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

LCM-MVSNet-Re		94.20 118	94.58 103	93.04 169	95.91 205	83.13 199	93.79 130	99.19 2	92.00 87	98.84 5	98.04 35	93.64 69	99.02 104	81.28 265	98.54 148	96.96 213

LTVRE_ROB	93.87 1	97.93 2	98.16 2	97.26 26	98.81 23	93.86 30	99.07 2	98.98 3	97.01 12	98.92 4	98.78 14	95.22 37	98.61 169	96.85 2	99.77 10	99.31 27
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
TDRefinement		97.68 3	97.60 4	97.93 2	99.02 11	95.95 5	98.61 3	98.81 4	97.41 9	97.28 46	98.46 25	94.62 56	98.84 131	94.64 17	99.53 35	98.99 53

ANet_high		94.83 93	96.28 36	90.47 253	96.65 150	73.16 319	94.33 114	98.74 5	96.39 22	98.09 24	98.93 8	93.37 76	98.70 160	90.38 128	99.68 18	99.53 14

ACMH+	88.43 11	96.48 30	96.82 16	95.47 81	98.54 41	89.06 95	95.65 65	98.61 6	96.10 25	98.16 22	97.52 59	96.90 7	98.62 168	90.30 133	99.60 25	98.72 90

SF-MVS		95.88 55	95.88 57	95.87 64	98.12 72	89.65 86	95.58 67	98.56 7	91.84 97	96.36 80	96.68 115	94.37 62	99.32 65	92.41 87	99.05 94	98.64 96

CS-MVS		92.54 167	92.31 164	93.23 166	95.89 207	84.07 187	93.58 136	98.48 8	88.60 173	90.41 262	86.23 344	92.00 108	99.35 55	87.54 194	98.06 200	96.26 239

HPM-MVS		96.81 12	96.62 23	97.36 24	98.89 18	93.53 37	97.51 7	98.44 9	92.35 78	95.95 104	96.41 130	96.71 8	99.42 28	93.99 31	99.36 56	99.13 39
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
AllTest		94.88 89	94.51 107	96.00 55	98.02 82	92.17 50	95.26 78	98.43 10	90.48 135	95.04 145	96.74 110	92.54 99	97.86 235	85.11 228	98.98 102	97.98 148

TestCases				96.00 55	98.02 82	92.17 50		98.43 10	90.48 135	95.04 145	96.74 110	92.54 99	97.86 235	85.11 228	98.98 102	97.98 148

xxxxxxxxxxxxxcwj		95.03 81	94.93 88	95.33 85	97.46 117	88.05 118	92.04 187	98.42 12	87.63 193	96.36 80	96.68 115	94.37 62	99.32 65	92.41 87	99.05 94	98.64 96

APDe-MVS		96.46 32	96.64 22	95.93 60	97.68 103	89.38 92	96.90 17	98.41 13	92.52 73	97.43 41	97.92 41	95.11 41	99.50 19	94.45 19	99.30 64	98.92 67

9.14			94.81 92		97.49 114		94.11 120	98.37 14	87.56 196	95.38 127	96.03 155	94.66 55	99.08 93	90.70 122	98.97 106

ETH3D-3000-0.1		94.86 90	94.55 104	95.81 65	97.61 107	89.72 84	94.05 122	98.37 14	88.09 182	95.06 144	95.85 161	92.58 97	99.10 92	90.33 132	98.99 101	98.62 100

abl_6		97.31 5	97.12 13	97.86 3	98.54 41	95.32 7	96.61 24	98.35 16	95.81 30	97.55 35	97.44 64	96.51 9	99.40 40	94.06 30	99.23 76	98.85 75

MP-MVS-pluss		96.08 49	95.92 56	96.57 45	99.06 9	91.21 64	93.25 143	98.32 17	87.89 186	96.86 62	97.38 67	95.55 24	99.39 45	95.47 10	99.47 39	99.11 41
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
FC-MVSNet-test		95.32 73	95.88 57	93.62 151	98.49 53	81.77 211	95.90 57	98.32 17	93.93 53	97.53 37	97.56 56	88.48 170	99.40 40	92.91 75	99.83 6	99.68 4

COLMAP_ROB	91.06 5	96.75 16	96.62 23	97.13 28	98.38 57	94.31 15	96.79 20	98.32 17	96.69 16	96.86 62	97.56 56	95.48 25	98.77 148	90.11 141	99.44 45	98.31 122
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
DPE-MVS		95.89 53	95.88 57	95.92 62	97.93 89	89.83 83	93.46 139	98.30 20	92.37 76	97.75 28	96.95 93	95.14 39	99.51 18	91.74 103	99.28 70	98.41 117
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
PGM-MVS		96.32 41	95.94 54	97.43 19	98.59 35	93.84 31	95.33 75	98.30 20	91.40 114	95.76 111	96.87 100	95.26 35	99.45 22	92.77 76	99.21 78	99.00 51

ACMH	88.36 12	96.59 27	97.43 5	94.07 135	98.56 36	85.33 171	96.33 39	98.30 20	94.66 39	98.72 8	98.30 30	97.51 5	98.00 223	94.87 14	99.59 27	98.86 72
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
nrg030		96.32 41	96.55 26	95.62 75	97.83 92	88.55 108	95.77 61	98.29 23	92.68 69	98.03 25	97.91 42	95.13 40	98.95 116	93.85 33	99.49 38	99.36 24

APD-MVS_3200maxsize		96.82 10	96.65 21	97.32 25	97.95 88	93.82 32	96.31 41	98.25 24	95.51 34	96.99 58	97.05 89	95.63 21	99.39 45	93.31 58	98.88 113	98.75 84

LPG-MVS_test		96.38 40	96.23 38	96.84 40	98.36 60	92.13 52	95.33 75	98.25 24	91.78 101	97.07 51	97.22 80	96.38 13	99.28 70	92.07 93	99.59 27	99.11 41

LGP-MVS_train				96.84 40	98.36 60	92.13 52		98.25 24	91.78 101	97.07 51	97.22 80	96.38 13	99.28 70	92.07 93	99.59 27	99.11 41

Anonymous20231211		96.60 25	97.13 12	95.00 99	97.46 117	86.35 155	97.11 14	98.24 27	97.58 7	98.72 8	98.97 7	93.15 83	99.15 83	93.18 64	99.74 13	99.50 16

canonicalmvs		94.59 101	94.69 98	94.30 130	95.60 224	87.03 136	95.59 66	98.24 27	91.56 111	95.21 138	92.04 288	94.95 49	98.66 165	91.45 112	97.57 227	97.20 206

test_0728_SECOND				94.88 102	98.55 39	86.72 143	95.20 81	98.22 29					99.38 51	93.44 51	99.31 62	98.53 107

Vis-MVSNet		95.50 66	95.48 70	95.56 79	98.11 73	89.40 91	95.35 73	98.22 29	92.36 77	94.11 170	98.07 33	92.02 107	99.44 23	93.38 56	97.67 223	97.85 164
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
UA-Net		97.35 4	97.24 11	97.69 5	98.22 67	93.87 29	98.42 4	98.19 31	96.95 13	95.46 125	99.23 4	93.45 72	99.57 13	95.34 12	99.89 2	99.63 9

test0726					98.51 45	86.69 144	95.34 74	98.18 32	91.85 94	97.63 31	97.37 68	95.58 22

MSP-MVS		95.34 72	94.63 102	97.48 14	98.67 27	94.05 21	96.41 35	98.18 32	91.26 117	95.12 139	95.15 195	86.60 206	99.50 19	93.43 53	96.81 250	98.89 69
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
ACMMP		96.61 24	96.34 34	97.43 19	98.61 32	93.88 28	96.95 16	98.18 32	92.26 81	96.33 82	96.84 104	95.10 42	99.40 40	93.47 48	99.33 60	99.02 50
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
EIA-MVS		92.35 171	92.03 169	93.30 164	95.81 211	83.97 188	92.80 154	98.17 35	87.71 190	89.79 277	87.56 334	91.17 135	99.18 81	87.97 187	97.27 235	96.77 220

HPM-MVS_fast		97.01 7	96.89 15	97.39 22	99.12 7	93.92 27	97.16 10	98.17 35	93.11 66	96.48 76	97.36 71	96.92 6	99.34 59	94.31 23	99.38 55	98.92 67

XVG-OURS		94.72 97	94.12 119	96.50 48	98.00 84	94.23 16	91.48 212	98.17 35	90.72 129	95.30 131	96.47 125	87.94 181	96.98 278	91.41 113	97.61 226	98.30 123

ZNCC-MVS		96.42 36	96.20 40	97.07 30	98.80 25	92.79 46	96.08 49	98.16 38	91.74 105	95.34 129	96.36 138	95.68 19	99.44 23	94.41 21	99.28 70	98.97 59

FIs		94.90 87	95.35 74	93.55 154	98.28 63	81.76 212	95.33 75	98.14 39	93.05 67	97.07 51	97.18 82	87.65 184	99.29 68	91.72 104	99.69 15	99.61 11

XVG-OURS-SEG-HR		95.38 70	95.00 87	96.51 47	98.10 74	94.07 18	92.46 166	98.13 40	90.69 130	93.75 183	96.25 146	98.03 2	97.02 277	92.08 92	95.55 275	98.45 114

SR-MVS-dyc-post		96.84 8	96.60 25	97.56 10	98.07 76	95.27 8	96.37 36	98.12 41	95.66 32	97.00 56	97.03 90	94.85 51	99.42 28	93.49 44	98.84 118	98.00 144

RE-MVS-def			96.66 20		98.07 76	95.27 8	96.37 36	98.12 41	95.66 32	97.00 56	97.03 90	95.40 27		93.49 44	98.84 118	98.00 144

RPMNet		90.31 216	90.14 215	90.81 246	91.01 318	78.93 258	92.52 162	98.12 41	91.91 91	89.10 284	96.89 99	68.84 307	99.41 35	90.17 139	92.70 323	94.08 297

SED-MVS		96.00 52	96.41 32	94.76 107	98.51 45	86.97 137	95.21 79	98.10 44	91.95 88	97.63 31	97.25 77	96.48 11	99.35 55	93.29 59	99.29 65	97.95 152

test_241102_TWO								98.10 44	91.95 88	97.54 36	97.25 77	95.37 28	99.35 55	93.29 59	99.25 73	98.49 110

test_241102_ONE					98.51 45	86.97 137		98.10 44	91.85 94	97.63 31	97.03 90	96.48 11	98.95 116

test_part1		94.39 107	94.55 104	93.92 142	96.14 187	82.86 202	95.54 69	98.09 47	95.36 35	98.27 20	98.36 28	75.91 288	99.44 23	93.41 54	99.84 3	99.47 17

WR-MVS_H		96.60 25	97.05 14	95.24 91	99.02 11	86.44 151	96.78 21	98.08 48	97.42 8	98.48 16	97.86 45	91.76 115	99.63 6	94.23 26	99.84 3	99.66 6

CP-MVS		96.44 35	96.08 48	97.54 11	98.29 62	94.62 13	96.80 19	98.08 48	92.67 71	95.08 143	96.39 135	94.77 53	99.42 28	93.17 65	99.44 45	98.58 105

