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		94.20 121	94.58 106	93.04 174	95.91 216	83.13 206	93.79 138	99.19 2	92.00 91	98.84 5	98.04 38	93.64 72	99.02 110	81.28 276	98.54 157	96.96 222

DROMVSNet		95.44 69	95.62 69	94.89 105	96.93 146	87.69 129	96.48 33	99.14 3	93.93 55	92.77 224	94.52 230	93.95 70	99.49 22	93.62 43	99.22 81	97.51 197

LTVRE_ROB	93.87 1	97.93 2	98.16 2	97.26 26	98.81 24	93.86 31	99.07 2	98.98 4	97.01 13	98.92 4	98.78 14	95.22 37	98.61 179	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
CS-MVS-test		93.33 138	93.53 141	92.71 188	95.74 225	83.08 207	94.55 115	98.85 5	91.02 127	89.30 294	91.91 298	91.79 118	99.23 80	90.23 144	98.41 167	95.82 267

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

TDRefinement		97.68 3	97.60 4	97.93 2	99.02 12	95.95 5	98.61 3	98.81 6	97.41 10	97.28 48	98.46 25	94.62 58	98.84 137	94.64 17	99.53 35	98.99 53

ANet_high		94.83 95	96.28 37	90.47 260	96.65 157	73.16 328	94.33 121	98.74 8	96.39 23	98.09 25	98.93 8	93.37 79	98.70 167	90.38 134	99.68 18	99.53 14

ACMH+	88.43 11	96.48 30	96.82 16	95.47 83	98.54 42	89.06 99	95.65 70	98.61 9	96.10 26	98.16 23	97.52 62	96.90 7	98.62 178	90.30 140	99.60 25	98.72 91

CS-MVS		92.12 180	92.62 162	90.60 257	94.57 269	78.12 278	92.00 198	98.58 10	87.75 199	90.08 277	91.88 300	89.79 166	99.10 97	90.35 136	98.60 152	94.58 299

SF-MVS		95.88 56	95.88 58	95.87 66	98.12 74	89.65 88	95.58 73	98.56 11	91.84 101	96.36 83	96.68 120	94.37 64	99.32 68	92.41 91	99.05 100	98.64 101

HPM-MVS		96.81 12	96.62 23	97.36 24	98.89 19	93.53 38	97.51 9	98.44 12	92.35 82	95.95 107	96.41 135	96.71 8	99.42 29	93.99 33	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 91	94.51 110	96.00 55	98.02 85	92.17 51	95.26 84	98.43 13	90.48 140	95.04 152	96.74 115	92.54 104	97.86 245	85.11 239	98.98 108	97.98 156

TestCases				96.00 55	98.02 85	92.17 51		98.43 13	90.48 140	95.04 152	96.74 115	92.54 104	97.86 245	85.11 239	98.98 108	97.98 156

xxxxxxxxxxxxxcwj		95.03 83	94.93 90	95.33 88	97.46 122	88.05 122	92.04 195	98.42 15	87.63 203	96.36 83	96.68 120	94.37 64	99.32 68	92.41 91	99.05 100	98.64 101

APDe-MVS		96.46 32	96.64 22	95.93 60	97.68 108	89.38 96	96.90 19	98.41 16	92.52 77	97.43 43	97.92 44	95.11 42	99.50 19	94.45 19	99.30 65	98.92 67

9.14			94.81 94		97.49 119		94.11 128	98.37 17	87.56 206	95.38 132	96.03 160	94.66 56	99.08 99	90.70 128	98.97 112

ETH3D-3000-0.1		94.86 92	94.55 107	95.81 67	97.61 112	89.72 86	94.05 130	98.37 17	88.09 191	95.06 151	95.85 166	92.58 102	99.10 97	90.33 139	98.99 107	98.62 105

abl_6		97.31 5	97.12 13	97.86 3	98.54 42	95.32 7	96.61 26	98.35 19	95.81 31	97.55 36	97.44 67	96.51 9	99.40 43	94.06 30	99.23 79	98.85 76

MP-MVS-pluss		96.08 49	95.92 57	96.57 45	99.06 10	91.21 65	93.25 150	98.32 20	87.89 195	96.86 65	97.38 70	95.55 24	99.39 48	95.47 10	99.47 39	99.11 41
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
FC-MVSNet-test		95.32 75	95.88 58	93.62 157	98.49 54	81.77 219	95.90 61	98.32 20	93.93 55	97.53 39	97.56 59	88.48 176	99.40 43	92.91 79	99.83 6	99.68 4

COLMAP_ROB	91.06 5	96.75 16	96.62 23	97.13 28	98.38 58	94.31 16	96.79 22	98.32 20	96.69 17	96.86 65	97.56 59	95.48 25	98.77 155	90.11 149	99.44 45	98.31 127
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 54	95.88 58	95.92 62	97.93 92	89.83 85	93.46 146	98.30 23	92.37 80	97.75 29	96.95 98	95.14 39	99.51 18	91.74 107	99.28 73	98.41 122
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 55	97.43 19	98.59 36	93.84 32	95.33 81	98.30 23	91.40 118	95.76 116	96.87 105	95.26 35	99.45 23	92.77 80	99.21 82	99.00 51

ACMH	88.36 12	96.59 27	97.43 5	94.07 141	98.56 37	85.33 178	96.33 42	98.30 23	94.66 40	98.72 8	98.30 30	97.51 5	98.00 233	94.87 14	99.59 27	98.86 73
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
nrg030		96.32 41	96.55 26	95.62 77	97.83 95	88.55 112	95.77 65	98.29 26	92.68 73	98.03 26	97.91 45	95.13 40	98.95 122	93.85 36	99.49 38	99.36 24

APD-MVS_3200maxsize		96.82 10	96.65 21	97.32 25	97.95 91	93.82 33	96.31 44	98.25 27	95.51 35	96.99 60	97.05 94	95.63 21	99.39 48	93.31 62	98.88 119	98.75 85

LPG-MVS_test		96.38 40	96.23 39	96.84 40	98.36 61	92.13 53	95.33 81	98.25 27	91.78 105	97.07 53	97.22 85	96.38 13	99.28 73	92.07 97	99.59 27	99.11 41

LGP-MVS_train				96.84 40	98.36 61	92.13 53		98.25 27	91.78 105	97.07 53	97.22 85	96.38 13	99.28 73	92.07 97	99.59 27	99.11 41

Anonymous20231211		96.60 25	97.13 12	95.00 102	97.46 122	86.35 162	97.11 16	98.24 30	97.58 8	98.72 8	98.97 7	93.15 86	99.15 87	93.18 68	99.74 13	99.50 16

canonicalmvs		94.59 103	94.69 100	94.30 135	95.60 235	87.03 142	95.59 71	98.24 30	91.56 115	95.21 145	92.04 297	94.95 50	98.66 174	91.45 116	97.57 237	97.20 215

DVP-MVS++		95.93 53	96.34 34	94.70 114	96.54 166	86.66 152	98.45 4	98.22 32	93.26 68	97.54 37	97.36 74	93.12 87	99.38 54	93.88 34	98.68 145	98.04 147

test_0728_SECOND				94.88 106	98.55 40	86.72 149	95.20 87	98.22 32					99.38 54	93.44 55	99.31 63	98.53 112

Vis-MVSNet		95.50 67	95.48 72	95.56 81	98.11 75	89.40 95	95.35 79	98.22 32	92.36 81	94.11 177	98.07 36	92.02 112	99.44 24	93.38 60	97.67 233	97.85 172
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 69	93.87 30	98.42 6	98.19 35	96.95 14	95.46 130	99.23 4	93.45 75	99.57 13	95.34 12	99.89 2	99.63 9

test_one_0601					98.26 66	87.14 138		98.18 36	94.25 48	96.99 60	97.36 74	95.13 40

test0726					98.51 46	86.69 150	95.34 80	98.18 36	91.85 98	97.63 32	97.37 71	95.58 22

MSP-MVS		95.34 74	94.63 105	97.48 14	98.67 28	94.05 22	96.41 38	98.18 36	91.26 121	95.12 146	95.15 202	86.60 212	99.50 19	93.43 57	96.81 260	98.89 70
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 33	93.88 29	96.95 18	98.18 36	92.26 85	96.33 85	96.84 109	95.10 43	99.40 43	93.47 52	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		92.35 174	92.03 173	93.30 170	95.81 221	83.97 195	92.80 161	98.17 40	87.71 200	89.79 287	87.56 345	91.17 140	99.18 85	87.97 197	97.27 245	96.77 230

HPM-MVS_fast		97.01 7	96.89 15	97.39 22	99.12 8	93.92 28	97.16 12	98.17 40	93.11 70	96.48 79	97.36 74	96.92 6	99.34 62	94.31 23	99.38 55	98.92 67

XVG-OURS		94.72 99	94.12 122	96.50 48	98.00 87	94.23 17	91.48 221	98.17 40	90.72 134	95.30 137	96.47 130	87.94 187	96.98 288	91.41 117	97.61 236	98.30 128

ZNCC-MVS		96.42 36	96.20 41	97.07 30	98.80 26	92.79 47	96.08 53	98.16 43	91.74 109	95.34 135	96.36 143	95.68 19	99.44 24	94.41 21	99.28 73	98.97 59

FIs		94.90 89	95.35 76	93.55 160	98.28 64	81.76 220	95.33 81	98.14 44	93.05 71	97.07 53	97.18 87	87.65 190	99.29 71	91.72 108	99.69 15	99.61 11

XVG-OURS-SEG-HR		95.38 72	95.00 89	96.51 47	98.10 76	94.07 19	92.46 174	98.13 45	90.69 135	93.75 191	96.25 151	98.03 2	97.02 287	92.08 96	95.55 286	98.45 119

SR-MVS-dyc-post		96.84 8	96.60 25	97.56 10	98.07 78	95.27 8	96.37 39	98.12 46	95.66 33	97.00 58	97.03 95	94.85 52	99.42 29	93.49 48	98.84 124	98.00 152

RE-MVS-def			96.66 20		98.07 78	95.27 8	96.37 39	98.12 46	95.66 33	97.00 58	97.03 95	95.40 27		93.49 48	98.84 124	98.00 152

RPMNet		90.31 222	90.14 222	90.81 252	91.01 330	78.93 266	92.52 169	98.12 46	91.91 95	89.10 295	96.89 104	68.84 316	99.41 36	90.17 147	92.70 334	94.08 308

SED-MVS		96.00 52	96.41 32	94.76 111	98.51 46	86.97 143	95.21 85	98.10 49	91.95 92	97.63 32	97.25 82	96.48 11	99.35 59	93.29 63	99.29 68	97.95 160

test_241102_TWO								98.10 49	91.95 92	97.54 37	97.25 82	95.37 28	99.35 59	93.29 63	99.25 76	98.49 115

test_241102_ONE					98.51 46	86.97 143		98.10 49	91.85 98	97.63 32	97.03 95	96.48 11	98.95 122

test_part1		94.39 110	94.55 107	93.92 148	96.14 198	82.86 210	95.54 75	98.09 52	95.36 36	98.27 20	98.36 28	75.91 295	99.44 24	93.41 58	99.84 3	99.47 17

WR-MVS_H		96.60 25	97.05 14	95.24 94	99.02 12	86.44 158	96.78 23	98.08 53	97.42 9	98.48 16	97.86 48	91.76 120	99.63 6	94.23 26	99.84 3	99.66 6

