High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

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

LTVRE_ROB	93.87 1	97.93 2	98.16 2	97.26 26	98.81 25	93.86 32	99.07 2	98.98 5	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
TDRefinement		97.68 3	97.60 4	97.93 2	99.02 12	95.95 5	98.61 3	98.81 7	97.41 10	97.28 48	98.46 25	94.62 58	98.84 137	94.64 17	99.53 35	98.99 54

DVP-MVS++		95.93 53	96.34 34	94.70 117	96.54 170	86.66 155	98.45 4	98.22 32	93.26 70	97.54 37	97.36 75	93.12 88	99.38 56	93.88 34	98.68 148	98.04 148

FOURS1					99.21 3	94.68 12	98.45 4	98.81 7	97.73 6	98.27 20

UA-Net		97.35 4	97.24 11	97.69 5	98.22 70	93.87 31	98.42 6	98.19 35	96.95 14	95.46 130	99.23 4	93.45 76	99.57 13	95.34 12	99.89 2	99.63 9

OurMVSNet-221017-0		96.80 13	96.75 18	96.96 36	99.03 11	91.85 59	97.98 7	98.01 69	94.15 52	98.93 3	99.07 5	88.07 185	99.57 13	95.86 9	99.69 15	99.46 18

UniMVSNet_ETH3D		97.13 6	97.72 3	95.35 89	99.51 2	87.38 136	97.70 8	97.54 110	98.16 2	98.94 2	99.33 2	97.84 4	99.08 99	90.73 130	99.73 14	99.59 12

HPM-MVS		96.81 12	96.62 23	97.36 24	98.89 19	93.53 39	97.51 9	98.44 12	92.35 84	95.95 107	96.41 137	96.71 8	99.42 31	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
EPP-MVSNet		93.91 129	93.68 135	94.59 125	98.08 78	85.55 179	97.44 10	94.03 263	94.22 50	94.94 155	96.19 155	82.07 251	99.57 13	87.28 211	98.89 120	98.65 98

LS3D		96.11 48	95.83 62	96.95 37	94.75 261	94.20 19	97.34 11	97.98 72	97.31 11	95.32 136	96.77 112	93.08 90	99.20 84	91.79 108	98.16 203	97.44 205

HPM-MVS_fast		97.01 7	96.89 15	97.39 22	99.12 8	93.92 29	97.16 12	98.17 40	93.11 72	96.48 79	97.36 75	96.92 6	99.34 64	94.31 23	99.38 55	98.92 68

MVSFormer		92.18 181	92.23 170	92.04 216	94.74 263	80.06 244	97.15 13	97.37 120	88.98 173	88.83 299	92.79 282	77.02 289	99.60 8	96.41 4	96.75 265	96.46 245

test_djsdf		96.62 23	96.49 28	97.01 33	98.55 41	91.77 61	97.15 13	97.37 120	88.98 173	98.26 22	98.86 10	93.35 81	99.60 8	96.41 4	99.45 43	99.66 6

IS-MVSNet		94.49 110	94.35 116	94.92 107	98.25 69	86.46 160	97.13 15	94.31 258	96.24 24	96.28 92	96.36 145	82.88 240	99.35 61	88.19 192	99.52 37	98.96 61

Anonymous20231211		96.60 25	97.13 12	95.00 105	97.46 123	86.35 165	97.11 16	98.24 30	97.58 8	98.72 8	98.97 7	93.15 87	99.15 88	93.18 70	99.74 13	99.50 16

anonymousdsp		96.74 17	96.42 29	97.68 7	98.00 88	94.03 26	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

ACMMP		96.61 24	96.34 34	97.43 19	98.61 34	93.88 30	96.95 18	98.18 36	92.26 87	96.33 85	96.84 110	95.10 43	99.40 45	93.47 53	99.33 60	99.02 51
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
APDe-MVS		96.46 32	96.64 22	95.93 63	97.68 109	89.38 99	96.90 19	98.41 16	92.52 79	97.43 43	97.92 45	95.11 42	99.50 19	94.45 19	99.30 65	98.92 68

EGC-MVSNET		80.97 328	75.73 340	96.67 44	98.85 22	94.55 15	96.83 20	96.60 181	2.44 375	5.32 376	98.25 31	92.24 109	98.02 232	91.85 107	99.21 82	97.45 203

v7n		96.82 10	97.31 10	95.33 91	98.54 43	86.81 150	96.83 20	98.07 56	96.59 20	98.46 17	98.43 27	92.91 95	99.52 17	96.25 6	99.76 11	99.65 8

CP-MVS		96.44 35	96.08 49	97.54 11	98.29 64	94.62 14	96.80 22	98.08 53	92.67 77	95.08 150	96.39 142	94.77 54	99.42 31	93.17 71	99.44 45	98.58 111

COLMAP_ROB	91.06 5	96.75 16	96.62 23	97.13 28	98.38 59	94.31 17	96.79 23	98.32 20	96.69 17	96.86 65	97.56 60	95.48 25	98.77 155	90.11 151	99.44 45	98.31 128
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
WR-MVS_H		96.60 25	97.05 14	95.24 97	99.02 12	86.44 161	96.78 24	98.08 53	97.42 9	98.48 16	97.86 49	91.76 122	99.63 6	94.23 26	99.84 3	99.66 6

CS-MVS		95.72 61	95.58 70	96.15 53	96.86 152	91.06 69	96.74 25	99.07 4	94.22 50	92.42 236	94.79 225	93.58 73	99.48 24	93.45 54	99.06 98	97.91 167

pmmvs6		96.80 13	97.36 9	95.15 101	99.12 8	87.82 131	96.68 26	97.86 83	96.10 26	98.14 24	99.28 3	97.94 3	98.21 216	91.38 121	99.69 15	99.42 19

3Dnovator	92.54 3	94.80 99	94.90 92	94.47 132	95.47 241	87.06 143	96.63 27	97.28 135	91.82 106	94.34 175	97.41 69	90.60 153	98.65 176	92.47 91	98.11 209	97.70 187

PS-CasMVS		96.69 20	97.43 5	94.49 131	99.13 6	84.09 197	96.61 28	97.97 75	97.91 5	98.64 13	98.13 34	95.24 36	99.65 3	93.39 61	99.84 3	99.72 2

abl_6		97.31 5	97.12 13	97.86 3	98.54 43	95.32 7	96.61 28	98.35 19	95.81 31	97.55 36	97.44 68	96.51 9	99.40 45	94.06 30	99.23 79	98.85 77

mvs_tets		96.83 9	96.71 19	97.17 27	98.83 23	92.51 50	96.58 30	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

PEN-MVS		96.69 20	97.39 8	94.61 120	99.16 4	84.50 188	96.54 31	98.05 60	98.06 4	98.64 13	98.25 31	95.01 48	99.65 3	92.95 80	99.83 6	99.68 4

DTE-MVSNet		96.74 17	97.43 5	94.67 118	99.13 6	84.68 187	96.51 32	97.94 81	98.14 3	98.67 12	98.32 29	95.04 45	99.69 2	93.27 67	99.82 8	99.62 10

XVS		96.49 29	96.18 42	97.44 17	98.56 38	93.99 27	96.50 33	97.95 78	94.58 41	94.38 173	96.49 131	94.56 59	99.39 50	93.57 44	99.05 103	98.93 64

X-MVStestdata		90.70 208	88.45 250	97.44 17	98.56 38	93.99 27	96.50 33	97.95 78	94.58 41	94.38 173	26.89 373	94.56 59	99.39 50	93.57 44	99.05 103	98.93 64

DROMVSNet		95.44 70	95.62 69	94.89 108	96.93 147	87.69 132	96.48 35	99.14 3	93.93 57	92.77 224	94.52 233	93.95 70	99.49 22	93.62 43	99.22 81	97.51 200

mPP-MVS		96.46 32	96.05 51	97.69 5	98.62 32	94.65 13	96.45 36	97.74 96	92.59 78	95.47 128	96.68 122	94.50 61	99.42 31	93.10 74	99.26 75	98.99 54

QAPM		92.88 158	92.77 157	93.22 175	95.82 223	83.31 204	96.45 36	97.35 127	83.91 256	93.75 191	96.77 112	89.25 173	98.88 129	84.56 249	97.02 254	97.49 201

jajsoiax		96.59 27	96.42 29	97.12 29	98.76 28	92.49 51	96.44 38	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

Gipuma		95.31 78	95.80 64	93.81 157	97.99 91	90.91 73	96.42 39	97.95 78	96.69 17	91.78 254	98.85 12	91.77 121	95.49 328	91.72 111	99.08 97	95.02 292
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MSP-MVS		95.34 75	94.63 107	97.48 14	98.67 29	94.05 23	96.41 40	98.18 36	91.26 123	95.12 146	95.15 204	86.60 214	99.50 19	93.43 59	96.81 262	98.89 71
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
SR-MVS-dyc-post		96.84 8	96.60 25	97.56 10	98.07 79	95.27 8	96.37 41	98.12 46	95.66 33	97.00 58	97.03 96	94.85 52	99.42 31	93.49 48	98.84 127	98.00 153

RE-MVS-def			96.66 20		98.07 79	95.27 8	96.37 41	98.12 46	95.66 33	97.00 58	97.03 96	95.40 27		93.49 48	98.84 127	98.00 153

TSAR-MVS + MP.		94.96 89	94.75 99	95.57 83	98.86 21	88.69 109	96.37 41	96.81 169	85.23 238	94.75 163	97.12 91	91.85 119	99.40 45	93.45 54	98.33 182	98.62 106
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
ACMH	88.36 12	96.59 27	97.43 5	94.07 144	98.56 38	85.33 181	96.33 44	98.30 23	94.66 40	98.72 8	98.30 30	97.51 5	98.00 234	94.87 14	99.59 27	98.86 74
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
region2R		96.41 37	96.09 48	97.38 23	98.62 32	93.81 36	96.32 45	97.96 76	92.26 87	95.28 139	96.57 129	95.02 47	99.41 38	93.63 42	99.11 96	98.94 63

APD-MVS_3200maxsize		96.82 10	96.65 21	97.32 25	97.95 92	93.82 34	96.31 46	98.25 27	95.51 35	96.99 60	97.05 95	95.63 21	99.39 50	93.31 64	98.88 122	98.75 86

CP-MVSNet		96.19 46	96.80 17	94.38 137	98.99 14	83.82 200	96.31 46	97.53 112	97.60 7	98.34 19	97.52 63	91.98 117	99.63 6	93.08 76	99.81 9	99.70 3

HFP-MVS		96.39 39	96.17 44	97.04 31	98.51 47	93.37 40	96.30 48	97.98 72	92.35 84	95.63 122	96.47 132	95.37 28	99.27 77	93.78 38	99.14 92	98.48 117

ACMMPR		96.46 32	96.14 45	97.41 21	98.60 35	93.82 34	96.30 48	97.96 76	92.35 84	95.57 125	96.61 127	94.93 51	99.41 38	93.78 38	99.15 91	99.00 52

3Dnovator+	92.74 2	95.86 57	95.77 65	96.13 55	96.81 156	90.79 76	96.30 48	97.82 89	96.13 25	94.74 164	97.23 85	91.33 132	99.16 87	93.25 68	98.30 187	98.46 119

