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 6	97.01 13	98.92 4	98.78 14	95.22 37	98.61 180	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 8	97.41 10	97.28 49	98.46 25	94.62 58	98.84 138	94.64 17	99.53 35	98.99 55

DVP-MVS++		95.93 53	96.34 34	94.70 117	96.54 171	86.66 155	98.45 4	98.22 33	93.26 70	97.54 38	97.36 75	93.12 89	99.38 56	93.88 34	98.68 148	98.04 149

FOURS1					99.21 3	94.68 12	98.45 4	98.81 8	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 36	96.95 14	95.46 131	99.23 4	93.45 77	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 70	94.15 51	98.93 3	99.07 5	88.07 186	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	97.54 111	98.16 2	98.94 2	99.33 2	97.84 4	99.08 100	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 13	92.35 84	95.95 108	96.41 138	96.71 8	99.42 31	93.99 33	99.36 57	99.13 40
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
EPP-MVSNet		93.91 130	93.68 136	94.59 125	98.08 78	85.55 179	97.44 10	94.03 264	94.22 50	94.94 156	96.19 156	82.07 252	99.57 13	87.28 212	98.89 120	98.65 99

LS3D		96.11 48	95.83 62	96.95 37	94.75 262	94.20 19	97.34 11	97.98 73	97.31 11	95.32 137	96.77 113	93.08 91	99.20 85	91.79 108	98.16 204	97.44 206

HPM-MVS_fast		97.01 7	96.89 15	97.39 22	99.12 8	93.92 29	97.16 12	98.17 41	93.11 72	96.48 80	97.36 75	96.92 6	99.34 64	94.31 23	99.38 56	98.92 69

MVSFormer		92.18 182	92.23 171	92.04 216	94.74 264	80.06 244	97.15 13	97.37 121	88.98 174	88.83 300	92.79 284	77.02 290	99.60 8	96.41 4	96.75 266	96.46 246

test_djsdf		96.62 23	96.49 28	97.01 33	98.55 41	91.77 61	97.15 13	97.37 121	88.98 174	98.26 22	98.86 10	93.35 82	99.60 8	96.41 4	99.45 43	99.66 6

IS-MVSNet		94.49 110	94.35 117	94.92 107	98.25 69	86.46 160	97.13 15	94.31 259	96.24 24	96.28 93	96.36 146	82.88 241	99.35 61	88.19 193	99.52 37	98.96 62

Anonymous20231211		96.60 25	97.13 12	95.00 105	97.46 124	86.35 165	97.11 16	98.24 31	97.58 8	98.72 8	98.97 7	93.15 88	99.15 89	93.18 69	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	97.61 106	87.68 203	98.45 18	98.77 15	94.20 67	99.50 20	96.70 3	99.40 54	99.53 14

ACMMP		96.61 24	96.34 34	97.43 19	98.61 34	93.88 30	96.95 18	98.18 37	92.26 87	96.33 86	96.84 111	95.10 43	99.40 45	93.47 52	99.33 61	99.02 52
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 17	92.52 79	97.43 44	97.92 45	95.11 42	99.50 20	94.45 19	99.30 66	98.92 69

EGC-MVSNET		80.97 329	75.73 341	96.67 44	98.85 22	94.55 15	96.83 20	96.60 182	2.44 376	5.32 377	98.25 31	92.24 110	98.02 233	91.85 106	99.21 83	97.45 204

v7n		96.82 10	97.31 10	95.33 91	98.54 43	86.81 150	96.83 20	98.07 57	96.59 20	98.46 17	98.43 27	92.91 96	99.52 18	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 54	92.67 77	95.08 151	96.39 143	94.77 54	99.42 31	93.17 70	99.44 46	98.58 112

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 21	96.69 17	96.86 66	97.56 60	95.48 25	98.77 156	90.11 152	99.44 46	98.31 129
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 54	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		95.77 59	95.58 70	96.37 52	96.84 153	91.72 63	96.73 25	99.06 5	94.23 49	92.48 233	94.79 226	93.56 73	99.49 23	93.47 52	99.05 102	97.89 170

pmmvs6		96.80 13	97.36 9	95.15 101	99.12 8	87.82 131	96.68 26	97.86 84	96.10 26	98.14 24	99.28 3	97.94 3	98.21 217	91.38 121	99.69 15	99.42 19

3Dnovator	92.54 3	94.80 99	94.90 94	94.47 132	95.47 242	87.06 143	96.63 27	97.28 136	91.82 106	94.34 176	97.41 69	90.60 154	98.65 177	92.47 90	98.11 210	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 76	97.91 5	98.64 13	98.13 34	95.24 36	99.65 3	93.39 60	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 20	95.81 31	97.55 37	97.44 68	96.51 9	99.40 45	94.06 30	99.23 80	98.85 78

mvs_tets		96.83 9	96.71 19	97.17 27	98.83 23	92.51 50	96.58 30	97.61 106	87.57 206	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 61	98.06 4	98.64 13	98.25 31	95.01 48	99.65 3	92.95 79	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 82	98.14 3	98.67 12	98.32 29	95.04 45	99.69 2	93.27 66	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 79	94.58 41	94.38 174	96.49 132	94.56 59	99.39 50	93.57 44	99.05 102	98.93 65

X-MVStestdata		90.70 209	88.45 251	97.44 17	98.56 38	93.99 27	96.50 33	97.95 79	94.58 41	94.38 174	26.89 374	94.56 59	99.39 50	93.57 44	99.05 102	98.93 65

DROMVSNet		95.44 70	95.62 69	94.89 108	96.93 148	87.69 132	96.48 35	99.14 4	93.93 57	92.77 225	94.52 234	93.95 70	99.49 23	93.62 43	99.22 82	97.51 201

mPP-MVS		96.46 32	96.05 51	97.69 5	98.62 32	94.65 13	96.45 36	97.74 97	92.59 78	95.47 129	96.68 123	94.50 61	99.42 31	93.10 73	99.26 76	98.99 55

QAPM		92.88 159	92.77 158	93.22 175	95.82 224	83.31 204	96.45 36	97.35 128	83.91 257	93.75 192	96.77 113	89.25 174	98.88 130	84.56 250	97.02 255	97.49 202

jajsoiax		96.59 27	96.42 29	97.12 29	98.76 28	92.49 51	96.44 38	97.42 119	86.96 215	98.71 10	98.72 17	95.36 31	99.56 16	95.92 8	99.45 43	99.32 26

Gipuma		95.31 79	95.80 64	93.81 157	97.99 91	90.91 72	96.42 39	97.95 79	96.69 17	91.78 254	98.85 12	91.77 121	95.49 329	91.72 111	99.08 98	95.02 293
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MSP-MVS		95.34 75	94.63 108	97.48 14	98.67 29	94.05 23	96.41 40	98.18 37	91.26 123	95.12 147	95.15 205	86.60 215	99.50 20	93.43 58	96.81 263	98.89 72
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 47	95.66 33	97.00 59	97.03 96	94.85 52	99.42 31	93.49 48	98.84 127	98.00 154

RE-MVS-def			96.66 20		98.07 79	95.27 8	96.37 41	98.12 47	95.66 33	97.00 59	97.03 96	95.40 27		93.49 48	98.84 127	98.00 154

TSAR-MVS + MP.		94.96 89	94.75 100	95.57 83	98.86 21	88.69 109	96.37 41	96.81 170	85.23 239	94.75 164	97.12 91	91.85 120	99.40 45	93.45 54	98.33 183	98.62 107
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
CS-MVS-test		95.32 76	95.10 89	95.96 59	96.86 152	90.75 76	96.33 44	99.20 2	93.99 53	91.03 265	93.73 262	93.52 76	99.55 17	91.81 107	99.45 43	97.58 195

ACMH	88.36 12	96.59 27	97.43 5	94.07 144	98.56 38	85.33 181	96.33 44	98.30 24	94.66 40	98.72 8	98.30 30	97.51 5	98.00 235	94.87 14	99.59 27	98.86 75
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 46	97.96 77	92.26 87	95.28 140	96.57 130	95.02 47	99.41 38	93.63 42	99.11 97	98.94 64

APD-MVS_3200maxsize		96.82 10	96.65 21	97.32 25	97.95 92	93.82 34	96.31 47	98.25 28	95.51 35	96.99 61	97.05 95	95.63 21	99.39 50	93.31 63	98.88 122	98.75 87

CP-MVSNet		96.19 46	96.80 17	94.38 137	98.99 14	83.82 200	96.31 47	97.53 113	97.60 7	98.34 19	97.52 63	91.98 118	99.63 6	93.08 75	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 49	97.98 73	92.35 84	95.63 123	96.47 133	95.37 28	99.27 77	93.78 38	99.14 93	98.48 118

ACMMPR		96.46 32	96.14 45	97.41 21	98.60 35	93.82 34	96.30 49	97.96 77	92.35 84	95.57 126	96.61 128	94.93 51	99.41 38	93.78 38	99.15 92	99.00 53

3Dnovator+	92.74 2	95.86 57	95.77 65	96.13 54	96.81 157	90.79 75	96.30 49	97.82 90	96.13 25	94.74 165	97.23 85	91.33 132	99.16 88	93.25 67	98.30 188	98.46 120