ACMP	88.15 13	95.71 60	95.43 73	96.54 46	98.17 70	91.73 60	94.24 116	98.08 48	89.46 154	96.61 73	96.47 125	95.85 17	99.12 89	90.45 125	99.56 33	98.77 83
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
SR-MVS		96.70 19	96.42 29	97.54 11	98.05 78	94.69 11	96.13 47	98.07 51	95.17 36	96.82 64	96.73 112	95.09 43	99.43 27	92.99 73	98.71 135	98.50 109

v7n		96.82 10	97.31 10	95.33 85	98.54 41	86.81 141	96.83 18	98.07 51	96.59 19	98.46 17	98.43 27	92.91 89	99.52 17	96.25 6	99.76 11	99.65 8

UniMVSNet (Re)		95.32 73	95.15 83	95.80 67	97.79 93	88.91 98	92.91 151	98.07 51	93.46 62	96.31 84	95.97 158	90.14 153	99.34 59	92.11 90	99.64 23	99.16 36

test1172		96.79 15	96.52 27	97.60 9	98.03 81	94.87 10	96.07 50	98.06 54	95.76 31	96.89 60	96.85 101	94.85 51	99.42 28	93.35 57	98.81 126	98.53 107

SD-MVS		95.19 79	95.73 65	93.55 154	96.62 153	88.88 101	94.67 100	98.05 55	91.26 117	97.25 48	96.40 131	95.42 26	94.36 332	92.72 80	99.19 80	97.40 196
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
casdiffmvs		94.32 112	94.80 93	92.85 179	96.05 194	81.44 217	92.35 174	98.05 55	91.53 112	95.75 112	96.80 105	93.35 77	98.49 183	91.01 118	98.32 171	98.64 96

PEN-MVS		96.69 20	97.39 8	94.61 112	99.16 3	84.50 177	96.54 27	98.05 55	98.06 4	98.64 13	98.25 31	95.01 47	99.65 3	92.95 74	99.83 6	99.68 4

XVG-ACMP-BASELINE		95.68 61	95.34 75	96.69 43	98.40 55	93.04 41	94.54 110	98.05 55	90.45 137	96.31 84	96.76 108	92.91 89	98.72 154	91.19 115	99.42 47	98.32 120

baseline		94.26 115	94.80 93	92.64 185	96.08 192	80.99 223	93.69 133	98.04 59	90.80 128	94.89 151	96.32 140	93.19 81	98.48 187	91.68 106	98.51 152	98.43 115

ACMMP_NAP		96.21 45	96.12 46	96.49 49	98.90 17	91.42 62	94.57 106	98.03 60	90.42 138	96.37 79	97.35 72	95.68 19	99.25 74	94.44 20	99.34 58	98.80 79

ACMM	88.83 9	96.30 43	96.07 49	96.97 35	98.39 56	92.95 44	94.74 98	98.03 60	90.82 127	97.15 49	96.85 101	96.25 15	99.00 108	93.10 68	99.33 60	98.95 61
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ETH3D cwj APD-0.16		93.99 123	93.38 140	95.80 67	96.82 144	89.92 80	92.72 155	98.02 62	84.73 241	93.65 187	95.54 182	91.68 117	99.22 77	88.78 172	98.49 155	98.26 126

DeepC-MVS	91.39 4	95.43 68	95.33 76	95.71 73	97.67 104	90.17 77	93.86 129	98.02 62	87.35 197	96.22 92	97.99 38	94.48 60	99.05 98	92.73 79	99.68 18	97.93 154
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
GST-MVS		96.24 44	95.99 53	97.00 34	98.65 28	92.71 47	95.69 64	98.01 64	92.08 86	95.74 113	96.28 143	95.22 37	99.42 28	93.17 65	99.06 91	98.88 71

OurMVSNet-221017-0		96.80 13	96.75 18	96.96 36	99.03 10	91.85 57	97.98 5	98.01 64	94.15 48	98.93 3	99.07 5	88.07 177	99.57 13	95.86 9	99.69 15	99.46 18

SteuartSystems-ACMMP		96.40 38	96.30 35	96.71 42	98.63 29	91.96 55	95.70 62	98.01 64	93.34 64	96.64 71	96.57 122	94.99 48	99.36 54	93.48 47	99.34 58	98.82 77
Skip Steuart: Steuart Systems R&D Blog.
HFP-MVS		96.39 39	96.17 43	97.04 31	98.51 45	93.37 38	96.30 43	97.98 67	92.35 78	95.63 117	96.47 125	95.37 28	99.27 72	93.78 35	99.14 85	98.48 111

#test#		95.89 53	95.51 69	97.04 31	98.51 45	93.37 38	95.14 84	97.98 67	89.34 157	95.63 117	96.47 125	95.37 28	99.27 72	91.99 95	99.14 85	98.48 111

LS3D		96.11 48	95.83 61	96.95 37	94.75 247	94.20 17	97.34 9	97.98 67	97.31 10	95.32 130	96.77 106	93.08 85	99.20 79	91.79 101	98.16 190	97.44 192

PS-CasMVS		96.69 20	97.43 5	94.49 123	99.13 5	84.09 186	96.61 24	97.97 70	97.91 5	98.64 13	98.13 32	95.24 36	99.65 3	93.39 55	99.84 3	99.72 2

region2R		96.41 37	96.09 47	97.38 23	98.62 30	93.81 34	96.32 40	97.96 71	92.26 81	95.28 133	96.57 122	95.02 46	99.41 35	93.63 39	99.11 89	98.94 62

ACMMPR		96.46 32	96.14 44	97.41 21	98.60 33	93.82 32	96.30 43	97.96 71	92.35 78	95.57 120	96.61 120	94.93 50	99.41 35	93.78 35	99.15 84	99.00 51

XVS		96.49 29	96.18 41	97.44 17	98.56 36	93.99 25	96.50 29	97.95 73	94.58 40	94.38 166	96.49 124	94.56 57	99.39 45	93.57 40	99.05 94	98.93 63

X-MVStestdata		90.70 202	88.45 242	97.44 17	98.56 36	93.99 25	96.50 29	97.95 73	94.58 40	94.38 166	26.89 360	94.56 57	99.39 45	93.57 40	99.05 94	98.93 63

Gipuma		95.31 75	95.80 63	93.81 148	97.99 87	90.91 69	96.42 34	97.95 73	96.69 16	91.78 241	98.85 12	91.77 114	95.49 315	91.72 104	99.08 90	95.02 279
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
DTE-MVSNet		96.74 17	97.43 5	94.67 110	99.13 5	84.68 176	96.51 28	97.94 76	98.14 3	98.67 12	98.32 29	95.04 44	99.69 2	93.27 61	99.82 8	99.62 10

PS-MVSNAJss		96.01 51	96.04 51	95.89 63	98.82 22	88.51 110	95.57 68	97.88 77	88.72 169	98.81 6	98.86 10	90.77 139	99.60 8	95.43 11	99.53 35	99.57 13

pmmvs6		96.80 13	97.36 9	95.15 95	99.12 7	87.82 124	96.68 22	97.86 78	96.10 25	98.14 23	99.28 3	97.94 3	98.21 206	91.38 114	99.69 15	99.42 19

TranMVSNet+NR-MVSNet		96.07 50	96.26 37	95.50 80	98.26 65	87.69 125	93.75 131	97.86 78	95.96 29	97.48 39	97.14 84	95.33 32	99.44 23	90.79 120	99.76 11	99.38 22

PHI-MVS		94.34 111	93.80 124	95.95 57	95.65 220	91.67 61	94.82 95	97.86 78	87.86 187	93.04 208	94.16 234	91.58 119	98.78 144	90.27 135	98.96 108	97.41 193

testtj		94.81 94	94.42 108	96.01 54	97.23 125	90.51 75	94.77 97	97.85 81	91.29 116	94.92 150	95.66 173	91.71 116	99.40 40	88.07 185	98.25 180	98.11 137

ETV-MVS		92.99 149	92.74 154	93.72 149	95.86 208	86.30 156	92.33 175	97.84 82	91.70 108	92.81 213	86.17 345	92.22 103	99.19 80	88.03 186	97.73 217	95.66 265

UniMVSNet_NR-MVSNet		95.35 71	95.21 81	95.76 70	97.69 102	88.59 106	92.26 179	97.84 82	94.91 37	96.80 65	95.78 169	90.42 148	99.41 35	91.60 108	99.58 31	99.29 28

3Dnovator+	92.74 2	95.86 56	95.77 64	96.13 52	96.81 146	90.79 72	96.30 43	97.82 84	96.13 24	94.74 157	97.23 79	91.33 125	99.16 82	93.25 62	98.30 174	98.46 113

HQP_MVS		94.26 115	93.93 121	95.23 92	97.71 99	88.12 116	94.56 107	97.81 85	91.74 105	93.31 194	95.59 175	86.93 198	98.95 116	89.26 161	98.51 152	98.60 103

plane_prior5								97.81 85					98.95 116	89.26 161	98.51 152	98.60 103

DU-MVS		95.28 76	95.12 85	95.75 71	97.75 95	88.59 106	92.58 160	97.81 85	93.99 50	96.80 65	95.90 159	90.10 157	99.41 35	91.60 108	99.58 31	99.26 29

APD-MVS		95.00 83	94.69 98	95.93 60	97.38 120	90.88 70	94.59 103	97.81 85	89.22 161	95.46 125	96.17 151	93.42 75	99.34 59	89.30 157	98.87 116	97.56 186
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
SMA-MVS		95.77 58	95.54 68	96.47 50	98.27 64	91.19 65	95.09 85	97.79 89	86.48 208	97.42 43	97.51 61	94.47 61	99.29 68	93.55 42	99.29 65	98.93 63
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
MP-MVS		96.14 47	95.68 66	97.51 13	98.81 23	94.06 19	96.10 48	97.78 90	92.73 68	93.48 190	96.72 113	94.23 64	99.42 28	91.99 95	99.29 65	99.05 48
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MSLP-MVS++		93.25 141	93.88 122	91.37 224	96.34 171	82.81 203	93.11 145	97.74 91	89.37 156	94.08 172	95.29 193	90.40 151	96.35 300	90.35 130	98.25 180	94.96 280

mPP-MVS		96.46 32	96.05 50	97.69 5	98.62 30	94.65 12	96.45 31	97.74 91	92.59 72	95.47 123	96.68 115	94.50 59	99.42 28	93.10 68	99.26 72	98.99 53

ETH3 D test6400		91.91 179	91.25 191	93.89 144	96.59 154	84.41 178	92.10 184	97.72 93	78.52 293	91.82 240	93.78 249	88.70 168	99.13 87	83.61 242	98.39 160	98.14 133

TAPA-MVS	88.58 10	92.49 168	91.75 179	94.73 108	96.50 160	89.69 85	92.91 151	97.68 94	78.02 297	92.79 214	94.10 235	90.85 138	97.96 227	84.76 234	98.16 190	96.54 224
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CPTT-MVS		94.74 96	94.12 119	96.60 44	98.15 71	93.01 42	95.84 59	97.66 95	89.21 162	93.28 197	95.46 184	88.89 167	98.98 109	89.80 148	98.82 124	97.80 169