CP-MVS		96.44 35	96.08 49	97.54 11	98.29 63	94.62 14	96.80 21	98.08 53	92.67 75	95.08 150	96.39 140	94.77 54	99.42 29	93.17 69	99.44 45	98.58 110

ACMP	88.15 13	95.71 61	95.43 75	96.54 46	98.17 72	91.73 61	94.24 123	98.08 53	89.46 159	96.61 76	96.47 130	95.85 17	99.12 93	90.45 131	99.56 33	98.77 84
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 80	94.69 11	96.13 51	98.07 56	95.17 37	96.82 67	96.73 117	95.09 44	99.43 28	92.99 77	98.71 141	98.50 114

v7n		96.82 10	97.31 10	95.33 88	98.54 42	86.81 147	96.83 20	98.07 56	96.59 20	98.46 17	98.43 27	92.91 94	99.52 17	96.25 6	99.76 11	99.65 8

UniMVSNet (Re)		95.32 75	95.15 85	95.80 69	97.79 98	88.91 102	92.91 158	98.07 56	93.46 65	96.31 87	95.97 163	90.14 158	99.34 62	92.11 94	99.64 23	99.16 36

test1172		96.79 15	96.52 27	97.60 9	98.03 84	94.87 10	96.07 54	98.06 59	95.76 32	96.89 63	96.85 106	94.85 52	99.42 29	93.35 61	98.81 132	98.53 112

SD-MVS		95.19 81	95.73 66	93.55 160	96.62 160	88.88 105	94.67 106	98.05 60	91.26 121	97.25 50	96.40 136	95.42 26	94.36 343	92.72 84	99.19 84	97.40 205
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		94.32 115	94.80 95	92.85 184	96.05 205	81.44 225	92.35 182	98.05 60	91.53 116	95.75 117	96.80 110	93.35 80	98.49 193	91.01 123	98.32 182	98.64 101

PEN-MVS		96.69 20	97.39 8	94.61 117	99.16 4	84.50 185	96.54 29	98.05 60	98.06 4	98.64 13	98.25 31	95.01 48	99.65 3	92.95 78	99.83 6	99.68 4

XVG-ACMP-BASELINE		95.68 62	95.34 77	96.69 43	98.40 56	93.04 42	94.54 117	98.05 60	90.45 142	96.31 87	96.76 113	92.91 94	98.72 161	91.19 119	99.42 47	98.32 125

baseline		94.26 118	94.80 95	92.64 191	96.08 203	80.99 231	93.69 141	98.04 64	90.80 133	94.89 158	96.32 145	93.19 84	98.48 197	91.68 110	98.51 161	98.43 120

ACMMP_NAP		96.21 45	96.12 47	96.49 49	98.90 18	91.42 63	94.57 112	98.03 65	90.42 143	96.37 82	97.35 77	95.68 19	99.25 77	94.44 20	99.34 58	98.80 80

ACMM	88.83 9	96.30 43	96.07 50	96.97 35	98.39 57	92.95 45	94.74 104	98.03 65	90.82 132	97.15 51	96.85 106	96.25 15	99.00 114	93.10 72	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		93.99 126	93.38 144	95.80 69	96.82 151	89.92 82	92.72 162	98.02 67	84.73 251	93.65 195	95.54 188	91.68 122	99.22 81	88.78 180	98.49 164	98.26 131

DeepC-MVS	91.39 4	95.43 70	95.33 78	95.71 75	97.67 109	90.17 79	93.86 137	98.02 67	87.35 207	96.22 95	97.99 41	94.48 62	99.05 104	92.73 83	99.68 18	97.93 162
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 54	97.00 34	98.65 29	92.71 48	95.69 69	98.01 69	92.08 90	95.74 118	96.28 148	95.22 37	99.42 29	93.17 69	99.06 97	98.88 72

OurMVSNet-221017-0		96.80 13	96.75 18	96.96 36	99.03 11	91.85 58	97.98 7	98.01 69	94.15 50	98.93 3	99.07 5	88.07 183	99.57 13	95.86 9	99.69 15	99.46 18

SteuartSystems-ACMMP		96.40 38	96.30 36	96.71 42	98.63 30	91.96 56	95.70 67	98.01 69	93.34 67	96.64 74	96.57 127	94.99 49	99.36 58	93.48 51	99.34 58	98.82 78
Skip Steuart: Steuart Systems R&D Blog.
HFP-MVS		96.39 39	96.17 44	97.04 31	98.51 46	93.37 39	96.30 46	97.98 72	92.35 82	95.63 122	96.47 130	95.37 28	99.27 75	93.78 38	99.14 91	98.48 116

#test#		95.89 54	95.51 71	97.04 31	98.51 46	93.37 39	95.14 90	97.98 72	89.34 163	95.63 122	96.47 130	95.37 28	99.27 75	91.99 99	99.14 91	98.48 116

LS3D		96.11 48	95.83 62	96.95 37	94.75 258	94.20 18	97.34 11	97.98 72	97.31 11	95.32 136	96.77 111	93.08 89	99.20 83	91.79 105	98.16 201	97.44 201

PS-CasMVS		96.69 20	97.43 5	94.49 128	99.13 6	84.09 194	96.61 26	97.97 75	97.91 5	98.64 13	98.13 33	95.24 36	99.65 3	93.39 59	99.84 3	99.72 2

region2R		96.41 37	96.09 48	97.38 23	98.62 31	93.81 35	96.32 43	97.96 76	92.26 85	95.28 139	96.57 127	95.02 47	99.41 36	93.63 42	99.11 95	98.94 62

ACMMPR		96.46 32	96.14 45	97.41 21	98.60 34	93.82 33	96.30 46	97.96 76	92.35 82	95.57 125	96.61 125	94.93 51	99.41 36	93.78 38	99.15 90	99.00 51

XVS		96.49 29	96.18 42	97.44 17	98.56 37	93.99 26	96.50 31	97.95 78	94.58 41	94.38 173	96.49 129	94.56 59	99.39 48	93.57 44	99.05 100	98.93 63

X-MVStestdata		90.70 207	88.45 249	97.44 17	98.56 37	93.99 26	96.50 31	97.95 78	94.58 41	94.38 173	26.89 371	94.56 59	99.39 48	93.57 44	99.05 100	98.93 63

Gipuma		95.31 77	95.80 64	93.81 154	97.99 90	90.91 70	96.42 37	97.95 78	96.69 17	91.78 251	98.85 12	91.77 119	95.49 326	91.72 108	99.08 96	95.02 289
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
DTE-MVSNet		96.74 17	97.43 5	94.67 115	99.13 6	84.68 184	96.51 30	97.94 81	98.14 3	98.67 12	98.32 29	95.04 45	99.69 2	93.27 65	99.82 8	99.62 10

PS-MVSNAJss		96.01 51	96.04 52	95.89 65	98.82 23	88.51 114	95.57 74	97.88 82	88.72 178	98.81 6	98.86 10	90.77 144	99.60 8	95.43 11	99.53 35	99.57 13

pmmvs6		96.80 13	97.36 9	95.15 98	99.12 8	87.82 128	96.68 24	97.86 83	96.10 26	98.14 24	99.28 3	97.94 3	98.21 216	91.38 118	99.69 15	99.42 19

TranMVSNet+NR-MVSNet		96.07 50	96.26 38	95.50 82	98.26 66	87.69 129	93.75 139	97.86 83	95.96 30	97.48 41	97.14 89	95.33 32	99.44 24	90.79 126	99.76 11	99.38 22

PHI-MVS		94.34 114	93.80 127	95.95 57	95.65 231	91.67 62	94.82 101	97.86 83	87.86 196	93.04 217	94.16 242	91.58 124	98.78 151	90.27 142	98.96 114	97.41 202

testtj		94.81 96	94.42 111	96.01 54	97.23 130	90.51 77	94.77 103	97.85 86	91.29 120	94.92 157	95.66 179	91.71 121	99.40 43	88.07 195	98.25 191	98.11 143

ETV-MVS		92.99 153	92.74 158	93.72 155	95.86 218	86.30 163	92.33 183	97.84 87	91.70 112	92.81 222	86.17 355	92.22 108	99.19 84	88.03 196	97.73 227	95.66 275

UniMVSNet_NR-MVSNet		95.35 73	95.21 83	95.76 72	97.69 107	88.59 110	92.26 187	97.84 87	94.91 38	96.80 68	95.78 174	90.42 153	99.41 36	91.60 112	99.58 31	99.29 28

3Dnovator+	92.74 2	95.86 57	95.77 65	96.13 52	96.81 153	90.79 73	96.30 46	97.82 89	96.13 25	94.74 164	97.23 84	91.33 130	99.16 86	93.25 66	98.30 185	98.46 118

HQP_MVS		94.26 118	93.93 124	95.23 95	97.71 104	88.12 120	94.56 113	97.81 90	91.74 109	93.31 203	95.59 181	86.93 204	98.95 122	89.26 169	98.51 161	98.60 108

plane_prior5								97.81 90					98.95 122	89.26 169	98.51 161	98.60 108

DU-MVS		95.28 78	95.12 87	95.75 73	97.75 100	88.59 110	92.58 167	97.81 90	93.99 52	96.80 68	95.90 164	90.10 162	99.41 36	91.60 112	99.58 31	99.26 29

APD-MVS		95.00 85	94.69 100	95.93 60	97.38 125	90.88 71	94.59 109	97.81 90	89.22 168	95.46 130	96.17 156	93.42 78	99.34 62	89.30 165	98.87 122	97.56 194
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
SMA-MVS		95.77 59	95.54 70	96.47 50	98.27 65	91.19 66	95.09 91	97.79 94	86.48 218	97.42 45	97.51 64	94.47 63	99.29 71	93.55 46	99.29 68	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 67	97.51 13	98.81 24	94.06 20	96.10 52	97.78 95	92.73 72	93.48 199	96.72 118	94.23 66	99.42 29	91.99 99	99.29 68	99.05 48
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MSLP-MVS++		93.25 145	93.88 125	91.37 230	96.34 181	82.81 211	93.11 152	97.74 96	89.37 162	94.08 179	95.29 200	90.40 156	96.35 310	90.35 136	98.25 191	94.96 290

mPP-MVS		96.46 32	96.05 51	97.69 5	98.62 31	94.65 13	96.45 34	97.74 96	92.59 76	95.47 128	96.68 120	94.50 61	99.42 29	93.10 72	99.26 75	98.99 53

ETH3 D test6400		91.91 184	91.25 195	93.89 150	96.59 161	84.41 186	92.10 192	97.72 98	78.52 303	91.82 250	93.78 257	88.70 174	99.13 91	83.61 253	98.39 171	98.14 139

TAPA-MVS	88.58 10	92.49 171	91.75 183	94.73 112	96.50 170	89.69 87	92.91 158	97.68 99	78.02 307	92.79 223	94.10 243	90.85 143	97.96 237	84.76 245	98.16 201	96.54 234
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CPTT-MVS		94.74 98	94.12 122	96.60 44	98.15 73	93.01 43	95.84 63	97.66 100	89.21 169	93.28 206	95.46 191	88.89 173	98.98 115	89.80 156	98.82 130	97.80 177

DP-MVS		95.62 63	95.84 61	94.97 103	97.16 135	88.62 109	94.54 117	97.64 101	96.94 15	96.58 77	97.32 80	93.07 90	98.72 161	90.45 131	98.84 124	97.57 192

zzz-MVS		96.47 31	96.14 45	97.47 15	98.95 16	94.05 22	93.69 141	97.62 102	94.46 45	96.29 89	96.94 99	93.56 73	99.37 56	94.29 24	99.42 47	98.99 53

