 182

CDPH-MVS		92.67 180	91.83 199	95.18 96	96.94 154	88.46 118	90.70 257	97.07 158	77.38 333	92.34 265	95.08 234	92.67 111	98.88 123	85.74 253	98.57 167	98.20 142

Anonymous202405211		92.58 182	92.50 183	92.83 191	96.55 180	83.22 217	92.43 200	91.64 323	94.10 56	95.59 137	96.64 154	81.88 272	97.50 273	85.12 262	98.52 172	97.77 189

XXY-MVS		92.58 182	93.16 167	90.84 267	97.75 109	79.84 266	91.87 228	96.22 213	85.94 235	95.53 139	97.68 69	92.69 110	94.48 360	83.21 280	97.51 247	98.21 141

MVS_Test		92.57 184	93.29 162	90.40 279	93.53 312	75.85 332	92.52 193	96.96 165	88.73 184	92.35 263	96.70 151	90.77 154	98.37 201	92.53 101	95.49 306	96.99 239

TAPA-MVS	88.58 10	92.49 185	91.75 201	94.73 110	96.50 185	89.69 87	92.91 180	97.68 107	78.02 331	92.79 245	94.10 268	90.85 152	97.96 235	84.76 269	98.16 207	96.54 254
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
patch_mono-2		92.46 186	92.72 178	91.71 232	96.65 172	78.91 288	88.85 313	97.17 150	83.89 271	92.45 257	96.76 143	89.86 175	97.09 296	90.24 162	98.59 165	99.12 43

test_fmvs3		92.42 187	92.40 186	92.46 209	93.80 309	87.28 136	93.86 151	97.05 159	76.86 338	96.25 103	98.66 20	82.87 258	91.26 383	95.44 25	96.83 275	98.82 82

ab-mvs		92.40 188	92.62 180	91.74 230	97.02 150	81.65 238	95.84 75	95.50 241	86.95 221	92.95 241	97.56 78	90.70 159	97.50 273	79.63 320	97.43 252	96.06 279

CANet		92.38 189	91.99 194	93.52 168	93.82 308	83.46 212	91.14 245	97.00 162	89.81 162	86.47 355	94.04 270	87.90 196	99.21 81	89.50 181	98.27 195	97.90 173

EIA-MVS		92.35 190	92.03 192	93.30 175	95.81 242	83.97 207	92.80 183	98.17 50	87.71 207	89.79 312	87.56 373	91.17 147	99.18 85	87.97 218	97.27 257	96.77 249

DP-MVS Recon		92.31 191	91.88 197	93.60 161	97.18 145	86.87 148	91.10 247	97.37 130	84.92 259	92.08 271	94.08 269	88.59 183	98.20 213	83.50 277	98.14 209	95.73 294

F-COLMAP		92.28 192	91.06 217	95.95 59	97.52 127	91.90 57	93.53 160	97.18 149	83.98 269	88.70 330	94.04 270	88.41 186	98.55 181	80.17 313	95.99 295	97.39 218

OpenMVS	89.45 8	92.27 193	92.13 191	92.68 196	94.53 291	84.10 205	95.70 79	97.03 160	82.44 291	91.14 286	96.42 164	88.47 185	98.38 197	85.95 251	97.47 250	95.55 304

hse-mvs2		92.24 194	91.20 213	95.38 83	96.16 213	90.65 76	92.52 193	92.01 319	89.23 173	93.95 206	92.99 300	76.88 313	98.69 162	91.02 137	96.03 293	96.81 247

MVSFormer		92.18 195	92.23 187	92.04 222	94.74 283	80.06 259	97.15 16	97.37 130	88.98 179	88.83 322	92.79 305	77.02 310	99.60 12	96.41 10	96.75 279	96.46 261

HQP-MVS		92.09 196	91.49 207	93.88 150	96.36 193	84.89 193	91.37 239	97.31 139	87.16 216	88.81 324	93.40 291	84.76 241	98.60 174	86.55 243	97.73 235	98.14 148

DELS-MVS		92.05 197	92.16 188	91.72 231	94.44 292	80.13 257	87.62 328	97.25 145	87.34 214	92.22 268	93.18 297	89.54 178	98.73 152	89.67 178	98.20 205	96.30 267
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		92.00 198	92.76 174	89.71 296	95.62 254	77.02 316	90.72 256	96.17 216	87.70 208	95.26 157	96.29 177	92.54 113	96.45 321	81.77 295	98.77 146	95.66 299

CLD-MVS		91.82 199	91.41 209	93.04 180	96.37 191	83.65 211	86.82 347	97.29 142	84.65 263	92.27 267	89.67 355	92.20 120	97.85 248	83.95 275	99.47 43	97.62 200
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)		91.81 200	91.85 198	91.68 234	94.95 273	79.99 263	96.00 66	93.44 290	87.80 204	94.02 204	97.29 104	77.60 301	98.45 192	88.04 216	97.49 248	96.61 253

diffmvs		91.74 201	91.93 196	91.15 256	93.06 319	78.17 300	88.77 316	97.51 124	86.28 227	92.42 259	93.96 275	88.04 192	97.46 276	90.69 146	96.67 281	97.82 184
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
CNLPA		91.72 202	91.20 213	93.26 176	96.17 212	91.02 68	91.14 245	95.55 239	90.16 157	90.87 289	93.56 288	86.31 225	94.40 363	79.92 319	97.12 262	94.37 337

IterMVS-SCA-FT		91.65 203	91.55 203	91.94 223	93.89 305	79.22 282	87.56 331	93.51 288	91.53 126	95.37 150	96.62 155	78.65 292	98.90 120	91.89 116	94.95 321	97.70 195

PVSNet_Blended_VisFu		91.63 204	91.20 213	92.94 186	97.73 112	83.95 208	92.14 214	97.46 125	78.85 327	92.35 263	94.98 237	84.16 246	99.08 96	86.36 247	96.77 278	95.79 292

AdaColmap		91.63 204	91.36 210	92.47 208	95.56 257	86.36 164	92.24 213	96.27 208	88.88 183	89.90 309	92.69 308	91.65 132	98.32 203	77.38 339	97.64 242	92.72 369

pmmvs-eth3d		91.54 206	90.73 225	93.99 142	95.76 245	87.86 128	90.83 252	93.98 282	78.23 330	94.02 204	96.22 183	82.62 264	96.83 310	86.57 241	98.33 190	97.29 224

API-MVS		91.52 207	91.61 202	91.26 250	94.16 297	86.26 167	94.66 119	94.82 261	91.17 135	92.13 270	91.08 337	90.03 174	97.06 299	79.09 327	97.35 256	90.45 385

xiu_mvs_v1_base_debu		91.47 208	91.52 204	91.33 246	95.69 248	81.56 239	89.92 283	96.05 220	83.22 278	91.26 282	90.74 341	91.55 134	98.82 132	89.29 186	95.91 296	93.62 356

xiu_mvs_v1_base		91.47 208	91.52 204	91.33 246	95.69 248	81.56 239	89.92 283	96.05 220	83.22 278	91.26 282	90.74 341	91.55 134	98.82 132	89.29 186	95.91 296	93.62 356

xiu_mvs_v1_base_debi		91.47 208	91.52 204	91.33 246	95.69 248	81.56 239	89.92 283	96.05 220	83.22 278	91.26 282	90.74 341	91.55 134	98.82 132	89.29 186	95.91 296	93.62 356