MIMVSNet1		95.52 67	95.45 74	95.72 77	99.14 5	89.02 103	96.23 51	96.87 165	93.73 61	97.87 27	98.49 24	90.73 150	99.05 104	86.43 226	99.60 25	99.10 45

test2506		85.42 299	84.57 301	87.96 309	97.81 97	66.53 361	96.14 52	56.35 378	89.04 171	93.55 198	98.10 35	42.88 380	98.68 171	88.09 196	99.18 87	98.67 96

SR-MVS		96.70 19	96.42 29	97.54 11	98.05 81	94.69 11	96.13 53	98.07 56	95.17 37	96.82 67	96.73 119	95.09 44	99.43 30	92.99 79	98.71 144	98.50 115

MP-MVS		96.14 47	95.68 67	97.51 13	98.81 25	94.06 21	96.10 54	97.78 95	92.73 74	93.48 199	96.72 120	94.23 66	99.42 31	91.99 101	99.29 68	99.05 49
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
ZNCC-MVS		96.42 36	96.20 41	97.07 30	98.80 27	92.79 48	96.08 55	98.16 43	91.74 111	95.34 135	96.36 145	95.68 19	99.44 26	94.41 21	99.28 73	98.97 60

test1172		96.79 15	96.52 27	97.60 9	98.03 85	94.87 10	96.07 56	98.06 59	95.76 32	96.89 63	96.85 107	94.85 52	99.42 31	93.35 63	98.81 135	98.53 113

GBi-Net		93.21 147	92.96 152	93.97 147	95.40 243	84.29 190	95.99 57	96.56 184	88.63 181	95.10 147	98.53 21	81.31 258	98.98 115	86.74 217	98.38 175	98.65 98

test1		93.21 147	92.96 152	93.97 147	95.40 243	84.29 190	95.99 57	96.56 184	88.63 181	95.10 147	98.53 21	81.31 258	98.98 115	86.74 217	98.38 175	98.65 98

FMVSNet1		94.84 96	95.13 87	93.97 147	97.60 114	84.29 190	95.99 57	96.56 184	92.38 81	97.03 57	98.53 21	90.12 161	98.98 115	88.78 182	99.16 90	98.65 98

RPSCF		95.58 66	94.89 93	97.62 8	97.58 115	96.30 4	95.97 60	97.53 112	92.42 80	93.41 200	97.78 50	91.21 138	97.77 255	91.06 123	97.06 252	98.80 81

SixPastTwentyTwo		94.91 90	95.21 84	93.98 146	98.52 46	83.19 207	95.93 61	94.84 244	94.86 39	98.49 15	98.74 16	81.45 256	99.60 8	94.69 16	99.39 54	99.15 37

ambc				92.98 179	96.88 150	83.01 211	95.92 62	96.38 194		96.41 80	97.48 66	88.26 181	97.80 251	89.96 156	98.93 119	98.12 143

FC-MVSNet-test		95.32 76	95.88 58	93.62 160	98.49 55	81.77 221	95.90 63	98.32 20	93.93 57	97.53 39	97.56 60	88.48 178	99.40 45	92.91 81	99.83 6	99.68 4

MTAPA		96.65 22	96.38 33	97.47 15	98.95 16	94.05 23	95.88 64	97.62 102	94.46 45	96.29 89	96.94 100	93.56 74	99.37 58	94.29 24	99.42 47	98.99 54

CPTT-MVS		94.74 100	94.12 124	96.60 45	98.15 74	93.01 44	95.84 65	97.66 100	89.21 170	93.28 206	95.46 193	88.89 175	98.98 115	89.80 158	98.82 133	97.80 180

ab-mvs		92.40 174	92.62 164	91.74 222	97.02 141	81.65 223	95.84 65	95.50 229	86.95 215	92.95 220	97.56 60	90.70 151	97.50 268	79.63 296	97.43 243	96.06 260

nrg030		96.32 41	96.55 26	95.62 80	97.83 96	88.55 115	95.77 67	98.29 26	92.68 75	98.03 26	97.91 46	95.13 40	98.95 122	93.85 36	99.49 38	99.36 24

ECVR-MVS		90.12 227	90.16 219	90.00 277	97.81 97	72.68 335	95.76 68	78.54 371	89.04 171	95.36 134	98.10 35	70.51 315	98.64 177	87.10 213	99.18 87	98.67 96

SteuartSystems-ACMMP		96.40 38	96.30 36	96.71 42	98.63 31	91.96 57	95.70 69	98.01 69	93.34 69	96.64 74	96.57 129	94.99 49	99.36 60	93.48 51	99.34 58	98.82 79
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OpenMVS	89.45 8	92.27 179	92.13 173	92.68 192	94.53 272	84.10 196	95.70 69	97.03 150	82.44 273	91.14 264	96.42 136	88.47 179	98.38 202	85.95 231	97.47 242	95.55 282

GST-MVS		96.24 44	95.99 54	97.00 34	98.65 30	92.71 49	95.69 71	98.01 69	92.08 92	95.74 118	96.28 150	95.22 37	99.42 31	93.17 71	99.06 98	98.88 73

ACMH+	88.43 11	96.48 30	96.82 16	95.47 86	98.54 43	89.06 102	95.65 72	98.61 10	96.10 26	98.16 23	97.52 63	96.90 7	98.62 178	90.30 142	99.60 25	98.72 92

test1111		90.39 217	90.61 211	89.74 280	98.04 84	71.50 341	95.59 73	79.72 370	89.41 161	95.94 109	98.14 33	70.79 314	98.81 144	88.52 188	99.32 62	98.90 70

canonicalmvs		94.59 105	94.69 102	94.30 138	95.60 238	87.03 145	95.59 73	98.24 30	91.56 117	95.21 145	92.04 300	94.95 50	98.66 174	91.45 119	97.57 239	97.20 219

SF-MVS		95.88 56	95.88 58	95.87 69	98.12 75	89.65 91	95.58 75	98.56 11	91.84 103	96.36 83	96.68 122	94.37 64	99.32 70	92.41 93	99.05 103	98.64 102

PS-MVSNAJss		96.01 51	96.04 52	95.89 68	98.82 24	88.51 117	95.57 76	97.88 82	88.72 179	98.81 6	98.86 10	90.77 146	99.60 8	95.43 11	99.53 35	99.57 13

test_part1		94.39 112	94.55 109	93.92 151	96.14 202	82.86 212	95.54 77	98.09 52	95.36 36	98.27 20	98.36 28	75.91 297	99.44 26	93.41 60	99.84 3	99.47 17

PMVS	87.21 14	94.97 88	95.33 79	93.91 152	98.97 15	97.16 2	95.54 77	95.85 214	96.47 21	93.40 202	97.46 67	95.31 33	95.47 329	86.18 230	98.78 139	89.11 356
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
VDDNet		94.03 127	94.27 121	93.31 172	98.87 20	82.36 216	95.51 79	91.78 305	97.19 12	96.32 86	98.60 18	84.24 231	98.75 156	87.09 214	98.83 132	98.81 80

pm-mvs1		95.43 71	95.94 55	93.93 150	98.38 59	85.08 184	95.46 80	97.12 146	91.84 103	97.28 48	98.46 25	95.30 34	97.71 260	90.17 149	99.42 47	98.99 54

Vis-MVSNet		95.50 68	95.48 73	95.56 84	98.11 76	89.40 98	95.35 81	98.22 32	92.36 83	94.11 177	98.07 37	92.02 114	99.44 26	93.38 62	97.67 235	97.85 175
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test0726					98.51 47	86.69 153	95.34 82	98.18 36	91.85 100	97.63 32	97.37 72	95.58 22

FIs		94.90 91	95.35 77	93.55 163	98.28 65	81.76 222	95.33 83	98.14 44	93.05 73	97.07 53	97.18 88	87.65 192	99.29 73	91.72 111	99.69 15	99.61 11

PGM-MVS		96.32 41	95.94 55	97.43 19	98.59 37	93.84 33	95.33 83	98.30 23	91.40 120	95.76 116	96.87 106	95.26 35	99.45 25	92.77 82	99.21 82	99.00 52

LPG-MVS_test		96.38 40	96.23 39	96.84 40	98.36 62	92.13 54	95.33 83	98.25 27	91.78 107	97.07 53	97.22 86	96.38 13	99.28 75	92.07 99	99.59 27	99.11 42

AllTest		94.88 93	94.51 112	96.00 58	98.02 86	92.17 52	95.26 86	98.43 13	90.48 141	95.04 152	96.74 117	92.54 105	97.86 246	85.11 241	98.98 111	97.98 157

SED-MVS		96.00 52	96.41 32	94.76 114	98.51 47	86.97 146	95.21 87	98.10 49	91.95 94	97.63 32	97.25 83	96.48 11	99.35 61	93.29 65	99.29 68	97.95 161

OPU-MVS				95.15 101	96.84 153	89.43 96	95.21 87				95.66 181	93.12 88	98.06 227	86.28 229	98.61 153	97.95 161

DVP-MVS		95.82 58	96.18 42	94.72 116	98.51 47	86.69 153	95.20 89	97.00 152	91.85 100	97.40 46	97.35 78	95.58 22	99.34 64	93.44 57	99.31 63	98.13 142
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
test_0728_SECOND				94.88 109	98.55 41	86.72 152	95.20 89	98.22 32					99.38 56	93.44 57	99.31 63	98.53 113

Anonymous20240529		95.50 68	95.83 62	94.50 129	97.33 129	85.93 173	95.19 91	96.77 173	96.64 19	97.61 35	98.05 38	93.23 84	98.79 147	88.60 187	99.04 108	98.78 83

#test#		95.89 54	95.51 72	97.04 31	98.51 47	93.37 40	95.14 92	97.98 72	89.34 164	95.63 122	96.47 132	95.37 28	99.27 77	91.99 101	99.14 92	98.48 117

SMA-MVS		95.77 59	95.54 71	96.47 51	98.27 66	91.19 67	95.09 93	97.79 94	86.48 218	97.42 45	97.51 65	94.47 63	99.29 73	93.55 46	99.29 68	98.93 64
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
NR-MVSNet		95.28 79	95.28 82	95.26 96	97.75 101	87.21 140	95.08 94	97.37 120	93.92 59	97.65 31	95.90 166	90.10 164	99.33 69	90.11 151	99.66 21	99.26 29

TransMVSNet (Re)		95.27 81	96.04 52	92.97 180	98.37 61	81.92 220	95.07 95	96.76 174	93.97 56	97.77 28	98.57 19	95.72 18	97.90 240	88.89 180	99.23 79	99.08 46

UGNet		93.08 150	92.50 167	94.79 113	93.87 287	87.99 127	95.07 95	94.26 260	90.64 138	87.33 324	97.67 55	86.89 209	98.49 193	88.10 195	98.71 144	97.91 167
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
tttt0517		89.81 238	88.90 244	92.55 199	97.00 142	79.73 255	95.03 97	83.65 359	89.88 153	95.30 137	94.79 225	53.64 367	99.39 50	91.99 101	98.79 138	98.54 112

LFMVS		91.33 198	91.16 200	91.82 219	96.27 191	79.36 261	95.01 98	85.61 347	96.04 29	94.82 160	97.06 94	72.03 311	98.46 199	84.96 244	98.70 146	97.65 191

CSCG		94.69 102	94.75 99	94.52 128	97.55 117	87.87 129	95.01 98	97.57 108	92.68 75	96.20 97	93.44 267	91.92 118	98.78 151	89.11 175	99.24 78	96.92 227

GG-mvs-BLEND				83.24 343	85.06 372	71.03 343	94.99 100	65.55 376		74.09 370	75.51 370	44.57 375	94.46 342	59.57 368	87.54 358	84.24 363