DP-MVS		95.62 62	95.84 60	94.97 100	97.16 129	88.62 105	94.54 110	97.64 96	96.94 14	96.58 74	97.32 75	93.07 86	98.72 154	90.45 125	98.84 118	97.57 184

zzz-MVS		96.47 31	96.14 44	97.47 15	98.95 15	94.05 21	93.69 133	97.62 97	94.46 44	96.29 86	96.94 94	93.56 70	99.37 52	94.29 24	99.42 47	98.99 53

MTGPA								97.62 97

MTAPA		96.65 22	96.38 33	97.47 15	98.95 15	94.05 21	95.88 58	97.62 97	94.46 44	96.29 86	96.94 94	93.56 70	99.37 52	94.29 24	99.42 47	98.99 53

anonymousdsp		96.74 17	96.42 29	97.68 7	98.00 84	94.03 24	96.97 15	97.61 100	87.68 192	98.45 18	98.77 15	94.20 65	99.50 19	96.70 3	99.40 53	99.53 14

mvs_tets		96.83 9	96.71 19	97.17 27	98.83 21	92.51 48	96.58 26	97.61 100	87.57 195	98.80 7	98.90 9	96.50 10	99.59 12	96.15 7	99.47 39	99.40 21

VPA-MVSNet		95.14 80	95.67 67	93.58 153	97.76 94	83.15 198	94.58 105	97.58 102	93.39 63	97.05 54	98.04 35	93.25 79	98.51 182	89.75 151	99.59 27	99.08 45

v10		94.68 99	95.27 80	92.90 177	96.57 156	80.15 230	94.65 102	97.57 103	90.68 131	97.43 41	98.00 37	88.18 174	99.15 83	94.84 15	99.55 34	99.41 20

CSCG		94.69 98	94.75 95	94.52 120	97.55 111	87.87 122	95.01 90	97.57 103	92.68 69	96.20 94	93.44 256	91.92 112	98.78 144	89.11 165	99.24 75	96.92 214

ZD-MVS					97.23 125	90.32 76		97.54 105	84.40 243	94.78 155	95.79 166	92.76 94	99.39 45	88.72 175	98.40 158

UniMVSNet_ETH3D		97.13 6	97.72 3	95.35 83	99.51 2	87.38 128	97.70 6	97.54 105	98.16 2	98.94 2	99.33 2	97.84 4	99.08 93	90.73 121	99.73 14	99.59 12

Effi-MVS+		92.79 156	92.74 154	92.94 175	95.10 237	83.30 195	94.00 124	97.53 107	91.36 115	89.35 283	90.65 310	94.01 67	98.66 165	87.40 198	95.30 283	96.88 217

CP-MVSNet		96.19 46	96.80 17	94.38 129	98.99 13	83.82 190	96.31 41	97.53 107	97.60 6	98.34 19	97.52 59	91.98 111	99.63 6	93.08 70	99.81 9	99.70 3

RPSCF		95.58 64	94.89 90	97.62 8	97.58 109	96.30 4	95.97 54	97.53 107	92.42 74	93.41 191	97.78 46	91.21 131	97.77 244	91.06 116	97.06 240	98.80 79

diffmvs		91.74 181	91.93 173	91.15 234	93.06 285	78.17 269	88.77 283	97.51 110	86.28 212	92.42 224	93.96 242	88.04 178	97.46 260	90.69 123	96.67 255	97.82 167

PVSNet_Blended_VisFu		91.63 184	91.20 192	92.94 175	97.73 98	83.95 189	92.14 183	97.46 111	78.85 292	92.35 228	94.98 205	84.16 224	99.08 93	86.36 214	96.77 252	95.79 259

DeepPCF-MVS	90.46 6	94.20 118	93.56 135	96.14 51	95.96 201	92.96 43	89.48 266	97.46 111	85.14 231	96.23 91	95.42 187	93.19 81	98.08 216	90.37 129	98.76 132	97.38 199

jajsoiax		96.59 27	96.42 29	97.12 29	98.76 26	92.49 49	96.44 33	97.42 113	86.96 204	98.71 10	98.72 17	95.36 31	99.56 16	95.92 8	99.45 43	99.32 26

OMC-MVS		94.22 117	93.69 129	95.81 65	97.25 124	91.27 63	92.27 178	97.40 114	87.10 203	94.56 161	95.42 187	93.74 68	98.11 215	86.62 208	98.85 117	98.06 138

v1240		93.29 136	93.71 128	92.06 207	96.01 199	77.89 273	91.81 205	97.37 115	85.12 233	96.69 69	96.40 131	86.67 204	99.07 97	94.51 18	98.76 132	99.22 32

NR-MVSNet		95.28 76	95.28 79	95.26 90	97.75 95	87.21 132	95.08 86	97.37 115	93.92 54	97.65 30	95.90 159	90.10 157	99.33 64	90.11 141	99.66 21	99.26 29

MVSFormer		92.18 175	92.23 165	92.04 208	94.74 249	80.06 234	97.15 11	97.37 115	88.98 163	88.83 287	92.79 270	77.02 281	99.60 8	96.41 4	96.75 253	96.46 231

test_djsdf		96.62 23	96.49 28	97.01 33	98.55 39	91.77 59	97.15 11	97.37 115	88.98 163	98.26 21	98.86 10	93.35 77	99.60 8	96.41 4	99.45 43	99.66 6

DP-MVS Recon		92.31 172	91.88 174	93.60 152	97.18 128	86.87 140	91.10 221	97.37 115	84.92 238	92.08 236	94.08 236	88.59 169	98.20 207	83.50 243	98.14 192	95.73 261

test_prior3		93.29 136	92.85 150	94.61 112	95.95 202	87.23 130	90.21 244	97.36 120	89.33 158	90.77 254	94.81 212	90.41 149	98.68 163	88.21 179	98.55 145	97.93 154

test_prior				94.61 112	95.95 202	87.23 130		97.36 120					98.68 163			97.93 154

QAPM		92.88 153	92.77 152	93.22 167	95.82 209	83.31 194	96.45 31	97.35 122	83.91 246	93.75 183	96.77 106	89.25 165	98.88 123	84.56 236	97.02 242	97.49 189

OPM-MVS		95.61 63	95.45 71	96.08 53	98.49 53	91.00 67	92.65 159	97.33 123	90.05 143	96.77 67	96.85 101	95.04 44	98.56 177	92.77 76	99.06 91	98.70 91

HQP3-MVS								97.31 124							97.73 217

HQP-MVS		92.09 176	91.49 185	93.88 145	96.36 167	84.89 174	91.37 213	97.31 124	87.16 200	88.81 289	93.40 257	84.76 220	98.60 171	86.55 210	97.73 217	98.14 133

PCF-MVS	84.52 17	89.12 239	87.71 258	93.34 161	96.06 193	85.84 165	86.58 320	97.31 124	68.46 341	93.61 188	93.89 245	87.51 187	98.52 181	67.85 345	98.11 196	95.66 265
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
114514_t		90.51 206	89.80 220	92.63 187	98.00 84	82.24 207	93.40 141	97.29 127	65.84 348	89.40 282	94.80 215	86.99 196	98.75 149	83.88 241	98.61 142	96.89 216

CLD-MVS		91.82 180	91.41 187	93.04 169	96.37 165	83.65 192	86.82 312	97.29 127	84.65 242	92.27 232	89.67 320	92.20 104	97.85 237	83.95 240	99.47 39	97.62 182
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
3Dnovator	92.54 3	94.80 95	94.90 89	94.47 124	95.47 227	87.06 134	96.63 23	97.28 129	91.82 100	94.34 168	97.41 65	90.60 146	98.65 167	92.47 85	98.11 196	97.70 176

DELS-MVS		92.05 177	92.16 166	91.72 215	94.44 259	80.13 232	87.62 293	97.25 130	87.34 198	92.22 233	93.18 263	89.54 163	98.73 153	89.67 152	98.20 188	96.30 237
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
v1921920		93.26 139	93.61 132	92.19 200	96.04 198	78.31 267	91.88 198	97.24 131	85.17 230	96.19 96	96.19 148	86.76 203	99.05 98	94.18 28	98.84 118	99.22 32

test_0402		95.73 59	96.22 39	94.26 131	98.19 69	85.77 166	93.24 144	97.24 131	96.88 15	97.69 29	97.77 48	94.12 66	99.13 87	91.54 111	99.29 65	97.88 160

v1192		93.49 131	93.78 125	92.62 188	96.16 186	79.62 246	91.83 204	97.22 133	86.07 216	96.10 100	96.38 136	87.22 191	99.02 104	94.14 29	98.88 113	99.22 32

F-COLMAP		92.28 173	91.06 195	95.95 57	97.52 112	91.90 56	93.53 137	97.18 134	83.98 245	88.70 295	94.04 237	88.41 172	98.55 179	80.17 276	95.99 266	97.39 197

v8		94.65 100	95.29 78	92.74 182	96.65 150	79.77 244	94.59 103	97.17 135	91.86 93	97.47 40	97.93 40	88.16 175	99.08 93	94.32 22	99.47 39	99.38 22

v144192		93.20 144	93.54 136	92.16 204	96.05 194	78.26 268	91.95 191	97.14 136	84.98 237	95.96 103	96.11 152	87.08 195	99.04 101	93.79 34	98.84 118	99.17 35

DeepC-MVS_fast	89.96 7	93.73 127	93.44 138	94.60 116	96.14 187	87.90 121	93.36 142	97.14 136	85.53 225	93.90 180	95.45 185	91.30 127	98.59 173	89.51 154	98.62 141	97.31 202
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MCST-MVS		92.91 151	92.51 160	94.10 134	97.52 112	85.72 167	91.36 216	97.13 138	80.33 274	92.91 212	94.24 230	91.23 130	98.72 154	89.99 145	97.93 210	97.86 162

DIV-MVS_2432*1600		94.10 120	94.73 97	92.19 200	97.66 105	79.49 249	94.86 94	97.12 139	89.59 153	96.87 61	97.65 52	90.40 151	98.34 196	89.08 166	99.35 57	98.75 84

pm-mvs1		95.43 68	95.94 54	93.93 141	98.38 57	85.08 173	95.46 72	97.12 139	91.84 97	97.28 46	98.46 25	95.30 34	97.71 249	90.17 139	99.42 47	98.99 53

save fliter					97.46 117	88.05 118	92.04 187	97.08 141	87.63 193

CDPH-MVS		92.67 161	91.83 175	95.18 94	96.94 138	88.46 111	90.70 230	97.07 142	77.38 299	92.34 230	95.08 200	92.67 96	98.88 123	85.74 219	98.57 144	98.20 130

OpenMVS	89.45 8	92.27 174	92.13 168	92.68 184	94.53 258	84.10 185	95.70 62	97.03 143	82.44 262	91.14 251	96.42 129	88.47 171	98.38 192	85.95 218	97.47 230	95.55 269

原ACMM1				92.87 178	96.91 140	84.22 182		97.01 144	76.84 304	89.64 280	94.46 223	88.00 179	98.70 160	81.53 263	98.01 206	95.70 263