MIMVSNet1		95.52 67	95.45 74	95.72 77	99.14 5	89.02 103	96.23 52	96.87 166	93.73 61	97.87 27	98.49 24	90.73 151	99.05 105	86.43 227	99.60 25	99.10 46

test2506		85.42 300	84.57 302	87.96 310	97.81 97	66.53 362	96.14 53	56.35 379	89.04 172	93.55 199	98.10 35	42.88 381	98.68 172	88.09 197	99.18 88	98.67 97

SR-MVS		96.70 19	96.42 29	97.54 11	98.05 81	94.69 11	96.13 54	98.07 57	95.17 37	96.82 68	96.73 120	95.09 44	99.43 30	92.99 78	98.71 144	98.50 116

MP-MVS		96.14 47	95.68 67	97.51 13	98.81 25	94.06 21	96.10 55	97.78 96	92.73 74	93.48 200	96.72 121	94.23 66	99.42 31	91.99 100	99.29 69	99.05 50
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 56	98.16 44	91.74 111	95.34 136	96.36 146	95.68 19	99.44 26	94.41 21	99.28 74	98.97 61

test1172		96.79 15	96.52 27	97.60 9	98.03 85	94.87 10	96.07 57	98.06 60	95.76 32	96.89 64	96.85 108	94.85 52	99.42 31	93.35 62	98.81 135	98.53 114

GBi-Net		93.21 148	92.96 153	93.97 147	95.40 244	84.29 190	95.99 58	96.56 185	88.63 182	95.10 148	98.53 21	81.31 259	98.98 116	86.74 218	98.38 176	98.65 99

test1		93.21 148	92.96 153	93.97 147	95.40 244	84.29 190	95.99 58	96.56 185	88.63 182	95.10 148	98.53 21	81.31 259	98.98 116	86.74 218	98.38 176	98.65 99

FMVSNet1		94.84 96	95.13 87	93.97 147	97.60 114	84.29 190	95.99 58	96.56 185	92.38 81	97.03 58	98.53 21	90.12 162	98.98 116	88.78 183	99.16 91	98.65 99

RPSCF		95.58 66	94.89 95	97.62 8	97.58 116	96.30 4	95.97 61	97.53 113	92.42 80	93.41 201	97.78 50	91.21 138	97.77 256	91.06 123	97.06 253	98.80 82

SixPastTwentyTwo		94.91 90	95.21 84	93.98 146	98.52 46	83.19 207	95.93 62	94.84 245	94.86 39	98.49 15	98.74 16	81.45 257	99.60 8	94.69 16	99.39 55	99.15 38

ambc				92.98 179	96.88 150	83.01 211	95.92 63	96.38 195		96.41 81	97.48 66	88.26 182	97.80 252	89.96 157	98.93 119	98.12 144

FC-MVSNet-test		95.32 76	95.88 58	93.62 160	98.49 55	81.77 221	95.90 64	98.32 21	93.93 57	97.53 40	97.56 60	88.48 179	99.40 45	92.91 80	99.83 6	99.68 4

MTAPA		96.65 22	96.38 33	97.47 15	98.95 16	94.05 23	95.88 65	97.62 103	94.46 45	96.29 90	96.94 101	93.56 73	99.37 58	94.29 24	99.42 48	98.99 55

CPTT-MVS		94.74 100	94.12 125	96.60 45	98.15 74	93.01 44	95.84 66	97.66 101	89.21 171	93.28 207	95.46 194	88.89 176	98.98 116	89.80 159	98.82 133	97.80 180

ab-mvs		92.40 175	92.62 165	91.74 222	97.02 142	81.65 223	95.84 66	95.50 230	86.95 216	92.95 221	97.56 60	90.70 152	97.50 269	79.63 297	97.43 244	96.06 261

nrg030		96.32 41	96.55 26	95.62 80	97.83 96	88.55 115	95.77 68	98.29 27	92.68 75	98.03 26	97.91 46	95.13 40	98.95 123	93.85 36	99.49 38	99.36 24

ECVR-MVS		90.12 228	90.16 220	90.00 278	97.81 97	72.68 336	95.76 69	78.54 372	89.04 172	95.36 135	98.10 35	70.51 316	98.64 178	87.10 214	99.18 88	98.67 97

SteuartSystems-ACMMP		96.40 38	96.30 36	96.71 42	98.63 31	91.96 57	95.70 70	98.01 70	93.34 69	96.64 75	96.57 130	94.99 49	99.36 60	93.48 51	99.34 59	98.82 80
Skip Steuart: Steuart Systems R&D Blog.
OpenMVS	89.45 8	92.27 180	92.13 174	92.68 192	94.53 273	84.10 196	95.70 70	97.03 151	82.44 274	91.14 264	96.42 137	88.47 180	98.38 203	85.95 232	97.47 243	95.55 283

GST-MVS		96.24 44	95.99 54	97.00 34	98.65 30	92.71 49	95.69 72	98.01 70	92.08 92	95.74 119	96.28 151	95.22 37	99.42 31	93.17 70	99.06 99	98.88 74

ACMH+	88.43 11	96.48 30	96.82 16	95.47 86	98.54 43	89.06 102	95.65 73	98.61 11	96.10 26	98.16 23	97.52 63	96.90 7	98.62 179	90.30 143	99.60 25	98.72 93

test1111		90.39 218	90.61 212	89.74 281	98.04 84	71.50 342	95.59 74	79.72 371	89.41 162	95.94 110	98.14 33	70.79 315	98.81 145	88.52 189	99.32 63	98.90 71

canonicalmvs		94.59 105	94.69 103	94.30 138	95.60 239	87.03 145	95.59 74	98.24 31	91.56 117	95.21 146	92.04 302	94.95 50	98.66 175	91.45 119	97.57 240	97.20 220

SF-MVS		95.88 56	95.88 58	95.87 69	98.12 75	89.65 91	95.58 76	98.56 12	91.84 103	96.36 84	96.68 123	94.37 64	99.32 70	92.41 92	99.05 102	98.64 103

PS-MVSNAJss		96.01 51	96.04 52	95.89 68	98.82 24	88.51 117	95.57 77	97.88 83	88.72 180	98.81 6	98.86 10	90.77 147	99.60 8	95.43 11	99.53 35	99.57 13

test_part1		94.39 112	94.55 110	93.92 151	96.14 203	82.86 212	95.54 78	98.09 53	95.36 36	98.27 20	98.36 28	75.91 298	99.44 26	93.41 59	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 78	95.85 215	96.47 21	93.40 203	97.46 67	95.31 33	95.47 330	86.18 231	98.78 139	89.11 357
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
VDDNet		94.03 127	94.27 122	93.31 172	98.87 20	82.36 216	95.51 80	91.78 306	97.19 12	96.32 87	98.60 18	84.24 232	98.75 157	87.09 215	98.83 132	98.81 81

pm-mvs1		95.43 71	95.94 55	93.93 150	98.38 59	85.08 184	95.46 81	97.12 147	91.84 103	97.28 49	98.46 25	95.30 34	97.71 261	90.17 150	99.42 48	98.99 55

Vis-MVSNet		95.50 68	95.48 73	95.56 84	98.11 76	89.40 98	95.35 82	98.22 33	92.36 83	94.11 178	98.07 37	92.02 115	99.44 26	93.38 61	97.67 236	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 83	98.18 37	91.85 100	97.63 33	97.37 72	95.58 22

FIs		94.90 91	95.35 77	93.55 163	98.28 65	81.76 222	95.33 84	98.14 45	93.05 73	97.07 54	97.18 88	87.65 193	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 84	98.30 24	91.40 120	95.76 117	96.87 107	95.26 35	99.45 25	92.77 81	99.21 83	99.00 53

LPG-MVS_test		96.38 40	96.23 39	96.84 40	98.36 62	92.13 54	95.33 84	98.25 28	91.78 107	97.07 54	97.22 86	96.38 13	99.28 75	92.07 98	99.59 27	99.11 43

AllTest		94.88 93	94.51 113	96.00 57	98.02 86	92.17 52	95.26 87	98.43 14	90.48 141	95.04 153	96.74 118	92.54 106	97.86 247	85.11 242	98.98 111	97.98 158

SED-MVS		96.00 52	96.41 32	94.76 114	98.51 47	86.97 146	95.21 88	98.10 50	91.95 94	97.63 33	97.25 83	96.48 11	99.35 61	93.29 64	99.29 69	97.95 162

OPU-MVS				95.15 101	96.84 153	89.43 96	95.21 88				95.66 182	93.12 89	98.06 228	86.28 230	98.61 153	97.95 162

DVP-MVS		95.82 58	96.18 42	94.72 116	98.51 47	86.69 153	95.20 90	97.00 153	91.85 100	97.40 47	97.35 78	95.58 22	99.34 64	93.44 56	99.31 64	98.13 143
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 90	98.22 33					99.38 56	93.44 56	99.31 64	98.53 114

Anonymous20240529		95.50 68	95.83 62	94.50 129	97.33 130	85.93 173	95.19 92	96.77 174	96.64 19	97.61 36	98.05 38	93.23 85	98.79 148	88.60 188	99.04 108	98.78 84