EU-MVSNet		87.39 281	86.71 284	89.44 284	93.40 292	76.11 307	94.93 101	90.00 316	57.17 369	95.71 120	97.37 72	64.77 339	97.68 262	92.67 87	94.37 314	94.52 303

KD-MVS_self_test		94.10 125	94.73 101	92.19 208	97.66 111	79.49 259	94.86 102	97.12 146	89.59 159	96.87 64	97.65 56	90.40 158	98.34 206	89.08 176	99.35 57	98.75 86

MTMP							94.82 103	54.62 379

PHI-MVS		94.34 116	93.80 129	95.95 60	95.65 234	91.67 63	94.82 103	97.86 83	87.86 197	93.04 217	94.16 245	91.58 126	98.78 151	90.27 144	98.96 117	97.41 206

testtj		94.81 98	94.42 113	96.01 57	97.23 131	90.51 80	94.77 105	97.85 86	91.29 122	94.92 157	95.66 181	91.71 123	99.40 45	88.07 197	98.25 193	98.11 144

gg-mvs-nofinetune		82.10 321	81.02 323	85.34 331	87.46 363	71.04 342	94.74 106	67.56 375	96.44 22	79.43 365	98.99 6	45.24 374	96.15 315	67.18 360	92.17 342	88.85 357

ACMM	88.83 9	96.30 43	96.07 50	96.97 35	98.39 58	92.95 46	94.74 106	98.03 65	90.82 133	97.15 51	96.85 107	96.25 15	99.00 114	93.10 74	99.33 60	98.95 62
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
SD-MVS		95.19 82	95.73 66	93.55 163	96.62 164	88.88 108	94.67 108	98.05 60	91.26 123	97.25 50	96.40 138	95.42 26	94.36 345	92.72 86	99.19 85	97.40 209
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
API-MVS		91.52 193	91.61 185	91.26 237	94.16 278	86.26 168	94.66 109	94.82 245	91.17 126	92.13 248	91.08 314	90.03 167	97.06 288	79.09 303	97.35 246	90.45 354

v10		94.68 103	95.27 83	92.90 185	96.57 167	80.15 240	94.65 110	97.57 108	90.68 137	97.43 43	98.00 41	88.18 182	99.15 88	94.84 15	99.55 34	99.41 20

v8		94.65 104	95.29 81	92.74 190	96.65 160	79.77 254	94.59 111	97.17 141	91.86 99	97.47 42	97.93 44	88.16 183	99.08 99	94.32 22	99.47 39	99.38 22

APD-MVS		95.00 87	94.69 102	95.93 63	97.38 126	90.88 74	94.59 111	97.81 90	89.22 169	95.46 130	96.17 158	93.42 79	99.34 64	89.30 167	98.87 125	97.56 197
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
VPA-MVSNet		95.14 84	95.67 68	93.58 162	97.76 100	83.15 208	94.58 113	97.58 107	93.39 68	97.05 56	98.04 39	93.25 83	98.51 192	89.75 161	99.59 27	99.08 46

ACMMP_NAP		96.21 45	96.12 47	96.49 50	98.90 18	91.42 64	94.57 114	98.03 65	90.42 144	96.37 82	97.35 78	95.68 19	99.25 79	94.44 20	99.34 58	98.80 81

HQP_MVS		94.26 120	93.93 126	95.23 98	97.71 105	88.12 123	94.56 115	97.81 90	91.74 111	93.31 203	95.59 183	86.93 206	98.95 122	89.26 171	98.51 164	98.60 109

plane_prior2							94.56 115		91.74 111

tfpnnormal		94.27 119	94.87 94	92.48 202	97.71 105	80.88 235	94.55 117	95.41 231	93.70 62	96.67 73	97.72 53	91.40 130	98.18 220	87.45 207	99.18 87	98.36 124

CS-MVS-test		95.15 83	94.81 95	96.19 52	96.89 149	91.14 68	94.55 117	98.85 6	94.31 48	92.43 235	91.91 301	91.79 120	99.49 22	93.48 51	99.06 98	97.93 163

XVG-ACMP-BASELINE		95.68 63	95.34 78	96.69 43	98.40 57	93.04 43	94.54 119	98.05 60	90.45 143	96.31 87	96.76 114	92.91 95	98.72 161	91.19 122	99.42 47	98.32 126

DP-MVS		95.62 64	95.84 61	94.97 106	97.16 136	88.62 112	94.54 119	97.64 101	96.94 15	96.58 77	97.32 81	93.07 91	98.72 161	90.45 134	98.84 127	97.57 195

MIMVSNet		87.13 289	86.54 287	88.89 294	96.05 209	76.11 307	94.39 121	88.51 321	81.37 279	88.27 313	96.75 116	72.38 308	95.52 326	65.71 363	95.47 291	95.03 291

K. test v3		93.37 139	93.27 149	93.66 159	98.05 81	82.62 214	94.35 122	86.62 336	96.05 28	97.51 40	98.85 12	76.59 295	99.65 3	93.21 69	98.20 201	98.73 91

Vis-MVSNet (Re-imp)		90.42 215	90.16 219	91.20 241	97.66 111	77.32 292	94.33 123	87.66 329	91.20 125	92.99 218	95.13 206	75.40 299	98.28 209	77.86 308	99.19 85	97.99 156

ANet_high		94.83 97	96.28 37	90.47 262	96.65 160	73.16 330	94.33 123	98.74 9	96.39 23	98.09 25	98.93 8	93.37 80	98.70 167	90.38 137	99.68 18	99.53 14

ACMP	88.15 13	95.71 62	95.43 76	96.54 47	98.17 73	91.73 62	94.24 125	98.08 53	89.46 160	96.61 76	96.47 132	95.85 17	99.12 94	90.45 134	99.56 33	98.77 85
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MAR-MVS		90.32 222	88.87 245	94.66 119	94.82 257	91.85 59	94.22 126	94.75 248	80.91 280	87.52 322	88.07 346	86.63 213	97.87 245	76.67 319	96.21 275	94.25 309
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
FMVSNet2		92.78 162	92.73 161	92.95 182	95.40 243	81.98 219	94.18 127	95.53 228	88.63 181	96.05 104	97.37 72	81.31 258	98.81 144	87.38 210	98.67 150	98.06 145

Anonymous20240521		92.86 160	93.57 139	90.74 256	96.57 167	75.50 314	94.15 128	95.60 220	89.38 162	95.90 112	97.90 48	80.39 265	97.96 238	92.60 89	99.68 18	98.75 86

GeoE		94.55 107	94.68 104	94.15 141	97.23 131	85.11 183	94.14 129	97.34 128	88.71 180	95.26 140	95.50 191	94.65 57	99.12 94	90.94 127	98.40 170	98.23 133

9.14			94.81 95		97.49 120		94.11 130	98.37 17	87.56 206	95.38 132	96.03 162	94.66 56	99.08 99	90.70 131	98.97 115

HPM-MVS++		95.02 86	94.39 114	96.91 38	97.88 94	93.58 38	94.09 131	96.99 154	91.05 128	92.40 238	95.22 203	91.03 144	99.25 79	92.11 96	98.69 147	97.90 169

ETH3D-3000-0.1		94.86 94	94.55 109	95.81 70	97.61 113	89.72 89	94.05 132	98.37 17	88.09 192	95.06 151	95.85 168	92.58 103	99.10 98	90.33 141	98.99 110	98.62 106

HY-MVS	82.50 18	86.81 293	85.93 294	89.47 283	93.63 290	77.93 282	94.02 133	91.58 307	75.68 317	83.64 345	93.64 261	77.40 284	97.42 274	71.70 346	92.07 343	93.05 334

Effi-MVS+-dtu		93.90 130	92.60 165	97.77 4	94.74 263	96.67 3	94.00 134	95.41 231	89.94 150	91.93 252	92.13 298	90.12 161	98.97 119	87.68 204	97.48 241	97.67 190

Effi-MVS+		92.79 161	92.74 159	92.94 183	95.10 251	83.30 205	94.00 134	97.53 112	91.36 121	89.35 295	90.65 323	94.01 69	98.66 174	87.40 209	95.30 296	96.88 230

VDD-MVS		94.37 113	94.37 115	94.40 136	97.49 120	86.07 171	93.97 136	93.28 275	94.49 44	96.24 93	97.78 50	87.99 188	98.79 147	88.92 178	99.14 92	98.34 125

h-mvs33		92.89 157	91.99 176	95.58 82	96.97 143	90.55 78	93.94 137	94.01 266	89.23 167	93.95 185	96.19 155	76.88 292	99.14 90	91.02 124	95.71 285	97.04 223

EPNet		89.80 239	88.25 255	94.45 134	83.91 374	86.18 169	93.87 138	87.07 334	91.16 127	80.64 362	94.72 227	78.83 272	98.89 128	85.17 236	98.89 120	98.28 130
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DeepC-MVS	91.39 4	95.43 71	95.33 79	95.71 78	97.67 110	90.17 82	93.86 139	98.02 67	87.35 207	96.22 95	97.99 42	94.48 62	99.05 104	92.73 85	99.68 18	97.93 163
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
LCM-MVSNet-Re		94.20 123	94.58 108	93.04 177	95.91 220	83.13 209	93.79 140	99.19 2	92.00 93	98.84 5	98.04 39	93.64 72	99.02 110	81.28 278	98.54 160	96.96 226

TranMVSNet+NR-MVSNet		96.07 50	96.26 38	95.50 85	98.26 67	87.69 132	93.75 141	97.86 83	95.96 30	97.48 41	97.14 90	95.33 32	99.44 26	90.79 129	99.76 11	99.38 22

PAPM_NR		91.03 202	90.81 206	91.68 226	96.73 158	81.10 232	93.72 142	96.35 195	88.19 190	88.77 305	92.12 299	85.09 227	97.25 281	82.40 268	93.90 320	96.68 237

zzz-MVS		96.47 31	96.14 45	97.47 15	98.95 16	94.05 23	93.69 143	97.62 102	94.46 45	96.29 89	96.94 100	93.56 74	99.37 58	94.29 24	99.42 47	98.99 54

baseline		94.26 120	94.80 97	92.64 193	96.08 207	80.99 233	93.69 143	98.04 64	90.80 134	94.89 158	96.32 147	93.19 85	98.48 197	91.68 113	98.51 164	98.43 121

MVS_0304		90.96 203	90.15 222	93.37 169	93.17 296	87.06 143	93.62 145	92.43 294	89.60 158	82.25 353	95.50 191	82.56 247	97.83 249	84.41 251	97.83 227	95.22 286

F-COLMAP		92.28 178	91.06 201	95.95 60	97.52 118	91.90 58	93.53 146	97.18 140	83.98 255	88.70 307	94.04 248	88.41 180	98.55 189	80.17 289	95.99 279	97.39 210

FMVSNet5		87.82 271	86.56 286	91.62 227	92.31 310	79.81 253	93.49 147	94.81 247	83.26 260	91.36 258	96.93 102	52.77 369	97.49 270	76.07 323	98.03 216	97.55 198

DPE-MVS		95.89 54	95.88 58	95.92 65	97.93 93	89.83 88	93.46 148	98.30 23	92.37 82	97.75 29	96.95 99	95.14 39	99.51 18	91.74 110	99.28 73	98.41 123
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
alignmvs		93.26 144	92.85 155	94.50 129	95.70 230	87.45 134	93.45 149	95.76 216	91.58 116	95.25 142	92.42 294	81.96 253	98.72 161	91.61 114	97.87 225	97.33 214