LFMVS		91.33 211	91.16 216	91.82 227	96.27 204	79.36 278	95.01 109	85.61 370	96.04 34	94.82 179	97.06 123	72.03 335	98.46 191	84.96 266	98.70 154	97.65 199

c3_l		91.32 212	91.42 208	91.00 261	92.29 334	76.79 322	87.52 334	96.42 203	85.76 239	94.72 185	93.89 278	82.73 261	98.16 218	90.93 141	98.55 168	98.04 155

Fast-Effi-MVS+		91.28 213	90.86 220	92.53 206	95.45 261	82.53 228	89.25 306	96.52 199	85.00 257	89.91 308	88.55 367	92.94 102	98.84 130	84.72 270	95.44 308	96.22 272

MDA-MVSNet-bldmvs		91.04 214	90.88 219	91.55 239	94.68 287	80.16 254	85.49 368	92.14 315	90.41 154	94.93 175	95.79 202	85.10 238	96.93 305	85.15 260	94.19 342	97.57 203

PAPM_NR		91.03 215	90.81 222	91.68 234	96.73 167	81.10 247	93.72 156	96.35 206	88.19 196	88.77 328	92.12 322	85.09 239	97.25 286	82.40 290	93.90 347	96.68 252

MSDG		90.82 216	90.67 226	91.26 250	94.16 297	83.08 222	86.63 352	96.19 214	90.60 149	91.94 273	91.89 325	89.16 181	95.75 339	80.96 306	94.51 332	94.95 320

test20.03		90.80 217	90.85 221	90.63 273	95.63 253	79.24 281	89.81 287	92.87 298	89.90 160	94.39 191	96.40 166	85.77 230	95.27 352	73.86 362	99.05 106	97.39 218

FMVSNet3		90.78 218	90.32 235	92.16 218	93.03 321	79.92 265	92.54 192	94.95 257	86.17 232	95.10 166	96.01 193	69.97 342	98.75 148	86.74 236	98.38 184	97.82 184

eth_miper_zixun_eth		90.72 219	90.61 227	91.05 257	92.04 344	76.84 321	86.91 343	96.67 188	85.21 251	94.41 190	93.92 276	79.53 286	98.26 209	89.76 176	97.02 266	98.06 152

X-MVStestdata		90.70 220	88.45 266	97.44 17	98.56 42	93.99 27	96.50 39	97.95 86	94.58 47	94.38 192	26.89 409	94.56 66	99.39 52	93.57 60	99.05 106	98.93 68

BH-untuned		90.68 221	90.90 218	90.05 290	95.98 229	79.57 274	90.04 279	94.94 258	87.91 200	94.07 200	93.00 299	87.76 197	97.78 255	79.19 326	95.17 316	92.80 368

cl____		90.65 222	90.56 229	90.91 265	91.85 349	76.98 319	86.75 348	95.36 247	85.53 246	94.06 201	94.89 240	77.36 307	97.98 234	90.27 160	98.98 115	97.76 190

DIV-MVS_self_test		90.65 222	90.56 229	90.91 265	91.85 349	76.99 318	86.75 348	95.36 247	85.52 248	94.06 201	94.89 240	77.37 306	97.99 233	90.28 159	98.97 120	97.76 190

test_fmvs2		90.62 224	90.40 233	91.29 249	91.93 348	85.46 187	92.70 186	96.48 201	74.44 353	94.91 176	97.59 76	75.52 321	90.57 385	93.44 70	96.56 283	97.84 181

114514_t		90.51 225	89.80 245	92.63 200	98.00 91	82.24 232	93.40 166	97.29 142	65.84 395	89.40 317	94.80 245	86.99 212	98.75 148	83.88 276	98.61 162	96.89 243

miper_ehance_all_eth		90.48 226	90.42 232	90.69 271	91.62 356	76.57 325	86.83 346	96.18 215	83.38 274	94.06 201	92.66 310	82.20 266	98.04 225	89.79 175	97.02 266	97.45 211

BH-RMVSNet		90.47 227	90.44 231	90.56 275	95.21 269	78.65 296	89.15 307	93.94 283	88.21 195	92.74 247	94.22 264	86.38 224	97.88 242	78.67 329	95.39 310	95.14 313

Vis-MVSNet (Re-imp)		90.42 228	90.16 236	91.20 254	97.66 120	77.32 313	94.33 133	87.66 352	91.20 134	92.99 238	95.13 231	75.40 322	98.28 205	77.86 332	99.19 92	97.99 163

test_vis3_rt		90.40 229	90.03 240	91.52 241	92.58 327	88.95 104	90.38 268	97.72 106	73.30 360	97.79 32	97.51 86	77.05 309	87.10 398	89.03 196	94.89 322	98.50 122

PLC	85.34 15	90.40 229	88.92 258	94.85 105	96.53 184	90.02 82	91.58 236	96.48 201	80.16 310	86.14 357	92.18 319	85.73 231	98.25 210	76.87 342	94.61 331	96.30 267
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
test1111		90.39 231	90.61 227	89.74 295	98.04 88	71.50 364	95.59 84	79.72 399	89.41 169	95.94 118	98.14 42	70.79 339	98.81 137	88.52 207	99.32 69	98.90 74

testgi		90.38 232	91.34 211	87.50 334	97.49 129	71.54 363	89.43 298	95.16 251	88.38 193	94.54 188	94.68 250	92.88 106	93.09 375	71.60 375	97.85 231	97.88 176

mvs_anonymous		90.37 233	91.30 212	87.58 333	92.17 340	68.00 378	89.84 286	94.73 265	83.82 272	93.22 231	97.40 91	87.54 200	97.40 281	87.94 219	95.05 319	97.34 221

PVSNet_BlendedMVS		90.35 234	89.96 241	91.54 240	94.81 278	78.80 293	90.14 276	96.93 167	79.43 317	88.68 331	95.06 235	86.27 226	98.15 219	80.27 309	98.04 217	97.68 197

UnsupCasMVSNet_eth		90.33 235	90.34 234	90.28 281	94.64 289	80.24 253	89.69 291	95.88 224	85.77 238	93.94 208	95.69 209	81.99 269	92.98 376	84.21 273	91.30 378	97.62 200

MAR-MVS		90.32 236	88.87 261	94.66 115	94.82 277	91.85 58	94.22 138	94.75 264	80.91 304	87.52 349	88.07 371	86.63 221	97.87 245	76.67 343	96.21 291	94.25 340
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		90.31 237	90.14 239	90.81 269	91.01 364	78.93 285	92.52 193	98.12 56	91.91 104	89.10 319	96.89 135	68.84 344	99.41 42	90.17 165	92.70 367	94.08 341

IterMVS		90.18 238	90.16 236	90.21 285	93.15 317	75.98 331	87.56 331	92.97 297	86.43 226	94.09 198	96.40 166	78.32 296	97.43 278	87.87 220	94.69 329	97.23 227
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
SSC-MVS		90.16 239	92.96 168	81.78 378	97.88 99	48.48 410	90.75 254	87.69 351	96.02 35	96.70 83	97.63 74	85.60 235	97.80 251	85.73 254	98.60 164	99.06 50