MTGPA								97.62 102

MTAPA		96.65 22	96.38 33	97.47 15	98.95 16	94.05 22	95.88 62	97.62 102	94.46 45	96.29 89	96.94 99	93.56 73	99.37 56	94.29 24	99.42 47	98.99 53

anonymousdsp		96.74 17	96.42 29	97.68 7	98.00 87	94.03 25	96.97 17	97.61 105	87.68 202	98.45 18	98.77 15	94.20 67	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 22	92.51 49	96.58 28	97.61 105	87.57 205	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 82	95.67 68	93.58 159	97.76 99	83.15 205	94.58 111	97.58 107	93.39 66	97.05 56	98.04 38	93.25 82	98.51 192	89.75 159	99.59 27	99.08 45

v10		94.68 101	95.27 82	92.90 182	96.57 163	80.15 238	94.65 108	97.57 108	90.68 136	97.43 43	98.00 40	88.18 180	99.15 87	94.84 15	99.55 34	99.41 20

CSCG		94.69 100	94.75 97	94.52 125	97.55 116	87.87 126	95.01 96	97.57 108	92.68 73	96.20 97	93.44 264	91.92 116	98.78 151	89.11 173	99.24 78	96.92 223

ZD-MVS					97.23 130	90.32 78		97.54 110	84.40 253	94.78 162	95.79 171	92.76 99	99.39 48	88.72 183	98.40 168

UniMVSNet_ETH3D		97.13 6	97.72 3	95.35 86	99.51 2	87.38 133	97.70 8	97.54 110	98.16 2	98.94 2	99.33 2	97.84 4	99.08 99	90.73 127	99.73 14	99.59 12

Effi-MVS+		92.79 160	92.74 158	92.94 180	95.10 248	83.30 202	94.00 132	97.53 112	91.36 119	89.35 293	90.65 321	94.01 69	98.66 174	87.40 207	95.30 294	96.88 226

CP-MVSNet		96.19 46	96.80 17	94.38 134	98.99 14	83.82 197	96.31 44	97.53 112	97.60 7	98.34 19	97.52 62	91.98 115	99.63 6	93.08 74	99.81 9	99.70 3

RPSCF		95.58 65	94.89 92	97.62 8	97.58 114	96.30 4	95.97 58	97.53 112	92.42 78	93.41 200	97.78 49	91.21 136	97.77 254	91.06 120	97.06 250	98.80 80

diffmvs		91.74 186	91.93 177	91.15 240	93.06 297	78.17 277	88.77 292	97.51 115	86.28 222	92.42 234	93.96 250	88.04 184	97.46 270	90.69 129	96.67 265	97.82 175

PVSNet_Blended_VisFu		91.63 189	91.20 196	92.94 180	97.73 103	83.95 196	92.14 191	97.46 116	78.85 302	92.35 238	94.98 212	84.16 230	99.08 99	86.36 225	96.77 262	95.79 269

DeepPCF-MVS	90.46 6	94.20 121	93.56 138	96.14 51	95.96 212	92.96 44	89.48 275	97.46 116	85.14 241	96.23 94	95.42 194	93.19 84	98.08 226	90.37 135	98.76 138	97.38 208

jajsoiax		96.59 27	96.42 29	97.12 29	98.76 27	92.49 50	96.44 36	97.42 118	86.96 214	98.71 10	98.72 17	95.36 31	99.56 16	95.92 8	99.45 43	99.32 26

OMC-MVS		94.22 120	93.69 132	95.81 67	97.25 129	91.27 64	92.27 186	97.40 119	87.10 213	94.56 168	95.42 194	93.74 71	98.11 225	86.62 219	98.85 123	98.06 144

v1240		93.29 140	93.71 131	92.06 213	96.01 210	77.89 282	91.81 214	97.37 120	85.12 243	96.69 72	96.40 136	86.67 210	99.07 103	94.51 18	98.76 138	99.22 32

NR-MVSNet		95.28 78	95.28 81	95.26 93	97.75 100	87.21 137	95.08 92	97.37 120	93.92 57	97.65 31	95.90 164	90.10 162	99.33 67	90.11 149	99.66 21	99.26 29

MVSFormer		92.18 179	92.23 169	92.04 214	94.74 260	80.06 242	97.15 13	97.37 120	88.98 172	88.83 298	92.79 279	77.02 287	99.60 8	96.41 4	96.75 263	96.46 241

test_djsdf		96.62 23	96.49 28	97.01 33	98.55 40	91.77 60	97.15 13	97.37 120	88.98 172	98.26 22	98.86 10	93.35 80	99.60 8	96.41 4	99.45 43	99.66 6

DP-MVS Recon		92.31 175	91.88 178	93.60 158	97.18 134	86.87 146	91.10 230	97.37 120	84.92 248	92.08 246	94.08 244	88.59 175	98.20 217	83.50 254	98.14 203	95.73 271

test_prior3		93.29 140	92.85 154	94.61 117	95.95 213	87.23 135	90.21 253	97.36 125	89.33 164	90.77 264	94.81 219	90.41 154	98.68 171	88.21 188	98.55 154	97.93 162

test_prior				94.61 117	95.95 213	87.23 135		97.36 125					98.68 171			97.93 162

QAPM		92.88 157	92.77 156	93.22 172	95.82 219	83.31 201	96.45 34	97.35 127	83.91 256	93.75 191	96.77 111	89.25 171	98.88 129	84.56 247	97.02 252	97.49 198

GeoE		94.55 105	94.68 102	94.15 138	97.23 130	85.11 180	94.14 127	97.34 128	88.71 179	95.26 140	95.50 189	94.65 57	99.12 93	90.94 124	98.40 168	98.23 132

OPM-MVS		95.61 64	95.45 73	96.08 53	98.49 54	91.00 68	92.65 166	97.33 129	90.05 148	96.77 70	96.85 106	95.04 45	98.56 187	92.77 80	99.06 97	98.70 94
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
HQP3-MVS								97.31 130							97.73 227

HQP-MVS		92.09 181	91.49 189	93.88 151	96.36 177	84.89 182	91.37 222	97.31 130	87.16 210	88.81 300	93.40 265	84.76 226	98.60 181	86.55 221	97.73 227	98.14 139

PCF-MVS	84.52 17	89.12 246	87.71 265	93.34 167	96.06 204	85.84 172	86.58 329	97.31 130	68.46 352	93.61 196	93.89 253	87.51 193	98.52 191	67.85 356	98.11 207	95.66 275
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
114514_t		90.51 211	89.80 227	92.63 193	98.00 87	82.24 215	93.40 148	97.29 133	65.84 359	89.40 292	94.80 222	86.99 202	98.75 156	83.88 252	98.61 150	96.89 225

CLD-MVS		91.82 185	91.41 191	93.04 174	96.37 175	83.65 199	86.82 321	97.29 133	84.65 252	92.27 242	89.67 331	92.20 109	97.85 247	83.95 251	99.47 39	97.62 190
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 97	94.90 91	94.47 129	95.47 238	87.06 140	96.63 25	97.28 135	91.82 104	94.34 175	97.41 68	90.60 151	98.65 176	92.47 89	98.11 207	97.70 184

DELS-MVS		92.05 182	92.16 170	91.72 221	94.44 271	80.13 240	87.62 302	97.25 136	87.34 208	92.22 243	93.18 271	89.54 169	98.73 160	89.67 160	98.20 199	96.30 247
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		93.26 143	93.61 135	92.19 206	96.04 209	78.31 275	91.88 207	97.24 137	85.17 240	96.19 99	96.19 153	86.76 209	99.05 104	94.18 28	98.84 124	99.22 32

test_0402		95.73 60	96.22 40	94.26 136	98.19 71	85.77 173	93.24 151	97.24 137	96.88 16	97.69 30	97.77 51	94.12 68	99.13 91	91.54 115	99.29 68	97.88 168

v1192		93.49 134	93.78 128	92.62 194	96.16 196	79.62 254	91.83 213	97.22 139	86.07 226	96.10 103	96.38 141	87.22 197	99.02 110	94.14 29	98.88 119	99.22 32

F-COLMAP		92.28 176	91.06 200	95.95 57	97.52 117	91.90 57	93.53 144	97.18 140	83.98 255	88.70 306	94.04 245	88.41 178	98.55 189	80.17 287	95.99 277	97.39 206

v8		94.65 102	95.29 80	92.74 187	96.65 157	79.77 252	94.59 109	97.17 141	91.86 97	97.47 42	97.93 43	88.16 181	99.08 99	94.32 22	99.47 39	99.38 22

v144192		93.20 148	93.54 139	92.16 210	96.05 205	78.26 276	91.95 200	97.14 142	84.98 247	95.96 106	96.11 157	87.08 201	99.04 107	93.79 37	98.84 124	99.17 35

DeepC-MVS_fast	89.96 7	93.73 130	93.44 142	94.60 121	96.14 198	87.90 125	93.36 149	97.14 142	85.53 235	93.90 188	95.45 192	91.30 132	98.59 183	89.51 162	98.62 149	97.31 211
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MCST-MVS		92.91 155	92.51 165	94.10 140	97.52 117	85.72 174	91.36 225	97.13 144	80.33 284	92.91 221	94.24 238	91.23 135	98.72 161	89.99 153	97.93 220	97.86 170

KD-MVS_self_test		94.10 123	94.73 99	92.19 206	97.66 110	79.49 257	94.86 100	97.12 145	89.59 158	96.87 64	97.65 55	90.40 156	98.34 206	89.08 174	99.35 57	98.75 85

pm-mvs1		95.43 70	95.94 55	93.93 147	98.38 58	85.08 181	95.46 78	97.12 145	91.84 101	97.28 48	98.46 25	95.30 34	97.71 259	90.17 147	99.42 47	98.99 53

save fliter					97.46 122	88.05 122	92.04 195	97.08 147	87.63 203

CDPH-MVS		92.67 165	91.83 179	95.18 97	96.94 144	88.46 115	90.70 239	97.07 148	77.38 309	92.34 240	95.08 207	92.67 101	98.88 129	85.74 230	98.57 153	98.20 136

OpenMVS	89.45 8	92.27 177	92.13 172	92.68 190	94.53 270	84.10 193	95.70 67	97.03 149	82.44 272	91.14 261	96.42 134	88.47 177	98.38 202	85.95 229	97.47 240	95.55 279

原ACMM1				92.87 183	96.91 147	84.22 190		97.01 150	76.84 314	89.64 290	94.46 231	88.00 185	98.70 167	81.53 274	98.01 216	95.70 273

DVP-MVS		95.82 58	96.18 42	94.72 113	98.51 46	86.69 150	95.20 87	97.00 151	91.85 98	97.40 46	97.35 77	95.58 22	99.34 62	93.44 55	99.31 63	98.13 141
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		92.38 173	91.99 175	93.52 164	93.82 287	83.46 200	91.14 228	97.00 151	89.81 153	86.47 327	94.04 245	87.90 188	99.21 82	89.50 163	98.27 187	97.90 166

HPM-MVS++		95.02 84	94.39 112	96.91 38	97.88 93	93.58 37	94.09 129	96.99 153	91.05 126	92.40 235	95.22 201	91.03 142	99.25 77	92.11 94	98.69 144	97.90 166

v1144		93.50 133	93.81 126	92.57 196	96.28 186	79.61 255	91.86 212	96.96 154	86.95 215	95.91 111	96.32 145	87.65 190	98.96 120	93.51 47	98.88 119	99.13 39