114514_t		90.51 212	89.80 228	92.63 195	98.00 88	82.24 217	93.40 150	97.29 133	65.84 360	89.40 294	94.80 224	86.99 204	98.75 156	83.88 254	98.61 153	96.89 229

DeepC-MVS_fast	89.96 7	93.73 132	93.44 143	94.60 124	96.14 202	87.90 128	93.36 151	97.14 143	85.53 235	93.90 188	95.45 194	91.30 134	98.59 183	89.51 164	98.62 152	97.31 215
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MP-MVS-pluss		96.08 49	95.92 57	96.57 46	99.06 10	91.21 66	93.25 152	98.32 20	87.89 196	96.86 65	97.38 71	95.55 24	99.39 50	95.47 10	99.47 39	99.11 42
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
test_0402		95.73 60	96.22 40	94.26 139	98.19 72	85.77 176	93.24 153	97.24 137	96.88 16	97.69 30	97.77 52	94.12 68	99.13 92	91.54 118	99.29 68	97.88 171

MSLP-MVS++		93.25 146	93.88 127	91.37 233	96.34 185	82.81 213	93.11 154	97.74 96	89.37 163	94.08 179	95.29 202	90.40 158	96.35 312	90.35 139	98.25 193	94.96 293

baseline1		87.62 276	87.31 271	88.54 300	94.71 267	74.27 324	93.10 155	88.20 325	86.20 223	92.18 247	93.04 275	73.21 305	95.52 326	79.32 300	85.82 360	95.83 270

plane_prior						88.12 123	93.01 156		88.98 173						98.06 213

thres100view900		87.35 282	86.89 280	88.72 297	96.14 202	73.09 331	93.00 157	85.31 350	92.13 91	93.26 208	90.96 316	63.42 345	98.28 209	71.27 349	96.54 269	94.79 296

Patchmtry		90.11 228	89.92 226	90.66 258	90.35 341	77.00 296	92.96 158	92.81 282	90.25 147	94.74 164	96.93 102	67.11 323	97.52 267	85.17 236	98.98 111	97.46 202

LF4IMVS		92.72 164	92.02 175	94.84 111	95.65 234	91.99 56	92.92 159	96.60 181	85.08 245	92.44 234	93.62 262	86.80 210	96.35 312	86.81 216	98.25 193	96.18 256

UniMVSNet (Re)		95.32 76	95.15 86	95.80 72	97.79 99	88.91 105	92.91 160	98.07 56	93.46 67	96.31 87	95.97 165	90.14 160	99.34 64	92.11 96	99.64 23	99.16 36

TAPA-MVS	88.58 10	92.49 172	91.75 184	94.73 115	96.50 174	89.69 90	92.91 160	97.68 99	78.02 308	92.79 223	94.10 246	90.85 145	97.96 238	84.76 247	98.16 203	96.54 238
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
thisisatest0530		88.69 258	87.52 269	92.20 207	96.33 186	79.36 261	92.81 162	84.01 358	86.44 219	93.67 194	92.68 286	53.62 368	99.25 79	89.65 163	98.45 168	98.00 153

EIA-MVS		92.35 176	92.03 174	93.30 173	95.81 225	83.97 198	92.80 163	98.17 40	87.71 200	89.79 289	87.56 347	91.17 142	99.18 86	87.97 199	97.27 247	96.77 234

ETH3D cwj APD-0.16		93.99 128	93.38 145	95.80 72	96.82 154	89.92 85	92.72 164	98.02 67	84.73 251	93.65 195	95.54 190	91.68 124	99.22 82	88.78 182	98.49 167	98.26 132

thres600view7		87.66 274	87.10 278	89.36 287	96.05 209	73.17 329	92.72 164	85.31 350	91.89 98	93.29 205	90.97 315	63.42 345	98.39 200	73.23 337	96.99 259	96.51 240

wuyk23d		87.83 270	90.79 207	78.96 351	90.46 340	88.63 111	92.72 164	90.67 313	91.65 115	98.68 11	97.64 57	96.06 16	77.53 372	59.84 367	99.41 52	70.73 370

V42		93.43 138	93.58 138	92.97 180	95.34 247	81.22 230	92.67 167	96.49 189	87.25 209	96.20 97	96.37 144	87.32 198	98.85 136	92.39 95	98.21 199	98.85 77

OPM-MVS		95.61 65	95.45 74	96.08 56	98.49 55	91.00 71	92.65 168	97.33 129	90.05 149	96.77 70	96.85 107	95.04 45	98.56 187	92.77 82	99.06 98	98.70 95
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
DU-MVS		95.28 79	95.12 88	95.75 76	97.75 101	88.59 113	92.58 169	97.81 90	93.99 54	96.80 68	95.90 166	90.10 164	99.41 38	91.60 115	99.58 31	99.26 29

FMVSNet3		90.78 206	90.32 218	92.16 212	93.03 301	79.92 249	92.54 170	94.95 240	86.17 225	95.10 147	96.01 163	69.97 317	98.75 156	86.74 217	98.38 175	97.82 178

hse-mvs2		92.24 180	91.20 197	95.38 88	96.16 200	90.65 77	92.52 171	92.01 303	89.23 167	93.95 185	92.99 277	76.88 292	98.69 169	91.02 124	96.03 277	96.81 232

MVS_Test		92.57 171	93.29 146	90.40 265	93.53 291	75.85 310	92.52 171	96.96 155	88.73 178	92.35 241	96.70 121	90.77 146	98.37 205	92.53 90	95.49 290	96.99 225

CR-MVSNet		87.89 268	87.12 277	90.22 270	91.01 332	78.93 268	92.52 171	92.81 282	73.08 333	89.10 296	96.93 102	67.11 323	97.64 263	88.80 181	92.70 336	94.08 310

RPMNet		90.31 223	90.14 223	90.81 255	91.01 332	78.93 268	92.52 171	98.12 46	91.91 97	89.10 296	96.89 105	68.84 318	99.41 38	90.17 149	92.70 336	94.08 310

RRT_MVS		91.36 197	90.05 224	95.29 95	89.21 353	88.15 122	92.51 175	94.89 242	86.73 217	95.54 126	95.68 180	61.82 352	99.30 72	94.91 13	99.13 95	98.43 121

XVG-OURS-SEG-HR		95.38 73	95.00 90	96.51 48	98.10 77	94.07 20	92.46 176	98.13 45	90.69 136	93.75 191	96.25 153	98.03 2	97.02 289	92.08 98	95.55 288	98.45 120

EI-MVSNet-Vis-set		94.36 114	94.28 119	94.61 120	92.55 308	85.98 172	92.44 177	94.69 251	93.70 62	96.12 102	95.81 172	91.24 136	98.86 134	93.76 41	98.22 198	98.98 59

Anonymous202405211		92.58 169	92.50 167	92.83 188	96.55 169	83.22 206	92.43 178	91.64 306	94.10 53	95.59 124	96.64 125	81.88 255	97.50 268	85.12 240	98.52 162	97.77 182

AUN-MVS		90.05 232	88.30 253	95.32 94	96.09 206	90.52 79	92.42 179	92.05 302	82.08 276	88.45 310	92.86 279	65.76 333	98.69 169	88.91 179	96.07 276	96.75 236

EI-MVSNet-UG-set		94.35 115	94.27 121	94.59 125	92.46 309	85.87 174	92.42 179	94.69 251	93.67 66	96.13 101	95.84 171	91.20 139	98.86 134	93.78 38	98.23 196	99.03 50

Regformer-3		94.28 118	94.23 123	94.46 133	92.78 306	86.28 167	92.39 181	94.70 250	93.69 65	95.97 105	95.56 188	91.34 131	98.48 197	93.45 54	98.14 205	98.62 106

Regformer-4		94.90 91	94.67 105	95.59 81	92.78 306	89.02 103	92.39 181	95.91 211	94.50 43	96.41 80	95.56 188	92.10 113	99.01 112	94.23 26	98.14 205	98.74 89

NCCC		94.08 126	93.54 141	95.70 79	96.49 175	89.90 87	92.39 181	96.91 161	90.64 138	92.33 244	94.60 230	90.58 154	98.96 120	90.21 148	97.70 233	98.23 133

casdiffmvs		94.32 117	94.80 97	92.85 187	96.05 209	81.44 227	92.35 184	98.05 60	91.53 118	95.75 117	96.80 111	93.35 81	98.49 193	91.01 126	98.32 184	98.64 102

ETV-MVS		92.99 154	92.74 159	93.72 158	95.86 222	86.30 166	92.33 185	97.84 87	91.70 114	92.81 222	86.17 357	92.22 110	99.19 85	88.03 198	97.73 229	95.66 278

EI-MVSNet		92.99 154	93.26 150	92.19 208	92.12 316	79.21 266	92.32 186	94.67 253	91.77 109	95.24 143	95.85 168	87.14 202	98.49 193	91.99 101	98.26 190	98.86 74

CVMVSNet		85.16 301	84.72 299	86.48 321	92.12 316	70.19 346	92.32 186	88.17 326	56.15 370	90.64 271	95.85 168	67.97 321	96.69 300	88.78 182	90.52 351	92.56 340

OMC-MVS		94.22 122	93.69 134	95.81 70	97.25 130	91.27 65	92.27 188	97.40 119	87.10 213	94.56 168	95.42 196	93.74 71	98.11 225	86.62 221	98.85 126	98.06 145

PM-MVS		93.33 140	92.67 163	95.33 91	96.58 166	94.06 21	92.26 189	92.18 296	85.92 229	96.22 95	96.61 127	85.64 225	95.99 321	90.35 139	98.23 196	95.93 265

UniMVSNet_NR-MVSNet		95.35 74	95.21 84	95.76 75	97.69 108	88.59 113	92.26 189	97.84 87	94.91 38	96.80 68	95.78 176	90.42 155	99.41 38	91.60 115	99.58 31	99.29 28

AdaColmap		91.63 190	91.36 193	92.47 203	95.56 239	86.36 164	92.24 191	96.27 197	88.88 177	89.90 285	92.69 285	91.65 125	98.32 207	77.38 315	97.64 236	92.72 339

mvs-test1		93.07 152	91.80 182	96.89 39	94.74 263	95.83 6	92.17 192	95.41 231	89.94 150	89.85 286	90.59 324	90.12 161	98.88 129	87.68 204	95.66 286	95.97 263

PVSNet_Blended_VisFu		91.63 190	91.20 197	92.94 183	97.73 104	83.95 199	92.14 193	97.46 116	78.85 303	92.35 241	94.98 214	84.16 232	99.08 99	86.36 227	96.77 264	95.79 272

ETH3 D test6400		91.91 185	91.25 196	93.89 153	96.59 165	84.41 189	92.10 194	97.72 98	78.52 304	91.82 253	93.78 260	88.70 176	99.13 92	83.61 255	98.39 173	98.14 140

Baseline_NR-MVSNet		94.47 111	95.09 89	92.60 197	98.50 54	80.82 236	92.08 195	96.68 177	93.82 60	96.29 89	98.56 20	90.10 164	97.75 258	90.10 153	99.66 21	99.24 31