TAMVS		90.16 239	89.05 254	93.49 170	96.49 186	86.37 163	90.34 270	92.55 308	80.84 307	92.99 238	94.57 255	81.94 271	98.20 213	73.51 363	98.21 203	95.90 288

ECVR-MVS		90.12 241	90.16 236	90.00 291	97.81 105	72.68 358	95.76 78	78.54 402	89.04 177	95.36 151	98.10 45	70.51 340	98.64 170	87.10 232	99.18 94	98.67 104

test_yl		90.11 242	89.73 248	91.26 250	94.09 300	79.82 267	90.44 264	92.65 304	90.90 138	93.19 232	93.30 293	73.90 326	98.03 226	82.23 291	96.87 273	95.93 285

DCV-MVSNet		90.11 242	89.73 248	91.26 250	94.09 300	79.82 267	90.44 264	92.65 304	90.90 138	93.19 232	93.30 293	73.90 326	98.03 226	82.23 291	96.87 273	95.93 285

Patchmtry		90.11 242	89.92 242	90.66 272	90.35 373	77.00 317	92.96 178	92.81 299	90.25 156	94.74 183	96.93 132	67.11 351	97.52 272	85.17 258	98.98 115	97.46 210

MVP-Stereo		90.07 245	88.92 258	93.54 165	96.31 200	86.49 158	90.93 250	95.59 236	79.80 311	91.48 278	95.59 212	80.79 279	97.39 282	78.57 330	91.19 379	96.76 250
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
AUN-MVS		90.05 246	88.30 270	95.32 88	96.09 220	90.52 78	92.42 201	92.05 318	82.08 295	88.45 334	92.86 302	65.76 361	98.69 162	88.91 199	96.07 292	96.75 251

CL-MVSNet_self_test		90.04 247	89.90 243	90.47 276	95.24 268	77.81 305	86.60 354	92.62 306	85.64 242	93.25 229	93.92 276	83.84 247	96.06 333	79.93 317	98.03 218	97.53 207

D2MVS		89.93 248	89.60 250	90.92 263	94.03 302	78.40 297	88.69 318	94.85 259	78.96 325	93.08 234	95.09 233	74.57 324	96.94 303	88.19 210	98.96 122	97.41 214

miper_lstm_enhance		89.90 249	89.80 245	90.19 287	91.37 360	77.50 310	83.82 384	95.00 255	84.84 261	93.05 236	94.96 238	76.53 318	95.20 353	89.96 172	98.67 158	97.86 178

CANet_DTU		89.85 250	89.17 252	91.87 225	92.20 338	80.02 262	90.79 253	95.87 225	86.02 234	82.53 386	91.77 327	80.01 283	98.57 178	85.66 255	97.70 238	97.01 238

tttt0517		89.81 251	88.90 260	92.55 205	97.00 151	79.73 271	95.03 108	83.65 383	89.88 161	95.30 154	94.79 246	53.64 395	99.39 52	91.99 112	98.79 144	98.54 119

EPNet		89.80 252	88.25 274	94.45 129	83.91 408	86.18 169	93.87 150	87.07 357	91.16 136	80.64 396	94.72 248	78.83 290	98.89 122	85.17 258	98.89 126	98.28 136
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CDS-MVSNet		89.55 253	88.22 277	93.53 166	95.37 265	86.49 158	89.26 304	93.59 285	79.76 313	91.15 285	92.31 317	77.12 308	98.38 197	77.51 337	97.92 228	95.71 295
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MG-MVS		89.54 254	89.80 245	88.76 312	94.88 274	72.47 360	89.60 292	92.44 310	85.82 237	89.48 316	95.98 194	82.85 259	97.74 261	81.87 294	95.27 314	96.08 278

OpenMVS_ROB	85.12 16	89.52 255	89.05 254	90.92 263	94.58 290	81.21 246	91.10 247	93.41 291	77.03 337	93.41 219	93.99 274	83.23 253	97.80 251	79.93 317	94.80 326	93.74 352

test_vis1_n_1920		89.45 256	89.85 244	88.28 323	93.59 311	76.71 323	90.67 258	97.78 102	79.67 315	90.30 301	96.11 188	76.62 316	92.17 379	90.31 157	93.57 352	95.96 283

WB-MVS		89.44 257	92.15 190	81.32 379	97.73 112	48.22 411	89.73 289	87.98 349	95.24 40	96.05 114	96.99 129	85.18 237	96.95 302	82.45 289	97.97 224	98.78 87

DPM-MVS		89.35 258	88.40 267	92.18 217	96.13 218	84.20 203	86.96 342	96.15 217	75.40 347	87.36 350	91.55 332	83.30 252	98.01 230	82.17 293	96.62 282	94.32 339

MVSTER		89.32 259	88.75 262	91.03 258	90.10 376	76.62 324	90.85 251	94.67 268	82.27 292	95.24 160	95.79 202	61.09 382	98.49 186	90.49 149	98.26 196	97.97 167

PatchMatch-RL		89.18 260	88.02 282	92.64 198	95.90 235	92.87 46	88.67 320	91.06 326	80.34 308	90.03 306	91.67 329	83.34 251	94.42 362	76.35 347	94.84 325	90.64 384

jason		89.17 261	88.32 269	91.70 233	95.73 246	80.07 258	88.10 324	93.22 293	71.98 368	90.09 303	92.79 305	78.53 295	98.56 179	87.43 227	97.06 264	96.46 261
jason: jason.
PCF-MVS	84.52 17	89.12 262	87.71 285	93.34 173	96.06 222	85.84 177	86.58 355	97.31 139	68.46 388	93.61 215	93.89 278	87.51 201	98.52 184	67.85 388	98.11 211	95.66 299
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
mvsany_test3		89.11 263	88.21 278	91.83 226	91.30 361	90.25 80	88.09 325	78.76 400	76.37 341	96.43 92	98.39 36	83.79 248	90.43 388	86.57 241	94.20 340	94.80 326

FE-MVS		89.06 264	88.29 271	91.36 245	94.78 280	79.57 274	96.77 29	90.99 327	84.87 260	92.96 240	96.29 177	60.69 384	98.80 140	80.18 312	97.11 263	95.71 295

cl22		89.02 265	88.50 265	90.59 274	89.76 378	76.45 326	86.62 353	94.03 278	82.98 284	92.65 249	92.49 311	72.05 334	97.53 271	88.93 197	97.02 266	97.78 188