DVP-MVS		95.82 57	96.18 41	94.72 109	98.51 45	86.69 144	95.20 81	97.00 145	91.85 94	97.40 44	97.35 72	95.58 22	99.34 59	93.44 51	99.31 62	98.13 135
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
CANet		92.38 170	91.99 171	93.52 158	93.82 275	83.46 193	91.14 219	97.00 145	89.81 148	86.47 316	94.04 237	87.90 182	99.21 78	89.50 155	98.27 176	97.90 158

HPM-MVS++		95.02 82	94.39 109	96.91 38	97.88 90	93.58 36	94.09 121	96.99 147	91.05 122	92.40 225	95.22 194	91.03 137	99.25 74	92.11 90	98.69 138	97.90 158

v1144		93.50 130	93.81 123	92.57 190	96.28 176	79.61 247	91.86 203	96.96 148	86.95 205	95.91 107	96.32 140	87.65 184	98.96 114	93.51 43	98.88 113	99.13 39

MVS_Test		92.57 166	93.29 141	90.40 256	93.53 277	75.85 299	92.52 162	96.96 148	88.73 168	92.35 228	96.70 114	90.77 139	98.37 195	92.53 84	95.49 277	96.99 212

PVSNet_BlendedMVS		90.35 213	89.96 217	91.54 221	94.81 244	78.80 263	90.14 248	96.93 150	79.43 282	88.68 296	95.06 201	86.27 209	98.15 213	80.27 273	98.04 203	97.68 178

PVSNet_Blended		88.74 249	88.16 253	90.46 255	94.81 244	78.80 263	86.64 316	96.93 150	74.67 311	88.68 296	89.18 326	86.27 209	98.15 213	80.27 273	96.00 265	94.44 292

TEST9					96.45 163	89.46 87	90.60 232	96.92 152	79.09 288	90.49 259	94.39 226	91.31 126	98.88 123

train_agg		92.71 160	91.83 175	95.35 83	96.45 163	89.46 87	90.60 232	96.92 152	79.37 283	90.49 259	94.39 226	91.20 132	98.88 123	88.66 176	98.43 157	97.72 175

NCCC		94.08 121	93.54 136	95.70 74	96.49 161	89.90 82	92.39 171	96.91 154	90.64 132	92.33 231	94.60 220	90.58 147	98.96 114	90.21 138	97.70 221	98.23 127

test_8					96.37 165	89.14 94	90.51 235	96.89 155	79.37 283	90.42 261	94.36 228	91.20 132	98.82 133

agg_prior1		92.60 163	91.76 178	95.10 97	96.20 182	88.89 99	90.37 239	96.88 156	79.67 280	90.21 265	94.41 224	91.30 127	98.78 144	88.46 178	98.37 167	97.64 181

agg_prior					96.20 182	88.89 99		96.88 156		90.21 265			98.78 144

MIMVSNet1		95.52 65	95.45 71	95.72 72	99.14 4	89.02 96	96.23 46	96.87 158	93.73 56	97.87 26	98.49 24	90.73 143	99.05 98	86.43 213	99.60 25	99.10 44

IU-MVS					98.51 45	86.66 146		96.83 159	72.74 323	95.83 109				93.00 72	99.29 65	98.64 96

TSAR-MVS + MP.		94.96 85	94.75 95	95.57 78	98.86 20	88.69 102	96.37 36	96.81 160	85.23 228	94.75 156	97.12 85	91.85 113	99.40 40	93.45 49	98.33 169	98.62 100
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
CNVR-MVS		94.58 102	94.29 113	95.46 82	96.94 138	89.35 93	91.81 205	96.80 161	89.66 150	93.90 180	95.44 186	92.80 93	98.72 154	92.74 78	98.52 150	98.32 120

cascas		87.02 283	86.28 284	89.25 279	91.56 313	76.45 293	84.33 335	96.78 162	71.01 331	86.89 315	85.91 346	81.35 249	96.94 279	83.09 247	95.60 274	94.35 294

IterMVS-LS		93.78 126	94.28 114	92.27 197	96.27 177	79.21 256	91.87 199	96.78 162	91.77 103	96.57 75	97.07 87	87.15 193	98.74 152	91.99 95	99.03 100	98.86 72
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Anonymous20240529		95.50 66	95.83 61	94.50 121	97.33 123	85.93 163	95.19 83	96.77 164	96.64 18	97.61 34	98.05 34	93.23 80	98.79 140	88.60 177	99.04 99	98.78 81

TransMVSNet (Re)		95.27 78	96.04 51	92.97 172	98.37 59	81.92 210	95.07 87	96.76 165	93.97 52	97.77 27	98.57 19	95.72 18	97.90 229	88.89 170	99.23 76	99.08 45

EG-PatchMatch MVS		94.54 104	94.67 100	94.14 133	97.87 91	86.50 147	92.00 190	96.74 166	88.16 181	96.93 59	97.61 54	93.04 87	97.90 229	91.60 108	98.12 195	98.03 142

1112_ss		88.42 253	87.41 262	91.45 222	96.69 149	80.99 223	89.72 261	96.72 167	73.37 319	87.00 314	90.69 308	77.38 277	98.20 207	81.38 264	93.72 310	95.15 275

Baseline_NR-MVSNet		94.47 106	95.09 86	92.60 189	98.50 52	80.82 226	92.08 185	96.68 168	93.82 55	96.29 86	98.56 20	90.10 157	97.75 247	90.10 143	99.66 21	99.24 31

eth_miper_zixun_eth		90.72 201	90.61 205	91.05 235	92.04 304	76.84 289	86.91 308	96.67 169	85.21 229	94.41 164	93.92 243	79.53 261	98.26 203	89.76 150	97.02 242	98.06 138

Fast-Effi-MVS+-dtu		92.77 158	92.16 166	94.58 119	94.66 255	88.25 113	92.05 186	96.65 170	89.62 151	90.08 268	91.23 298	92.56 98	98.60 171	86.30 215	96.27 262	96.90 215

test11								96.65 170

RRT_test8_iter05		88.21 256	88.17 251	88.33 294	91.62 311	66.82 347	91.73 208	96.60 172	86.34 211	94.14 169	95.38 192	47.72 361	99.11 90	91.78 102	98.26 177	99.06 47

LF4IMVS		92.72 159	92.02 170	94.84 104	95.65 220	91.99 54	92.92 150	96.60 172	85.08 235	92.44 223	93.62 251	86.80 202	96.35 300	86.81 203	98.25 180	96.18 243

GBi-Net		93.21 142	92.96 147	93.97 138	95.40 229	84.29 179	95.99 51	96.56 174	88.63 170	95.10 140	98.53 21	81.31 250	98.98 109	86.74 204	98.38 162	98.65 92

test1		93.21 142	92.96 147	93.97 138	95.40 229	84.29 179	95.99 51	96.56 174	88.63 170	95.10 140	98.53 21	81.31 250	98.98 109	86.74 204	98.38 162	98.65 92

FMVSNet1		94.84 92	95.13 84	93.97 138	97.60 108	84.29 179	95.99 51	96.56 174	92.38 75	97.03 55	98.53 21	90.12 154	98.98 109	88.78 172	99.16 83	98.65 92

ITE_SJBPF				95.95 57	97.34 122	93.36 40		96.55 177	91.93 90	94.82 153	95.39 190	91.99 110	97.08 275	85.53 221	97.96 208	97.41 193

Fast-Effi-MVS+		91.28 194	90.86 198	92.53 192	95.45 228	82.53 205	89.25 275	96.52 178	85.00 236	89.91 272	88.55 330	92.94 88	98.84 131	84.72 235	95.44 279	96.22 241

V42		93.43 133	93.58 133	92.97 172	95.34 233	81.22 220	92.67 158	96.49 179	87.25 199	96.20 94	96.37 137	87.32 190	98.85 130	92.39 89	98.21 186	98.85 75

PLC	85.34 15	90.40 210	88.92 234	94.85 103	96.53 159	90.02 78	91.58 210	96.48 180	80.16 275	86.14 318	92.18 284	85.73 214	98.25 204	76.87 305	94.61 298	96.30 237
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
cl_fuxian		91.32 193	91.42 186	91.00 239	92.29 297	76.79 290	87.52 299	96.42 181	85.76 222	94.72 159	93.89 245	82.73 235	98.16 212	90.93 119	98.55 145	98.04 141

Regformer-2		94.86 90	94.55 104	95.77 69	92.83 290	89.98 79	91.87 199	96.40 182	94.38 46	96.19 96	95.04 202	92.47 102	99.04 101	93.49 44	98.31 172	98.28 124

USDC		89.02 240	89.08 230	88.84 284	95.07 238	74.50 310	88.97 278	96.39 183	73.21 320	93.27 198	96.28 143	82.16 242	96.39 297	77.55 299	98.80 128	95.62 268

ambc				92.98 171	96.88 141	83.01 201	95.92 56	96.38 184		96.41 77	97.48 62	88.26 173	97.80 240	89.96 146	98.93 110	98.12 136

PAPM_NR		91.03 196	90.81 200	91.68 217	96.73 148	81.10 222	93.72 132	96.35 185	88.19 180	88.77 293	92.12 287	85.09 219	97.25 270	82.40 255	93.90 307	96.68 223

v2v482		93.29 136	93.63 131	92.29 196	96.35 170	78.82 261	91.77 207	96.28 186	88.45 175	95.70 116	96.26 145	86.02 212	98.90 120	93.02 71	98.81 126	99.14 38

AdaColmap		91.63 184	91.36 188	92.47 195	95.56 225	86.36 154	92.24 181	96.27 187	88.88 167	89.90 273	92.69 273	91.65 118	98.32 197	77.38 302	97.64 224	92.72 326

Test_1112_low_res		87.50 271	86.58 277	90.25 260	96.80 147	77.75 275	87.53 298	96.25 188	69.73 337	86.47 316	93.61 252	75.67 289	97.88 231	79.95 278	93.20 315	95.11 277

test12				94.43 127	95.95 202	86.75 142		96.24 189		89.76 278		89.79 161	98.79 140		97.95 209	97.75 174

PAPR		87.65 267	86.77 275	90.27 259	92.85 289	77.38 280	88.56 288	96.23 190	76.82 305	84.98 324	89.75 319	86.08 211	97.16 273	72.33 329	93.35 313	96.26 239

MVS_111021_HR		93.63 129	93.42 139	94.26 131	96.65 150	86.96 139	89.30 272	96.23 190	88.36 178	93.57 189	94.60 220	93.45 72	97.77 244	90.23 137	98.38 162	98.03 142

XXY-MVS		92.58 164	93.16 146	90.84 245	97.75 95	79.84 240	91.87 199	96.22 192	85.94 218	95.53 122	97.68 50	92.69 95	94.48 328	83.21 246	97.51 228	98.21 129

MSDG		90.82 198	90.67 204	91.26 228	94.16 264	83.08 200	86.63 317	96.19 193	90.60 134	91.94 238	91.89 289	89.16 166	95.75 310	80.96 271	94.51 299	94.95 281

miper_ehance_all_eth		90.48 207	90.42 209	90.69 248	91.62 311	76.57 292	86.83 311	96.18 194	83.38 248	94.06 174	92.66 275	82.20 241	98.04 218	89.79 149	97.02 242	97.45 191