Fast-Effi-MVS+-dtu		92.77 163	92.16 171	94.58 127	94.66 269	88.25 120	92.05 196	96.65 179	89.62 157	90.08 280	91.23 311	92.56 104	98.60 181	86.30 228	96.27 274	96.90 228

xxxxxxxxxxxxxcwj		95.03 85	94.93 91	95.33 91	97.46 123	88.05 125	92.04 197	98.42 15	87.63 203	96.36 83	96.68 122	94.37 64	99.32 70	92.41 93	99.05 103	98.64 102

save fliter					97.46 123	88.05 125	92.04 197	97.08 148	87.63 203

PatchT		87.51 278	88.17 259	85.55 328	90.64 335	66.91 356	92.02 199	86.09 340	92.20 89	89.05 298	97.16 89	64.15 341	96.37 311	89.21 174	92.98 334	93.37 329

EG-PatchMatch MVS		94.54 109	94.67 105	94.14 142	97.87 95	86.50 157	92.00 200	96.74 175	88.16 191	96.93 62	97.61 58	93.04 92	97.90 240	91.60 115	98.12 208	98.03 151

v144192		93.20 149	93.54 141	92.16 212	96.05 209	78.26 279	91.95 201	97.14 143	84.98 247	95.96 106	96.11 159	87.08 203	99.04 107	93.79 37	98.84 127	99.17 35

VNet		92.67 166	92.96 152	91.79 220	96.27 191	80.15 240	91.95 201	94.98 239	92.19 90	94.52 170	96.07 160	87.43 196	97.39 277	84.83 245	98.38 175	97.83 176

1314		86.46 294	86.33 291	86.87 320	91.65 324	74.54 319	91.94 203	94.10 262	74.28 325	84.78 338	87.33 351	83.03 239	95.00 338	78.72 304	91.16 349	91.06 351

1121		90.26 224	89.23 234	93.34 170	97.15 138	87.40 135	91.94 203	94.39 256	67.88 355	91.02 265	94.91 217	86.91 208	98.59 183	81.17 281	97.71 232	94.02 315

MVS		84.98 303	84.30 303	87.01 318	91.03 331	77.69 288	91.94 203	94.16 261	59.36 368	84.23 342	87.50 349	85.66 223	96.80 297	71.79 344	93.05 333	86.54 361

tfpn200view9		87.05 290	86.52 288	88.67 298	95.77 226	72.94 332	91.89 206	86.00 342	90.84 131	92.61 228	89.80 328	63.93 342	98.28 209	71.27 349	96.54 269	94.79 296

thres400		87.20 286	86.52 288	89.24 291	95.77 226	72.94 332	91.89 206	86.00 342	90.84 131	92.61 228	89.80 328	63.93 342	98.28 209	71.27 349	96.54 269	96.51 240

v1921920		93.26 144	93.61 137	92.19 208	96.04 213	78.31 278	91.88 208	97.24 137	85.17 240	96.19 99	96.19 155	86.76 211	99.05 104	94.18 28	98.84 127	99.22 32

Regformer-1		94.55 107	94.33 117	95.19 99	92.83 304	88.54 116	91.87 209	95.84 215	93.99 54	95.95 107	95.04 211	92.00 115	98.79 147	93.14 73	98.31 185	98.23 133

Regformer-2		94.86 94	94.55 109	95.77 74	92.83 304	89.98 84	91.87 209	96.40 192	94.38 47	96.19 99	95.04 211	92.47 108	99.04 107	93.49 48	98.31 185	98.28 130

XXY-MVS		92.58 169	93.16 151	90.84 254	97.75 101	79.84 250	91.87 209	96.22 202	85.94 228	95.53 127	97.68 54	92.69 101	94.48 341	83.21 259	97.51 240	98.21 136

IterMVS-LS		93.78 131	94.28 119	92.27 205	96.27 191	79.21 266	91.87 209	96.78 171	91.77 109	96.57 78	97.07 93	87.15 201	98.74 159	91.99 101	99.03 109	98.86 74
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1144		93.50 135	93.81 128	92.57 198	96.28 190	79.61 257	91.86 213	96.96 155	86.95 215	95.91 111	96.32 147	87.65 192	98.96 120	93.51 47	98.88 122	99.13 39

v1192		93.49 136	93.78 130	92.62 196	96.16 200	79.62 256	91.83 214	97.22 139	86.07 226	96.10 103	96.38 143	87.22 199	99.02 110	94.14 29	98.88 122	99.22 32

v1240		93.29 141	93.71 133	92.06 215	96.01 214	77.89 284	91.81 215	97.37 120	85.12 243	96.69 72	96.40 138	86.67 212	99.07 103	94.51 18	98.76 141	99.22 32

CNVR-MVS		94.58 106	94.29 118	95.46 87	96.94 145	89.35 100	91.81 215	96.80 170	89.66 156	93.90 188	95.44 195	92.80 99	98.72 161	92.74 84	98.52 162	98.32 126

v2v482		93.29 141	93.63 136	92.29 204	96.35 184	78.82 272	91.77 217	96.28 196	88.45 185	95.70 121	96.26 152	86.02 220	98.90 126	93.02 77	98.81 135	99.14 38

RRT_test8_iter05		88.21 264	88.17 259	88.33 305	91.62 325	66.82 360	91.73 218	96.60 181	86.34 221	94.14 176	95.38 201	47.72 373	99.11 96	91.78 109	98.26 190	99.06 48

EPNet_dtu		85.63 298	84.37 302	89.40 286	86.30 368	74.33 323	91.64 219	88.26 323	84.84 249	72.96 371	89.85 326	71.27 313	97.69 261	76.60 320	97.62 237	96.18 256
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PLC	85.34 15	90.40 216	88.92 242	94.85 110	96.53 173	90.02 83	91.58 220	96.48 190	80.16 286	86.14 330	92.18 296	85.73 222	98.25 214	76.87 318	94.61 311	96.30 251
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
VPNet		93.08 150	93.76 131	91.03 245	98.60 35	75.83 312	91.51 221	95.62 219	91.84 103	95.74 118	97.10 92	89.31 172	98.32 207	85.07 243	99.06 98	98.93 64

XVG-OURS		94.72 101	94.12 124	96.50 49	98.00 88	94.23 18	91.48 222	98.17 40	90.72 135	95.30 137	96.47 132	87.94 189	96.98 290	91.41 120	97.61 238	98.30 129

HQP-NCC					96.36 181		91.37 223		87.16 210	88.81 301

ACMP_Plane					96.36 181		91.37 223		87.16 210	88.81 301

HQP-MVS		92.09 182	91.49 190	93.88 154	96.36 181	84.89 185	91.37 223	97.31 130	87.16 210	88.81 301	93.40 268	84.76 228	98.60 181	86.55 223	97.73 229	98.14 140

MCST-MVS		92.91 156	92.51 166	94.10 143	97.52 118	85.72 177	91.36 226	97.13 145	80.33 285	92.91 221	94.24 241	91.23 137	98.72 161	89.99 155	97.93 222	97.86 173

v148		92.87 159	93.29 146	91.62 227	96.25 194	77.72 287	91.28 227	95.05 237	89.69 155	95.93 110	96.04 161	87.34 197	98.38 202	90.05 154	97.99 219	98.78 83

tpmvs		84.22 307	83.97 306	84.94 333	87.09 365	65.18 364	91.21 228	88.35 322	82.87 268	85.21 333	90.96 316	65.24 337	96.75 298	79.60 299	85.25 361	92.90 336

CANet		92.38 175	91.99 176	93.52 167	93.82 289	83.46 203	91.14 229	97.00 152	89.81 154	86.47 328	94.04 248	87.90 190	99.21 83	89.50 165	98.27 189	97.90 169

CNLPA		91.72 188	91.20 197	93.26 174	96.17 199	91.02 70	91.14 229	95.55 227	90.16 148	90.87 266	93.56 265	86.31 216	94.40 344	79.92 295	97.12 251	94.37 306

DP-MVS Recon		92.31 177	91.88 179	93.60 161	97.18 135	86.87 149	91.10 231	97.37 120	84.92 248	92.08 249	94.08 247	88.59 177	98.20 217	83.50 256	98.14 205	95.73 274

OpenMVS_ROB	85.12 16	89.52 242	89.05 239	90.92 250	94.58 271	81.21 231	91.10 231	93.41 274	77.03 314	93.41 200	93.99 252	83.23 237	97.80 251	79.93 293	94.80 306	93.74 322

TSAR-MVS + GP.		93.07 152	92.41 169	95.06 104	95.82 223	90.87 75	90.97 233	92.61 290	88.04 193	94.61 167	93.79 259	88.08 184	97.81 250	89.41 166	98.39 173	96.50 243

MVP-Stereo		90.07 231	88.92 242	93.54 165	96.31 188	86.49 158	90.93 234	95.59 224	79.80 287	91.48 256	95.59 183	80.79 262	97.39 277	78.57 306	91.19 348	96.76 235
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MVSTER		89.32 244	88.75 246	91.03 245	90.10 343	76.62 302	90.85 235	94.67 253	82.27 274	95.24 143	95.79 173	61.09 355	98.49 193	90.49 133	98.26 190	97.97 160

pmmvs-eth3d		91.54 192	90.73 209	93.99 145	95.76 228	87.86 130	90.83 236	93.98 267	78.23 307	94.02 184	96.22 154	82.62 246	96.83 296	86.57 222	98.33 182	97.29 216

CANet_DTU		89.85 237	89.17 236	91.87 218	92.20 314	80.02 247	90.79 237	95.87 213	86.02 227	82.53 352	91.77 304	80.01 266	98.57 186	85.66 233	97.70 233	97.01 224

test_prior4						89.91 86	90.74 238

TinyColmap		92.00 184	92.76 158	89.71 281	95.62 237	77.02 295	90.72 239	96.17 205	87.70 201	95.26 140	96.29 149	92.54 105	96.45 307	81.77 273	98.77 140	95.66 278

CDPH-MVS		92.67 166	91.83 180	95.18 100	96.94 145	88.46 118	90.70 240	97.07 149	77.38 310	92.34 243	95.08 209	92.67 102	98.88 129	85.74 232	98.57 156	98.20 137

DSMNet-mixed		82.21 318	81.56 317	84.16 339	89.57 349	70.00 350	90.65 241	77.66 373	54.99 371	83.30 348	97.57 59	77.89 282	90.50 364	66.86 361	95.54 289	91.97 344

TEST9					96.45 177	89.46 94	90.60 242	96.92 159	79.09 299	90.49 272	94.39 237	91.31 133	98.88 129

train_agg		92.71 165	91.83 180	95.35 89	96.45 177	89.46 94	90.60 242	96.92 159	79.37 294	90.49 272	94.39 237	91.20 139	98.88 129	88.66 186	98.43 169	97.72 186

PatchmatchNet		85.22 300	84.64 300	86.98 319	89.51 350	69.83 351	90.52 244	87.34 332	78.87 302	87.22 325	92.74 284	66.91 325	96.53 303	81.77 273	86.88 359	94.58 302
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test_8					96.37 179	89.14 101	90.51 245	96.89 162	79.37 294	90.42 274	94.36 239	91.20 139	98.82 139

test_yl		90.11 228	89.73 231	91.26 237	94.09 281	79.82 251	90.44 246	92.65 287	90.90 129	93.19 212	93.30 270	73.90 302	98.03 229	82.23 269	96.87 260	95.93 265