MVS_Test		92.57 170	93.29 145	90.40 263	93.53 289	75.85 308	92.52 169	96.96 154	88.73 177	92.35 238	96.70 119	90.77 144	98.37 205	92.53 88	95.49 288	96.99 221

PVSNet_BlendedMVS		90.35 219	89.96 224	91.54 227	94.81 255	78.80 271	90.14 257	96.93 156	79.43 292	88.68 307	95.06 208	86.27 215	98.15 223	80.27 284	98.04 213	97.68 186

PVSNet_Blended		88.74 256	88.16 260	90.46 262	94.81 255	78.80 271	86.64 325	96.93 156	74.67 322	88.68 307	89.18 337	86.27 215	98.15 223	80.27 284	96.00 276	94.44 303

TEST9					96.45 173	89.46 91	90.60 241	96.92 158	79.09 298	90.49 269	94.39 234	91.31 131	98.88 129

train_agg		92.71 164	91.83 179	95.35 86	96.45 173	89.46 91	90.60 241	96.92 158	79.37 293	90.49 269	94.39 234	91.20 137	98.88 129	88.66 184	98.43 166	97.72 183

NCCC		94.08 124	93.54 139	95.70 76	96.49 171	89.90 84	92.39 179	96.91 160	90.64 137	92.33 241	94.60 227	90.58 152	98.96 120	90.21 146	97.70 231	98.23 132

test_8					96.37 175	89.14 98	90.51 244	96.89 161	79.37 293	90.42 271	94.36 236	91.20 137	98.82 139

agg_prior1		92.60 167	91.76 182	95.10 100	96.20 192	88.89 103	90.37 248	96.88 162	79.67 290	90.21 274	94.41 232	91.30 132	98.78 151	88.46 187	98.37 178	97.64 189

agg_prior					96.20 192	88.89 103		96.88 162		90.21 274			98.78 151

MSC_two_6792asdad				95.90 63	96.54 166	89.57 89		96.87 164					99.41 36	94.06 30	99.30 65	98.72 91

No_MVS				95.90 63	96.54 166	89.57 89		96.87 164					99.41 36	94.06 30	99.30 65	98.72 91

MIMVSNet1		95.52 66	95.45 73	95.72 74	99.14 5	89.02 100	96.23 49	96.87 164	93.73 59	97.87 27	98.49 24	90.73 148	99.05 104	86.43 224	99.60 25	99.10 44

IU-MVS					98.51 46	86.66 152		96.83 167	72.74 334	95.83 114				93.00 76	99.29 68	98.64 101

TSAR-MVS + MP.		94.96 87	94.75 97	95.57 80	98.86 21	88.69 106	96.37 39	96.81 168	85.23 238	94.75 163	97.12 90	91.85 117	99.40 43	93.45 53	98.33 180	98.62 105
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
CNVR-MVS		94.58 104	94.29 116	95.46 84	96.94 144	89.35 97	91.81 214	96.80 169	89.66 155	93.90 188	95.44 193	92.80 98	98.72 161	92.74 82	98.52 159	98.32 125

cascas		87.02 290	86.28 291	89.25 288	91.56 325	76.45 302	84.33 344	96.78 170	71.01 342	86.89 326	85.91 356	81.35 255	96.94 289	83.09 258	95.60 285	94.35 305

IterMVS-LS		93.78 129	94.28 117	92.27 203	96.27 187	79.21 264	91.87 208	96.78 170	91.77 107	96.57 78	97.07 92	87.15 199	98.74 159	91.99 99	99.03 106	98.86 73
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Anonymous20240529		95.50 67	95.83 62	94.50 126	97.33 128	85.93 170	95.19 89	96.77 172	96.64 19	97.61 35	98.05 37	93.23 83	98.79 147	88.60 185	99.04 105	98.78 82

TransMVSNet (Re)		95.27 80	96.04 52	92.97 177	98.37 60	81.92 218	95.07 93	96.76 173	93.97 54	97.77 28	98.57 19	95.72 18	97.90 239	88.89 178	99.23 79	99.08 45

EG-PatchMatch MVS		94.54 107	94.67 103	94.14 139	97.87 94	86.50 154	92.00 198	96.74 174	88.16 190	96.93 62	97.61 57	93.04 91	97.90 239	91.60 112	98.12 206	98.03 150

1112_ss		88.42 260	87.41 269	91.45 228	96.69 156	80.99 231	89.72 270	96.72 175	73.37 330	87.00 325	90.69 319	77.38 283	98.20 217	81.38 275	93.72 321	95.15 285

Baseline_NR-MVSNet		94.47 109	95.09 88	92.60 195	98.50 53	80.82 234	92.08 193	96.68 176	93.82 58	96.29 89	98.56 20	90.10 162	97.75 257	90.10 151	99.66 21	99.24 31

eth_miper_zixun_eth		90.72 206	90.61 210	91.05 241	92.04 316	76.84 298	86.91 317	96.67 177	85.21 239	94.41 171	93.92 251	79.53 267	98.26 213	89.76 158	97.02 252	98.06 144

Fast-Effi-MVS+-dtu		92.77 162	92.16 170	94.58 124	94.66 266	88.25 117	92.05 194	96.65 178	89.62 156	90.08 277	91.23 309	92.56 103	98.60 181	86.30 226	96.27 272	96.90 224

test11								96.65 178

RRT_test8_iter05		88.21 263	88.17 258	88.33 303	91.62 323	66.82 358	91.73 217	96.60 180	86.34 221	94.14 176	95.38 199	47.72 371	99.11 95	91.78 106	98.26 188	99.06 47

LF4IMVS		92.72 163	92.02 174	94.84 108	95.65 231	91.99 55	92.92 157	96.60 180	85.08 245	92.44 233	93.62 259	86.80 208	96.35 310	86.81 214	98.25 191	96.18 252

GBi-Net		93.21 146	92.96 151	93.97 144	95.40 240	84.29 187	95.99 55	96.56 182	88.63 180	95.10 147	98.53 21	81.31 256	98.98 115	86.74 215	98.38 173	98.65 97

test1		93.21 146	92.96 151	93.97 144	95.40 240	84.29 187	95.99 55	96.56 182	88.63 180	95.10 147	98.53 21	81.31 256	98.98 115	86.74 215	98.38 173	98.65 97

FMVSNet1		94.84 94	95.13 86	93.97 144	97.60 113	84.29 187	95.99 55	96.56 182	92.38 79	97.03 57	98.53 21	90.12 159	98.98 115	88.78 180	99.16 89	98.65 97

ITE_SJBPF				95.95 57	97.34 127	93.36 41		96.55 185	91.93 94	94.82 160	95.39 197	91.99 114	97.08 285	85.53 232	97.96 218	97.41 202

Fast-Effi-MVS+		91.28 199	90.86 203	92.53 198	95.45 239	82.53 213	89.25 284	96.52 186	85.00 246	89.91 282	88.55 341	92.94 92	98.84 137	84.72 246	95.44 290	96.22 250

V42		93.43 136	93.58 136	92.97 177	95.34 244	81.22 228	92.67 165	96.49 187	87.25 209	96.20 97	96.37 142	87.32 196	98.85 136	92.39 93	98.21 197	98.85 76

PLC	85.34 15	90.40 215	88.92 241	94.85 107	96.53 169	90.02 80	91.58 219	96.48 188	80.16 285	86.14 329	92.18 293	85.73 220	98.25 214	76.87 316	94.61 309	96.30 247
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
c3_l		91.32 198	91.42 190	91.00 245	92.29 309	76.79 299	87.52 308	96.42 189	85.76 232	94.72 166	93.89 253	82.73 241	98.16 222	90.93 125	98.55 154	98.04 147

Regformer-2		94.86 92	94.55 107	95.77 71	92.83 302	89.98 81	91.87 208	96.40 190	94.38 47	96.19 99	95.04 209	92.47 107	99.04 107	93.49 48	98.31 183	98.28 129

USDC		89.02 247	89.08 237	88.84 293	95.07 249	74.50 319	88.97 287	96.39 191	73.21 331	93.27 207	96.28 148	82.16 248	96.39 307	77.55 310	98.80 134	95.62 278

ambc				92.98 176	96.88 148	83.01 209	95.92 60	96.38 192		96.41 80	97.48 65	88.26 179	97.80 250	89.96 154	98.93 116	98.12 142

PAPM_NR		91.03 201	90.81 205	91.68 223	96.73 155	81.10 230	93.72 140	96.35 193	88.19 189	88.77 304	92.12 296	85.09 225	97.25 280	82.40 266	93.90 318	96.68 233

v2v482		93.29 140	93.63 134	92.29 202	96.35 180	78.82 269	91.77 216	96.28 194	88.45 184	95.70 121	96.26 150	86.02 218	98.90 126	93.02 75	98.81 132	99.14 38

AdaColmap		91.63 189	91.36 192	92.47 201	95.56 236	86.36 161	92.24 189	96.27 195	88.88 176	89.90 283	92.69 282	91.65 123	98.32 207	77.38 313	97.64 234	92.72 337

Test_1112_low_res		87.50 278	86.58 284	90.25 267	96.80 154	77.75 284	87.53 307	96.25 196	69.73 348	86.47 327	93.61 260	75.67 296	97.88 241	79.95 289	93.20 326	95.11 287

test12				94.43 132	95.95 213	86.75 148		96.24 197		89.76 288		89.79 166	98.79 147		97.95 219	97.75 182

PAPR		87.65 274	86.77 282	90.27 266	92.85 301	77.38 289	88.56 297	96.23 198	76.82 315	84.98 335	89.75 330	86.08 217	97.16 283	72.33 340	93.35 324	96.26 249

MVS_111021_HR		93.63 132	93.42 143	94.26 136	96.65 157	86.96 145	89.30 281	96.23 198	88.36 187	93.57 197	94.60 227	93.45 75	97.77 254	90.23 144	98.38 173	98.03 150

XXY-MVS		92.58 168	93.16 150	90.84 251	97.75 100	79.84 248	91.87 208	96.22 200	85.94 228	95.53 127	97.68 53	92.69 100	94.48 339	83.21 257	97.51 238	98.21 135

MSDG		90.82 203	90.67 209	91.26 234	94.16 276	83.08 207	86.63 326	96.19 201	90.60 139	91.94 248	91.89 299	89.16 172	95.75 321	80.96 282	94.51 310	94.95 291

miper_ehance_all_eth		90.48 212	90.42 215	90.69 254	91.62 323	76.57 301	86.83 320	96.18 202	83.38 258	94.06 181	92.66 284	82.20 247	98.04 228	89.79 157	97.02 252	97.45 200

TinyColmap		92.00 183	92.76 157	89.71 279	95.62 234	77.02 293	90.72 238	96.17 203	87.70 201	95.26 140	96.29 147	92.54 104	96.45 305	81.77 271	98.77 137	95.66 275

DPM-MVS		89.35 242	88.40 250	92.18 209	96.13 201	84.20 191	86.96 316	96.15 204	75.40 320	87.36 322	91.55 307	83.30 234	98.01 232	82.17 269	96.62 266	94.32 306