TinyColmap		92.00 178	92.76 153	89.71 270	95.62 223	77.02 284	90.72 229	96.17 195	87.70 191	95.26 134	96.29 142	92.54 99	96.45 295	81.77 260	98.77 131	95.66 265

DPM-MVS		89.35 235	88.40 243	92.18 203	96.13 190	84.20 183	86.96 307	96.15 196	75.40 310	87.36 311	91.55 296	83.30 228	98.01 222	82.17 258	96.62 256	94.32 295

HyFIR lowres test		87.19 279	85.51 289	92.24 198	97.12 133	80.51 227	85.03 327	96.06 197	66.11 347	91.66 242	92.98 266	70.12 305	99.14 85	75.29 314	95.23 285	97.07 207

xiu_mvs_v1_base_debu		91.47 188	91.52 182	91.33 225	95.69 217	81.56 214	89.92 255	96.05 198	83.22 250	91.26 247	90.74 305	91.55 120	98.82 133	89.29 158	95.91 267	93.62 312

xiu_mvs_v1_base		91.47 188	91.52 182	91.33 225	95.69 217	81.56 214	89.92 255	96.05 198	83.22 250	91.26 247	90.74 305	91.55 120	98.82 133	89.29 158	95.91 267	93.62 312

xiu_mvs_v1_base_debi		91.47 188	91.52 182	91.33 225	95.69 217	81.56 214	89.92 255	96.05 198	83.22 250	91.26 247	90.74 305	91.55 120	98.82 133	89.29 158	95.91 267	93.62 312

Regformer-4		94.90 87	94.67 100	95.59 76	92.78 292	89.02 96	92.39 171	95.91 201	94.50 42	96.41 77	95.56 180	92.10 106	99.01 106	94.23 26	98.14 192	98.74 87

UnsupCasMVSNet_eth		90.33 214	90.34 210	90.28 258	94.64 256	80.24 228	89.69 262	95.88 202	85.77 221	93.94 179	95.69 171	81.99 244	92.98 343	84.21 239	91.30 334	97.62 182

CANet_DTU		89.85 229	89.17 228	91.87 210	92.20 300	80.02 237	90.79 227	95.87 203	86.02 217	82.53 340	91.77 291	80.01 258	98.57 176	85.66 220	97.70 221	97.01 211

PMVS	87.21 14	94.97 84	95.33 76	93.91 143	98.97 14	97.16 2	95.54 69	95.85 204	96.47 20	93.40 193	97.46 63	95.31 33	95.47 316	86.18 217	98.78 130	89.11 343
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Regformer-1		94.55 103	94.33 112	95.19 93	92.83 290	88.54 109	91.87 199	95.84 205	93.99 50	95.95 104	95.04 202	92.00 108	98.79 140	93.14 67	98.31 172	98.23 127

alignmvs		93.26 139	92.85 150	94.50 121	95.70 216	87.45 126	93.45 140	95.76 206	91.58 110	95.25 135	92.42 282	81.96 245	98.72 154	91.61 107	97.87 213	97.33 201

无先验							89.94 254	95.75 207	70.81 333				98.59 173	81.17 268		94.81 282

WR-MVS		93.49 131	93.72 127	92.80 181	97.57 110	80.03 236	90.14 248	95.68 208	93.70 57	96.62 72	95.39 190	87.21 192	99.04 101	87.50 195	99.64 23	99.33 25

VPNet		93.08 145	93.76 126	91.03 236	98.60 33	75.83 301	91.51 211	95.62 209	91.84 97	95.74 113	97.10 86	89.31 164	98.32 197	85.07 230	99.06 91	98.93 63

Anonymous20240521		92.86 155	93.57 134	90.74 247	96.57 156	75.50 303	94.15 119	95.60 210	89.38 155	95.90 108	97.90 44	80.39 257	97.96 227	92.60 83	99.68 18	98.75 84

xiu_mvs_v2_base		89.00 242	89.19 227	88.46 292	94.86 242	74.63 307	86.97 306	95.60 210	80.88 270	87.83 306	88.62 329	91.04 136	98.81 138	82.51 254	94.38 300	91.93 332

PS-MVSNAJ		88.86 246	88.99 233	88.48 291	94.88 240	74.71 305	86.69 315	95.60 210	80.88 270	87.83 306	87.37 337	90.77 139	98.82 133	82.52 253	94.37 301	91.93 332

CHOSEN 1792x2688		87.19 279	85.92 287	91.00 239	97.13 132	79.41 250	84.51 333	95.60 210	64.14 351	90.07 269	94.81 212	78.26 271	97.14 274	73.34 323	95.38 282	96.46 231

miper_enhance_ethall		88.42 253	87.87 256	90.07 265	88.67 344	75.52 302	85.10 326	95.59 214	75.68 306	92.49 221	89.45 323	78.96 263	97.88 231	87.86 190	97.02 242	96.81 219

MVP-Stereo		90.07 223	88.92 234	93.54 156	96.31 174	86.49 148	90.93 224	95.59 214	79.80 276	91.48 243	95.59 175	80.79 254	97.39 266	78.57 293	91.19 335	96.76 221
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
cdsmvs_eth3d_5k		23.35 331	31.13 334	0.00 347	0.00 368	0.00 369	0.00 359	95.58 216	0.00 364	0.00 365	91.15 299	93.43 74	0.00 365	0.00 363	0.00 363	0.00 361

CNLPA		91.72 182	91.20 192	93.26 165	96.17 185	91.02 66	91.14 219	95.55 217	90.16 142	90.87 253	93.56 254	86.31 208	94.40 331	79.92 282	97.12 239	94.37 293

FMVSNet2		92.78 157	92.73 156	92.95 174	95.40 229	81.98 209	94.18 118	95.53 218	88.63 170	96.05 101	97.37 68	81.31 250	98.81 138	87.38 199	98.67 139	98.06 138

ab-mvs		92.40 169	92.62 158	91.74 214	97.02 134	81.65 213	95.84 59	95.50 219	86.95 205	92.95 211	97.56 56	90.70 144	97.50 257	79.63 283	97.43 231	96.06 247

MVS_111021_LR		93.66 128	93.28 143	94.80 105	96.25 180	90.95 68	90.21 244	95.43 220	87.91 184	93.74 185	94.40 225	92.88 91	96.38 298	90.39 127	98.28 175	97.07 207

tfpnnormal		94.27 114	94.87 91	92.48 194	97.71 99	80.88 225	94.55 109	95.41 221	93.70 57	96.67 70	97.72 49	91.40 123	98.18 210	87.45 196	99.18 82	98.36 118

Effi-MVS+-dtu		93.90 125	92.60 159	97.77 4	94.74 249	96.67 3	94.00 124	95.41 221	89.94 144	91.93 239	92.13 286	90.12 154	98.97 113	87.68 192	97.48 229	97.67 179

mvs-test1		93.07 147	91.80 177	96.89 39	94.74 249	95.83 6	92.17 182	95.41 221	89.94 144	89.85 274	90.59 311	90.12 154	98.88 123	87.68 192	95.66 273	95.97 250

cl-mvsnet_		90.65 204	90.56 206	90.91 243	91.85 306	76.98 287	86.75 313	95.36 224	85.53 225	94.06 174	94.89 209	77.36 279	97.98 226	90.27 135	98.98 102	97.76 172

cl-mvsnet1		90.65 204	90.56 206	90.91 243	91.85 306	76.99 286	86.75 313	95.36 224	85.52 227	94.06 174	94.89 209	77.37 278	97.99 225	90.28 134	98.97 106	97.76 172

testgi		90.38 211	91.34 189	87.50 303	97.49 114	71.54 328	89.43 267	95.16 226	88.38 177	94.54 162	94.68 219	92.88 91	93.09 342	71.60 334	97.85 214	97.88 160

v148		92.87 154	93.29 141	91.62 218	96.25 180	77.72 276	91.28 217	95.05 227	89.69 149	95.93 106	96.04 154	87.34 189	98.38 192	90.05 144	97.99 207	98.78 81

miper_lstm_enhance		89.90 228	89.80 220	90.19 264	91.37 315	77.50 278	83.82 340	95.00 228	84.84 239	93.05 207	94.96 206	76.53 287	95.20 324	89.96 146	98.67 139	97.86 162

VNet		92.67 161	92.96 147	91.79 212	96.27 177	80.15 230	91.95 191	94.98 229	92.19 84	94.52 163	96.07 153	87.43 188	97.39 266	84.83 232	98.38 162	97.83 165

FMVSNet3		90.78 200	90.32 211	92.16 204	93.03 287	79.92 239	92.54 161	94.95 230	86.17 215	95.10 140	96.01 156	69.97 306	98.75 149	86.74 204	98.38 162	97.82 167

BH-untuned		90.68 203	90.90 196	90.05 267	95.98 200	79.57 248	90.04 251	94.94 231	87.91 184	94.07 173	93.00 265	87.76 183	97.78 243	79.19 289	95.17 286	92.80 324

RRT_MVS		91.36 191	90.05 216	95.29 89	89.21 339	88.15 115	92.51 165	94.89 232	86.73 207	95.54 121	95.68 172	61.82 341	99.30 67	94.91 13	99.13 88	98.43 115

D2MVS		89.93 227	89.60 225	90.92 241	94.03 269	78.40 266	88.69 285	94.85 233	78.96 290	93.08 205	95.09 199	74.57 291	96.94 279	88.19 181	98.96 108	97.41 193

SixPastTwentyTwo		94.91 86	95.21 81	93.98 137	98.52 44	83.19 197	95.93 55	94.84 234	94.86 38	98.49 15	98.74 16	81.45 248	99.60 8	94.69 16	99.39 54	99.15 37

旧先验1					96.20 182	84.17 184		94.82 235			95.57 179	89.57 162			97.89 212	96.32 236

API-MVS		91.52 187	91.61 180	91.26 228	94.16 264	86.26 158	94.66 101	94.82 235	91.17 120	92.13 235	91.08 301	90.03 160	97.06 276	79.09 290	97.35 234	90.45 341

FMVSNet5		87.82 263	86.56 278	91.62 218	92.31 296	79.81 243	93.49 138	94.81 237	83.26 249	91.36 245	96.93 96	52.77 357	97.49 259	76.07 310	98.03 204	97.55 187

MAR-MVS		90.32 215	88.87 237	94.66 111	94.82 243	91.85 57	94.22 117	94.75 238	80.91 269	87.52 310	88.07 333	86.63 205	97.87 234	76.67 306	96.21 263	94.25 296
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
mvs_anonymous		90.37 212	91.30 190	87.58 302	92.17 301	68.00 341	89.84 259	94.73 239	83.82 247	93.22 202	97.40 66	87.54 186	97.40 265	87.94 188	95.05 288	97.34 200

Regformer-3		94.28 113	94.23 118	94.46 125	92.78 292	86.28 157	92.39 171	94.70 240	93.69 60	95.97 102	95.56 180	91.34 124	98.48 187	93.45 49	98.14 192	98.62 100

EI-MVSNet-UG-set		94.35 110	94.27 116	94.59 117	92.46 295	85.87 164	92.42 169	94.69 241	93.67 61	96.13 98	95.84 164	91.20 132	98.86 128	93.78 35	98.23 183	99.03 49