#test#		95.89 54	95.51 72	97.04 31	98.51 47	93.37 40	95.14 93	97.98 73	89.34 165	95.63 123	96.47 133	95.37 28	99.27 77	91.99 100	99.14 93	98.48 118

SMA-MVS		95.77 59	95.54 71	96.47 51	98.27 66	91.19 68	95.09 94	97.79 95	86.48 219	97.42 46	97.51 65	94.47 63	99.29 73	93.55 46	99.29 69	98.93 65
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 80	95.28 82	95.26 96	97.75 101	87.21 140	95.08 95	97.37 121	93.92 59	97.65 32	95.90 167	90.10 165	99.33 69	90.11 152	99.66 21	99.26 29

TransMVSNet (Re)		95.27 82	96.04 52	92.97 180	98.37 61	81.92 220	95.07 96	96.76 175	93.97 56	97.77 29	98.57 19	95.72 18	97.90 241	88.89 181	99.23 80	99.08 47

UGNet		93.08 151	92.50 168	94.79 113	93.87 288	87.99 127	95.07 96	94.26 261	90.64 138	87.33 325	97.67 55	86.89 210	98.49 194	88.10 196	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		89.81 239	88.90 245	92.55 199	97.00 143	79.73 255	95.03 98	83.65 360	89.88 153	95.30 138	94.79 226	53.64 368	99.39 50	91.99 100	98.79 138	98.54 113

LFMVS		91.33 199	91.16 201	91.82 219	96.27 192	79.36 261	95.01 99	85.61 348	96.04 29	94.82 161	97.06 94	72.03 312	98.46 200	84.96 245	98.70 146	97.65 191

CSCG		94.69 102	94.75 100	94.52 128	97.55 118	87.87 129	95.01 99	97.57 109	92.68 75	96.20 98	93.44 269	91.92 119	98.78 152	89.11 176	99.24 79	96.92 228

GG-mvs-BLEND				83.24 344	85.06 373	71.03 344	94.99 101	65.55 377		74.09 371	75.51 371	44.57 376	94.46 343	59.57 369	87.54 359	84.24 364

EU-MVSNet		87.39 282	86.71 285	89.44 285	93.40 293	76.11 307	94.93 102	90.00 317	57.17 370	95.71 121	97.37 72	64.77 340	97.68 263	92.67 86	94.37 315	94.52 304

KD-MVS_self_test		94.10 125	94.73 102	92.19 208	97.66 111	79.49 259	94.86 103	97.12 147	89.59 160	96.87 65	97.65 56	90.40 159	98.34 207	89.08 177	99.35 58	98.75 87

MTMP							94.82 104	54.62 380

PHI-MVS		94.34 116	93.80 130	95.95 60	95.65 235	91.67 64	94.82 104	97.86 84	87.86 198	93.04 218	94.16 246	91.58 126	98.78 152	90.27 145	98.96 117	97.41 207

testtj		94.81 98	94.42 114	96.01 56	97.23 132	90.51 80	94.77 106	97.85 87	91.29 122	94.92 158	95.66 182	91.71 123	99.40 45	88.07 198	98.25 194	98.11 145

gg-mvs-nofinetune		82.10 322	81.02 324	85.34 332	87.46 364	71.04 343	94.74 107	67.56 376	96.44 22	79.43 366	98.99 6	45.24 375	96.15 316	67.18 361	92.17 343	88.85 358

ACMM	88.83 9	96.30 43	96.07 50	96.97 35	98.39 58	92.95 46	94.74 107	98.03 66	90.82 133	97.15 52	96.85 108	96.25 15	99.00 115	93.10 73	99.33 61	98.95 63
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
SD-MVS		95.19 83	95.73 66	93.55 163	96.62 165	88.88 108	94.67 109	98.05 61	91.26 123	97.25 51	96.40 139	95.42 26	94.36 346	92.72 85	99.19 86	97.40 210
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		91.52 194	91.61 186	91.26 237	94.16 279	86.26 168	94.66 110	94.82 246	91.17 126	92.13 248	91.08 315	90.03 168	97.06 289	79.09 304	97.35 247	90.45 355

v10		94.68 103	95.27 83	92.90 185	96.57 168	80.15 240	94.65 111	97.57 109	90.68 137	97.43 44	98.00 41	88.18 183	99.15 89	94.84 15	99.55 34	99.41 20

v8		94.65 104	95.29 81	92.74 190	96.65 161	79.77 254	94.59 112	97.17 142	91.86 99	97.47 43	97.93 44	88.16 184	99.08 100	94.32 22	99.47 39	99.38 22

APD-MVS		95.00 87	94.69 103	95.93 63	97.38 127	90.88 73	94.59 112	97.81 91	89.22 170	95.46 131	96.17 159	93.42 80	99.34 64	89.30 168	98.87 125	97.56 198
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	94.58 114	97.58 108	93.39 68	97.05 57	98.04 39	93.25 84	98.51 193	89.75 162	99.59 27	99.08 47

ACMMP_NAP		96.21 45	96.12 47	96.49 50	98.90 18	91.42 65	94.57 115	98.03 66	90.42 144	96.37 83	97.35 78	95.68 19	99.25 79	94.44 20	99.34 59	98.80 82

HQP_MVS		94.26 120	93.93 127	95.23 98	97.71 105	88.12 123	94.56 116	97.81 91	91.74 111	93.31 204	95.59 184	86.93 207	98.95 123	89.26 172	98.51 165	98.60 110

plane_prior2							94.56 116		91.74 111

tfpnnormal		94.27 119	94.87 96	92.48 202	97.71 105	80.88 235	94.55 118	95.41 232	93.70 62	96.67 74	97.72 53	91.40 130	98.18 221	87.45 208	99.18 88	98.36 125

XVG-ACMP-BASELINE		95.68 63	95.34 78	96.69 43	98.40 57	93.04 43	94.54 119	98.05 61	90.45 143	96.31 88	96.76 115	92.91 96	98.72 162	91.19 122	99.42 48	98.32 127

DP-MVS		95.62 64	95.84 61	94.97 106	97.16 137	88.62 112	94.54 119	97.64 102	96.94 15	96.58 78	97.32 81	93.07 92	98.72 162	90.45 135	98.84 127	97.57 196

MIMVSNet		87.13 290	86.54 288	88.89 295	96.05 210	76.11 307	94.39 121	88.51 322	81.37 280	88.27 314	96.75 117	72.38 309	95.52 327	65.71 364	95.47 292	95.03 292

K. test v3		93.37 140	93.27 150	93.66 159	98.05 81	82.62 214	94.35 122	86.62 337	96.05 28	97.51 41	98.85 12	76.59 296	99.65 3	93.21 68	98.20 202	98.73 92

Vis-MVSNet (Re-imp)		90.42 216	90.16 220	91.20 241	97.66 111	77.32 292	94.33 123	87.66 330	91.20 125	92.99 219	95.13 207	75.40 300	98.28 210	77.86 309	99.19 86	97.99 157

ANet_high		94.83 97	96.28 37	90.47 263	96.65 161	73.16 331	94.33 123	98.74 10	96.39 23	98.09 25	98.93 8	93.37 81	98.70 168	90.38 138	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 54	89.46 161	96.61 77	96.47 133	95.85 17	99.12 95	90.45 135	99.56 33	98.77 86
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MAR-MVS		90.32 223	88.87 246	94.66 119	94.82 258	91.85 59	94.22 126	94.75 249	80.91 281	87.52 323	88.07 347	86.63 214	97.87 246	76.67 320	96.21 276	94.25 310
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		92.78 163	92.73 162	92.95 182	95.40 244	81.98 219	94.18 127	95.53 229	88.63 182	96.05 105	97.37 72	81.31 259	98.81 145	87.38 211	98.67 150	98.06 146

Anonymous20240521		92.86 161	93.57 140	90.74 257	96.57 168	75.50 314	94.15 128	95.60 221	89.38 163	95.90 113	97.90 48	80.39 266	97.96 239	92.60 88	99.68 18	98.75 87

GeoE		94.55 107	94.68 105	94.15 141	97.23 132	85.11 183	94.14 129	97.34 129	88.71 181	95.26 141	95.50 192	94.65 57	99.12 95	90.94 127	98.40 171	98.23 134

9.14			94.81 97		97.49 121		94.11 130	98.37 18	87.56 207	95.38 133	96.03 163	94.66 56	99.08 100	90.70 131	98.97 115

HPM-MVS++		95.02 86	94.39 115	96.91 38	97.88 94	93.58 38	94.09 131	96.99 155	91.05 128	92.40 238	95.22 204	91.03 145	99.25 79	92.11 95	98.69 147	97.90 168

ETH3D-3000-0.1		94.86 94	94.55 110	95.81 70	97.61 113	89.72 89	94.05 132	98.37 18	88.09 193	95.06 152	95.85 169	92.58 104	99.10 99	90.33 142	98.99 110	98.62 107

HY-MVS	82.50 18	86.81 294	85.93 295	89.47 284	93.63 291	77.93 282	94.02 133	91.58 308	75.68 318	83.64 346	93.64 263	77.40 285	97.42 275	71.70 347	92.07 344	93.05 335