HyFIR lowres test		87.19 286	85.51 296	92.24 204	97.12 139	80.51 235	85.03 336	96.06 205	66.11 358	91.66 252	92.98 275	70.12 314	99.14 89	75.29 325	95.23 296	97.07 216

xiu_mvs_v1_base_debu		91.47 193	91.52 186	91.33 231	95.69 228	81.56 222	89.92 264	96.05 206	83.22 260	91.26 257	90.74 316	91.55 125	98.82 139	89.29 166	95.91 278	93.62 323

xiu_mvs_v1_base		91.47 193	91.52 186	91.33 231	95.69 228	81.56 222	89.92 264	96.05 206	83.22 260	91.26 257	90.74 316	91.55 125	98.82 139	89.29 166	95.91 278	93.62 323

xiu_mvs_v1_base_debi		91.47 193	91.52 186	91.33 231	95.69 228	81.56 222	89.92 264	96.05 206	83.22 260	91.26 257	90.74 316	91.55 125	98.82 139	89.29 166	95.91 278	93.62 323

Regformer-4		94.90 89	94.67 103	95.59 78	92.78 304	89.02 100	92.39 179	95.91 209	94.50 43	96.41 80	95.56 186	92.10 111	99.01 112	94.23 26	98.14 203	98.74 88

UnsupCasMVSNet_eth		90.33 220	90.34 216	90.28 265	94.64 267	80.24 236	89.69 271	95.88 210	85.77 231	93.94 187	95.69 177	81.99 250	92.98 354	84.21 250	91.30 345	97.62 190

CANet_DTU		89.85 236	89.17 235	91.87 216	92.20 312	80.02 245	90.79 236	95.87 211	86.02 227	82.53 351	91.77 302	80.01 264	98.57 186	85.66 231	97.70 231	97.01 220

PMVS	87.21 14	94.97 86	95.33 78	93.91 149	98.97 15	97.16 2	95.54 75	95.85 212	96.47 21	93.40 202	97.46 66	95.31 33	95.47 327	86.18 228	98.78 136	89.11 354
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Regformer-1		94.55 105	94.33 115	95.19 96	92.83 302	88.54 113	91.87 208	95.84 213	93.99 52	95.95 107	95.04 209	92.00 113	98.79 147	93.14 71	98.31 183	98.23 132

alignmvs		93.26 143	92.85 154	94.50 126	95.70 227	87.45 131	93.45 147	95.76 214	91.58 114	95.25 142	92.42 291	81.96 251	98.72 161	91.61 111	97.87 223	97.33 210

无先验							89.94 263	95.75 215	70.81 344				98.59 183	81.17 279		94.81 292

WR-MVS		93.49 134	93.72 130	92.80 186	97.57 115	80.03 244	90.14 257	95.68 216	93.70 60	96.62 75	95.39 197	87.21 198	99.04 107	87.50 204	99.64 23	99.33 25

VPNet		93.08 149	93.76 129	91.03 242	98.60 34	75.83 310	91.51 220	95.62 217	91.84 101	95.74 118	97.10 91	89.31 170	98.32 207	85.07 241	99.06 97	98.93 63

Anonymous20240521		92.86 159	93.57 137	90.74 253	96.57 163	75.50 312	94.15 126	95.60 218	89.38 161	95.90 112	97.90 47	80.39 263	97.96 237	92.60 87	99.68 18	98.75 85

xiu_mvs_v2_base		89.00 249	89.19 234	88.46 301	94.86 253	74.63 316	86.97 315	95.60 218	80.88 280	87.83 317	88.62 340	91.04 141	98.81 144	82.51 265	94.38 311	91.93 343

PS-MVSNAJ		88.86 253	88.99 240	88.48 300	94.88 251	74.71 314	86.69 324	95.60 218	80.88 280	87.83 317	87.37 348	90.77 144	98.82 139	82.52 264	94.37 312	91.93 343

CHOSEN 1792x2688		87.19 286	85.92 294	91.00 245	97.13 138	79.41 258	84.51 342	95.60 218	64.14 362	90.07 279	94.81 219	78.26 277	97.14 284	73.34 334	95.38 293	96.46 241

miper_enhance_ethall		88.42 260	87.87 263	90.07 272	88.67 356	75.52 311	85.10 335	95.59 222	75.68 316	92.49 231	89.45 334	78.96 269	97.88 241	87.86 200	97.02 252	96.81 228

MVP-Stereo		90.07 230	88.92 241	93.54 162	96.31 184	86.49 155	90.93 233	95.59 222	79.80 286	91.48 253	95.59 181	80.79 260	97.39 276	78.57 304	91.19 346	96.76 231
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
cdsmvs_eth3d_5k		23.35 340	31.13 343	0.00 358	0.00 381	0.00 382	0.00 369	95.58 224	0.00 376	0.00 377	91.15 310	93.43 77	0.00 377	0.00 375	0.00 375	0.00 373

CNLPA		91.72 187	91.20 196	93.26 171	96.17 195	91.02 67	91.14 228	95.55 225	90.16 147	90.87 263	93.56 262	86.31 214	94.40 342	79.92 293	97.12 249	94.37 304

FMVSNet2		92.78 161	92.73 160	92.95 179	95.40 240	81.98 217	94.18 125	95.53 226	88.63 180	96.05 104	97.37 71	81.31 256	98.81 144	87.38 208	98.67 147	98.06 144

ab-mvs		92.40 172	92.62 162	91.74 220	97.02 140	81.65 221	95.84 63	95.50 227	86.95 215	92.95 220	97.56 59	90.70 149	97.50 267	79.63 294	97.43 241	96.06 256

MVS_111021_LR		93.66 131	93.28 147	94.80 109	96.25 190	90.95 69	90.21 253	95.43 228	87.91 193	93.74 193	94.40 233	92.88 96	96.38 308	90.39 133	98.28 186	97.07 216

tfpnnormal		94.27 117	94.87 93	92.48 200	97.71 104	80.88 233	94.55 115	95.41 229	93.70 60	96.67 73	97.72 52	91.40 128	98.18 220	87.45 205	99.18 86	98.36 123

Effi-MVS+-dtu		93.90 128	92.60 164	97.77 4	94.74 260	96.67 3	94.00 132	95.41 229	89.94 149	91.93 249	92.13 295	90.12 159	98.97 119	87.68 202	97.48 239	97.67 187

mvs-test1		93.07 151	91.80 181	96.89 39	94.74 260	95.83 6	92.17 190	95.41 229	89.94 149	89.85 284	90.59 322	90.12 159	98.88 129	87.68 202	95.66 284	95.97 259

cl____		90.65 209	90.56 212	90.91 249	91.85 318	76.98 296	86.75 322	95.36 232	85.53 235	94.06 181	94.89 216	77.36 285	97.98 236	90.27 142	98.98 108	97.76 180

DIV-MVS_self_test		90.65 209	90.56 212	90.91 249	91.85 318	76.99 295	86.75 322	95.36 232	85.52 237	94.06 181	94.89 216	77.37 284	97.99 235	90.28 141	98.97 112	97.76 180

testgi		90.38 217	91.34 193	87.50 313	97.49 119	71.54 338	89.43 276	95.16 234	88.38 186	94.54 169	94.68 226	92.88 96	93.09 353	71.60 345	97.85 224	97.88 168

v148		92.87 158	93.29 145	91.62 224	96.25 190	77.72 285	91.28 226	95.05 235	89.69 154	95.93 110	96.04 159	87.34 195	98.38 202	90.05 152	97.99 217	98.78 82

miper_lstm_enhance		89.90 235	89.80 227	90.19 271	91.37 327	77.50 287	83.82 349	95.00 236	84.84 249	93.05 216	94.96 213	76.53 294	95.20 335	89.96 154	98.67 147	97.86 170

VNet		92.67 165	92.96 151	91.79 218	96.27 187	80.15 238	91.95 200	94.98 237	92.19 88	94.52 170	96.07 158	87.43 194	97.39 276	84.83 243	98.38 173	97.83 173

FMVSNet3		90.78 205	90.32 217	92.16 210	93.03 299	79.92 247	92.54 168	94.95 238	86.17 225	95.10 147	96.01 161	69.97 315	98.75 156	86.74 215	98.38 173	97.82 175

BH-untuned		90.68 208	90.90 201	90.05 274	95.98 211	79.57 256	90.04 260	94.94 239	87.91 193	94.07 180	93.00 273	87.76 189	97.78 253	79.19 300	95.17 297	92.80 335

RRT_MVS		91.36 196	90.05 223	95.29 92	89.21 351	88.15 119	92.51 173	94.89 240	86.73 217	95.54 126	95.68 178	61.82 350	99.30 70	94.91 13	99.13 94	98.43 120

D2MVS		89.93 234	89.60 232	90.92 247	94.03 281	78.40 274	88.69 294	94.85 241	78.96 300	93.08 214	95.09 206	74.57 298	96.94 289	88.19 190	98.96 114	97.41 202

SixPastTwentyTwo		94.91 88	95.21 83	93.98 143	98.52 45	83.19 204	95.93 59	94.84 242	94.86 39	98.49 15	98.74 16	81.45 254	99.60 8	94.69 16	99.39 54	99.15 37

旧先验1					96.20 192	84.17 192		94.82 243			95.57 185	89.57 168			97.89 222	96.32 246

API-MVS		91.52 192	91.61 184	91.26 234	94.16 276	86.26 165	94.66 107	94.82 243	91.17 124	92.13 245	91.08 312	90.03 165	97.06 286	79.09 301	97.35 244	90.45 352

FMVSNet5		87.82 270	86.56 285	91.62 224	92.31 308	79.81 251	93.49 145	94.81 245	83.26 259	91.36 255	96.93 101	52.77 367	97.49 269	76.07 321	98.03 214	97.55 195

MAR-MVS		90.32 221	88.87 244	94.66 116	94.82 254	91.85 58	94.22 124	94.75 246	80.91 279	87.52 321	88.07 344	86.63 211	97.87 244	76.67 317	96.21 273	94.25 307
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		90.37 218	91.30 194	87.58 312	92.17 313	68.00 352	89.84 268	94.73 247	83.82 257	93.22 211	97.40 69	87.54 192	97.40 275	87.94 198	95.05 299	97.34 209

Regformer-3		94.28 116	94.23 121	94.46 130	92.78 304	86.28 164	92.39 179	94.70 248	93.69 63	95.97 105	95.56 186	91.34 129	98.48 197	93.45 53	98.14 203	98.62 105

EI-MVSNet-UG-set		94.35 113	94.27 119	94.59 122	92.46 307	85.87 171	92.42 177	94.69 249	93.67 64	96.13 101	95.84 169	91.20 137	98.86 134	93.78 38	98.23 194	99.03 49

EI-MVSNet-Vis-set		94.36 112	94.28 117	94.61 117	92.55 306	85.98 169	92.44 175	94.69 249	93.70 60	96.12 102	95.81 170	91.24 134	98.86 134	93.76 41	98.22 196	98.98 58

EI-MVSNet		92.99 153	93.26 149	92.19 206	92.12 314	79.21 264	92.32 184	94.67 251	91.77 107	95.24 143	95.85 166	87.14 200	98.49 193	91.99 99	98.26 188	98.86 73

MVSTER		89.32 243	88.75 245	91.03 242	90.10 341	76.62 300	90.85 234	94.67 251	82.27 273	95.24 143	95.79 171	61.09 353	98.49 193	90.49 130	98.26 188	97.97 159

新几何1				93.17 173	97.16 135	87.29 134		94.43 253	67.95 353	91.29 256	94.94 214	86.97 203	98.23 215	81.06 281	97.75 226	93.98 314

1121		90.26 223	89.23 233	93.34 167	97.15 137	87.40 132	91.94 202	94.39 254	67.88 354	91.02 262	94.91 215	86.91 206	98.59 183	81.17 279	97.71 230	94.02 313