EI-MVSNet-Vis-set		94.36 109	94.28 114	94.61 112	92.55 294	85.98 162	92.44 167	94.69 241	93.70 57	96.12 99	95.81 165	91.24 129	98.86 128	93.76 38	98.22 185	98.98 58

EI-MVSNet		92.99 149	93.26 145	92.19 200	92.12 302	79.21 256	92.32 176	94.67 243	91.77 103	95.24 136	95.85 161	87.14 194	98.49 183	91.99 95	98.26 177	98.86 72

MVSTER		89.32 236	88.75 238	91.03 236	90.10 329	76.62 291	90.85 225	94.67 243	82.27 263	95.24 136	95.79 166	61.09 344	98.49 183	90.49 124	98.26 177	97.97 151

新几何1				93.17 168	97.16 129	87.29 129		94.43 245	67.95 342	91.29 246	94.94 207	86.97 197	98.23 205	81.06 270	97.75 216	93.98 303

1121		90.26 217	89.23 226	93.34 161	97.15 131	87.40 127	91.94 193	94.39 246	67.88 343	91.02 252	94.91 208	86.91 200	98.59 173	81.17 268	97.71 220	94.02 302

CMPMVS	68.83 22	87.28 275	85.67 288	92.09 206	88.77 343	85.42 170	90.31 242	94.38 247	70.02 336	88.00 304	93.30 259	73.78 295	94.03 336	75.96 312	96.54 257	96.83 218
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
IS-MVSNet		94.49 105	94.35 111	94.92 101	98.25 66	86.46 150	97.13 13	94.31 248	96.24 23	96.28 89	96.36 138	82.88 232	99.35 55	88.19 181	99.52 37	98.96 60

testdata				91.03 236	96.87 142	82.01 208		94.28 249	71.55 327	92.46 222	95.42 187	85.65 216	97.38 268	82.64 251	97.27 235	93.70 310

UGNet		93.08 145	92.50 161	94.79 106	93.87 273	87.99 120	95.07 87	94.26 250	90.64 132	87.33 312	97.67 51	86.89 201	98.49 183	88.10 184	98.71 135	97.91 157
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		84.98 294	84.30 294	87.01 306	91.03 317	77.69 277	91.94 193	94.16 251	59.36 356	84.23 330	87.50 336	85.66 215	96.80 285	71.79 331	93.05 320	86.54 348

1314		86.46 286	86.33 283	86.87 308	91.65 310	74.54 308	91.94 193	94.10 252	74.28 313	84.78 326	87.33 338	83.03 231	95.00 325	78.72 291	91.16 336	91.06 338

cl-mvsnet2		89.02 240	88.50 241	90.59 251	89.76 331	76.45 293	86.62 318	94.03 253	82.98 256	92.65 217	92.49 276	72.05 301	97.53 255	88.93 167	97.02 242	97.78 170

EPP-MVSNet		93.91 124	93.68 130	94.59 117	98.08 75	85.55 169	97.44 8	94.03 253	94.22 47	94.94 148	96.19 148	82.07 243	99.57 13	87.28 200	98.89 111	98.65 92

UnsupCasMVSNet_bld		88.50 252	88.03 254	89.90 268	95.52 226	78.88 260	87.39 300	94.02 255	79.32 286	93.06 206	94.02 239	80.72 255	94.27 333	75.16 315	93.08 319	96.54 224

hse-mvs3		92.89 152	91.99 171	95.58 77	96.97 136	90.55 73	93.94 127	94.01 256	89.23 160	93.95 178	96.19 148	76.88 284	99.14 85	91.02 117	95.71 272	97.04 210

pmmvs-eth3d		91.54 186	90.73 203	93.99 136	95.76 214	87.86 123	90.83 226	93.98 257	78.23 296	94.02 177	96.22 147	82.62 238	96.83 284	86.57 209	98.33 169	97.29 203

BH-RMVSNet		90.47 208	90.44 208	90.56 252	95.21 236	78.65 265	89.15 276	93.94 258	88.21 179	92.74 215	94.22 231	86.38 207	97.88 231	78.67 292	95.39 281	95.14 276

test222					96.95 137	85.27 172	88.83 281	93.61 259	65.09 350	90.74 256	94.85 211	84.62 222			97.36 233	93.91 304

CDS-MVSNet		89.55 232	88.22 250	93.53 157	95.37 232	86.49 148	89.26 273	93.59 260	79.76 278	91.15 250	92.31 283	77.12 280	98.38 192	77.51 300	97.92 211	95.71 262
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
new-patchmatchnet		88.97 243	90.79 201	83.50 330	94.28 263	55.83 362	85.34 325	93.56 261	86.18 214	95.47 123	95.73 170	83.10 230	96.51 293	85.40 222	98.06 200	98.16 131

IterMVS-SCA-FT		91.65 183	91.55 181	91.94 209	93.89 272	79.22 255	87.56 296	93.51 262	91.53 112	95.37 128	96.62 119	78.65 266	98.90 120	91.89 100	94.95 289	97.70 176

Anonymous20231206		88.77 248	88.29 246	90.20 263	96.31 174	78.81 262	89.56 265	93.49 263	74.26 314	92.38 226	95.58 178	82.21 240	95.43 318	72.07 330	98.75 134	96.34 235

OpenMVS_ROB	85.12 16	89.52 234	89.05 231	90.92 241	94.58 257	81.21 221	91.10 221	93.41 264	77.03 303	93.41 191	93.99 241	83.23 229	97.80 240	79.93 280	94.80 293	93.74 309

VDD-MVS		94.37 108	94.37 110	94.40 128	97.49 114	86.07 161	93.97 126	93.28 265	94.49 43	96.24 90	97.78 46	87.99 180	98.79 140	88.92 168	99.14 85	98.34 119

jason		89.17 238	88.32 244	91.70 216	95.73 215	80.07 233	88.10 290	93.22 266	71.98 326	90.09 267	92.79 270	78.53 269	98.56 177	87.43 197	97.06 240	96.46 231
jason: jason.
PAPM		81.91 313	80.11 323	87.31 305	93.87 273	72.32 326	84.02 338	93.22 266	69.47 338	76.13 357	89.84 314	72.15 300	97.23 271	53.27 358	89.02 341	92.37 329

BH-w/o		87.21 277	87.02 271	87.79 301	94.77 246	77.27 282	87.90 291	93.21 268	81.74 267	89.99 271	88.39 332	83.47 226	96.93 281	71.29 335	92.43 327	89.15 342

ppachtmachnet_test		88.61 251	88.64 239	88.50 290	91.76 308	70.99 331	84.59 332	92.98 269	79.30 287	92.38 226	93.53 255	79.57 260	97.45 261	86.50 212	97.17 238	97.07 207

IterMVS		90.18 218	90.16 212	90.21 262	93.15 283	75.98 298	87.56 296	92.97 270	86.43 210	94.09 171	96.40 131	78.32 270	97.43 262	87.87 189	94.69 296	97.23 204
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
test20.03		90.80 199	90.85 199	90.63 250	95.63 222	79.24 254	89.81 260	92.87 271	89.90 146	94.39 165	96.40 131	85.77 213	95.27 323	73.86 321	99.05 94	97.39 197

CR-MVSNet		87.89 260	87.12 269	90.22 261	91.01 318	78.93 258	92.52 162	92.81 272	73.08 321	89.10 284	96.93 96	67.11 312	97.64 252	88.80 171	92.70 323	94.08 297

Patchmtry		90.11 220	89.92 218	90.66 249	90.35 327	77.00 285	92.96 149	92.81 272	90.25 141	94.74 157	96.93 96	67.11 312	97.52 256	85.17 223	98.98 102	97.46 190

GA-MVS		87.70 264	86.82 273	90.31 257	93.27 280	77.22 283	84.72 331	92.79 274	85.11 234	89.82 275	90.07 312	66.80 315	97.76 246	84.56 236	94.27 304	95.96 251

sss		87.23 276	86.82 273	88.46 292	93.96 270	77.94 270	86.84 310	92.78 275	77.59 298	87.61 309	91.83 290	78.75 265	91.92 346	77.84 296	94.20 305	95.52 270

Patchmatch-RL test		88.81 247	88.52 240	89.69 271	95.33 234	79.94 238	86.22 321	92.71 276	78.46 294	95.80 110	94.18 233	66.25 320	95.33 321	89.22 163	98.53 149	93.78 307

test_yl		90.11 220	89.73 223	91.26 228	94.09 267	79.82 241	90.44 236	92.65 277	90.90 123	93.19 203	93.30 259	73.90 293	98.03 219	82.23 256	96.87 248	95.93 252

DCV-MVSNet		90.11 220	89.73 223	91.26 228	94.09 267	79.82 241	90.44 236	92.65 277	90.90 123	93.19 203	93.30 259	73.90 293	98.03 219	82.23 256	96.87 248	95.93 252

CL-MVSNet_2432*1600		90.04 225	89.90 219	90.47 253	95.24 235	77.81 274	86.60 319	92.62 279	85.64 224	93.25 201	93.92 243	83.84 225	96.06 306	79.93 280	98.03 204	97.53 188

TSAR-MVS + GP.		93.07 147	92.41 163	95.06 98	95.82 209	90.87 71	90.97 223	92.61 280	88.04 183	94.61 160	93.79 248	88.08 176	97.81 239	89.41 156	98.39 160	96.50 229

TAMVS		90.16 219	89.05 231	93.49 159	96.49 161	86.37 153	90.34 241	92.55 281	80.84 272	92.99 209	94.57 222	81.94 246	98.20 207	73.51 322	98.21 186	95.90 255

MS-PatchMatch		88.05 259	87.75 257	88.95 281	93.28 279	77.93 271	87.88 292	92.49 282	75.42 309	92.57 220	93.59 253	80.44 256	94.24 335	81.28 265	92.75 322	94.69 288

MG-MVS		89.54 233	89.80 220	88.76 285	94.88 240	72.47 325	89.60 263	92.44 283	85.82 220	89.48 281	95.98 157	82.85 233	97.74 248	81.87 259	95.27 284	96.08 246

MVS_0304		90.96 197	90.15 214	93.37 160	93.17 282	87.06 134	93.62 135	92.43 284	89.60 152	82.25 341	95.50 183	82.56 239	97.83 238	84.41 238	97.83 215	95.22 273

lupinMVS		88.34 255	87.31 263	91.45 222	94.74 249	80.06 234	87.23 301	92.27 285	71.10 330	88.83 287	91.15 299	77.02 281	98.53 180	86.67 207	96.75 253	95.76 260

pmmvs5		87.87 261	87.14 268	90.07 265	93.26 281	76.97 288	88.89 280	92.18 286	73.71 318	88.36 299	93.89 245	76.86 285	96.73 287	80.32 272	96.81 250	96.51 226

PM-MVS		93.33 135	92.67 157	95.33 85	96.58 155	94.06 19	92.26 179	92.18 286	85.92 219	96.22 92	96.61 120	85.64 217	95.99 308	90.35 130	98.23 183	95.93 252

pmmvs4		88.95 244	87.70 259	92.70 183	94.30 262	85.60 168	87.22 302	92.16 288	74.62 312	89.75 279	94.19 232	77.97 273	96.41 296	82.71 250	96.36 261	96.09 245