Effi-MVS+-dtu		93.90 131	92.60 166	97.77 4	94.74 264	96.67 3	94.00 134	95.41 232	89.94 150	91.93 252	92.13 300	90.12 162	98.97 120	87.68 205	97.48 242	97.67 190

Effi-MVS+		92.79 162	92.74 160	92.94 183	95.10 252	83.30 205	94.00 134	97.53 113	91.36 121	89.35 296	90.65 324	94.01 69	98.66 175	87.40 210	95.30 297	96.88 231

VDD-MVS		94.37 113	94.37 116	94.40 136	97.49 121	86.07 171	93.97 136	93.28 276	94.49 44	96.24 94	97.78 50	87.99 189	98.79 148	88.92 179	99.14 93	98.34 126

h-mvs33		92.89 158	91.99 177	95.58 82	96.97 144	90.55 78	93.94 137	94.01 267	89.23 168	93.95 186	96.19 156	76.88 293	99.14 91	91.02 124	95.71 286	97.04 224

EPNet		89.80 240	88.25 256	94.45 134	83.91 375	86.18 169	93.87 138	87.07 335	91.16 127	80.64 363	94.72 228	78.83 273	98.89 129	85.17 237	98.89 120	98.28 131
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 68	87.35 208	96.22 96	97.99 42	94.48 62	99.05 105	92.73 84	99.68 18	97.93 164
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	94.58 109	93.04 177	95.91 221	83.13 209	93.79 140	99.19 3	92.00 93	98.84 5	98.04 39	93.64 72	99.02 111	81.28 279	98.54 161	96.96 227

TranMVSNet+NR-MVSNet		96.07 50	96.26 38	95.50 85	98.26 67	87.69 132	93.75 141	97.86 84	95.96 30	97.48 42	97.14 90	95.33 32	99.44 26	90.79 129	99.76 11	99.38 22

PAPM_NR		91.03 203	90.81 207	91.68 226	96.73 159	81.10 232	93.72 142	96.35 196	88.19 191	88.77 306	92.12 301	85.09 228	97.25 282	82.40 269	93.90 321	96.68 238

zzz-MVS		96.47 31	96.14 45	97.47 15	98.95 16	94.05 23	93.69 143	97.62 103	94.46 45	96.29 90	96.94 101	93.56 73	99.37 58	94.29 24	99.42 48	98.99 55

baseline		94.26 120	94.80 98	92.64 193	96.08 208	80.99 233	93.69 143	98.04 65	90.80 134	94.89 159	96.32 148	93.19 86	98.48 198	91.68 113	98.51 165	98.43 122

dcpmvs_2		93.96 129	95.01 91	90.82 255	97.60 114	74.04 326	93.68 145	98.85 7	89.80 155	97.82 28	97.01 99	91.14 143	99.21 83	90.56 133	98.59 155	99.19 35

MVS_0304		90.96 204	90.15 223	93.37 169	93.17 297	87.06 143	93.62 146	92.43 295	89.60 159	82.25 354	95.50 192	82.56 248	97.83 250	84.41 252	97.83 228	95.22 287

F-COLMAP		92.28 179	91.06 202	95.95 60	97.52 119	91.90 58	93.53 147	97.18 141	83.98 256	88.70 308	94.04 249	88.41 181	98.55 190	80.17 290	95.99 280	97.39 211

FMVSNet5		87.82 272	86.56 287	91.62 227	92.31 311	79.81 253	93.49 148	94.81 248	83.26 261	91.36 258	96.93 103	52.77 370	97.49 271	76.07 324	98.03 217	97.55 199

DPE-MVS		95.89 54	95.88 58	95.92 65	97.93 93	89.83 88	93.46 149	98.30 24	92.37 82	97.75 30	96.95 100	95.14 39	99.51 19	91.74 110	99.28 74	98.41 124
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
alignmvs		93.26 145	92.85 156	94.50 129	95.70 231	87.45 134	93.45 150	95.76 217	91.58 116	95.25 143	92.42 296	81.96 254	98.72 162	91.61 114	97.87 226	97.33 215

114514_t		90.51 213	89.80 229	92.63 195	98.00 88	82.24 217	93.40 151	97.29 134	65.84 361	89.40 295	94.80 225	86.99 205	98.75 157	83.88 255	98.61 153	96.89 230

DeepC-MVS_fast	89.96 7	93.73 133	93.44 144	94.60 124	96.14 203	87.90 128	93.36 152	97.14 144	85.53 236	93.90 189	95.45 195	91.30 134	98.59 184	89.51 165	98.62 152	97.31 216
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 67	93.25 153	98.32 21	87.89 197	96.86 66	97.38 71	95.55 24	99.39 50	95.47 10	99.47 39	99.11 43
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
test_0402		95.73 61	96.22 40	94.26 139	98.19 72	85.77 176	93.24 154	97.24 138	96.88 16	97.69 31	97.77 52	94.12 68	99.13 93	91.54 118	99.29 69	97.88 171

MSLP-MVS++		93.25 147	93.88 128	91.37 233	96.34 186	82.81 213	93.11 155	97.74 97	89.37 164	94.08 180	95.29 203	90.40 159	96.35 313	90.35 140	98.25 194	94.96 294

baseline1		87.62 277	87.31 272	88.54 301	94.71 268	74.27 324	93.10 156	88.20 326	86.20 224	92.18 247	93.04 277	73.21 306	95.52 327	79.32 301	85.82 361	95.83 271

plane_prior						88.12 123	93.01 157		88.98 174						98.06 214

thres100view900		87.35 283	86.89 281	88.72 298	96.14 203	73.09 332	93.00 158	85.31 351	92.13 91	93.26 209	90.96 317	63.42 346	98.28 210	71.27 350	96.54 270	94.79 297

Patchmtry		90.11 229	89.92 227	90.66 259	90.35 342	77.00 296	92.96 159	92.81 283	90.25 147	94.74 165	96.93 103	67.11 324	97.52 268	85.17 237	98.98 111	97.46 203

LF4IMVS		92.72 165	92.02 176	94.84 111	95.65 235	91.99 56	92.92 160	96.60 182	85.08 246	92.44 236	93.62 264	86.80 211	96.35 313	86.81 217	98.25 194	96.18 257

UniMVSNet (Re)		95.32 76	95.15 86	95.80 72	97.79 99	88.91 105	92.91 161	98.07 57	93.46 67	96.31 88	95.97 166	90.14 161	99.34 64	92.11 95	99.64 23	99.16 37

TAPA-MVS	88.58 10	92.49 173	91.75 185	94.73 115	96.50 175	89.69 90	92.91 161	97.68 100	78.02 309	92.79 224	94.10 247	90.85 146	97.96 239	84.76 248	98.16 204	96.54 239
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
thisisatest0530		88.69 259	87.52 270	92.20 207	96.33 187	79.36 261	92.81 163	84.01 359	86.44 220	93.67 195	92.68 288	53.62 369	99.25 79	89.65 164	98.45 169	98.00 154

EIA-MVS		92.35 177	92.03 175	93.30 173	95.81 226	83.97 198	92.80 164	98.17 41	87.71 201	89.79 290	87.56 348	91.17 142	99.18 87	87.97 200	97.27 248	96.77 235

ETH3D cwj APD-0.16		93.99 128	93.38 146	95.80 72	96.82 155	89.92 85	92.72 165	98.02 68	84.73 252	93.65 196	95.54 191	91.68 124	99.22 82	88.78 183	98.49 168	98.26 133

thres600view7		87.66 275	87.10 279	89.36 288	96.05 210	73.17 330	92.72 165	85.31 351	91.89 98	93.29 206	90.97 316	63.42 346	98.39 201	73.23 338	96.99 260	96.51 241

wuyk23d		87.83 271	90.79 208	78.96 352	90.46 341	88.63 111	92.72 165	90.67 314	91.65 115	98.68 11	97.64 57	96.06 16	77.53 373	59.84 368	99.41 53	70.73 371

V42		93.43 139	93.58 139	92.97 180	95.34 248	81.22 230	92.67 168	96.49 190	87.25 210	96.20 98	96.37 145	87.32 199	98.85 137	92.39 94	98.21 200	98.85 78

OPM-MVS		95.61 65	95.45 74	96.08 55	98.49 55	91.00 70	92.65 169	97.33 130	90.05 149	96.77 71	96.85 108	95.04 45	98.56 188	92.77 81	99.06 99	98.70 96
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
DU-MVS		95.28 80	95.12 88	95.75 76	97.75 101	88.59 113	92.58 170	97.81 91	93.99 53	96.80 69	95.90 167	90.10 165	99.41 38	91.60 115	99.58 31	99.26 29

FMVSNet3		90.78 207	90.32 219	92.16 212	93.03 302	79.92 249	92.54 171	94.95 241	86.17 226	95.10 148	96.01 164	69.97 318	98.75 157	86.74 218	98.38 176	97.82 178

hse-mvs2		92.24 181	91.20 198	95.38 88	96.16 201	90.65 77	92.52 172	92.01 304	89.23 168	93.95 186	92.99 279	76.88 293	98.69 170	91.02 124	96.03 278	96.81 233