CMPMVS	68.83 22	87.28 282	85.67 295	92.09 212	88.77 355	85.42 177	90.31 251	94.38 255	70.02 347	88.00 315	93.30 267	73.78 302	94.03 347	75.96 323	96.54 267	96.83 227
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
IS-MVSNet		94.49 108	94.35 114	94.92 104	98.25 68	86.46 157	97.13 15	94.31 256	96.24 24	96.28 92	96.36 143	82.88 238	99.35 59	88.19 190	99.52 37	98.96 60

testdata				91.03 242	96.87 149	82.01 216		94.28 257	71.55 338	92.46 232	95.42 194	85.65 222	97.38 278	82.64 262	97.27 245	93.70 321

UGNet		93.08 149	92.50 166	94.79 110	93.87 285	87.99 124	95.07 93	94.26 258	90.64 137	87.33 323	97.67 54	86.89 207	98.49 193	88.10 193	98.71 141	97.91 165
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		84.98 302	84.30 302	87.01 316	91.03 329	77.69 286	91.94 202	94.16 259	59.36 367	84.23 341	87.50 347	85.66 221	96.80 295	71.79 342	93.05 331	86.54 359

1314		86.46 293	86.33 290	86.87 318	91.65 322	74.54 317	91.94 202	94.10 260	74.28 324	84.78 337	87.33 349	83.03 237	95.00 336	78.72 302	91.16 347	91.06 349

cl22		89.02 247	88.50 248	90.59 258	89.76 343	76.45 302	86.62 327	94.03 261	82.98 266	92.65 227	92.49 285	72.05 308	97.53 265	88.93 175	97.02 252	97.78 178

EPP-MVSNet		93.91 127	93.68 133	94.59 122	98.08 77	85.55 176	97.44 10	94.03 261	94.22 49	94.94 155	96.19 153	82.07 249	99.57 13	87.28 209	98.89 117	98.65 97

UnsupCasMVSNet_bld		88.50 259	88.03 261	89.90 276	95.52 237	78.88 268	87.39 309	94.02 263	79.32 296	93.06 215	94.02 247	80.72 261	94.27 344	75.16 326	93.08 330	96.54 234

h-mvs33		92.89 156	91.99 175	95.58 79	96.97 142	90.55 75	93.94 135	94.01 264	89.23 166	93.95 185	96.19 153	76.88 290	99.14 89	91.02 121	95.71 283	97.04 219

pmmvs-eth3d		91.54 191	90.73 208	93.99 142	95.76 224	87.86 127	90.83 235	93.98 265	78.23 306	94.02 184	96.22 152	82.62 244	96.83 294	86.57 220	98.33 180	97.29 212

BH-RMVSNet		90.47 213	90.44 214	90.56 259	95.21 247	78.65 273	89.15 285	93.94 266	88.21 188	92.74 225	94.22 239	86.38 213	97.88 241	78.67 303	95.39 292	95.14 286

test222					96.95 143	85.27 179	88.83 290	93.61 267	65.09 361	90.74 266	94.85 218	84.62 228			97.36 243	93.91 315

CDS-MVSNet		89.55 239	88.22 257	93.53 163	95.37 243	86.49 155	89.26 282	93.59 268	79.76 288	91.15 260	92.31 292	77.12 286	98.38 202	77.51 311	97.92 221	95.71 272
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
new-patchmatchnet		88.97 250	90.79 206	83.50 340	94.28 275	55.83 374	85.34 334	93.56 269	86.18 224	95.47 128	95.73 176	83.10 236	96.51 303	85.40 233	98.06 211	98.16 137

IterMVS-SCA-FT		91.65 188	91.55 185	91.94 215	93.89 284	79.22 263	87.56 305	93.51 270	91.53 116	95.37 133	96.62 124	78.65 272	98.90 126	91.89 104	94.95 300	97.70 184

Anonymous20231206		88.77 255	88.29 253	90.20 270	96.31 184	78.81 270	89.56 274	93.49 271	74.26 325	92.38 236	95.58 184	82.21 246	95.43 329	72.07 341	98.75 140	96.34 245

OpenMVS_ROB	85.12 16	89.52 241	89.05 238	90.92 247	94.58 268	81.21 229	91.10 230	93.41 272	77.03 313	93.41 200	93.99 249	83.23 235	97.80 250	79.93 291	94.80 304	93.74 320

VDD-MVS		94.37 111	94.37 113	94.40 133	97.49 119	86.07 168	93.97 134	93.28 273	94.49 44	96.24 93	97.78 49	87.99 186	98.79 147	88.92 176	99.14 91	98.34 124

jason		89.17 245	88.32 251	91.70 222	95.73 226	80.07 241	88.10 299	93.22 274	71.98 337	90.09 276	92.79 279	78.53 275	98.56 187	87.43 206	97.06 250	96.46 241
jason: jason.
PAPM		81.91 321	80.11 331	87.31 315	93.87 285	72.32 336	84.02 347	93.22 274	69.47 349	76.13 368	89.84 325	72.15 307	97.23 281	53.27 369	89.02 352	92.37 340

BH-w/o		87.21 284	87.02 278	87.79 311	94.77 257	77.27 291	87.90 300	93.21 276	81.74 277	89.99 281	88.39 343	83.47 232	96.93 291	71.29 346	92.43 338	89.15 353

ppachtmachnet_test		88.61 258	88.64 246	88.50 299	91.76 320	70.99 342	84.59 341	92.98 277	79.30 297	92.38 236	93.53 263	79.57 266	97.45 271	86.50 223	97.17 248	97.07 216

IterMVS		90.18 224	90.16 218	90.21 269	93.15 295	75.98 307	87.56 305	92.97 278	86.43 220	94.09 178	96.40 136	78.32 276	97.43 272	87.87 199	94.69 307	97.23 213
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
test20.03		90.80 204	90.85 204	90.63 256	95.63 233	79.24 262	89.81 269	92.87 279	89.90 151	94.39 172	96.40 136	85.77 219	95.27 334	73.86 332	99.05 100	97.39 206

CR-MVSNet		87.89 267	87.12 276	90.22 268	91.01 330	78.93 266	92.52 169	92.81 280	73.08 332	89.10 295	96.93 101	67.11 321	97.64 262	88.80 179	92.70 334	94.08 308

Patchmtry		90.11 227	89.92 225	90.66 255	90.35 339	77.00 294	92.96 156	92.81 280	90.25 146	94.74 164	96.93 101	67.11 321	97.52 266	85.17 234	98.98 108	97.46 199

GA-MVS		87.70 271	86.82 280	90.31 264	93.27 292	77.22 292	84.72 340	92.79 282	85.11 244	89.82 285	90.07 323	66.80 324	97.76 256	84.56 247	94.27 315	95.96 260

sss		87.23 283	86.82 280	88.46 301	93.96 282	77.94 279	86.84 319	92.78 283	77.59 308	87.61 320	91.83 301	78.75 271	91.92 357	77.84 307	94.20 316	95.52 280

Patchmatch-RL test		88.81 254	88.52 247	89.69 280	95.33 245	79.94 246	86.22 330	92.71 284	78.46 304	95.80 115	94.18 241	66.25 329	95.33 332	89.22 171	98.53 158	93.78 318

test_yl		90.11 227	89.73 230	91.26 234	94.09 279	79.82 249	90.44 245	92.65 285	90.90 128	93.19 212	93.30 267	73.90 300	98.03 229	82.23 267	96.87 258	95.93 261

DCV-MVSNet		90.11 227	89.73 230	91.26 234	94.09 279	79.82 249	90.44 245	92.65 285	90.90 128	93.19 212	93.30 267	73.90 300	98.03 229	82.23 267	96.87 258	95.93 261

CL-MVSNet_self_test		90.04 232	89.90 226	90.47 260	95.24 246	77.81 283	86.60 328	92.62 287	85.64 234	93.25 210	93.92 251	83.84 231	96.06 317	79.93 291	98.03 214	97.53 196

TSAR-MVS + GP.		93.07 151	92.41 168	95.06 101	95.82 219	90.87 72	90.97 232	92.61 288	88.04 192	94.61 167	93.79 256	88.08 182	97.81 249	89.41 164	98.39 171	96.50 239

TAMVS		90.16 225	89.05 238	93.49 165	96.49 171	86.37 160	90.34 250	92.55 289	80.84 282	92.99 218	94.57 229	81.94 252	98.20 217	73.51 333	98.21 197	95.90 264

MS-PatchMatch		88.05 266	87.75 264	88.95 290	93.28 291	77.93 280	87.88 301	92.49 290	75.42 319	92.57 230	93.59 261	80.44 262	94.24 346	81.28 276	92.75 333	94.69 298

MG-MVS		89.54 240	89.80 227	88.76 294	94.88 251	72.47 335	89.60 272	92.44 291	85.82 230	89.48 291	95.98 162	82.85 239	97.74 258	81.87 270	95.27 295	96.08 255

MVS_0304		90.96 202	90.15 221	93.37 166	93.17 294	87.06 140	93.62 143	92.43 292	89.60 157	82.25 352	95.50 189	82.56 245	97.83 248	84.41 249	97.83 225	95.22 283

lupinMVS		88.34 262	87.31 270	91.45 228	94.74 260	80.06 242	87.23 310	92.27 293	71.10 341	88.83 298	91.15 310	77.02 287	98.53 190	86.67 218	96.75 263	95.76 270

pmmvs5		87.87 268	87.14 275	90.07 272	93.26 293	76.97 297	88.89 289	92.18 294	73.71 329	88.36 310	93.89 253	76.86 292	96.73 297	80.32 283	96.81 260	96.51 236

PM-MVS		93.33 138	92.67 161	95.33 88	96.58 162	94.06 20	92.26 187	92.18 294	85.92 229	96.22 95	96.61 125	85.64 223	95.99 319	90.35 136	98.23 194	95.93 261

pmmvs4		88.95 251	87.70 266	92.70 189	94.30 274	85.60 175	87.22 311	92.16 296	74.62 323	89.75 289	94.19 240	77.97 279	96.41 306	82.71 261	96.36 271	96.09 254

MDA-MVSNet-bldmvs		91.04 200	90.88 202	91.55 226	94.68 265	80.16 237	85.49 333	92.14 297	90.41 144	94.93 156	95.79 171	85.10 224	96.93 291	85.15 236	94.19 317	97.57 192

door-mid								92.13 298

WTY-MVS		86.93 291	86.50 289	88.24 304	94.96 250	74.64 315	87.19 312	92.07 299	78.29 305	88.32 311	91.59 306	78.06 278	94.27 344	74.88 327	93.15 328	95.80 268

AUN-MVS		90.05 231	88.30 252	95.32 91	96.09 202	90.52 76	92.42 177	92.05 300	82.08 275	88.45 309	92.86 276	65.76 331	98.69 169	88.91 177	96.07 274	96.75 232

hse-mvs2		92.24 178	91.20 196	95.38 85	96.16 196	90.65 74	92.52 169	92.01 301	89.23 166	93.95 185	92.99 274	76.88 290	98.69 169	91.02 121	96.03 275	96.81 228

TR-MVS		87.70 271	87.17 274	89.27 287	94.11 278	79.26 261	88.69 294	91.86 302	81.94 276	90.69 267	89.79 328	82.82 240	97.42 273	72.65 339	91.98 342	91.14 348

VDDNet		94.03 125	94.27 119	93.31 169	98.87 20	82.36 214	95.51 77	91.78 303	97.19 12	96.32 86	98.60 18	84.24 229	98.75 156	87.09 212	98.83 129	98.81 79