USDC		89.02 265	89.08 253	88.84 311	95.07 271	74.50 343	88.97 309	96.39 204	73.21 361	93.27 226	96.28 179	82.16 267	96.39 323	77.55 336	98.80 142	95.62 302

test_vis1_n		89.01 267	89.01 256	89.03 307	92.57 328	82.46 230	92.62 190	96.06 218	73.02 363	90.40 298	95.77 206	74.86 323	89.68 391	90.78 143	94.98 320	94.95 320

xiu_mvs_v2_base		89.00 268	89.19 251	88.46 321	94.86 276	74.63 340	86.97 341	95.60 232	80.88 305	87.83 343	88.62 366	91.04 149	98.81 137	82.51 288	94.38 334	91.93 375

new-patchmatchnet		88.97 269	90.79 223	83.50 373	94.28 296	55.83 408	85.34 370	93.56 287	86.18 231	95.47 143	95.73 208	83.10 254	96.51 318	85.40 257	98.06 215	98.16 146

pmmvs4		88.95 270	87.70 286	92.70 194	94.30 295	85.60 184	87.22 337	92.16 314	74.62 352	89.75 314	94.19 265	77.97 299	96.41 322	82.71 284	96.36 288	96.09 277

N_pmnet		88.90 271	87.25 293	93.83 154	94.40 294	93.81 36	84.73 374	87.09 356	79.36 320	93.26 227	92.43 315	79.29 288	91.68 381	77.50 338	97.22 259	96.00 281

PS-MVSNAJ		88.86 272	88.99 257	88.48 320	94.88 274	74.71 338	86.69 350	95.60 232	80.88 305	87.83 343	87.37 376	90.77 154	98.82 132	82.52 287	94.37 335	91.93 375

Patchmatch-RL test		88.81 273	88.52 264	89.69 297	95.33 267	79.94 264	86.22 360	92.71 303	78.46 328	95.80 125	94.18 266	66.25 359	95.33 350	89.22 191	98.53 171	93.78 350

Anonymous20231206		88.77 274	88.29 271	90.20 286	96.31 200	78.81 292	89.56 294	93.49 289	74.26 355	92.38 261	95.58 215	82.21 265	95.43 347	72.07 371	98.75 149	96.34 265

PVSNet_Blended		88.74 275	88.16 280	90.46 278	94.81 278	78.80 293	86.64 351	96.93 167	74.67 351	88.68 331	89.18 362	86.27 226	98.15 219	80.27 309	96.00 294	94.44 336

test_fmvs1_n		88.73 276	88.38 268	89.76 294	92.06 343	82.53 228	92.30 209	96.59 193	71.14 373	92.58 252	95.41 224	68.55 345	89.57 393	91.12 135	95.66 302	97.18 230

thisisatest0530		88.69 277	87.52 288	92.20 213	96.33 198	79.36 278	92.81 182	84.01 382	86.44 225	93.67 214	92.68 309	53.62 396	99.25 78	89.65 179	98.45 178	98.00 160

ppachtmachnet_test		88.61 278	88.64 263	88.50 319	91.76 351	70.99 367	84.59 377	92.98 296	79.30 322	92.38 261	93.53 289	79.57 285	97.45 277	86.50 245	97.17 261	97.07 233

UnsupCasMVSNet_bld		88.50 279	88.03 281	89.90 292	95.52 259	78.88 289	87.39 335	94.02 280	79.32 321	93.06 235	94.02 272	80.72 280	94.27 365	75.16 354	93.08 363	96.54 254

miper_enhance_ethall		88.42 280	87.87 283	90.07 288	88.67 390	75.52 335	85.10 371	95.59 236	75.68 343	92.49 254	89.45 358	78.96 289	97.88 242	87.86 221	97.02 266	96.81 247

1112_ss		88.42 280	87.41 289	91.45 242	96.69 169	80.99 248	89.72 290	96.72 185	73.37 359	87.00 353	90.69 344	77.38 305	98.20 213	81.38 300	93.72 350	95.15 312

lupinMVS		88.34 282	87.31 290	91.45 242	94.74 283	80.06 259	87.23 336	92.27 311	71.10 374	88.83 322	91.15 335	77.02 310	98.53 183	86.67 239	96.75 279	95.76 293

test_cas_vis1_n_1920		88.25 283	88.27 273	88.20 325	92.19 339	78.92 287	89.45 297	95.44 242	75.29 350	93.23 230	95.65 211	71.58 336	90.23 389	88.05 215	93.55 354	95.44 306

YYNet1		88.17 284	88.24 275	87.93 329	92.21 337	73.62 350	80.75 393	88.77 338	82.51 290	94.99 173	95.11 232	82.70 262	93.70 369	83.33 278	93.83 348	96.48 260

MDA-MVSNet_test_wron		88.16 285	88.23 276	87.93 329	92.22 336	73.71 349	80.71 394	88.84 337	82.52 289	94.88 178	95.14 230	82.70 262	93.61 370	83.28 279	93.80 349	96.46 261

MS-PatchMatch		88.05 286	87.75 284	88.95 308	93.28 314	77.93 302	87.88 327	92.49 309	75.42 346	92.57 253	93.59 287	80.44 281	94.24 367	81.28 301	92.75 366	94.69 332

CR-MVSNet		87.89 287	87.12 298	90.22 284	91.01 364	78.93 285	92.52 193	92.81 299	73.08 362	89.10 319	96.93 132	67.11 351	97.64 268	88.80 201	92.70 367	94.08 341

pmmvs5		87.87 288	87.14 296	90.07 288	93.26 316	76.97 320	88.89 311	92.18 312	73.71 358	88.36 335	93.89 278	76.86 315	96.73 313	80.32 308	96.81 276	96.51 256

wuyk23d		87.83 289	90.79 223	78.96 384	90.46 372	88.63 110	92.72 184	90.67 332	91.65 122	98.68 14	97.64 73	96.06 15	77.53 406	59.84 400	99.41 56	70.73 404

FMVSNet5		87.82 290	86.56 307	91.62 236	92.31 333	79.81 269	93.49 162	94.81 263	83.26 276	91.36 280	96.93 132	52.77 397	97.49 275	76.07 349	98.03 218	97.55 206

GA-MVS		87.70 291	86.82 302	90.31 280	93.27 315	77.22 315	84.72 376	92.79 301	85.11 255	89.82 310	90.07 347	66.80 354	97.76 258	84.56 271	94.27 338	95.96 283

TR-MVS		87.70 291	87.17 295	89.27 304	94.11 299	79.26 280	88.69 318	91.86 320	81.94 296	90.69 293	89.79 352	82.82 260	97.42 279	72.65 369	91.98 375	91.14 381

thres600view7		87.66 293	87.10 299	89.36 302	96.05 223	73.17 352	92.72 184	85.31 373	91.89 105	93.29 224	90.97 338	63.42 373	98.39 194	73.23 365	96.99 271	96.51 256

PAPR		87.65 294	86.77 304	90.27 282	92.85 325	77.38 312	88.56 321	96.23 211	76.82 340	84.98 366	89.75 354	86.08 228	97.16 294	72.33 370	93.35 356	96.26 271

baseline1		87.62 295	87.31 290	88.54 317	94.71 286	74.27 346	93.10 175	88.20 344	86.20 230	92.18 269	93.04 298	73.21 329	95.52 342	79.32 324	85.82 394	95.83 290

test_fmvs1		87.59 296	87.27 292	88.54 317	88.32 391	81.26 244	90.43 267	95.72 229	70.55 379	91.70 276	94.63 251	68.13 346	89.42 394	90.59 147	95.34 312	94.94 322

our_test_3		87.55 297	87.59 287	87.44 335	91.76 351	70.48 368	83.83 383	90.55 333	79.79 312	92.06 272	92.17 320	78.63 294	95.63 340	84.77 268	94.73 327	96.22 272