DCV-MVSNet		90.11 228	89.73 231	91.26 237	94.09 281	79.82 251	90.44 246	92.65 287	90.90 129	93.19 212	93.30 270	73.90 302	98.03 229	82.23 269	96.87 260	95.93 265

tpm2		81.46 323	80.35 330	84.80 334	89.90 344	65.14 365	90.44 246	85.36 349	65.82 361	82.05 356	92.44 292	57.94 359	96.69 300	70.71 352	88.49 356	92.56 340

agg_prior1		92.60 168	91.76 183	95.10 103	96.20 196	88.89 106	90.37 249	96.88 163	79.67 291	90.21 277	94.41 235	91.30 134	98.78 151	88.46 189	98.37 180	97.64 192

CostFormer		83.09 312	82.21 315	85.73 327	89.27 352	67.01 355	90.35 250	86.47 337	70.42 346	83.52 347	93.23 273	61.18 354	96.85 295	77.21 316	88.26 357	93.34 330

TAMVS		90.16 226	89.05 239	93.49 168	96.49 175	86.37 163	90.34 251	92.55 291	80.84 283	92.99 218	94.57 232	81.94 254	98.20 217	73.51 335	98.21 199	95.90 268

EPMVS		81.17 327	80.37 329	83.58 341	85.58 370	65.08 366	90.31 252	71.34 374	77.31 312	85.80 332	91.30 310	59.38 357	92.70 357	79.99 290	82.34 367	92.96 335

CMPMVS	68.83 22	87.28 283	85.67 296	92.09 214	88.77 357	85.42 180	90.31 252	94.38 257	70.02 348	88.00 316	93.30 270	73.78 304	94.03 349	75.96 325	96.54 269	96.83 231
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test_post1							90.21 254				5.85 377	65.36 335	96.00 320	79.61 297

test_prior3		93.29 141	92.85 155	94.61 120	95.95 217	87.23 138	90.21 254	97.36 125	89.33 165	90.77 267	94.81 221	90.41 156	98.68 171	88.21 190	98.55 157	97.93 163

test_prior2							90.21 254		89.33 165	90.77 267	94.81 221	90.41 156		88.21 190	98.55 157

MVS_111021_LR		93.66 133	93.28 148	94.80 112	96.25 194	90.95 72	90.21 254	95.43 230	87.91 194	93.74 193	94.40 236	92.88 97	96.38 310	90.39 136	98.28 188	97.07 220

WR-MVS		93.49 136	93.72 132	92.80 189	97.57 116	80.03 246	90.14 258	95.68 218	93.70 62	96.62 75	95.39 199	87.21 200	99.04 107	87.50 206	99.64 23	99.33 25

tpmrst		82.85 315	82.93 313	82.64 344	87.65 359	58.99 374	90.14 258	87.90 328	75.54 319	83.93 343	91.63 307	66.79 328	95.36 332	81.21 280	81.54 368	93.57 328

PVSNet_BlendedMVS		90.35 220	89.96 225	91.54 230	94.81 258	78.80 274	90.14 258	96.93 157	79.43 293	88.68 308	95.06 210	86.27 217	98.15 223	80.27 286	98.04 215	97.68 189

BH-untuned		90.68 209	90.90 202	90.05 276	95.98 215	79.57 258	90.04 261	94.94 241	87.91 194	94.07 180	93.00 276	87.76 191	97.78 254	79.19 302	95.17 299	92.80 337

新几何2							90.02 262

旧先验2							90.00 263		68.65 352	92.71 226			96.52 304	85.15 238

无先验							89.94 264	95.75 217	70.81 345				98.59 183	81.17 281		94.81 295

xiu_mvs_v1_base_debu		91.47 194	91.52 187	91.33 234	95.69 231	81.56 224	89.92 265	96.05 208	83.22 261	91.26 260	90.74 318	91.55 127	98.82 139	89.29 168	95.91 280	93.62 325

xiu_mvs_v1_base		91.47 194	91.52 187	91.33 234	95.69 231	81.56 224	89.92 265	96.05 208	83.22 261	91.26 260	90.74 318	91.55 127	98.82 139	89.29 168	95.91 280	93.62 325

xiu_mvs_v1_base_debi		91.47 194	91.52 187	91.33 234	95.69 231	81.56 224	89.92 265	96.05 208	83.22 261	91.26 260	90.74 318	91.55 127	98.82 139	89.29 168	95.91 280	93.62 325

DWT-MVSNet_test		80.74 330	79.18 335	85.43 330	87.51 362	66.87 357	89.87 268	86.01 341	74.20 327	80.86 361	80.62 367	48.84 371	96.68 302	81.54 275	83.14 366	92.75 338

mvs_anonymous		90.37 219	91.30 195	87.58 314	92.17 315	68.00 354	89.84 269	94.73 249	83.82 258	93.22 211	97.40 70	87.54 194	97.40 276	87.94 200	95.05 301	97.34 213

test20.03		90.80 205	90.85 205	90.63 259	95.63 236	79.24 264	89.81 270	92.87 281	89.90 152	94.39 172	96.40 138	85.77 221	95.27 336	73.86 334	99.05 103	97.39 210

1112_ss		88.42 261	87.41 270	91.45 231	96.69 159	80.99 233	89.72 271	96.72 176	73.37 331	87.00 326	90.69 321	77.38 285	98.20 217	81.38 277	93.72 323	95.15 288

UnsupCasMVSNet_eth		90.33 221	90.34 217	90.28 267	94.64 270	80.24 238	89.69 272	95.88 212	85.77 231	93.94 187	95.69 179	81.99 252	92.98 356	84.21 252	91.30 347	97.62 193

MG-MVS		89.54 241	89.80 228	88.76 296	94.88 254	72.47 337	89.60 273	92.44 293	85.82 230	89.48 293	95.98 164	82.85 241	97.74 259	81.87 272	95.27 297	96.08 259

Patchmatch-test		86.10 296	86.01 293	86.38 325	90.63 336	74.22 325	89.57 274	86.69 335	85.73 233	89.81 288	92.83 280	65.24 337	91.04 362	77.82 311	95.78 284	93.88 319

Anonymous20231206		88.77 256	88.29 254	90.20 272	96.31 188	78.81 273	89.56 275	93.49 273	74.26 326	92.38 239	95.58 186	82.21 248	95.43 331	72.07 343	98.75 143	96.34 249

DeepPCF-MVS	90.46 6	94.20 123	93.56 140	96.14 54	95.96 216	92.96 45	89.48 276	97.46 116	85.14 241	96.23 94	95.42 196	93.19 85	98.08 226	90.37 138	98.76 141	97.38 212

SCA		87.43 280	87.21 274	88.10 308	92.01 319	71.98 339	89.43 277	88.11 327	82.26 275	88.71 306	92.83 280	78.65 274	97.59 264	79.61 297	93.30 327	94.75 298

testgi		90.38 218	91.34 194	87.50 315	97.49 120	71.54 340	89.43 277	95.16 236	88.38 187	94.54 169	94.68 229	92.88 97	93.09 355	71.60 347	97.85 226	97.88 171

JIA-IIPM		85.08 302	83.04 311	91.19 242	87.56 360	86.14 170	89.40 279	84.44 357	88.98 173	82.20 354	97.95 43	56.82 362	96.15 315	76.55 321	83.45 364	91.30 349

原ACMM2							89.34 280

tpm		84.38 306	84.08 305	85.30 332	90.47 339	63.43 371	89.34 280	85.63 346	77.24 313	87.62 320	95.03 213	61.00 356	97.30 280	79.26 301	91.09 350	95.16 287

MVS_111021_HR		93.63 134	93.42 144	94.26 139	96.65 160	86.96 148	89.30 282	96.23 200	88.36 188	93.57 197	94.60 230	93.45 76	97.77 255	90.23 147	98.38 175	98.03 151

tpm cat1		80.61 332	79.46 334	84.07 340	88.78 356	65.06 367	89.26 283	88.23 324	62.27 366	81.90 358	89.66 334	62.70 350	95.29 335	71.72 345	80.60 369	91.86 347

CDS-MVSNet		89.55 240	88.22 258	93.53 166	95.37 246	86.49 158	89.26 283	93.59 270	79.76 289	91.15 263	92.31 295	77.12 288	98.38 202	77.51 313	97.92 223	95.71 275
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Fast-Effi-MVS+		91.28 200	90.86 204	92.53 200	95.45 242	82.53 215	89.25 285	96.52 188	85.00 246	89.91 284	88.55 343	92.94 93	98.84 137	84.72 248	95.44 292	96.22 254

BH-RMVSNet		90.47 214	90.44 215	90.56 261	95.21 250	78.65 276	89.15 286	93.94 268	88.21 189	92.74 225	94.22 242	86.38 215	97.88 242	78.67 305	95.39 294	95.14 289

thres200		85.85 297	85.18 298	87.88 312	94.44 273	72.52 336	89.08 287	86.21 338	88.57 184	91.44 257	88.40 344	64.22 340	98.00 234	68.35 357	95.88 283	93.12 331

USDC		89.02 248	89.08 238	88.84 295	95.07 252	74.50 321	88.97 288	96.39 193	73.21 332	93.27 207	96.28 150	82.16 250	96.39 309	77.55 312	98.80 137	95.62 281

testdata1							88.96 289		88.44 186

pmmvs5		87.87 269	87.14 276	90.07 274	93.26 295	76.97 299	88.89 290	92.18 296	73.71 330	88.36 311	93.89 256	76.86 294	96.73 299	80.32 285	96.81 262	96.51 240

patch_mono-2		92.46 173	92.72 162	91.71 224	96.65 160	78.91 270	88.85 291	97.17 141	83.89 257	92.45 233	96.76 114	89.86 168	97.09 286	90.24 146	98.59 155	99.12 41

test222					96.95 144	85.27 182	88.83 292	93.61 269	65.09 362	90.74 269	94.85 220	84.62 230			97.36 245	93.91 317

baseline2		83.38 310	81.54 319	88.90 293	91.38 328	72.84 334	88.78 293	81.22 365	78.97 300	79.82 364	87.56 347	61.73 353	97.80 251	74.30 332	90.05 353	96.05 261

diffmvs		91.74 187	91.93 178	91.15 243	93.06 299	78.17 280	88.77 294	97.51 115	86.28 222	92.42 236	93.96 253	88.04 186	97.46 271	90.69 132	96.67 267	97.82 178

MDTV_nov1_ep13			83.88 307		89.42 351	61.52 372	88.74 295	87.41 331	73.99 328	84.96 337	94.01 251	65.25 336	95.53 325	78.02 307	93.16 329

D2MVS		89.93 235	89.60 233	90.92 250	94.03 283	78.40 277	88.69 296	94.85 243	78.96 301	93.08 214	95.09 208	74.57 300	96.94 291	88.19 192	98.96 117	97.41 206

TR-MVS		87.70 272	87.17 275	89.27 289	94.11 280	79.26 263	88.69 296	91.86 304	81.94 277	90.69 270	89.79 330	82.82 242	97.42 274	72.65 341	91.98 344	91.14 350

PatchMatch-RL		89.18 245	88.02 263	92.64 193	95.90 221	92.87 47	88.67 298	91.06 309	80.34 284	90.03 282	91.67 306	83.34 235	94.42 343	76.35 322	94.84 305	90.64 353

PAPR		87.65 275	86.77 283	90.27 268	92.85 303	77.38 291	88.56 299	96.23 200	76.82 316	84.98 336	89.75 332	86.08 219	97.16 284	72.33 342	93.35 326	96.26 253