Anonymous202405211		92.58 168	92.50 166	92.83 185	96.55 165	83.22 203	92.43 176	91.64 304	94.10 51	95.59 124	96.64 123	81.88 253	97.50 267	85.12 238	98.52 159	97.77 179

HY-MVS	82.50 18	86.81 292	85.93 293	89.47 281	93.63 288	77.93 280	94.02 131	91.58 305	75.68 316	83.64 344	93.64 258	77.40 282	97.42 273	71.70 344	92.07 341	93.05 332

door								91.26 306

PatchMatch-RL		89.18 244	88.02 262	92.64 191	95.90 217	92.87 46	88.67 296	91.06 307	80.34 283	90.03 280	91.67 304	83.34 233	94.42 341	76.35 320	94.84 303	90.64 351

ADS-MVSNet2		84.01 307	82.20 315	89.41 283	89.04 352	76.37 304	87.57 303	90.98 308	72.71 335	84.46 338	92.45 287	68.08 317	96.48 304	70.58 351	83.97 360	95.38 281

KD-MVS_2432*1600		82.17 318	80.75 325	86.42 321	82.04 374	70.09 346	81.75 355	90.80 309	82.56 268	90.37 272	89.30 335	42.90 376	96.11 315	74.47 328	92.55 336	93.06 330

miper_refine_blended		82.17 318	80.75 325	86.42 321	82.04 374	70.09 346	81.75 355	90.80 309	82.56 268	90.37 272	89.30 335	42.90 376	96.11 315	74.47 328	92.55 336	93.06 330

wuyk23d		87.83 269	90.79 206	78.96 349	90.46 338	88.63 108	92.72 162	90.67 311	91.65 113	98.68 11	97.64 56	96.06 16	77.53 370	59.84 365	99.41 52	70.73 368

our_test_3		87.55 276	87.59 267	87.44 314	91.76 320	70.48 343	83.83 348	90.55 312	79.79 287	92.06 247	92.17 294	78.63 274	95.63 322	84.77 244	94.73 305	96.22 250

test_method		50.44 338	48.94 341	54.93 353	39.68 377	12.38 379	28.59 368	90.09 313	6.82 372	41.10 374	78.41 366	54.41 363	70.69 372	50.12 370	51.26 372	81.72 366

EU-MVSNet		87.39 280	86.71 283	89.44 282	93.40 290	76.11 305	94.93 99	90.00 314	57.17 368	95.71 120	97.37 71	64.77 337	97.68 261	92.67 85	94.37 312	94.52 301

CHOSEN 280x420		80.04 332	77.97 338	86.23 324	90.13 340	74.53 318	72.87 363	89.59 315	66.38 357	76.29 367	85.32 358	56.96 359	95.36 330	69.49 354	94.72 306	88.79 356

MDA-MVSNet_test_wron		88.16 265	88.23 256	87.93 308	92.22 310	73.71 324	80.71 358	88.84 316	82.52 270	94.88 159	95.14 203	82.70 242	93.61 349	83.28 256	93.80 320	96.46 241

YYNet1		88.17 264	88.24 255	87.93 308	92.21 311	73.62 325	80.75 357	88.77 317	82.51 271	94.99 154	95.11 205	82.70 242	93.70 348	83.33 255	93.83 319	96.48 240

PVSNet	76.22 20	82.89 313	82.37 313	84.48 335	93.96 282	64.38 367	78.60 360	88.61 318	71.50 339	84.43 340	86.36 354	74.27 299	94.60 338	69.87 353	93.69 322	94.46 302

MIMVSNet		87.13 288	86.54 286	88.89 292	96.05 205	76.11 305	94.39 119	88.51 319	81.37 278	88.27 312	96.75 114	72.38 306	95.52 324	65.71 361	95.47 289	95.03 288

tpmvs		84.22 306	83.97 305	84.94 331	87.09 363	65.18 362	91.21 227	88.35 320	82.87 267	85.21 332	90.96 314	65.24 335	96.75 296	79.60 297	85.25 359	92.90 334

EPNet_dtu		85.63 297	84.37 301	89.40 284	86.30 366	74.33 321	91.64 218	88.26 321	84.84 249	72.96 370	89.85 324	71.27 311	97.69 260	76.60 318	97.62 235	96.18 252
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tpm cat1		80.61 330	79.46 333	84.07 338	88.78 354	65.06 365	89.26 282	88.23 322	62.27 365	81.90 357	89.66 332	62.70 348	95.29 333	71.72 343	80.60 367	91.86 345

baseline1		87.62 275	87.31 270	88.54 298	94.71 264	74.27 322	93.10 153	88.20 323	86.20 223	92.18 244	93.04 272	73.21 303	95.52 324	79.32 298	85.82 358	95.83 266

CVMVSNet		85.16 300	84.72 298	86.48 319	92.12 314	70.19 344	92.32 184	88.17 324	56.15 369	90.64 268	95.85 166	67.97 319	96.69 298	88.78 180	90.52 349	92.56 338

SCA		87.43 279	87.21 273	88.10 306	92.01 317	71.98 337	89.43 276	88.11 325	82.26 274	88.71 305	92.83 277	78.65 272	97.59 263	79.61 295	93.30 325	94.75 295

tpmrst		82.85 314	82.93 312	82.64 342	87.65 357	58.99 372	90.14 257	87.90 326	75.54 318	83.93 342	91.63 305	66.79 326	95.36 330	81.21 278	81.54 366	93.57 326

Vis-MVSNet (Re-imp)		90.42 214	90.16 218	91.20 238	97.66 110	77.32 290	94.33 121	87.66 327	91.20 123	92.99 218	95.13 204	75.40 297	98.28 209	77.86 306	99.19 84	97.99 155

bset_n11_16_dypcd		89.99 233	89.15 236	92.53 198	94.75 258	81.34 226	84.19 345	87.56 328	85.13 242	93.77 190	92.46 286	72.82 304	99.01 112	92.46 90	99.21 82	97.23 213

MDTV_nov1_ep13			83.88 306		89.42 349	61.52 370	88.74 293	87.41 329	73.99 327	84.96 336	94.01 248	65.25 334	95.53 323	78.02 305	93.16 327

PatchmatchNet		85.22 299	84.64 299	86.98 317	89.51 348	69.83 349	90.52 243	87.34 330	78.87 301	87.22 324	92.74 281	66.91 323	96.53 301	81.77 271	86.88 357	94.58 299
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
N_pmnet		88.90 252	87.25 272	93.83 153	94.40 273	93.81 35	84.73 338	87.09 331	79.36 295	93.26 208	92.43 290	79.29 268	91.68 358	77.50 312	97.22 247	96.00 258

EPNet		89.80 238	88.25 254	94.45 131	83.91 372	86.18 166	93.87 136	87.07 332	91.16 125	80.64 361	94.72 224	78.83 270	98.89 128	85.17 234	98.89 117	98.28 129
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Patchmatch-test		86.10 295	86.01 292	86.38 323	90.63 334	74.22 323	89.57 273	86.69 333	85.73 233	89.81 286	92.83 277	65.24 335	91.04 360	77.82 309	95.78 282	93.88 317

K. test v3		93.37 137	93.27 148	93.66 156	98.05 80	82.62 212	94.35 120	86.62 334	96.05 28	97.51 40	98.85 12	76.59 293	99.65 3	93.21 67	98.20 199	98.73 90

CostFormer		83.09 311	82.21 314	85.73 325	89.27 350	67.01 353	90.35 249	86.47 335	70.42 345	83.52 346	93.23 270	61.18 352	96.85 293	77.21 314	88.26 355	93.34 328

thres200		85.85 296	85.18 297	87.88 310	94.44 271	72.52 334	89.08 286	86.21 336	88.57 183	91.44 254	88.40 342	64.22 338	98.00 233	68.35 355	95.88 281	93.12 329

ET-MVSNet_ETH3D		86.15 294	84.27 303	91.79 218	93.04 298	81.28 227	87.17 313	86.14 337	79.57 291	83.65 343	88.66 339	57.10 358	98.18 220	87.74 201	95.40 291	95.90 264

PatchT		87.51 277	88.17 258	85.55 326	90.64 333	66.91 354	92.02 197	86.09 338	92.20 87	89.05 297	97.16 88	64.15 339	96.37 309	89.21 172	92.98 332	93.37 327

DWT-MVSNet_test		80.74 328	79.18 334	85.43 328	87.51 360	66.87 355	89.87 267	86.01 339	74.20 326	80.86 360	80.62 365	48.84 369	96.68 300	81.54 273	83.14 364	92.75 336

tfpn200view9		87.05 289	86.52 287	88.67 296	95.77 222	72.94 330	91.89 205	86.00 340	90.84 130	92.61 228	89.80 326	63.93 340	98.28 209	71.27 347	96.54 267	94.79 293

thres400		87.20 285	86.52 287	89.24 289	95.77 222	72.94 330	91.89 205	86.00 340	90.84 130	92.61 228	89.80 326	63.93 340	98.28 209	71.27 347	96.54 267	96.51 236

IB-MVS	77.21 19	83.11 310	81.05 321	89.29 286	91.15 328	75.85 308	85.66 332	86.00 340	79.70 289	82.02 356	86.61 351	48.26 370	98.39 200	77.84 307	92.22 339	93.63 322
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		83.00 312	81.11 320	88.66 297	83.81 373	86.44 158	82.24 354	85.65 343	61.75 366	82.07 354	85.64 357	79.75 265	91.59 359	75.99 322	93.09 329	87.94 358

tpm		84.38 305	84.08 304	85.30 330	90.47 337	63.43 369	89.34 279	85.63 344	77.24 312	87.62 319	95.03 211	61.00 354	97.30 279	79.26 299	91.09 348	95.16 284

LFMVS		91.33 197	91.16 199	91.82 217	96.27 187	79.36 259	95.01 96	85.61 345	96.04 29	94.82 160	97.06 93	72.03 309	98.46 199	84.96 242	98.70 143	97.65 188

FPMVS		84.50 304	83.28 308	88.16 305	96.32 183	94.49 15	85.76 331	85.47 346	83.09 263	85.20 333	94.26 237	63.79 342	86.58 367	63.72 363	91.88 344	83.40 362

tpm2		81.46 322	80.35 329	84.80 332	89.90 342	65.14 363	90.44 245	85.36 347	65.82 360	82.05 355	92.44 289	57.94 357	96.69 298	70.71 350	88.49 354	92.56 338

thres100view900		87.35 281	86.89 279	88.72 295	96.14 198	73.09 329	93.00 155	85.31 348	92.13 89	93.26 208	90.96 314	63.42 343	98.28 209	71.27 347	96.54 267	94.79 293

thres600view7		87.66 273	87.10 277	89.36 285	96.05 205	73.17 327	92.72 162	85.31 348	91.89 96	93.29 205	90.97 313	63.42 343	98.39 200	73.23 335	96.99 257	96.51 236

dp		79.28 333	78.62 336	81.24 345	85.97 367	56.45 373	86.91 317	85.26 350	72.97 333	81.45 359	89.17 338	56.01 362	95.45 328	73.19 336	76.68 368	91.82 346

PMMVS2		81.31 323	83.44 307	74.92 351	90.52 336	46.49 376	69.19 365	85.23 351	84.30 254	87.95 316	94.71 225	76.95 289	84.36 369	64.07 362	98.09 209	93.89 316