PatchT		87.51 298	88.17 279	85.55 355	90.64 367	66.91 382	92.02 218	86.09 363	92.20 96	89.05 321	97.16 114	64.15 369	96.37 325	89.21 192	92.98 365	93.37 360

Test_1112_low_res		87.50 299	86.58 306	90.25 283	96.80 166	77.75 306	87.53 333	96.25 209	69.73 384	86.47 355	93.61 286	75.67 320	97.88 242	79.95 315	93.20 359	95.11 316

SCA		87.43 300	87.21 294	88.10 327	92.01 345	71.98 362	89.43 298	88.11 347	82.26 293	88.71 329	92.83 303	78.65 292	97.59 269	79.61 321	93.30 357	94.75 329

EU-MVSNet		87.39 301	86.71 305	89.44 299	93.40 313	76.11 329	94.93 112	90.00 335	57.17 404	95.71 133	97.37 93	64.77 367	97.68 265	92.67 98	94.37 335	94.52 334

thres100view900		87.35 302	86.89 301	88.72 313	96.14 216	73.09 354	93.00 177	85.31 373	92.13 98	93.26 227	90.96 339	63.42 373	98.28 205	71.27 377	96.54 284	94.79 327

CMPMVS	68.83 22	87.28 303	85.67 317	92.09 220	88.77 389	85.42 188	90.31 271	94.38 271	70.02 382	88.00 340	93.30 293	73.78 328	94.03 368	75.96 351	96.54 284	96.83 246
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
sss		87.23 304	86.82 302	88.46 321	93.96 303	77.94 301	86.84 345	92.78 302	77.59 332	87.61 348	91.83 326	78.75 291	91.92 380	77.84 333	94.20 340	95.52 305

BH-w/o		87.21 305	87.02 300	87.79 332	94.77 281	77.27 314	87.90 326	93.21 295	81.74 297	89.99 307	88.39 369	83.47 250	96.93 305	71.29 376	92.43 371	89.15 386

thres400		87.20 306	86.52 309	89.24 306	95.77 243	72.94 355	91.89 225	86.00 364	90.84 140	92.61 250	89.80 350	63.93 370	98.28 205	71.27 377	96.54 284	96.51 256

CHOSEN 1792x2688		87.19 307	85.92 316	91.00 261	97.13 148	79.41 277	84.51 378	95.60 232	64.14 398	90.07 305	94.81 243	78.26 297	97.14 295	73.34 364	95.38 311	96.46 261

HyFIR lowres test		87.19 307	85.51 318	92.24 212	97.12 149	80.51 252	85.03 372	96.06 218	66.11 394	91.66 277	92.98 301	70.12 341	99.14 90	75.29 353	95.23 315	97.07 233

MIMVSNet		87.13 309	86.54 308	88.89 310	96.05 223	76.11 329	94.39 130	88.51 340	81.37 300	88.27 337	96.75 145	72.38 332	95.52 342	65.71 393	95.47 307	95.03 317

tfpn200view9		87.05 310	86.52 309	88.67 314	95.77 243	72.94 355	91.89 225	86.00 364	90.84 140	92.61 250	89.80 350	63.93 370	98.28 205	71.27 377	96.54 284	94.79 327

cascas		87.02 311	86.28 313	89.25 305	91.56 358	76.45 326	84.33 380	96.78 180	71.01 375	86.89 354	85.91 384	81.35 274	96.94 303	83.09 281	95.60 303	94.35 338

WTY-MVS		86.93 312	86.50 311	88.24 324	94.96 272	74.64 339	87.19 338	92.07 317	78.29 329	88.32 336	91.59 331	78.06 298	94.27 365	74.88 355	93.15 361	95.80 291

HY-MVS	82.50 18	86.81 313	85.93 315	89.47 298	93.63 310	77.93 302	94.02 145	91.58 324	75.68 343	83.64 377	93.64 283	77.40 304	97.42 279	71.70 374	92.07 374	93.05 365

test_f		86.65 314	87.13 297	85.19 359	90.28 374	86.11 171	86.52 356	91.66 322	69.76 383	95.73 132	97.21 112	69.51 343	81.28 405	89.15 193	94.40 333	88.17 391

1314		86.46 315	86.33 312	86.87 342	91.65 355	74.54 341	91.94 222	94.10 277	74.28 354	84.78 368	87.33 377	83.03 256	95.00 354	78.72 328	91.16 380	91.06 382

ET-MVSNet_ETH3D		86.15 316	84.27 327	91.79 228	93.04 320	81.28 243	87.17 339	86.14 362	79.57 316	83.65 376	88.66 364	57.10 388	98.18 216	87.74 222	95.40 309	95.90 288

Patchmatch-test		86.10 317	86.01 314	86.38 350	90.63 368	74.22 347	89.57 293	86.69 358	85.73 240	89.81 311	92.83 303	65.24 365	91.04 384	77.82 335	95.78 300	93.88 349

thres200		85.85 318	85.18 319	87.88 331	94.44 292	72.52 359	89.08 308	86.21 361	88.57 190	91.44 279	88.40 368	64.22 368	98.00 231	68.35 386	95.88 299	93.12 362

EPNet_dtu		85.63 319	84.37 325	89.40 301	86.30 401	74.33 345	91.64 235	88.26 342	84.84 261	72.96 405	89.85 348	71.27 338	97.69 264	76.60 344	97.62 243	96.18 274
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test_vis1_rt		85.58 320	84.58 323	88.60 316	87.97 392	86.76 150	85.45 369	93.59 285	66.43 392	87.64 346	89.20 361	79.33 287	85.38 402	81.59 298	89.98 386	93.66 354

test2506		85.42 321	84.57 324	87.96 328	97.81 105	66.53 385	96.14 61	56.35 412	89.04 177	93.55 217	98.10 45	42.88 410	98.68 164	88.09 214	99.18 94	98.67 104

PatchmatchNet		85.22 322	84.64 322	86.98 339	89.51 383	69.83 374	90.52 262	87.34 355	78.87 326	87.22 352	92.74 307	66.91 353	96.53 316	81.77 295	86.88 392	94.58 333
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CVMVSNet		85.16 323	84.72 321	86.48 346	92.12 341	70.19 369	92.32 206	88.17 345	56.15 405	90.64 294	95.85 198	67.97 349	96.69 314	88.78 202	90.52 383	92.56 370

JIA-IIPM		85.08 324	83.04 336	91.19 255	87.56 394	86.14 170	89.40 300	84.44 381	88.98 179	82.20 387	97.95 56	56.82 390	96.15 329	76.55 346	83.45 398	91.30 380

MVS		84.98 325	84.30 326	87.01 338	91.03 363	77.69 308	91.94 222	94.16 276	59.36 403	84.23 373	87.50 375	85.66 232	96.80 311	71.79 372	93.05 364	86.54 395