MVS_Test		92.57 172	93.29 147	90.40 266	93.53 292	75.85 310	92.52 172	96.96 156	88.73 179	92.35 241	96.70 122	90.77 147	98.37 206	92.53 89	95.49 291	96.99 226

CR-MVSNet		87.89 269	87.12 278	90.22 271	91.01 333	78.93 268	92.52 172	92.81 283	73.08 334	89.10 297	96.93 103	67.11 324	97.64 264	88.80 182	92.70 337	94.08 311

RPMNet		90.31 224	90.14 224	90.81 256	91.01 333	78.93 268	92.52 172	98.12 47	91.91 97	89.10 297	96.89 106	68.84 319	99.41 38	90.17 150	92.70 337	94.08 311

RRT_MVS		91.36 198	90.05 225	95.29 95	89.21 354	88.15 122	92.51 176	94.89 243	86.73 218	95.54 127	95.68 181	61.82 353	99.30 72	94.91 13	99.13 96	98.43 122

XVG-OURS-SEG-HR		95.38 73	95.00 92	96.51 48	98.10 77	94.07 20	92.46 177	98.13 46	90.69 136	93.75 192	96.25 154	98.03 2	97.02 290	92.08 97	95.55 289	98.45 121

EI-MVSNet-Vis-set		94.36 114	94.28 120	94.61 120	92.55 309	85.98 172	92.44 178	94.69 252	93.70 62	96.12 103	95.81 173	91.24 136	98.86 135	93.76 41	98.22 199	98.98 60

Anonymous202405211		92.58 170	92.50 168	92.83 188	96.55 170	83.22 206	92.43 179	91.64 307	94.10 52	95.59 125	96.64 126	81.88 256	97.50 269	85.12 241	98.52 163	97.77 182

AUN-MVS		90.05 233	88.30 254	95.32 94	96.09 207	90.52 79	92.42 180	92.05 303	82.08 277	88.45 311	92.86 281	65.76 334	98.69 170	88.91 180	96.07 277	96.75 237

EI-MVSNet-UG-set		94.35 115	94.27 122	94.59 125	92.46 310	85.87 174	92.42 180	94.69 252	93.67 66	96.13 102	95.84 172	91.20 139	98.86 135	93.78 38	98.23 197	99.03 51

Regformer-3		94.28 118	94.23 124	94.46 133	92.78 307	86.28 167	92.39 182	94.70 251	93.69 65	95.97 106	95.56 189	91.34 131	98.48 198	93.45 54	98.14 206	98.62 107

Regformer-4		94.90 91	94.67 106	95.59 81	92.78 307	89.02 103	92.39 182	95.91 212	94.50 43	96.41 81	95.56 189	92.10 114	99.01 113	94.23 26	98.14 206	98.74 90

NCCC		94.08 126	93.54 142	95.70 79	96.49 176	89.90 87	92.39 182	96.91 162	90.64 138	92.33 244	94.60 231	90.58 155	98.96 121	90.21 149	97.70 234	98.23 134

casdiffmvs		94.32 117	94.80 98	92.85 187	96.05 210	81.44 227	92.35 185	98.05 61	91.53 118	95.75 118	96.80 112	93.35 82	98.49 194	91.01 126	98.32 185	98.64 103

ETV-MVS		92.99 155	92.74 160	93.72 158	95.86 223	86.30 166	92.33 186	97.84 88	91.70 114	92.81 223	86.17 358	92.22 111	99.19 86	88.03 199	97.73 230	95.66 279

EI-MVSNet		92.99 155	93.26 151	92.19 208	92.12 317	79.21 266	92.32 187	94.67 254	91.77 109	95.24 144	95.85 169	87.14 203	98.49 194	91.99 100	98.26 191	98.86 75

CVMVSNet		85.16 302	84.72 300	86.48 322	92.12 317	70.19 347	92.32 187	88.17 327	56.15 371	90.64 272	95.85 169	67.97 322	96.69 301	88.78 183	90.52 352	92.56 341

OMC-MVS		94.22 122	93.69 135	95.81 70	97.25 131	91.27 66	92.27 189	97.40 120	87.10 214	94.56 169	95.42 197	93.74 71	98.11 226	86.62 222	98.85 126	98.06 146

PM-MVS		93.33 141	92.67 164	95.33 91	96.58 167	94.06 21	92.26 190	92.18 297	85.92 230	96.22 96	96.61 128	85.64 226	95.99 322	90.35 140	98.23 197	95.93 266

UniMVSNet_NR-MVSNet		95.35 74	95.21 84	95.76 75	97.69 108	88.59 113	92.26 190	97.84 88	94.91 38	96.80 69	95.78 177	90.42 156	99.41 38	91.60 115	99.58 31	99.29 28

AdaColmap		91.63 191	91.36 194	92.47 203	95.56 240	86.36 164	92.24 192	96.27 198	88.88 178	89.90 286	92.69 287	91.65 125	98.32 208	77.38 316	97.64 237	92.72 340

mvs-test1		93.07 153	91.80 183	96.89 39	94.74 264	95.83 6	92.17 193	95.41 232	89.94 150	89.85 287	90.59 325	90.12 162	98.88 130	87.68 205	95.66 287	95.97 264

PVSNet_Blended_VisFu		91.63 191	91.20 198	92.94 183	97.73 104	83.95 199	92.14 194	97.46 117	78.85 304	92.35 241	94.98 215	84.16 233	99.08 100	86.36 228	96.77 265	95.79 273

ETH3 D test6400		91.91 186	91.25 197	93.89 153	96.59 166	84.41 189	92.10 195	97.72 99	78.52 305	91.82 253	93.78 261	88.70 177	99.13 93	83.61 256	98.39 174	98.14 141

Baseline_NR-MVSNet		94.47 111	95.09 90	92.60 197	98.50 54	80.82 236	92.08 196	96.68 178	93.82 60	96.29 90	98.56 20	90.10 165	97.75 259	90.10 154	99.66 21	99.24 31

Fast-Effi-MVS+-dtu		92.77 164	92.16 172	94.58 127	94.66 270	88.25 120	92.05 197	96.65 180	89.62 158	90.08 281	91.23 312	92.56 105	98.60 182	86.30 229	96.27 275	96.90 229

xxxxxxxxxxxxxcwj		95.03 85	94.93 93	95.33 91	97.46 124	88.05 125	92.04 198	98.42 16	87.63 204	96.36 84	96.68 123	94.37 64	99.32 70	92.41 92	99.05 102	98.64 103

save fliter					97.46 124	88.05 125	92.04 198	97.08 149	87.63 204

PatchT		87.51 279	88.17 260	85.55 329	90.64 336	66.91 357	92.02 200	86.09 341	92.20 89	89.05 299	97.16 89	64.15 342	96.37 312	89.21 175	92.98 335	93.37 330

EG-PatchMatch MVS		94.54 109	94.67 106	94.14 142	97.87 95	86.50 157	92.00 201	96.74 176	88.16 192	96.93 63	97.61 58	93.04 93	97.90 241	91.60 115	98.12 209	98.03 152

v144192		93.20 150	93.54 142	92.16 212	96.05 210	78.26 279	91.95 202	97.14 144	84.98 248	95.96 107	96.11 160	87.08 204	99.04 108	93.79 37	98.84 127	99.17 36

VNet		92.67 167	92.96 153	91.79 220	96.27 192	80.15 240	91.95 202	94.98 240	92.19 90	94.52 171	96.07 161	87.43 197	97.39 278	84.83 246	98.38 176	97.83 176

1314		86.46 295	86.33 292	86.87 321	91.65 325	74.54 319	91.94 204	94.10 263	74.28 326	84.78 339	87.33 352	83.03 240	95.00 339	78.72 305	91.16 350	91.06 352

1121		90.26 225	89.23 235	93.34 170	97.15 139	87.40 135	91.94 204	94.39 257	67.88 356	91.02 266	94.91 218	86.91 209	98.59 184	81.17 282	97.71 233	94.02 316

MVS		84.98 304	84.30 304	87.01 319	91.03 332	77.69 288	91.94 204	94.16 262	59.36 369	84.23 343	87.50 350	85.66 224	96.80 298	71.79 345	93.05 334	86.54 362

tfpn200view9		87.05 291	86.52 289	88.67 299	95.77 227	72.94 333	91.89 207	86.00 343	90.84 131	92.61 229	89.80 329	63.93 343	98.28 210	71.27 350	96.54 270	94.79 297

thres400		87.20 287	86.52 289	89.24 292	95.77 227	72.94 333	91.89 207	86.00 343	90.84 131	92.61 229	89.80 329	63.93 343	98.28 210	71.27 350	96.54 270	96.51 241

v1921920		93.26 145	93.61 138	92.19 208	96.04 214	78.31 278	91.88 209	97.24 138	85.17 241	96.19 100	96.19 156	86.76 212	99.05 105	94.18 28	98.84 127	99.22 32

Regformer-1		94.55 107	94.33 118	95.19 99	92.83 305	88.54 116	91.87 210	95.84 216	93.99 53	95.95 108	95.04 212	92.00 116	98.79 148	93.14 72	98.31 186	98.23 134