MDTV_nov1_ep13_2view						42.48 379	88.45 300		67.22 357	83.56 346		66.80 326		72.86 340		94.06 312

jason		89.17 246	88.32 252	91.70 225	95.73 229	80.07 243	88.10 301	93.22 276	71.98 338	90.09 279	92.79 282	78.53 277	98.56 187	87.43 208	97.06 252	96.46 245
jason: jason.
BH-w/o		87.21 285	87.02 279	87.79 313	94.77 260	77.27 293	87.90 302	93.21 278	81.74 278	89.99 283	88.39 345	83.47 234	96.93 293	71.29 348	92.43 340	89.15 355

MS-PatchMatch		88.05 267	87.75 265	88.95 292	93.28 293	77.93 282	87.88 303	92.49 292	75.42 320	92.57 230	93.59 264	80.44 264	94.24 348	81.28 278	92.75 335	94.69 301

DELS-MVS		92.05 183	92.16 171	91.72 223	94.44 273	80.13 242	87.62 304	97.25 136	87.34 208	92.22 246	93.18 274	89.54 171	98.73 160	89.67 162	98.20 201	96.30 251
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
ADS-MVSNet2		84.01 308	82.20 316	89.41 285	89.04 354	76.37 306	87.57 305	90.98 310	72.71 336	84.46 339	92.45 290	68.08 319	96.48 306	70.58 353	83.97 362	95.38 284

ADS-MVSNet		82.25 317	81.55 318	84.34 338	89.04 354	65.30 363	87.57 305	85.13 354	72.71 336	84.46 339	92.45 290	68.08 319	92.33 358	70.58 353	83.97 362	95.38 284

IterMVS-SCA-FT		91.65 189	91.55 186	91.94 217	93.89 286	79.22 265	87.56 307	93.51 272	91.53 118	95.37 133	96.62 126	78.65 274	98.90 126	91.89 106	94.95 302	97.70 187

IterMVS		90.18 225	90.16 219	90.21 271	93.15 297	75.98 309	87.56 307	92.97 280	86.43 220	94.09 178	96.40 138	78.32 278	97.43 273	87.87 201	94.69 309	97.23 217
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Test_1112_low_res		87.50 279	86.58 285	90.25 269	96.80 157	77.75 286	87.53 309	96.25 198	69.73 349	86.47 328	93.61 263	75.67 298	97.88 242	79.95 291	93.20 328	95.11 290

c3_l		91.32 199	91.42 191	91.00 248	92.29 311	76.79 301	87.52 310	96.42 191	85.76 232	94.72 166	93.89 256	82.73 243	98.16 222	90.93 128	98.55 157	98.04 148

UnsupCasMVSNet_bld		88.50 260	88.03 262	89.90 278	95.52 240	78.88 271	87.39 311	94.02 265	79.32 297	93.06 215	94.02 250	80.72 263	94.27 346	75.16 328	93.08 332	96.54 238

lupinMVS		88.34 263	87.31 271	91.45 231	94.74 263	80.06 244	87.23 312	92.27 295	71.10 342	88.83 299	91.15 312	77.02 289	98.53 190	86.67 220	96.75 265	95.76 273

pmmvs4		88.95 252	87.70 267	92.70 191	94.30 276	85.60 178	87.22 313	92.16 298	74.62 324	89.75 291	94.19 243	77.97 281	96.41 308	82.71 263	96.36 273	96.09 258

WTY-MVS		86.93 292	86.50 290	88.24 306	94.96 253	74.64 317	87.19 314	92.07 301	78.29 306	88.32 312	91.59 308	78.06 280	94.27 346	74.88 329	93.15 330	95.80 271

ET-MVSNet_ETH3D		86.15 295	84.27 304	91.79 220	93.04 300	81.28 229	87.17 315	86.14 339	79.57 292	83.65 344	88.66 341	57.10 360	98.18 220	87.74 203	95.40 293	95.90 268

MVS-HIRNet		78.83 337	80.60 328	73.51 354	93.07 298	47.37 377	87.10 316	78.00 372	68.94 351	77.53 367	97.26 82	71.45 312	94.62 339	63.28 366	88.74 355	78.55 369

xiu_mvs_v2_base		89.00 250	89.19 235	88.46 303	94.86 256	74.63 318	86.97 317	95.60 220	80.88 281	87.83 318	88.62 342	91.04 143	98.81 144	82.51 267	94.38 313	91.93 345

DPM-MVS		89.35 243	88.40 251	92.18 211	96.13 205	84.20 194	86.96 318	96.15 206	75.40 321	87.36 323	91.55 309	83.30 236	98.01 233	82.17 271	96.62 268	94.32 308

eth_miper_zixun_eth		90.72 207	90.61 211	91.05 244	92.04 318	76.84 300	86.91 319	96.67 178	85.21 239	94.41 171	93.92 254	79.53 269	98.26 213	89.76 160	97.02 254	98.06 145

dp		79.28 335	78.62 337	81.24 347	85.97 369	56.45 375	86.91 319	85.26 352	72.97 334	81.45 360	89.17 340	56.01 364	95.45 330	73.19 338	76.68 370	91.82 348

sss		87.23 284	86.82 281	88.46 303	93.96 284	77.94 281	86.84 321	92.78 285	77.59 309	87.61 321	91.83 303	78.75 273	91.92 359	77.84 309	94.20 318	95.52 283

miper_ehance_all_eth		90.48 213	90.42 216	90.69 257	91.62 325	76.57 303	86.83 322	96.18 204	83.38 259	94.06 181	92.66 287	82.20 249	98.04 228	89.79 159	97.02 254	97.45 203

CLD-MVS		91.82 186	91.41 192	93.04 177	96.37 179	83.65 202	86.82 323	97.29 133	84.65 252	92.27 245	89.67 333	92.20 111	97.85 248	83.95 253	99.47 39	97.62 193
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
cl____		90.65 210	90.56 213	90.91 252	91.85 320	76.98 298	86.75 324	95.36 234	85.53 235	94.06 181	94.89 218	77.36 287	97.98 237	90.27 144	98.98 111	97.76 183

DIV-MVS_self_test		90.65 210	90.56 213	90.91 252	91.85 320	76.99 297	86.75 324	95.36 234	85.52 237	94.06 181	94.89 218	77.37 286	97.99 236	90.28 143	98.97 115	97.76 183

PS-MVSNAJ		88.86 254	88.99 241	88.48 302	94.88 254	74.71 316	86.69 326	95.60 220	80.88 281	87.83 318	87.37 350	90.77 146	98.82 139	82.52 266	94.37 314	91.93 345

PVSNet_Blended		88.74 257	88.16 261	90.46 264	94.81 258	78.80 274	86.64 327	96.93 157	74.67 323	88.68 308	89.18 339	86.27 217	98.15 223	80.27 286	96.00 278	94.44 305

MSDG		90.82 204	90.67 210	91.26 237	94.16 278	83.08 210	86.63 328	96.19 203	90.60 140	91.94 251	91.89 302	89.16 174	95.75 323	80.96 284	94.51 312	94.95 294

cl22		89.02 248	88.50 249	90.59 260	89.76 345	76.45 304	86.62 329	94.03 263	82.98 267	92.65 227	92.49 288	72.05 310	97.53 266	88.93 177	97.02 254	97.78 181

CL-MVSNet_self_test		90.04 233	89.90 227	90.47 262	95.24 249	77.81 285	86.60 330	92.62 289	85.64 234	93.25 210	93.92 254	83.84 233	96.06 319	79.93 293	98.03 216	97.53 199

PCF-MVS	84.52 17	89.12 247	87.71 266	93.34 170	96.06 208	85.84 175	86.58 331	97.31 130	68.46 353	93.61 196	93.89 256	87.51 195	98.52 191	67.85 358	98.11 209	95.66 278
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Patchmatch-RL test		88.81 255	88.52 248	89.69 282	95.33 248	79.94 248	86.22 332	92.71 286	78.46 305	95.80 115	94.18 244	66.25 331	95.33 334	89.22 173	98.53 161	93.78 320

FPMVS		84.50 305	83.28 309	88.16 307	96.32 187	94.49 16	85.76 333	85.47 348	83.09 264	85.20 334	94.26 240	63.79 344	86.58 369	63.72 365	91.88 346	83.40 364

IB-MVS	77.21 19	83.11 311	81.05 322	89.29 288	91.15 330	75.85 310	85.66 334	86.00 342	79.70 290	82.02 357	86.61 353	48.26 372	98.39 200	77.84 309	92.22 341	93.63 324
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
MDA-MVSNet-bldmvs		91.04 201	90.88 203	91.55 229	94.68 268	80.16 239	85.49 335	92.14 299	90.41 145	94.93 156	95.79 173	85.10 226	96.93 293	85.15 238	94.19 319	97.57 195

new-patchmatchnet		88.97 251	90.79 207	83.50 342	94.28 277	55.83 376	85.34 336	93.56 271	86.18 224	95.47 128	95.73 178	83.10 238	96.51 305	85.40 235	98.06 213	98.16 138

miper_enhance_ethall		88.42 261	87.87 264	90.07 274	88.67 358	75.52 313	85.10 337	95.59 224	75.68 317	92.49 231	89.45 336	78.96 271	97.88 242	87.86 202	97.02 254	96.81 232

HyFIR lowres test		87.19 287	85.51 297	92.24 206	97.12 140	80.51 237	85.03 338	96.06 207	66.11 359	91.66 255	92.98 278	70.12 316	99.14 90	75.29 327	95.23 298	97.07 220

pmmvs3		80.83 329	78.96 336	86.45 322	87.23 364	77.48 290	84.87 339	82.31 362	63.83 364	85.03 335	89.50 335	49.66 370	93.10 354	73.12 339	95.10 300	88.78 359

test0.0.03 1		82.48 316	81.47 320	85.48 329	89.70 346	73.57 328	84.73 340	81.64 364	83.07 265	88.13 315	86.61 353	62.86 348	89.10 368	66.24 362	90.29 352	93.77 321

N_pmnet		88.90 253	87.25 273	93.83 156	94.40 275	93.81 36	84.73 340	87.09 333	79.36 296	93.26 208	92.43 293	79.29 270	91.68 360	77.50 314	97.22 249	96.00 262

GA-MVS		87.70 272	86.82 281	90.31 266	93.27 294	77.22 294	84.72 342	92.79 284	85.11 244	89.82 287	90.07 325	66.80 326	97.76 257	84.56 249	94.27 317	95.96 264

ppachtmachnet_test		88.61 259	88.64 247	88.50 301	91.76 322	70.99 344	84.59 343	92.98 279	79.30 298	92.38 239	93.53 266	79.57 268	97.45 272	86.50 225	97.17 250	97.07 220

CHOSEN 1792x2688		87.19 287	85.92 295	91.00 248	97.13 139	79.41 260	84.51 344	95.60 220	64.14 363	90.07 281	94.81 221	78.26 279	97.14 285	73.34 336	95.38 295	96.46 245

thisisatest0515		84.72 304	82.99 312	89.90 278	92.96 302	75.33 315	84.36 345	83.42 360	77.37 311	88.27 313	86.65 352	53.94 366	98.72 161	82.56 265	97.40 244	95.67 277

cascas		87.02 291	86.28 292	89.25 290	91.56 327	76.45 304	84.33 346	96.78 171	71.01 343	86.89 327	85.91 358	81.35 257	96.94 291	83.09 260	95.60 287	94.35 307

bset_n11_16_dypcd		89.99 234	89.15 237	92.53 200	94.75 261	81.34 228	84.19 347	87.56 330	85.13 242	93.77 190	92.46 289	72.82 306	99.01 112	92.46 92	99.21 82	97.23 217

new_pmnet		81.22 325	81.01 324	81.86 346	90.92 334	70.15 347	84.03 348	80.25 369	70.83 344	85.97 331	89.78 331	67.93 322	84.65 370	67.44 359	91.90 345	90.78 352