Syy-MVS		84.81 326	84.93 320	84.42 366	91.71 353	63.36 400	85.89 363	81.49 390	81.03 302	85.13 363	81.64 399	77.44 303	95.00 354	85.94 252	94.12 343	94.91 323

thisisatest0515		84.72 327	82.99 337	89.90 292	92.96 323	75.33 337	84.36 379	83.42 384	77.37 334	88.27 337	86.65 378	53.94 394	98.72 153	82.56 286	97.40 254	95.67 298

dmvs_re		84.69 328	83.94 330	86.95 340	92.24 335	82.93 224	89.51 295	87.37 354	84.38 267	85.37 360	85.08 390	72.44 331	86.59 399	68.05 387	91.03 382	91.33 379

FPMVS		84.50 329	83.28 334	88.16 326	96.32 199	94.49 17	85.76 366	85.47 371	83.09 281	85.20 362	94.26 262	63.79 372	86.58 400	63.72 396	91.88 377	83.40 398

tpm		84.38 330	84.08 328	85.30 358	90.47 371	63.43 399	89.34 301	85.63 369	77.24 336	87.62 347	95.03 236	61.00 383	97.30 285	79.26 325	91.09 381	95.16 311

tpmvs		84.22 331	83.97 329	84.94 361	87.09 398	65.18 392	91.21 244	88.35 341	82.87 285	85.21 361	90.96 339	65.24 365	96.75 312	79.60 323	85.25 395	92.90 367

WB-MVSnew		84.20 332	83.89 331	85.16 360	91.62 356	66.15 389	88.44 323	81.00 393	76.23 342	87.98 341	87.77 372	84.98 240	93.35 373	62.85 398	94.10 345	95.98 282

ADS-MVSNet2		84.01 333	82.20 344	89.41 300	89.04 386	76.37 328	87.57 329	90.98 328	72.71 366	84.46 369	92.45 312	68.08 347	96.48 319	70.58 382	83.97 396	95.38 307

mvsany_test1		83.91 334	82.93 338	86.84 343	86.18 402	85.93 174	81.11 392	75.03 407	70.80 378	88.57 333	94.63 251	83.08 255	87.38 397	80.39 307	86.57 393	87.21 393

testing3		83.66 335	82.52 340	87.08 337	95.84 238	65.84 390	89.80 288	77.17 406	88.17 197	90.84 290	88.63 365	30.95 414	98.11 221	84.05 274	97.19 260	97.28 225

test-LLR		83.58 336	83.17 335	84.79 363	89.68 380	66.86 383	83.08 385	84.52 379	83.07 282	82.85 383	84.78 391	62.86 376	93.49 371	82.85 282	94.86 323	94.03 344

testing91		83.56 337	82.45 341	86.91 341	92.92 324	67.29 379	86.33 358	88.07 348	86.22 229	84.26 372	85.76 385	48.15 400	97.17 292	76.27 348	94.08 346	96.27 270

baseline2		83.38 338	81.54 348	88.90 309	91.38 359	72.84 357	88.78 315	81.22 392	78.97 324	79.82 398	87.56 373	61.73 380	97.80 251	74.30 359	90.05 385	96.05 280

IB-MVS	77.21 19	83.11 339	81.05 351	89.29 303	91.15 362	75.85 332	85.66 367	86.00 364	79.70 314	82.02 390	86.61 379	48.26 399	98.39 194	77.84 333	92.22 372	93.63 355
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		83.09 340	82.21 343	85.73 353	89.27 385	67.01 381	90.35 269	86.47 360	70.42 380	83.52 379	93.23 296	61.18 381	96.85 309	77.21 340	88.26 390	93.34 361

PMMVS		83.00 341	81.11 350	88.66 315	83.81 409	86.44 161	82.24 389	85.65 368	61.75 402	82.07 388	85.64 387	79.75 284	91.59 382	75.99 350	93.09 362	87.94 392

testing99		82.94 342	81.72 345	86.59 344	92.55 329	66.53 385	86.08 362	85.70 367	85.47 249	83.95 374	85.70 386	45.87 401	97.07 298	76.58 345	93.56 353	96.17 276

PVSNet	76.22 20	82.89 343	82.37 342	84.48 365	93.96 303	64.38 397	78.60 396	88.61 339	71.50 371	84.43 371	86.36 382	74.27 325	94.60 359	69.87 384	93.69 351	94.46 335

tpmrst		82.85 344	82.93 338	82.64 375	87.65 393	58.99 406	90.14 276	87.90 350	75.54 345	83.93 375	91.63 330	66.79 356	95.36 348	81.21 303	81.54 402	93.57 359

test0.0.03 1		82.48 345	81.47 349	85.48 356	89.70 379	73.57 351	84.73 374	81.64 389	83.07 282	88.13 339	86.61 379	62.86 376	89.10 396	66.24 392	90.29 384	93.77 351

ADS-MVSNet		82.25 346	81.55 347	84.34 367	89.04 386	65.30 391	87.57 329	85.13 377	72.71 366	84.46 369	92.45 312	68.08 347	92.33 378	70.58 382	83.97 396	95.38 307

DSMNet-mixed		82.21 347	81.56 346	84.16 368	89.57 382	70.00 373	90.65 259	77.66 404	54.99 406	83.30 381	97.57 77	77.89 300	90.50 387	66.86 391	95.54 305	91.97 374

KD-MVS_2432*1600		82.17 348	80.75 355	86.42 348	82.04 410	70.09 371	81.75 390	90.80 330	82.56 287	90.37 299	89.30 359	42.90 408	96.11 331	74.47 357	92.55 369	93.06 363

miper_refine_blended		82.17 348	80.75 355	86.42 348	82.04 410	70.09 371	81.75 390	90.80 330	82.56 287	90.37 299	89.30 359	42.90 408	96.11 331	74.47 357	92.55 369	93.06 363

gg-mvs-nofinetune		82.10 350	81.02 352	85.34 357	87.46 396	71.04 365	94.74 116	67.56 409	96.44 27	79.43 399	98.99 6	45.24 402	96.15 329	67.18 390	92.17 373	88.85 388

testing11		81.98 351	80.52 358	86.38 350	92.69 326	67.13 380	85.79 365	84.80 378	82.16 294	81.19 395	85.41 388	45.24 402	96.88 308	74.14 360	93.24 358	95.14 313

PAPM		81.91 352	80.11 362	87.31 336	93.87 306	72.32 361	84.02 382	93.22 293	69.47 385	76.13 403	89.84 349	72.15 333	97.23 287	53.27 405	89.02 387	92.37 372

tpm2		81.46 353	80.35 360	84.80 362	89.90 377	65.14 393	90.44 264	85.36 372	65.82 396	82.05 389	92.44 314	57.94 387	96.69 314	70.71 381	88.49 389	92.56 370

PMMVS2		81.31 354	83.44 333	74.92 387	90.52 370	46.49 413	69.19 403	85.23 376	84.30 268	87.95 342	94.71 249	76.95 312	84.36 404	64.07 395	98.09 213	93.89 348

new_pmnet		81.22 355	81.01 353	81.86 377	90.92 366	70.15 370	84.03 381	80.25 398	70.83 376	85.97 358	89.78 353	67.93 350	84.65 403	67.44 389	91.90 376	90.78 383

test-mter		81.21 356	80.01 363	84.79 363	89.68 380	66.86 383	83.08 385	84.52 379	73.85 357	82.85 383	84.78 391	43.66 407	93.49 371	82.85 282	94.86 323	94.03 344