Regformer-2		94.86 94	94.55 110	95.77 74	92.83 305	89.98 84	91.87 210	96.40 193	94.38 47	96.19 100	95.04 212	92.47 109	99.04 108	93.49 48	98.31 186	98.28 131

XXY-MVS		92.58 170	93.16 152	90.84 254	97.75 101	79.84 250	91.87 210	96.22 203	85.94 229	95.53 128	97.68 54	92.69 102	94.48 342	83.21 260	97.51 241	98.21 137

IterMVS-LS		93.78 132	94.28 120	92.27 205	96.27 192	79.21 266	91.87 210	96.78 172	91.77 109	96.57 79	97.07 93	87.15 202	98.74 160	91.99 100	99.03 109	98.86 75
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1144		93.50 136	93.81 129	92.57 198	96.28 191	79.61 257	91.86 214	96.96 156	86.95 216	95.91 112	96.32 148	87.65 193	98.96 121	93.51 47	98.88 122	99.13 40

v1192		93.49 137	93.78 131	92.62 196	96.16 201	79.62 256	91.83 215	97.22 140	86.07 227	96.10 104	96.38 144	87.22 200	99.02 111	94.14 29	98.88 122	99.22 32

v1240		93.29 142	93.71 134	92.06 215	96.01 215	77.89 284	91.81 216	97.37 121	85.12 244	96.69 73	96.40 139	86.67 213	99.07 104	94.51 18	98.76 141	99.22 32

CNVR-MVS		94.58 106	94.29 119	95.46 87	96.94 146	89.35 100	91.81 216	96.80 171	89.66 157	93.90 189	95.44 196	92.80 100	98.72 162	92.74 83	98.52 163	98.32 127

v2v482		93.29 142	93.63 137	92.29 204	96.35 185	78.82 272	91.77 218	96.28 197	88.45 186	95.70 122	96.26 153	86.02 221	98.90 127	93.02 76	98.81 135	99.14 39

RRT_test8_iter05		88.21 265	88.17 260	88.33 306	91.62 326	66.82 361	91.73 219	96.60 182	86.34 222	94.14 177	95.38 202	47.72 374	99.11 97	91.78 109	98.26 191	99.06 49

EPNet_dtu		85.63 299	84.37 303	89.40 287	86.30 369	74.33 323	91.64 220	88.26 324	84.84 250	72.96 372	89.85 327	71.27 314	97.69 262	76.60 321	97.62 238	96.18 257
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PLC	85.34 15	90.40 217	88.92 243	94.85 110	96.53 174	90.02 83	91.58 221	96.48 191	80.16 287	86.14 331	92.18 298	85.73 223	98.25 215	76.87 319	94.61 312	96.30 252
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
VPNet		93.08 151	93.76 132	91.03 245	98.60 35	75.83 312	91.51 222	95.62 220	91.84 103	95.74 119	97.10 92	89.31 173	98.32 208	85.07 244	99.06 99	98.93 65

XVG-OURS		94.72 101	94.12 125	96.50 49	98.00 88	94.23 18	91.48 223	98.17 41	90.72 135	95.30 138	96.47 133	87.94 190	96.98 291	91.41 120	97.61 239	98.30 130

HQP-NCC					96.36 182		91.37 224		87.16 211	88.81 302

ACMP_Plane					96.36 182		91.37 224		87.16 211	88.81 302

HQP-MVS		92.09 183	91.49 191	93.88 154	96.36 182	84.89 185	91.37 224	97.31 131	87.16 211	88.81 302	93.40 270	84.76 229	98.60 182	86.55 224	97.73 230	98.14 141

MCST-MVS		92.91 157	92.51 167	94.10 143	97.52 119	85.72 177	91.36 227	97.13 146	80.33 286	92.91 222	94.24 242	91.23 137	98.72 162	89.99 156	97.93 223	97.86 173

v148		92.87 160	93.29 147	91.62 227	96.25 195	77.72 287	91.28 228	95.05 238	89.69 156	95.93 111	96.04 162	87.34 198	98.38 203	90.05 155	97.99 220	98.78 84

tpmvs		84.22 308	83.97 307	84.94 334	87.09 366	65.18 365	91.21 229	88.35 323	82.87 269	85.21 334	90.96 317	65.24 338	96.75 299	79.60 300	85.25 362	92.90 337

CANet		92.38 176	91.99 177	93.52 167	93.82 290	83.46 203	91.14 230	97.00 153	89.81 154	86.47 329	94.04 249	87.90 191	99.21 83	89.50 166	98.27 190	97.90 168

CNLPA		91.72 189	91.20 198	93.26 174	96.17 200	91.02 69	91.14 230	95.55 228	90.16 148	90.87 267	93.56 267	86.31 217	94.40 345	79.92 296	97.12 252	94.37 307

DP-MVS Recon		92.31 178	91.88 180	93.60 161	97.18 136	86.87 149	91.10 232	97.37 121	84.92 249	92.08 249	94.08 248	88.59 178	98.20 218	83.50 257	98.14 206	95.73 275

OpenMVS_ROB	85.12 16	89.52 243	89.05 240	90.92 250	94.58 272	81.21 231	91.10 232	93.41 275	77.03 315	93.41 201	93.99 253	83.23 238	97.80 252	79.93 294	94.80 307	93.74 323

TSAR-MVS + GP.		93.07 153	92.41 170	95.06 104	95.82 224	90.87 74	90.97 234	92.61 291	88.04 194	94.61 168	93.79 260	88.08 185	97.81 251	89.41 167	98.39 174	96.50 244

MVP-Stereo		90.07 232	88.92 243	93.54 165	96.31 189	86.49 158	90.93 235	95.59 225	79.80 288	91.48 256	95.59 184	80.79 263	97.39 278	78.57 307	91.19 349	96.76 236
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MVSTER		89.32 245	88.75 247	91.03 245	90.10 344	76.62 302	90.85 236	94.67 254	82.27 275	95.24 144	95.79 174	61.09 356	98.49 194	90.49 134	98.26 191	97.97 161

pmmvs-eth3d		91.54 193	90.73 210	93.99 145	95.76 229	87.86 130	90.83 237	93.98 268	78.23 308	94.02 185	96.22 155	82.62 247	96.83 297	86.57 223	98.33 183	97.29 217

CANet_DTU		89.85 238	89.17 237	91.87 218	92.20 315	80.02 247	90.79 238	95.87 214	86.02 228	82.53 353	91.77 305	80.01 267	98.57 187	85.66 234	97.70 234	97.01 225

test_prior4						89.91 86	90.74 239

TinyColmap		92.00 185	92.76 159	89.71 282	95.62 238	77.02 295	90.72 240	96.17 206	87.70 202	95.26 141	96.29 150	92.54 106	96.45 308	81.77 274	98.77 140	95.66 279

CDPH-MVS		92.67 167	91.83 181	95.18 100	96.94 146	88.46 118	90.70 241	97.07 150	77.38 311	92.34 243	95.08 210	92.67 103	98.88 130	85.74 233	98.57 157	98.20 138

DSMNet-mixed		82.21 319	81.56 318	84.16 340	89.57 350	70.00 351	90.65 242	77.66 374	54.99 372	83.30 349	97.57 59	77.89 283	90.50 365	66.86 362	95.54 290	91.97 345

TEST9					96.45 178	89.46 94	90.60 243	96.92 160	79.09 300	90.49 273	94.39 238	91.31 133	98.88 130

train_agg		92.71 166	91.83 181	95.35 89	96.45 178	89.46 94	90.60 243	96.92 160	79.37 295	90.49 273	94.39 238	91.20 139	98.88 130	88.66 187	98.43 170	97.72 186

PatchmatchNet		85.22 301	84.64 301	86.98 320	89.51 351	69.83 352	90.52 245	87.34 333	78.87 303	87.22 326	92.74 286	66.91 326	96.53 304	81.77 274	86.88 360	94.58 303
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test_8					96.37 180	89.14 101	90.51 246	96.89 163	79.37 295	90.42 275	94.36 240	91.20 139	98.82 140

test_yl		90.11 229	89.73 232	91.26 237	94.09 282	79.82 251	90.44 247	92.65 288	90.90 129	93.19 213	93.30 272	73.90 303	98.03 230	82.23 270	96.87 261	95.93 266

DCV-MVSNet		90.11 229	89.73 232	91.26 237	94.09 282	79.82 251	90.44 247	92.65 288	90.90 129	93.19 213	93.30 272	73.90 303	98.03 230	82.23 270	96.87 261	95.93 266

tpm2		81.46 324	80.35 331	84.80 335	89.90 345	65.14 366	90.44 247	85.36 350	65.82 362	82.05 357	92.44 294	57.94 360	96.69 301	70.71 353	88.49 357	92.56 341

agg_prior1		92.60 169	91.76 184	95.10 103	96.20 197	88.89 106	90.37 250	96.88 164	79.67 292	90.21 278	94.41 236	91.30 134	98.78 152	88.46 190	98.37 181	97.64 192