PAPM		81.91 322	80.11 332	87.31 317	93.87 287	72.32 338	84.02 349	93.22 276	69.47 350	76.13 369	89.84 327	72.15 309	97.23 282	53.27 371	89.02 354	92.37 342

our_test_3		87.55 277	87.59 268	87.44 316	91.76 322	70.48 345	83.83 350	90.55 314	79.79 288	92.06 250	92.17 297	78.63 276	95.63 324	84.77 246	94.73 307	96.22 254

miper_lstm_enhance		89.90 236	89.80 228	90.19 273	91.37 329	77.50 289	83.82 351	95.00 238	84.84 249	93.05 216	94.96 215	76.53 296	95.20 337	89.96 156	98.67 150	97.86 173

test-LLR		83.58 309	83.17 310	84.79 335	89.68 347	66.86 358	83.08 352	84.52 355	83.07 265	82.85 350	84.78 361	62.86 348	93.49 352	82.85 261	94.86 303	94.03 313

TESTMET0.1,1		79.09 336	78.04 338	82.25 345	87.52 361	64.03 370	83.08 352	80.62 367	70.28 347	80.16 363	83.22 364	44.13 376	90.56 363	79.95 291	93.36 325	92.15 343

test-mter		81.21 326	80.01 333	84.79 335	89.68 347	66.86 358	83.08 352	84.52 355	73.85 329	82.85 350	84.78 361	43.66 377	93.49 352	82.85 261	94.86 303	94.03 313

test123		9.49 343	12.01 346	1.91 358	2.87 381	1.30 382	82.38 355	1.34 383	1.36 376	2.84 377	6.56 375	2.45 381	0.97 377	2.73 375	5.56 375	3.47 373

PMMVS		83.00 313	81.11 321	88.66 299	83.81 375	86.44 161	82.24 356	85.65 345	61.75 367	82.07 355	85.64 359	79.75 267	91.59 361	75.99 324	93.09 331	87.94 360

KD-MVS_2432*1600		82.17 319	80.75 326	86.42 323	82.04 376	70.09 348	81.75 357	90.80 311	82.56 269	90.37 275	89.30 337	42.90 378	96.11 317	74.47 330	92.55 338	93.06 332

miper_refine_blended		82.17 319	80.75 326	86.42 323	82.04 376	70.09 348	81.75 357	90.80 311	82.56 269	90.37 275	89.30 337	42.90 378	96.11 317	74.47 330	92.55 338	93.06 332

YYNet1		88.17 265	88.24 256	87.93 310	92.21 313	73.62 327	80.75 359	88.77 319	82.51 272	94.99 154	95.11 207	82.70 244	93.70 350	83.33 257	93.83 321	96.48 244

MDA-MVSNet_test_wron		88.16 266	88.23 257	87.93 310	92.22 312	73.71 326	80.71 360	88.84 318	82.52 271	94.88 159	95.14 205	82.70 244	93.61 351	83.28 258	93.80 322	96.46 245

testmvs		9.02 344	11.42 347	1.81 359	2.77 382	1.13 383	79.44 361	1.90 382	1.18 377	2.65 378	6.80 374	1.95 382	0.87 378	2.62 376	3.45 376	3.44 374

PVSNet	76.22 20	82.89 314	82.37 314	84.48 337	93.96 284	64.38 369	78.60 362	88.61 320	71.50 340	84.43 341	86.36 356	74.27 301	94.60 340	69.87 355	93.69 324	94.46 304

PVSNet_0	70.34 21	74.58 338	72.96 341	79.47 350	90.63 336	66.24 362	73.26 363	83.40 361	63.67 365	78.02 366	78.35 369	72.53 307	89.59 366	56.68 369	60.05 373	82.57 367

E-PMN		80.72 331	80.86 325	80.29 349	85.11 371	68.77 353	72.96 364	81.97 363	87.76 199	83.25 349	83.01 365	62.22 351	89.17 367	77.15 317	94.31 316	82.93 365

CHOSEN 280x420		80.04 334	77.97 339	86.23 326	90.13 342	74.53 320	72.87 365	89.59 317	66.38 358	76.29 368	85.32 360	56.96 361	95.36 332	69.49 356	94.72 308	88.79 358

EMVS		80.35 333	80.28 331	80.54 348	84.73 373	69.07 352	72.54 366	80.73 366	87.80 198	81.66 359	81.73 366	62.89 347	89.84 365	75.79 326	94.65 310	82.71 366

PMMVS2		81.31 324	83.44 308	74.92 353	90.52 338	46.49 378	69.19 367	85.23 353	84.30 254	87.95 317	94.71 228	76.95 291	84.36 371	64.07 364	98.09 211	93.89 318

tmp_tt		37.97 341	44.33 344	18.88 357	11.80 380	21.54 380	63.51 368	45.66 381	4.23 374	51.34 374	50.48 372	59.08 358	22.11 376	44.50 373	68.35 372	13.00 372

MVE	59.87 23	73.86 339	72.65 342	77.47 352	87.00 367	74.35 322	61.37 369	60.93 377	67.27 356	69.69 372	86.49 355	81.24 261	72.33 373	56.45 370	83.45 364	85.74 362
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		50.44 340	48.94 343	54.93 355	39.68 379	12.38 381	28.59 370	90.09 315	6.82 373	41.10 375	78.41 368	54.41 365	70.69 374	50.12 372	51.26 374	81.72 368

test_blank		0.00 347	0.00 350	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.00 378	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

uanet_test		0.00 347	0.00 350	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.00 378	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

cdsmvs_eth3d_5k		23.35 342	31.13 345	0.00 360	0.00 383	0.00 384	0.00 371	95.58 226	0.00 378	0.00 379	91.15 312	93.43 78	0.00 379	0.00 377	0.00 377	0.00 375

pcd_1.5k_mvsjas		7.56 345	10.09 348	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.00 378	90.77 146	0.00 379	0.00 377	0.00 377	0.00 375

sosnet-low-res		0.00 347	0.00 350	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.00 378	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

sosnet		0.00 347	0.00 350	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.00 378	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

uncertanet		0.00 347	0.00 350	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.00 378	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

Regformer		0.00 347	0.00 350	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.00 378	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

ab-mvs-re		7.56 345	10.08 349	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	90.69 321	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

uanet		0.00 347	0.00 350	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.00 378	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

MSC_two_6792asdad				95.90 66	96.54 170	89.57 92		96.87 165					99.41 38	94.06 30	99.30 65	98.72 92

PC_three_1452									75.31 322	95.87 113	95.75 177	92.93 94	96.34 314	87.18 212	98.68 148	98.04 148

No_MVS				95.90 66	96.54 170	89.57 92		96.87 165					99.41 38	94.06 30	99.30 65	98.72 92

test_one_0601					98.26 67	87.14 141		98.18 36	94.25 49	96.99 60	97.36 75	95.13 40

eth-test2					0.00 383

eth-test					0.00 383

ZD-MVS					97.23 131	90.32 81		97.54 110	84.40 253	94.78 162	95.79 173	92.76 100	99.39 50	88.72 185	98.40 170

IU-MVS					98.51 47	86.66 155		96.83 168	72.74 335	95.83 114				93.00 78	99.29 68	98.64 102

test_241102_TWO								98.10 49	91.95 94	97.54 37	97.25 83	95.37 28	99.35 61	93.29 65	99.25 76	98.49 116

test_241102_ONE					98.51 47	86.97 146		98.10 49	91.85 100	97.63 32	97.03 96	96.48 11	98.95 122

test_0728_THIRD									93.26 70	97.40 46	97.35 78	94.69 55	99.34 64	93.88 34	99.42 47	98.89 71

GSMVS																94.75 298

test_part2					98.21 71	89.41 97				96.72 71

sam_mvs1												66.64 329				94.75 298

sam_mvs												66.41 330

MTGPA								97.62 102

test_post											6.07 376	65.74 334	95.84 322

patchmatchnet-post											91.71 305	66.22 332	97.59 264

gm-plane-assit					87.08 366	59.33 373			71.22 341		83.58 363		97.20 283	73.95 333

test9_res														88.16 194	98.40 170	97.83 176

agg_prior2														87.06 215	98.36 181	97.98 157

agg_prior					96.20 196	88.89 106		96.88 163		90.21 277			98.78 151

TestCases				96.00 58	98.02 86	92.17 52		98.43 13	90.48 141	95.04 152	96.74 117	92.54 105	97.86 246	85.11 241	98.98 111	97.98 157

test_prior				94.61 120	95.95 217	87.23 138		97.36 125					98.68 171			97.93 163

新几何1				93.17 176	97.16 136	87.29 137		94.43 255	67.95 354	91.29 259	94.94 216	86.97 205	98.23 215	81.06 283	97.75 228	93.98 316

旧先验1					96.20 196	84.17 195		94.82 245			95.57 187	89.57 170			97.89 224	96.32 250

原ACMM1				92.87 186	96.91 148	84.22 193		97.01 151	76.84 315	89.64 292	94.46 234	88.00 187	98.70 167	81.53 276	98.01 218	95.70 276

testdata2													98.03 229	80.24 288

segment_acmp												92.14 112

testdata				91.03 245	96.87 151	82.01 218		94.28 259	71.55 339	92.46 232	95.42 196	85.65 224	97.38 279	82.64 264	97.27 247	93.70 323

test12				94.43 135	95.95 217	86.75 151		96.24 199		89.76 290		89.79 169	98.79 147		97.95 221	97.75 185

plane_prior7					97.71 105	88.68 110

plane_prior6					97.21 134	88.23 121						86.93 206

plane_prior5								97.81 90					98.95 122	89.26 171	98.51 164	98.60 109

plane_prior4											95.59 183

plane_prior3						88.43 119			90.35 146	93.31 203

plane_prior1					97.38 126

n2								0.00 384

nn								0.00 384

door-mid								92.13 300

lessismore_v0				93.87 155	98.05 81	83.77 201		80.32 368		97.13 52	97.91 46	77.49 283	99.11 96	92.62 88	98.08 212	98.74 89

LGP-MVS_train				96.84 40	98.36 62	92.13 54		98.25 27	91.78 107	97.07 53	97.22 86	96.38 13	99.28 75	92.07 99	99.59 27	99.11 42

test11								96.65 179

door								91.26 308

HQP5-MVS						84.89 185

BP-MVS														86.55 223

HQP4-MVS										88.81 301			98.61 179			98.15 139

HQP3-MVS								97.31 130							97.73 229

HQP2-MVS												84.76 228

NP-MVS					96.82 154	87.10 142					93.40 268

ACMMP++_ref															98.82 133

ACMMP++															99.25 76

Test By Simon												90.61 152

ITE_SJBPF				95.95 60	97.34 128	93.36 42		96.55 187	91.93 96	94.82 160	95.39 199	91.99 116	97.08 287	85.53 234	97.96 220	97.41 206

DeepMVS_CX				53.83 356	70.38 378	64.56 368		48.52 380	33.01 372	65.50 373	74.21 371	56.19 363	46.64 375	38.45 374	70.07 371	50.30 371

High-resolution multi-view benchmark