ADS-MVSNet		82.25 316	81.55 317	84.34 336	89.04 352	65.30 361	87.57 303	85.13 352	72.71 335	84.46 338	92.45 287	68.08 317	92.33 356	70.58 351	83.97 360	95.38 281

test-LLR		83.58 308	83.17 309	84.79 333	89.68 345	66.86 356	83.08 350	84.52 353	83.07 264	82.85 349	84.78 359	62.86 346	93.49 350	82.85 259	94.86 301	94.03 311

test-mter		81.21 325	80.01 332	84.79 333	89.68 345	66.86 356	83.08 350	84.52 353	73.85 328	82.85 349	84.78 359	43.66 375	93.49 350	82.85 259	94.86 301	94.03 311

JIA-IIPM		85.08 301	83.04 310	91.19 239	87.56 358	86.14 167	89.40 278	84.44 355	88.98 172	82.20 353	97.95 42	56.82 360	96.15 313	76.55 319	83.45 362	91.30 347

thisisatest0530		88.69 257	87.52 268	92.20 205	96.33 182	79.36 259	92.81 160	84.01 356	86.44 219	93.67 194	92.68 283	53.62 366	99.25 77	89.65 161	98.45 165	98.00 152

tttt0517		89.81 237	88.90 243	92.55 197	97.00 141	79.73 253	95.03 95	83.65 357	89.88 152	95.30 137	94.79 223	53.64 365	99.39 48	91.99 99	98.79 135	98.54 111

thisisatest0515		84.72 303	82.99 311	89.90 276	92.96 300	75.33 313	84.36 343	83.42 358	77.37 310	88.27 312	86.65 350	53.94 364	98.72 161	82.56 263	97.40 242	95.67 274

PVSNet_0	70.34 21	74.58 336	72.96 339	79.47 348	90.63 334	66.24 360	73.26 361	83.40 359	63.67 364	78.02 365	78.35 367	72.53 305	89.59 364	56.68 367	60.05 371	82.57 365

pmmvs3		80.83 327	78.96 335	86.45 320	87.23 362	77.48 288	84.87 337	82.31 360	63.83 363	85.03 334	89.50 333	49.66 368	93.10 352	73.12 337	95.10 298	88.78 357

E-PMN		80.72 329	80.86 324	80.29 347	85.11 369	68.77 351	72.96 362	81.97 361	87.76 198	83.25 348	83.01 363	62.22 349	89.17 365	77.15 315	94.31 314	82.93 363

test0.0.03 1		82.48 315	81.47 319	85.48 327	89.70 344	73.57 326	84.73 338	81.64 362	83.07 264	88.13 314	86.61 351	62.86 346	89.10 366	66.24 360	90.29 350	93.77 319

baseline2		83.38 309	81.54 318	88.90 291	91.38 326	72.84 332	88.78 291	81.22 363	78.97 299	79.82 363	87.56 345	61.73 351	97.80 250	74.30 330	90.05 351	96.05 257

EMVS		80.35 331	80.28 330	80.54 346	84.73 371	69.07 350	72.54 364	80.73 364	87.80 197	81.66 358	81.73 364	62.89 345	89.84 363	75.79 324	94.65 308	82.71 364

TESTMET0.1,1		79.09 334	78.04 337	82.25 343	87.52 359	64.03 368	83.08 350	80.62 365	70.28 346	80.16 362	83.22 362	44.13 374	90.56 361	79.95 289	93.36 323	92.15 341

lessismore_v0				93.87 152	98.05 80	83.77 198		80.32 366		97.13 52	97.91 45	77.49 281	99.11 95	92.62 86	98.08 210	98.74 88

new_pmnet		81.22 324	81.01 323	81.86 344	90.92 332	70.15 345	84.03 346	80.25 367	70.83 343	85.97 330	89.78 329	67.93 320	84.65 368	67.44 357	91.90 343	90.78 350

test1111		90.39 216	90.61 210	89.74 278	98.04 83	71.50 339	95.59 71	79.72 368	89.41 160	95.94 109	98.14 32	70.79 312	98.81 144	88.52 186	99.32 62	98.90 69

ECVR-MVS		90.12 226	90.16 218	90.00 275	97.81 96	72.68 333	95.76 66	78.54 369	89.04 170	95.36 134	98.10 34	70.51 313	98.64 177	87.10 211	99.18 86	98.67 95

MVS-HIRNet		78.83 335	80.60 327	73.51 352	93.07 296	47.37 375	87.10 314	78.00 370	68.94 350	77.53 366	97.26 81	71.45 310	94.62 337	63.28 364	88.74 353	78.55 367

DSMNet-mixed		82.21 317	81.56 316	84.16 337	89.57 347	70.00 348	90.65 240	77.66 371	54.99 370	83.30 347	97.57 58	77.89 280	90.50 362	66.86 359	95.54 287	91.97 342

EPMVS		81.17 326	80.37 328	83.58 339	85.58 368	65.08 364	90.31 251	71.34 372	77.31 311	85.80 331	91.30 308	59.38 355	92.70 355	79.99 288	82.34 365	92.96 333

gg-mvs-nofinetune		82.10 320	81.02 322	85.34 329	87.46 361	71.04 340	94.74 104	67.56 373	96.44 22	79.43 364	98.99 6	45.24 372	96.15 313	67.18 358	92.17 340	88.85 355

GG-mvs-BLEND				83.24 341	85.06 370	71.03 341	94.99 98	65.55 374		74.09 369	75.51 368	44.57 373	94.46 340	59.57 366	87.54 356	84.24 361

MVE	59.87 23	73.86 337	72.65 340	77.47 350	87.00 365	74.35 320	61.37 367	60.93 375	67.27 355	69.69 371	86.49 353	81.24 259	72.33 371	56.45 368	83.45 362	85.74 360
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test2506		85.42 298	84.57 300	87.96 307	97.81 96	66.53 359	96.14 50	56.35 376	89.04 170	93.55 198	98.10 34	42.88 378	98.68 171	88.09 194	99.18 86	98.67 95

MTMP							94.82 101	54.62 377

DeepMVS_CX				53.83 354	70.38 376	64.56 366		48.52 378	33.01 371	65.50 372	74.21 369	56.19 361	46.64 373	38.45 372	70.07 369	50.30 369

tmp_tt		37.97 339	44.33 342	18.88 355	11.80 378	21.54 378	63.51 366	45.66 379	4.23 373	51.34 373	50.48 370	59.08 356	22.11 374	44.50 371	68.35 370	13.00 370

testmvs		9.02 342	11.42 345	1.81 357	2.77 380	1.13 381	79.44 359	1.90 380	1.18 375	2.65 376	6.80 372	1.95 380	0.87 376	2.62 374	3.45 374	3.44 372

test123		9.49 341	12.01 344	1.91 356	2.87 379	1.30 380	82.38 353	1.34 381	1.36 374	2.84 375	6.56 373	2.45 379	0.97 375	2.73 373	5.56 373	3.47 371

test_blank		0.00 345	0.00 348	0.00 358	0.00 381	0.00 382	0.00 369	0.00 382	0.00 376	0.00 377	0.00 376	0.00 381	0.00 377	0.00 375	0.00 375	0.00 373

uanet_test		0.00 345	0.00 348	0.00 358	0.00 381	0.00 382	0.00 369	0.00 382	0.00 376	0.00 377	0.00 376	0.00 381	0.00 377	0.00 375	0.00 375	0.00 373

pcd_1.5k_mvsjas		7.56 343	10.09 346	0.00 358	0.00 381	0.00 382	0.00 369	0.00 382	0.00 376	0.00 377	0.00 376	90.77 144	0.00 377	0.00 375	0.00 375	0.00 373

sosnet-low-res		0.00 345	0.00 348	0.00 358	0.00 381	0.00 382	0.00 369	0.00 382	0.00 376	0.00 377	0.00 376	0.00 381	0.00 377	0.00 375	0.00 375	0.00 373

sosnet		0.00 345	0.00 348	0.00 358	0.00 381	0.00 382	0.00 369	0.00 382	0.00 376	0.00 377	0.00 376	0.00 381	0.00 377	0.00 375	0.00 375	0.00 373

uncertanet		0.00 345	0.00 348	0.00 358	0.00 381	0.00 382	0.00 369	0.00 382	0.00 376	0.00 377	0.00 376	0.00 381	0.00 377	0.00 375	0.00 375	0.00 373

Regformer		0.00 345	0.00 348	0.00 358	0.00 381	0.00 382	0.00 369	0.00 382	0.00 376	0.00 377	0.00 376	0.00 381	0.00 377	0.00 375	0.00 375	0.00 373

n2								0.00 382

nn								0.00 382

ab-mvs-re		7.56 343	10.08 347	0.00 358	0.00 381	0.00 382	0.00 369	0.00 382	0.00 376	0.00 377	90.69 319	0.00 381	0.00 377	0.00 375	0.00 375	0.00 373

uanet		0.00 345	0.00 348	0.00 358	0.00 381	0.00 382	0.00 369	0.00 382	0.00 376	0.00 377	0.00 376	0.00 381	0.00 377	0.00 375	0.00 375	0.00 373

PC_three_1452									75.31 321	95.87 113	95.75 175	92.93 93	96.34 312	87.18 210	98.68 145	98.04 147

eth-test2					0.00 381

eth-test					0.00 381

OPU-MVS				95.15 98	96.84 150	89.43 93	95.21 85				95.66 179	93.12 87	98.06 227	86.28 227	98.61 150	97.95 160

test_0728_THIRD									93.26 68	97.40 46	97.35 77	94.69 55	99.34 62	93.88 34	99.42 47	98.89 70

GSMVS																94.75 295

test_part2					98.21 70	89.41 94				96.72 71

sam_mvs1												66.64 327				94.75 295

sam_mvs												66.41 328

test_post1							90.21 253				5.85 375	65.36 333	96.00 318	79.61 295

test_post											6.07 374	65.74 332	95.84 320

patchmatchnet-post											91.71 303	66.22 330	97.59 263

gm-plane-assit					87.08 364	59.33 371			71.22 340		83.58 361		97.20 282	73.95 331

test9_res														88.16 192	98.40 168	97.83 173

agg_prior2														87.06 213	98.36 179	97.98 156

test_prior4						89.91 83	90.74 237

test_prior2							90.21 253		89.33 164	90.77 264	94.81 219	90.41 154		88.21 188	98.55 154

旧先验2							90.00 262		68.65 351	92.71 226			96.52 302	85.15 236

新几何2							90.02 261

原ACMM2							89.34 279

testdata2													98.03 229	80.24 286

segment_acmp												92.14 110

testdata1							88.96 288		88.44 185

plane_prior7					97.71 104	88.68 107

plane_prior6					97.21 133	88.23 118						86.93 204

plane_prior4											95.59 181

plane_prior3						88.43 116			90.35 145	93.31 203

plane_prior2							94.56 113		91.74 109

plane_prior1					97.38 125

plane_prior						88.12 120	93.01 154		88.98 172						98.06 211

HQP5-MVS						84.89 182

HQP-NCC					96.36 177		91.37 222		87.16 210	88.81 300

ACMP_Plane					96.36 177		91.37 222		87.16 210	88.81 300

BP-MVS														86.55 221

HQP4-MVS										88.81 300			98.61 179			98.15 138

HQP2-MVS												84.76 226

NP-MVS					96.82 151	87.10 139					93.40 265

MDTV_nov1_ep13_2view						42.48 377	88.45 298		67.22 356	83.56 345		66.80 324		72.86 338		94.06 310

ACMMP++_ref															98.82 130

ACMMP++															99.25 76

Test By Simon												90.61 150

High-resolution multi-view benchmark