EPMVS		81.17 357	80.37 359	83.58 372	85.58 404	65.08 394	90.31 271	71.34 408	77.31 335	85.80 359	91.30 333	59.38 385	92.70 377	79.99 314	82.34 401	92.96 366

EGC-MVSNET		80.97 358	75.73 374	96.67 43	98.85 23	94.55 16	96.83 24	96.60 191	2.44 411	5.32 412	98.25 40	92.24 117	98.02 229	91.85 117	99.21 90	97.45 211

pmmvs3		80.83 359	78.96 367	86.45 347	87.23 397	77.48 311	84.87 373	82.31 387	63.83 399	85.03 365	89.50 357	49.66 398	93.10 374	73.12 367	95.10 317	88.78 390

E-PMN		80.72 360	80.86 354	80.29 382	85.11 405	68.77 376	72.96 400	81.97 388	87.76 206	83.25 382	83.01 397	62.22 379	89.17 395	77.15 341	94.31 337	82.93 399

tpm cat1		80.61 361	79.46 364	84.07 369	88.78 388	65.06 395	89.26 304	88.23 343	62.27 401	81.90 391	89.66 356	62.70 378	95.29 351	71.72 373	80.60 403	91.86 377

testing222		80.54 362	78.53 369	86.58 345	92.54 331	68.60 377	86.24 359	82.72 386	83.78 273	82.68 385	84.24 393	39.25 412	95.94 336	60.25 399	95.09 318	95.20 309

EMVS		80.35 363	80.28 361	80.54 381	84.73 407	69.07 375	72.54 402	80.73 395	87.80 204	81.66 392	81.73 398	62.89 375	89.84 390	75.79 352	94.65 330	82.71 400

UWE-MVS		80.29 364	79.10 365	83.87 370	91.97 347	59.56 404	86.50 357	77.43 405	75.40 347	87.79 345	88.10 370	44.08 406	96.90 307	64.23 394	96.36 288	95.14 313

CHOSEN 280x420		80.04 365	77.97 372	86.23 352	90.13 375	74.53 342	72.87 401	89.59 336	66.38 393	76.29 402	85.32 389	56.96 389	95.36 348	69.49 385	94.72 328	88.79 389

ETVMVS		79.85 366	77.94 373	85.59 354	92.97 322	66.20 388	86.13 361	80.99 394	81.41 299	83.52 379	83.89 394	41.81 411	94.98 357	56.47 403	94.25 339	95.61 303

myMVS_eth3d		79.62 367	78.26 370	83.72 371	91.71 353	61.25 402	85.89 363	81.49 390	81.03 302	85.13 363	81.64 399	32.12 413	95.00 354	71.17 380	94.12 343	94.91 323

dp		79.28 368	78.62 368	81.24 380	85.97 403	56.45 407	86.91 343	85.26 375	72.97 364	81.45 394	89.17 363	56.01 392	95.45 346	73.19 366	76.68 404	91.82 378

TESTMET0.1,1		79.09 369	78.04 371	82.25 376	87.52 395	64.03 398	83.08 385	80.62 396	70.28 381	80.16 397	83.22 396	44.13 405	90.56 386	79.95 315	93.36 355	92.15 373

MVS-HIRNet		78.83 370	80.60 357	73.51 388	93.07 318	47.37 412	87.10 340	78.00 403	68.94 386	77.53 401	97.26 105	71.45 337	94.62 358	63.28 397	88.74 388	78.55 403

dmvs_testset		78.23 371	78.99 366	75.94 386	91.99 346	55.34 409	88.86 312	78.70 401	82.69 286	81.64 393	79.46 401	75.93 319	85.74 401	48.78 407	82.85 400	86.76 394

PVSNet_0	70.34 21	74.58 372	72.96 375	79.47 383	90.63 368	66.24 387	73.26 399	83.40 385	63.67 400	78.02 400	78.35 403	72.53 330	89.59 392	56.68 402	60.05 407	82.57 401

MVE	59.87 23	73.86 373	72.65 376	77.47 385	87.00 400	74.35 344	61.37 405	60.93 411	67.27 390	69.69 406	86.49 381	81.24 278	72.33 408	56.45 404	83.45 398	85.74 396
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dongtai		53.72 374	53.79 377	53.51 391	79.69 412	36.70 415	77.18 397	32.53 417	71.69 369	68.63 407	60.79 406	26.65 415	73.11 407	30.67 410	36.29 409	50.73 405

test_method		50.44 375	48.94 378	54.93 389	39.68 415	12.38 418	28.59 406	90.09 334	6.82 409	41.10 411	78.41 402	54.41 393	70.69 409	50.12 406	51.26 408	81.72 402

kuosan		43.63 376	44.25 380	41.78 392	66.04 414	34.37 416	75.56 398	32.62 416	53.25 407	50.46 410	51.18 407	25.28 416	49.13 410	13.44 411	30.41 410	41.84 407

tmp_tt		37.97 377	44.33 379	18.88 393	11.80 416	21.54 417	63.51 404	45.66 415	4.23 410	51.34 409	50.48 408	59.08 386	22.11 412	44.50 408	68.35 406	13.00 408

cdsmvs_eth3d_5k		23.35 378	31.13 381	0.00 396	0.00 419	0.00 421	0.00 407	95.58 238	0.00 414	0.00 415	91.15 335	93.43 86	0.00 415	0.00 414	0.00 413	0.00 411

test123		9.49 379	12.01 382	1.91 394	2.87 417	1.30 419	82.38 388	1.34 419	1.36 412	2.84 413	6.56 411	2.45 417	0.97 413	2.73 412	5.56 411	3.47 409

testmvs		9.02 380	11.42 383	1.81 395	2.77 418	1.13 420	79.44 395	1.90 418	1.18 413	2.65 414	6.80 410	1.95 418	0.87 414	2.62 413	3.45 412	3.44 410

pcd_1.5k_mvsjas		7.56 381	10.09 384	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	90.77 154	0.00 415	0.00 414	0.00 413	0.00 411

ab-mvs-re		7.56 381	10.08 385	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	90.69 344	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

test_blank		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

uanet_test		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

DCPMVS		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

sosnet-low-res		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

sosnet		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

uncertanet		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

Regformer		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

uanet		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

WAC-MVS						61.25 402								74.55 356

FOURS1					99.21 3	94.68 13	98.45 4	98.81 10	97.73 7	98.27 23

MSC_two_6792asdad				95.90 65	96.54 181	89.57 89		96.87 174					99.41 42	94.06 47	99.30 72	98.72 96

PC_three_1452									75.31 349	95.87 123	95.75 207	92.93 103	96.34 328	87.18 231	98.68 156	98.04 155

No_MVS				95.90 65	96.54 181	89.57 89		96.87 174					99.41 42	94.06 47	99.30 72	98.72 96

test_one_0601					98.26 70	87.14 140		98.18 46	94.25 52	96.99 71	97.36 96	95.13 43

eth-test2					0.00 419

eth-test					0.00 419

ZD-MVS					97.23 141	90.32 79		97.54 119	84.40 266	94.78 181	95.79 202	92.76 109	99.39 52	88.72 204	98.40 180

RE-MVS-def			96.66 22		98.07 83	95.27 10	96.37 47	98.12 56	95.66 37	97.00 69	97.03 125	95.40 29		93.49 64	98.84 133	98.00 160