MDA-MVSNet-bldmvs		91.04 195	90.88 197	91.55 220	94.68 254	80.16 229	85.49 324	92.14 289	90.41 139	94.93 149	95.79 166	85.10 218	96.93 281	85.15 225	94.19 306	97.57 184

door-mid								92.13 290

WTY-MVS		86.93 284	86.50 282	88.24 295	94.96 239	74.64 306	87.19 303	92.07 291	78.29 295	88.32 300	91.59 295	78.06 272	94.27 333	74.88 316	93.15 317	95.80 258

AUN-MVS		90.05 224	88.30 245	95.32 88	96.09 191	90.52 74	92.42 169	92.05 292	82.08 265	88.45 298	92.86 267	65.76 322	98.69 162	88.91 169	96.07 264	96.75 222

TR-MVS		87.70 264	87.17 267	89.27 278	94.11 266	79.26 253	88.69 285	91.86 293	81.94 266	90.69 257	89.79 317	82.82 234	97.42 263	72.65 328	91.98 331	91.14 337

VDDNet		94.03 122	94.27 116	93.31 163	98.87 19	82.36 206	95.51 71	91.78 294	97.19 11	96.32 83	98.60 18	84.24 223	98.75 149	87.09 201	98.83 123	98.81 78

Anonymous202405211		92.58 164	92.50 161	92.83 180	96.55 158	83.22 196	92.43 168	91.64 295	94.10 49	95.59 119	96.64 118	81.88 247	97.50 257	85.12 227	98.52 150	97.77 171

HY-MVS	82.50 18	86.81 285	85.93 286	89.47 272	93.63 276	77.93 271	94.02 123	91.58 296	75.68 306	83.64 333	93.64 250	77.40 276	97.42 263	71.70 333	92.07 330	93.05 321

door								91.26 297

PatchMatch-RL		89.18 237	88.02 255	92.64 185	95.90 206	92.87 45	88.67 287	91.06 298	80.34 273	90.03 270	91.67 293	83.34 227	94.42 330	76.35 309	94.84 292	90.64 340

ADS-MVSNet2		84.01 299	82.20 307	89.41 274	89.04 340	76.37 295	87.57 294	90.98 299	72.71 324	84.46 327	92.45 278	68.08 308	96.48 294	70.58 340	83.97 349	95.38 271

KD-MVS_2432*1600		82.17 310	80.75 317	86.42 311	82.04 362	70.09 335	81.75 346	90.80 300	82.56 258	90.37 263	89.30 324	42.90 366	96.11 304	74.47 317	92.55 325	93.06 319

miper_refine_blended		82.17 310	80.75 317	86.42 311	82.04 362	70.09 335	81.75 346	90.80 300	82.56 258	90.37 263	89.30 324	42.90 366	96.11 304	74.47 317	92.55 325	93.06 319

wuyk23d		87.83 262	90.79 201	78.96 339	90.46 326	88.63 104	92.72 155	90.67 302	91.65 109	98.68 11	97.64 53	96.06 16	77.53 359	59.84 354	99.41 52	70.73 356

our_test_3		87.55 269	87.59 260	87.44 304	91.76 308	70.48 332	83.83 339	90.55 303	79.79 277	92.06 237	92.17 285	78.63 268	95.63 311	84.77 233	94.73 294	96.22 241

EU-MVSNet		87.39 273	86.71 276	89.44 273	93.40 278	76.11 296	94.93 93	90.00 304	57.17 357	95.71 115	97.37 68	64.77 328	97.68 251	92.67 81	94.37 301	94.52 290

CHOSEN 280x420		80.04 324	77.97 330	86.23 314	90.13 328	74.53 309	72.87 354	89.59 305	66.38 346	76.29 356	85.32 348	56.96 350	95.36 319	69.49 343	94.72 295	88.79 345

MDA-MVSNet_test_wron		88.16 258	88.23 249	87.93 298	92.22 298	73.71 315	80.71 349	88.84 306	82.52 260	94.88 152	95.14 196	82.70 236	93.61 338	83.28 245	93.80 309	96.46 231

YYNet1		88.17 257	88.24 248	87.93 298	92.21 299	73.62 316	80.75 348	88.77 307	82.51 261	94.99 147	95.11 198	82.70 236	93.70 337	83.33 244	93.83 308	96.48 230

PVSNet	76.22 20	82.89 305	82.37 305	84.48 325	93.96 270	64.38 355	78.60 351	88.61 308	71.50 328	84.43 329	86.36 343	74.27 292	94.60 327	69.87 342	93.69 311	94.46 291

MIMVSNet		87.13 281	86.54 279	88.89 283	96.05 194	76.11 296	94.39 112	88.51 309	81.37 268	88.27 301	96.75 109	72.38 299	95.52 313	65.71 350	95.47 278	95.03 278

tpmvs		84.22 298	83.97 297	84.94 321	87.09 351	65.18 350	91.21 218	88.35 310	82.87 257	85.21 321	90.96 303	65.24 326	96.75 286	79.60 286	85.25 348	92.90 323

EPNet_dtu		85.63 290	84.37 293	89.40 275	86.30 354	74.33 312	91.64 209	88.26 311	84.84 239	72.96 359	89.85 313	71.27 304	97.69 250	76.60 307	97.62 225	96.18 243
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tpm cat1		80.61 322	79.46 325	84.07 328	88.78 342	65.06 353	89.26 273	88.23 312	62.27 354	81.90 346	89.66 321	62.70 339	95.29 322	71.72 332	80.60 356	91.86 334

baseline1		87.62 268	87.31 263	88.54 289	94.71 253	74.27 313	93.10 146	88.20 313	86.20 213	92.18 234	93.04 264	73.21 296	95.52 313	79.32 287	85.82 347	95.83 257

CVMVSNet		85.16 292	84.72 291	86.48 309	92.12 302	70.19 333	92.32 176	88.17 314	56.15 358	90.64 258	95.85 161	67.97 310	96.69 288	88.78 172	90.52 338	92.56 327

SCA		87.43 272	87.21 266	88.10 297	92.01 305	71.98 327	89.43 267	88.11 315	82.26 264	88.71 294	92.83 268	78.65 266	97.59 253	79.61 284	93.30 314	94.75 285

tpmrst		82.85 306	82.93 304	82.64 332	87.65 345	58.99 360	90.14 248	87.90 316	75.54 308	83.93 331	91.63 294	66.79 317	95.36 319	81.21 267	81.54 355	93.57 315

Vis-MVSNet (Re-imp)		90.42 209	90.16 212	91.20 232	97.66 105	77.32 281	94.33 114	87.66 317	91.20 119	92.99 209	95.13 197	75.40 290	98.28 199	77.86 295	99.19 80	97.99 147

bset_n11_16_dypcd		89.99 226	89.15 229	92.53 192	94.75 247	81.34 218	84.19 336	87.56 318	85.13 232	93.77 182	92.46 277	72.82 297	99.01 106	92.46 86	99.21 78	97.23 204

MDTV_nov1_ep13			83.88 298		89.42 337	61.52 358	88.74 284	87.41 319	73.99 316	84.96 325	94.01 240	65.25 325	95.53 312	78.02 294	93.16 316

PatchmatchNet		85.22 291	84.64 292	86.98 307	89.51 336	69.83 338	90.52 234	87.34 320	78.87 291	87.22 313	92.74 272	66.91 314	96.53 291	81.77 260	86.88 346	94.58 289
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
N_pmnet		88.90 245	87.25 265	93.83 147	94.40 261	93.81 34	84.73 329	87.09 321	79.36 285	93.26 199	92.43 281	79.29 262	91.68 347	77.50 301	97.22 237	96.00 249

EPNet		89.80 231	88.25 247	94.45 126	83.91 360	86.18 159	93.87 128	87.07 322	91.16 121	80.64 350	94.72 217	78.83 264	98.89 122	85.17 223	98.89 111	98.28 124
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Patchmatch-test		86.10 288	86.01 285	86.38 313	90.63 322	74.22 314	89.57 264	86.69 323	85.73 223	89.81 276	92.83 268	65.24 326	91.04 349	77.82 298	95.78 271	93.88 306

K. test v3		93.37 134	93.27 144	93.66 150	98.05 78	82.62 204	94.35 113	86.62 324	96.05 27	97.51 38	98.85 12	76.59 286	99.65 3	93.21 63	98.20 188	98.73 89

CostFormer		83.09 303	82.21 306	85.73 315	89.27 338	67.01 342	90.35 240	86.47 325	70.42 334	83.52 335	93.23 262	61.18 343	96.85 283	77.21 303	88.26 344	93.34 317

thres200		85.85 289	85.18 290	87.88 300	94.44 259	72.52 324	89.08 277	86.21 326	88.57 174	91.44 244	88.40 331	64.22 329	98.00 223	68.35 344	95.88 270	93.12 318

ET-MVSNet_ETH3D		86.15 287	84.27 295	91.79 212	93.04 286	81.28 219	87.17 304	86.14 327	79.57 281	83.65 332	88.66 328	57.10 349	98.18 210	87.74 191	95.40 280	95.90 255

PatchT		87.51 270	88.17 251	85.55 316	90.64 321	66.91 343	92.02 189	86.09 328	92.20 83	89.05 286	97.16 83	64.15 330	96.37 299	89.21 164	92.98 321	93.37 316

DWT-MVSNet_test		80.74 320	79.18 326	85.43 318	87.51 348	66.87 344	89.87 258	86.01 329	74.20 315	80.86 349	80.62 355	48.84 359	96.68 290	81.54 262	83.14 353	92.75 325

tfpn200view9		87.05 282	86.52 280	88.67 287	95.77 212	72.94 321	91.89 196	86.00 330	90.84 125	92.61 218	89.80 315	63.93 331	98.28 199	71.27 336	96.54 257	94.79 283

thres400		87.20 278	86.52 280	89.24 280	95.77 212	72.94 321	91.89 196	86.00 330	90.84 125	92.61 218	89.80 315	63.93 331	98.28 199	71.27 336	96.54 257	96.51 226

IB-MVS	77.21 19	83.11 302	81.05 313	89.29 277	91.15 316	75.85 299	85.66 323	86.00 330	79.70 279	82.02 345	86.61 340	48.26 360	98.39 190	77.84 296	92.22 328	93.63 311
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
PMMVS		83.00 304	81.11 312	88.66 288	83.81 361	86.44 151	82.24 345	85.65 333	61.75 355	82.07 343	85.64 347	79.75 259	91.59 348	75.99 311	93.09 318	87.94 347

tpm		84.38 297	84.08 296	85.30 320	90.47 325	63.43 357	89.34 270	85.63 334	77.24 302	87.62 308	95.03 204	61.00 345	97.30 269	79.26 288	91.09 337	95.16 274

LFMVS		91.33 192	91.16 194	91.82 211	96.27 177	79.36 251	95.01 90	85.61 335	96.04 28	94.82 153	97.06 88	72.03 302	98.46 189	84.96 231	98.70 137	97.65 180