CostFormer		83.09 313	82.21 316	85.73 328	89.27 353	67.01 356	90.35 251	86.47 338	70.42 347	83.52 348	93.23 275	61.18 355	96.85 296	77.21 317	88.26 358	93.34 331

TAMVS		90.16 227	89.05 240	93.49 168	96.49 176	86.37 163	90.34 252	92.55 292	80.84 284	92.99 219	94.57 233	81.94 255	98.20 218	73.51 336	98.21 200	95.90 269

EPMVS		81.17 328	80.37 330	83.58 342	85.58 371	65.08 367	90.31 253	71.34 375	77.31 313	85.80 333	91.30 311	59.38 358	92.70 358	79.99 291	82.34 368	92.96 336

CMPMVS	68.83 22	87.28 284	85.67 297	92.09 214	88.77 358	85.42 180	90.31 253	94.38 258	70.02 349	88.00 317	93.30 272	73.78 305	94.03 350	75.96 326	96.54 270	96.83 232
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test_post1							90.21 255				5.85 378	65.36 336	96.00 321	79.61 298

test_prior3		93.29 142	92.85 156	94.61 120	95.95 218	87.23 138	90.21 255	97.36 126	89.33 166	90.77 268	94.81 222	90.41 157	98.68 172	88.21 191	98.55 158	97.93 164

test_prior2							90.21 255		89.33 166	90.77 268	94.81 222	90.41 157		88.21 191	98.55 158

MVS_111021_LR		93.66 134	93.28 149	94.80 112	96.25 195	90.95 71	90.21 255	95.43 231	87.91 195	93.74 194	94.40 237	92.88 98	96.38 311	90.39 137	98.28 189	97.07 221

WR-MVS		93.49 137	93.72 133	92.80 189	97.57 117	80.03 246	90.14 259	95.68 219	93.70 62	96.62 76	95.39 200	87.21 201	99.04 108	87.50 207	99.64 23	99.33 25

tpmrst		82.85 316	82.93 314	82.64 345	87.65 360	58.99 375	90.14 259	87.90 329	75.54 320	83.93 344	91.63 308	66.79 329	95.36 333	81.21 281	81.54 369	93.57 329

PVSNet_BlendedMVS		90.35 221	89.96 226	91.54 230	94.81 259	78.80 274	90.14 259	96.93 158	79.43 294	88.68 309	95.06 211	86.27 218	98.15 224	80.27 287	98.04 216	97.68 189

BH-untuned		90.68 210	90.90 203	90.05 277	95.98 216	79.57 258	90.04 262	94.94 242	87.91 195	94.07 181	93.00 278	87.76 192	97.78 255	79.19 303	95.17 300	92.80 338

新几何2							90.02 263

旧先验2							90.00 264		68.65 353	92.71 227			96.52 305	85.15 239

无先验							89.94 265	95.75 218	70.81 346				98.59 184	81.17 282		94.81 296

xiu_mvs_v1_base_debu		91.47 195	91.52 188	91.33 234	95.69 232	81.56 224	89.92 266	96.05 209	83.22 262	91.26 260	90.74 319	91.55 127	98.82 140	89.29 169	95.91 281	93.62 326

xiu_mvs_v1_base		91.47 195	91.52 188	91.33 234	95.69 232	81.56 224	89.92 266	96.05 209	83.22 262	91.26 260	90.74 319	91.55 127	98.82 140	89.29 169	95.91 281	93.62 326

xiu_mvs_v1_base_debi		91.47 195	91.52 188	91.33 234	95.69 232	81.56 224	89.92 266	96.05 209	83.22 262	91.26 260	90.74 319	91.55 127	98.82 140	89.29 169	95.91 281	93.62 326

DWT-MVSNet_test		80.74 331	79.18 336	85.43 331	87.51 363	66.87 358	89.87 269	86.01 342	74.20 328	80.86 362	80.62 368	48.84 372	96.68 303	81.54 276	83.14 367	92.75 339

mvs_anonymous		90.37 220	91.30 196	87.58 315	92.17 316	68.00 355	89.84 270	94.73 250	83.82 259	93.22 212	97.40 70	87.54 195	97.40 277	87.94 201	95.05 302	97.34 214

test20.03		90.80 206	90.85 206	90.63 260	95.63 237	79.24 264	89.81 271	92.87 282	89.90 152	94.39 173	96.40 139	85.77 222	95.27 337	73.86 335	99.05 102	97.39 211

1112_ss		88.42 262	87.41 271	91.45 231	96.69 160	80.99 233	89.72 272	96.72 177	73.37 332	87.00 327	90.69 322	77.38 286	98.20 218	81.38 278	93.72 324	95.15 289

UnsupCasMVSNet_eth		90.33 222	90.34 218	90.28 268	94.64 271	80.24 238	89.69 273	95.88 213	85.77 232	93.94 188	95.69 180	81.99 253	92.98 357	84.21 253	91.30 348	97.62 193

MG-MVS		89.54 242	89.80 229	88.76 297	94.88 255	72.47 338	89.60 274	92.44 294	85.82 231	89.48 294	95.98 165	82.85 242	97.74 260	81.87 273	95.27 298	96.08 260

Patchmatch-test		86.10 297	86.01 294	86.38 326	90.63 337	74.22 325	89.57 275	86.69 336	85.73 234	89.81 289	92.83 282	65.24 338	91.04 363	77.82 312	95.78 285	93.88 320

Anonymous20231206		88.77 257	88.29 255	90.20 273	96.31 189	78.81 273	89.56 276	93.49 274	74.26 327	92.38 239	95.58 187	82.21 249	95.43 332	72.07 344	98.75 143	96.34 250

DeepPCF-MVS	90.46 6	94.20 123	93.56 141	96.14 53	95.96 217	92.96 45	89.48 277	97.46 117	85.14 242	96.23 95	95.42 197	93.19 86	98.08 227	90.37 139	98.76 141	97.38 213

SCA		87.43 281	87.21 275	88.10 309	92.01 320	71.98 340	89.43 278	88.11 328	82.26 276	88.71 307	92.83 282	78.65 275	97.59 265	79.61 298	93.30 328	94.75 299

testgi		90.38 219	91.34 195	87.50 316	97.49 121	71.54 341	89.43 278	95.16 237	88.38 188	94.54 170	94.68 230	92.88 98	93.09 356	71.60 348	97.85 227	97.88 171

JIA-IIPM		85.08 303	83.04 312	91.19 242	87.56 361	86.14 170	89.40 280	84.44 358	88.98 174	82.20 355	97.95 43	56.82 363	96.15 316	76.55 322	83.45 365	91.30 350

原ACMM2							89.34 281

tpm		84.38 307	84.08 306	85.30 333	90.47 340	63.43 372	89.34 281	85.63 347	77.24 314	87.62 321	95.03 214	61.00 357	97.30 281	79.26 302	91.09 351	95.16 288

MVS_111021_HR		93.63 135	93.42 145	94.26 139	96.65 161	86.96 148	89.30 283	96.23 201	88.36 189	93.57 198	94.60 231	93.45 77	97.77 256	90.23 148	98.38 176	98.03 152

tpm cat1		80.61 333	79.46 335	84.07 341	88.78 357	65.06 368	89.26 284	88.23 325	62.27 367	81.90 359	89.66 335	62.70 351	95.29 336	71.72 346	80.60 370	91.86 348

CDS-MVSNet		89.55 241	88.22 259	93.53 166	95.37 247	86.49 158	89.26 284	93.59 271	79.76 290	91.15 263	92.31 297	77.12 289	98.38 203	77.51 314	97.92 224	95.71 276
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Fast-Effi-MVS+		91.28 201	90.86 205	92.53 200	95.45 243	82.53 215	89.25 286	96.52 189	85.00 247	89.91 285	88.55 344	92.94 94	98.84 138	84.72 249	95.44 293	96.22 255

BH-RMVSNet		90.47 215	90.44 216	90.56 262	95.21 251	78.65 276	89.15 287	93.94 269	88.21 190	92.74 226	94.22 243	86.38 216	97.88 243	78.67 306	95.39 295	95.14 290

thres200		85.85 298	85.18 299	87.88 313	94.44 274	72.52 337	89.08 288	86.21 339	88.57 185	91.44 257	88.40 345	64.22 341	98.00 235	68.35 358	95.88 284	93.12 332

USDC		89.02 249	89.08 239	88.84 296	95.07 253	74.50 321	88.97 289	96.39 194	73.21 333	93.27 208	96.28 151	82.16 251	96.39 310	77.55 313	98.80 137	95.62 282

testdata1							88.96 290		88.44 187

pmmvs5		87.87 270	87.14 277	90.07 275	93.26 296	76.97 299	88.89 291	92.18 297	73.71 331	88.36 312	93.89 257	76.86 295	96.73 300	80.32 286	96.81 263	96.51 241

patch_mono-2		92.46 174	92.72 163	91.71 224	96.65 161	78.91 270	88.85 292	97.17 142	83.89 258	92.45 235	96.76 115	89.86 169	97.09 287	90.24 147	98.59 155	99.12 42

test222					96.95 145	85.27 182	88.83 293	93.61 270	65.09 363	90.74 270	94.85 221	84.62 231			97.36 246	93.91 318

baseline2		83.38 311	81.54 320	88.90 294	91.38 329	72.84 335	88.78 294	81.22 366	78.97 301	79.82 365	87.56 348	61.73 354	97.80 252	74.30 333	90.05 354	96.05 262

diffmvs		91.74 188	91.93 179	91.15 243	93.06 300	78.17 280	88.77 295	97.51 116	86.28 223	92.42 237	93.96 254	88.04 187	97.46 272	90.69 132	96.67 268	97.82 178