IU-MVS					98.51 50	86.66 155		96.83 177	72.74 365	95.83 124				93.00 89	99.29 75	98.64 111

OPU-MVS				95.15 97	96.84 162	89.43 93	95.21 99				95.66 210	93.12 97	98.06 224	86.28 249	98.61 162	97.95 168

test_241102_TWO								98.10 59	91.95 101	97.54 42	97.25 106	95.37 30	99.35 63	93.29 77	99.25 83	98.49 124

test_241102_ONE					98.51 50	86.97 145		98.10 59	91.85 107	97.63 37	97.03 125	96.48 10	98.95 116

9.14			94.81 107		97.49 129		94.11 143	98.37 23	87.56 212	95.38 148	96.03 192	94.66 62	99.08 96	90.70 145	98.97 120

save fliter					97.46 132	88.05 124	92.04 217	97.08 157	87.63 210

test_0728_THIRD									93.26 75	97.40 54	97.35 99	94.69 61	99.34 66	93.88 50	99.42 52	98.89 75

test_0728_SECOND				94.88 104	98.55 45	86.72 152	95.20 101	98.22 41					99.38 58	93.44 70	99.31 70	98.53 121

test0726					98.51 50	86.69 153	95.34 93	98.18 46	91.85 107	97.63 37	97.37 93	95.58 24

GSMVS																94.75 329

test_part2					98.21 75	89.41 94				96.72 82

sam_mvs1												66.64 357				94.75 329

sam_mvs												66.41 358

ambc				92.98 182	96.88 158	83.01 223	95.92 72	96.38 205		96.41 93	97.48 88	88.26 187	97.80 251	89.96 172	98.93 125	98.12 150

MTGPA								97.62 111

test_post1							90.21 273				5.85 413	65.36 363	96.00 334	79.61 321

test_post											6.07 412	65.74 362	95.84 338

patchmatchnet-post											91.71 328	66.22 360	97.59 269

GG-mvs-BLEND				83.24 374	85.06 406	71.03 366	94.99 111	65.55 410		74.09 404	75.51 404	44.57 404	94.46 361	59.57 401	87.54 391	84.24 397

MTMP							94.82 114	54.62 413

gm-plane-assit					87.08 399	59.33 405			71.22 372		83.58 395		97.20 289	73.95 361

test9_res														88.16 212	98.40 180	97.83 182

TEST9					96.45 189	89.46 91	90.60 260	96.92 169	79.09 323	90.49 295	94.39 259	91.31 139	98.88 123

test_8					96.37 191	89.14 101	90.51 263	96.89 172	79.37 318	90.42 297	94.36 261	91.20 144	98.82 132

agg_prior2														87.06 234	98.36 189	97.98 164

agg_prior					96.20 210	88.89 106		96.88 173		90.21 302			98.78 144

TestCases				96.00 56	98.02 89	92.17 51		98.43 20	90.48 150	95.04 170	96.74 147	92.54 113	97.86 246	85.11 263	98.98 115	97.98 164

test_prior4						89.91 83	90.74 255

test_prior2							90.21 273		89.33 172	90.77 291	94.81 243	90.41 164		88.21 208	98.55 168

test_prior				94.61 117	95.95 231	87.23 137		97.36 135					98.68 164			97.93 170

旧先验2							90.00 281		68.65 387	92.71 248			96.52 317	85.15 260

新几何2							90.02 280

新几何1				93.17 178	97.16 146	87.29 135		94.43 270	67.95 389	91.29 281	94.94 239	86.97 213	98.23 211	81.06 305	97.75 234	93.98 346

旧先验1					96.20 210	84.17 204		94.82 261			95.57 216	89.57 177			97.89 229	96.32 266

无先验							89.94 282	95.75 228	70.81 377				98.59 176	81.17 304		94.81 325

原ACMM2							89.34 301

原ACMM1				92.87 189	96.91 157	84.22 202		97.01 161	76.84 339	89.64 315	94.46 257	88.00 193	98.70 160	81.53 299	98.01 221	95.70 297

test222					96.95 153	85.27 190	88.83 314	93.61 284	65.09 397	90.74 292	94.85 242	84.62 243			97.36 255	93.91 347

testdata2													98.03 226	80.24 311

segment_acmp												92.14 121

testdata				91.03 258	96.87 159	82.01 233		94.28 274	71.55 370	92.46 256	95.42 221	85.65 233	97.38 284	82.64 285	97.27 257	93.70 353

testdata1							88.96 310		88.44 192

test12				94.43 130	95.95 231	86.75 151		96.24 210		89.76 313		89.79 176	98.79 141		97.95 226	97.75 192

plane_prior7					97.71 114	88.68 109

plane_prior6					97.21 144	88.23 121						86.93 214

plane_prior5								97.81 97					98.95 116	89.26 189	98.51 174	98.60 116

plane_prior4											95.59 212

plane_prior3						88.43 119			90.35 155	93.31 222

plane_prior2							94.56 125		91.74 118

plane_prior1					97.38 134

plane_prior						88.12 122	93.01 176		88.98 179						98.06 215

n2								0.00 420

nn								0.00 420

door-mid								92.13 316

lessismore_v0				93.87 151	98.05 85	83.77 210		80.32 397		97.13 62	97.91 61	77.49 302	99.11 95	92.62 99	98.08 214	98.74 94

LGP-MVS_train				96.84 39	98.36 65	92.13 53		98.25 34	91.78 114	97.07 64	97.22 110	96.38 12	99.28 75	92.07 110	99.59 29	99.11 44

test11								96.65 189

door								91.26 325

HQP5-MVS						84.89 193

HQP-NCC					96.36 193		91.37 239		87.16 216	88.81 324

ACMP_Plane					96.36 193		91.37 239		87.16 216	88.81 324

BP-MVS														86.55 243

HQP4-MVS										88.81 324			98.61 172			98.15 147

HQP3-MVS								97.31 139							97.73 235

HQP2-MVS												84.76 241

NP-MVS					96.82 164	87.10 141					93.40 291

MDTV_nov1_ep13_2view						42.48 414	88.45 322		67.22 391	83.56 378		66.80 354		72.86 368		94.06 343

MDTV_nov1_ep13			83.88 332		89.42 384	61.52 401	88.74 317	87.41 353	73.99 356	84.96 367	94.01 273	65.25 364	95.53 341	78.02 331	93.16 360

ACMMP++_ref															98.82 139

ACMMP++															99.25 83

Test By Simon												90.61 160

ITE_SJBPF				95.95 59	97.34 137	93.36 41		96.55 198	91.93 103	94.82 179	95.39 225	91.99 123	97.08 297	85.53 256	97.96 225	97.41 214

DeepMVS_CX				53.83 390	70.38 413	64.56 396		48.52 414	33.01 408	65.50 408	74.21 405	56.19 391	46.64 411	38.45 409	70.07 405	50.30 406

High-resolution multi-view benchmark