FPMVS		84.50 296	83.28 300	88.16 296	96.32 173	94.49 14	85.76 322	85.47 336	83.09 253	85.20 322	94.26 229	63.79 333	86.58 356	63.72 352	91.88 333	83.40 351

tpm2		81.46 314	80.35 321	84.80 322	89.90 330	65.14 351	90.44 236	85.36 337	65.82 349	82.05 344	92.44 280	57.94 348	96.69 288	70.71 339	88.49 343	92.56 327

thres100view900		87.35 274	86.89 272	88.72 286	96.14 187	73.09 320	93.00 148	85.31 338	92.13 85	93.26 199	90.96 303	63.42 334	98.28 199	71.27 336	96.54 257	94.79 283

thres600view7		87.66 266	87.10 270	89.36 276	96.05 194	73.17 318	92.72 155	85.31 338	91.89 92	93.29 196	90.97 302	63.42 334	98.39 190	73.23 324	96.99 247	96.51 226

dp		79.28 325	78.62 328	81.24 335	85.97 355	56.45 361	86.91 308	85.26 340	72.97 322	81.45 348	89.17 327	56.01 353	95.45 317	73.19 325	76.68 357	91.82 335

PMMVS2		81.31 315	83.44 299	74.92 341	90.52 324	46.49 364	69.19 356	85.23 341	84.30 244	87.95 305	94.71 218	76.95 283	84.36 358	64.07 351	98.09 198	93.89 305

ADS-MVSNet		82.25 308	81.55 309	84.34 326	89.04 340	65.30 349	87.57 294	85.13 342	72.71 324	84.46 327	92.45 278	68.08 308	92.33 345	70.58 340	83.97 349	95.38 271

test-LLR		83.58 300	83.17 301	84.79 323	89.68 333	66.86 345	83.08 341	84.52 343	83.07 254	82.85 338	84.78 349	62.86 337	93.49 339	82.85 248	94.86 290	94.03 300

test-mter		81.21 317	80.01 324	84.79 323	89.68 333	66.86 345	83.08 341	84.52 343	73.85 317	82.85 338	84.78 349	43.66 365	93.49 339	82.85 248	94.86 290	94.03 300

JIA-IIPM		85.08 293	83.04 302	91.19 233	87.56 346	86.14 160	89.40 269	84.44 345	88.98 163	82.20 342	97.95 39	56.82 351	96.15 302	76.55 308	83.45 351	91.30 336

thisisatest0530		88.69 250	87.52 261	92.20 199	96.33 172	79.36 251	92.81 153	84.01 346	86.44 209	93.67 186	92.68 274	53.62 356	99.25 74	89.65 153	98.45 156	98.00 144

tttt0517		89.81 230	88.90 236	92.55 191	97.00 135	79.73 245	95.03 89	83.65 347	89.88 147	95.30 131	94.79 216	53.64 355	99.39 45	91.99 95	98.79 129	98.54 106

thisisatest0515		84.72 295	82.99 303	89.90 268	92.96 288	75.33 304	84.36 334	83.42 348	77.37 300	88.27 301	86.65 339	53.94 354	98.72 154	82.56 252	97.40 232	95.67 264

PVSNet_0	70.34 21	74.58 328	72.96 331	79.47 338	90.63 322	66.24 348	73.26 352	83.40 349	63.67 353	78.02 354	78.35 356	72.53 298	89.59 353	56.68 356	60.05 360	82.57 354

pmmvs3		80.83 319	78.96 327	86.45 310	87.23 350	77.48 279	84.87 328	82.31 350	63.83 352	85.03 323	89.50 322	49.66 358	93.10 341	73.12 326	95.10 287	88.78 346

E-PMN		80.72 321	80.86 316	80.29 337	85.11 357	68.77 340	72.96 353	81.97 351	87.76 189	83.25 337	83.01 353	62.22 340	89.17 354	77.15 304	94.31 303	82.93 352

test0.0.03 1		82.48 307	81.47 311	85.48 317	89.70 332	73.57 317	84.73 329	81.64 352	83.07 254	88.13 303	86.61 340	62.86 337	89.10 355	66.24 349	90.29 339	93.77 308

baseline2		83.38 301	81.54 310	88.90 282	91.38 314	72.84 323	88.78 282	81.22 353	78.97 289	79.82 352	87.56 334	61.73 342	97.80 240	74.30 319	90.05 340	96.05 248

EMVS		80.35 323	80.28 322	80.54 336	84.73 359	69.07 339	72.54 355	80.73 354	87.80 188	81.66 347	81.73 354	62.89 336	89.84 352	75.79 313	94.65 297	82.71 353

TESTMET0.1,1		79.09 326	78.04 329	82.25 333	87.52 347	64.03 356	83.08 341	80.62 355	70.28 335	80.16 351	83.22 352	44.13 364	90.56 350	79.95 278	93.36 312	92.15 330

lessismore_v0				93.87 146	98.05 78	83.77 191		80.32 356		97.13 50	97.91 42	77.49 275	99.11 90	92.62 82	98.08 199	98.74 87

new_pmnet		81.22 316	81.01 315	81.86 334	90.92 320	70.15 334	84.03 337	80.25 357	70.83 332	85.97 319	89.78 318	67.93 311	84.65 357	67.44 346	91.90 332	90.78 339

MVS-HIRNet		78.83 327	80.60 319	73.51 342	93.07 284	47.37 363	87.10 305	78.00 358	68.94 339	77.53 355	97.26 76	71.45 303	94.62 326	63.28 353	88.74 342	78.55 355

DSMNet-mixed		82.21 309	81.56 308	84.16 327	89.57 335	70.00 337	90.65 231	77.66 359	54.99 359	83.30 336	97.57 55	77.89 274	90.50 351	66.86 348	95.54 276	91.97 331

EPMVS		81.17 318	80.37 320	83.58 329	85.58 356	65.08 352	90.31 242	71.34 360	77.31 301	85.80 320	91.30 297	59.38 346	92.70 344	79.99 277	82.34 354	92.96 322

gg-mvs-nofinetune		82.10 312	81.02 314	85.34 319	87.46 349	71.04 329	94.74 98	67.56 361	96.44 21	79.43 353	98.99 6	45.24 362	96.15 302	67.18 347	92.17 329	88.85 344

GG-mvs-BLEND				83.24 331	85.06 358	71.03 330	94.99 92	65.55 362		74.09 358	75.51 357	44.57 363	94.46 329	59.57 355	87.54 345	84.24 350

MVE	59.87 23	73.86 329	72.65 332	77.47 340	87.00 353	74.35 311	61.37 358	60.93 363	67.27 344	69.69 360	86.49 342	81.24 253	72.33 360	56.45 357	83.45 351	85.74 349
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
MTMP							94.82 95	54.62 364

DeepMVS_CX				53.83 343	70.38 364	64.56 354		48.52 365	33.01 360	65.50 361	74.21 358	56.19 352	46.64 361	38.45 360	70.07 358	50.30 357

tmp_tt		37.97 330	44.33 333	18.88 344	11.80 365	21.54 366	63.51 357	45.66 366	4.23 361	51.34 362	50.48 359	59.08 347	22.11 362	44.50 359	68.35 359	13.00 358

testmvs		9.02 333	11.42 336	1.81 346	2.77 367	1.13 368	79.44 350	1.90 367	1.18 363	2.65 364	6.80 361	1.95 369	0.87 364	2.62 362	3.45 362	3.44 360

test123		9.49 332	12.01 335	1.91 345	2.87 366	1.30 367	82.38 344	1.34 368	1.36 362	2.84 363	6.56 362	2.45 368	0.97 363	2.73 361	5.56 361	3.47 359

uanet_test		0.00 336	0.00 339	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	0.00 365	0.00 370	0.00 365	0.00 363	0.00 363	0.00 361

pcd_1.5k_mvsjas		7.56 334	10.09 337	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	0.00 365	90.77 139	0.00 365	0.00 363	0.00 363	0.00 361

sosnet-low-res		0.00 336	0.00 339	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	0.00 365	0.00 370	0.00 365	0.00 363	0.00 363	0.00 361

sosnet		0.00 336	0.00 339	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	0.00 365	0.00 370	0.00 365	0.00 363	0.00 363	0.00 361

uncertanet		0.00 336	0.00 339	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	0.00 365	0.00 370	0.00 365	0.00 363	0.00 363	0.00 361

Regformer		0.00 336	0.00 339	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	0.00 365	0.00 370	0.00 365	0.00 363	0.00 363	0.00 361

n2								0.00 369

nn								0.00 369

ab-mvs-re		7.56 334	10.08 338	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	90.69 308	0.00 370	0.00 365	0.00 363	0.00 363	0.00 361

uanet		0.00 336	0.00 339	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	0.00 365	0.00 370	0.00 365	0.00 363	0.00 363	0.00 361

OPU-MVS				95.15 95	96.84 143	89.43 89	95.21 79				95.66 173	93.12 84	98.06 217	86.28 216	98.61 142	97.95 152

test_0728_THIRD									93.26 65	97.40 44	97.35 72	94.69 54	99.34 59	93.88 32	99.42 47	98.89 69

GSMVS																94.75 285

test_part2					98.21 68	89.41 90				96.72 68

sam_mvs1												66.64 318				94.75 285

sam_mvs												66.41 319

test_post1							90.21 244				5.85 364	65.36 324	96.00 307	79.61 284

test_post											6.07 363	65.74 323	95.84 309

patchmatchnet-post											91.71 292	66.22 321	97.59 253

gm-plane-assit					87.08 352	59.33 359			71.22 329		83.58 351		97.20 272	73.95 320

test9_res														88.16 183	98.40 158	97.83 165

agg_prior2														87.06 202	98.36 168	97.98 148

test_prior4						89.91 81	90.74 228

test_prior2							90.21 244		89.33 158	90.77 254	94.81 212	90.41 149		88.21 179	98.55 145

旧先验2							90.00 253		68.65 340	92.71 216			96.52 292	85.15 225

新几何2							90.02 252

原ACMM2							89.34 270

testdata2													98.03 219	80.24 275

segment_acmp												92.14 105

testdata1							88.96 279		88.44 176

plane_prior7					97.71 99	88.68 103

plane_prior6					97.21 127	88.23 114						86.93 198

plane_prior4											95.59 175

plane_prior3						88.43 112			90.35 140	93.31 194

plane_prior2							94.56 107		91.74 105

plane_prior1					97.38 120

plane_prior						88.12 116	93.01 147		88.98 163						98.06 200

HQP5-MVS						84.89 174

HQP-NCC					96.36 167		91.37 213		87.16 200	88.81 289

ACMP_Plane					96.36 167		91.37 213		87.16 200	88.81 289

BP-MVS														86.55 210

HQP4-MVS										88.81 289			98.61 169			98.15 132

HQP2-MVS												84.76 220

NP-MVS					96.82 144	87.10 133					93.40 257

MDTV_nov1_ep13_2view						42.48 365	88.45 289		67.22 345	83.56 334		66.80 315		72.86 327		94.06 299

ACMMP++_ref															98.82 124

ACMMP++															99.25 73

Test By Simon												90.61 145

High-resolution multi-view benchmark