MDTV_nov1_ep13			83.88 308		89.42 352	61.52 373	88.74 296	87.41 332	73.99 329	84.96 338	94.01 252	65.25 337	95.53 326	78.02 308	93.16 330

D2MVS		89.93 236	89.60 234	90.92 250	94.03 284	78.40 277	88.69 297	94.85 244	78.96 302	93.08 215	95.09 209	74.57 301	96.94 292	88.19 193	98.96 117	97.41 207

TR-MVS		87.70 273	87.17 276	89.27 290	94.11 281	79.26 263	88.69 297	91.86 305	81.94 278	90.69 271	89.79 331	82.82 243	97.42 275	72.65 342	91.98 345	91.14 351

PatchMatch-RL		89.18 246	88.02 264	92.64 193	95.90 222	92.87 47	88.67 299	91.06 310	80.34 285	90.03 283	91.67 307	83.34 236	94.42 344	76.35 323	94.84 306	90.64 354

PAPR		87.65 276	86.77 284	90.27 269	92.85 304	77.38 291	88.56 300	96.23 201	76.82 317	84.98 337	89.75 333	86.08 220	97.16 285	72.33 343	93.35 327	96.26 254

MDTV_nov1_ep13_2view						42.48 380	88.45 301		67.22 358	83.56 347		66.80 327		72.86 341		94.06 313

jason		89.17 247	88.32 253	91.70 225	95.73 230	80.07 243	88.10 302	93.22 277	71.98 339	90.09 280	92.79 284	78.53 278	98.56 188	87.43 209	97.06 253	96.46 246
jason: jason.
BH-w/o		87.21 286	87.02 280	87.79 314	94.77 261	77.27 293	87.90 303	93.21 279	81.74 279	89.99 284	88.39 346	83.47 235	96.93 294	71.29 349	92.43 341	89.15 356

MS-PatchMatch		88.05 268	87.75 266	88.95 293	93.28 294	77.93 282	87.88 304	92.49 293	75.42 321	92.57 231	93.59 266	80.44 265	94.24 349	81.28 279	92.75 336	94.69 302

DELS-MVS		92.05 184	92.16 172	91.72 223	94.44 274	80.13 242	87.62 305	97.25 137	87.34 209	92.22 246	93.18 276	89.54 172	98.73 161	89.67 163	98.20 202	96.30 252
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		84.01 309	82.20 317	89.41 286	89.04 355	76.37 306	87.57 306	90.98 311	72.71 337	84.46 340	92.45 292	68.08 320	96.48 307	70.58 354	83.97 363	95.38 285

ADS-MVSNet		82.25 318	81.55 319	84.34 339	89.04 355	65.30 364	87.57 306	85.13 355	72.71 337	84.46 340	92.45 292	68.08 320	92.33 359	70.58 354	83.97 363	95.38 285

IterMVS-SCA-FT		91.65 190	91.55 187	91.94 217	93.89 287	79.22 265	87.56 308	93.51 273	91.53 118	95.37 134	96.62 127	78.65 275	98.90 127	91.89 105	94.95 303	97.70 187

IterMVS		90.18 226	90.16 220	90.21 272	93.15 298	75.98 309	87.56 308	92.97 281	86.43 221	94.09 179	96.40 139	78.32 279	97.43 274	87.87 202	94.69 310	97.23 218
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Test_1112_low_res		87.50 280	86.58 286	90.25 270	96.80 158	77.75 286	87.53 310	96.25 199	69.73 350	86.47 329	93.61 265	75.67 299	97.88 243	79.95 292	93.20 329	95.11 291

c3_l		91.32 200	91.42 192	91.00 248	92.29 312	76.79 301	87.52 311	96.42 192	85.76 233	94.72 167	93.89 257	82.73 244	98.16 223	90.93 128	98.55 158	98.04 149

UnsupCasMVSNet_bld		88.50 261	88.03 263	89.90 279	95.52 241	78.88 271	87.39 312	94.02 266	79.32 298	93.06 216	94.02 251	80.72 264	94.27 347	75.16 329	93.08 333	96.54 239

lupinMVS		88.34 264	87.31 272	91.45 231	94.74 264	80.06 244	87.23 313	92.27 296	71.10 343	88.83 300	91.15 313	77.02 290	98.53 191	86.67 221	96.75 266	95.76 274

pmmvs4		88.95 253	87.70 268	92.70 191	94.30 277	85.60 178	87.22 314	92.16 299	74.62 325	89.75 292	94.19 244	77.97 282	96.41 309	82.71 264	96.36 274	96.09 259

WTY-MVS		86.93 293	86.50 291	88.24 307	94.96 254	74.64 317	87.19 315	92.07 302	78.29 307	88.32 313	91.59 309	78.06 281	94.27 347	74.88 330	93.15 331	95.80 272

ET-MVSNet_ETH3D		86.15 296	84.27 305	91.79 220	93.04 301	81.28 229	87.17 316	86.14 340	79.57 293	83.65 345	88.66 342	57.10 361	98.18 221	87.74 204	95.40 294	95.90 269

MVS-HIRNet		78.83 338	80.60 329	73.51 355	93.07 299	47.37 378	87.10 317	78.00 373	68.94 352	77.53 368	97.26 82	71.45 313	94.62 340	63.28 367	88.74 356	78.55 370

xiu_mvs_v2_base		89.00 251	89.19 236	88.46 304	94.86 257	74.63 318	86.97 318	95.60 221	80.88 282	87.83 319	88.62 343	91.04 144	98.81 145	82.51 268	94.38 314	91.93 346

DPM-MVS		89.35 244	88.40 252	92.18 211	96.13 206	84.20 194	86.96 319	96.15 207	75.40 322	87.36 324	91.55 310	83.30 237	98.01 234	82.17 272	96.62 269	94.32 309

eth_miper_zixun_eth		90.72 208	90.61 212	91.05 244	92.04 319	76.84 300	86.91 320	96.67 179	85.21 240	94.41 172	93.92 255	79.53 270	98.26 214	89.76 161	97.02 255	98.06 146

dp		79.28 336	78.62 338	81.24 348	85.97 370	56.45 376	86.91 320	85.26 353	72.97 335	81.45 361	89.17 341	56.01 365	95.45 331	73.19 339	76.68 371	91.82 349

sss		87.23 285	86.82 282	88.46 304	93.96 285	77.94 281	86.84 322	92.78 286	77.59 310	87.61 322	91.83 304	78.75 274	91.92 360	77.84 310	94.20 319	95.52 284

miper_ehance_all_eth		90.48 214	90.42 217	90.69 258	91.62 326	76.57 303	86.83 323	96.18 205	83.38 260	94.06 182	92.66 289	82.20 250	98.04 229	89.79 160	97.02 255	97.45 204

CLD-MVS		91.82 187	91.41 193	93.04 177	96.37 180	83.65 202	86.82 324	97.29 134	84.65 253	92.27 245	89.67 334	92.20 112	97.85 249	83.95 254	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____		90.65 211	90.56 214	90.91 252	91.85 321	76.98 298	86.75 325	95.36 235	85.53 236	94.06 182	94.89 219	77.36 288	97.98 238	90.27 145	98.98 111	97.76 183

DIV-MVS_self_test		90.65 211	90.56 214	90.91 252	91.85 321	76.99 297	86.75 325	95.36 235	85.52 238	94.06 182	94.89 219	77.37 287	97.99 237	90.28 144	98.97 115	97.76 183

PS-MVSNAJ		88.86 255	88.99 242	88.48 303	94.88 255	74.71 316	86.69 327	95.60 221	80.88 282	87.83 319	87.37 351	90.77 147	98.82 140	82.52 267	94.37 315	91.93 346

PVSNet_Blended		88.74 258	88.16 262	90.46 265	94.81 259	78.80 274	86.64 328	96.93 158	74.67 324	88.68 309	89.18 340	86.27 218	98.15 224	80.27 287	96.00 279	94.44 306

MSDG		90.82 205	90.67 211	91.26 237	94.16 279	83.08 210	86.63 329	96.19 204	90.60 140	91.94 251	91.89 303	89.16 175	95.75 324	80.96 285	94.51 313	94.95 295

cl22		89.02 249	88.50 250	90.59 261	89.76 346	76.45 304	86.62 330	94.03 264	82.98 268	92.65 228	92.49 290	72.05 311	97.53 267	88.93 178	97.02 255	97.78 181

CL-MVSNet_self_test		90.04 234	89.90 228	90.47 263	95.24 250	77.81 285	86.60 331	92.62 290	85.64 235	93.25 211	93.92 255	83.84 234	96.06 320	79.93 294	98.03 217	97.53 200

PCF-MVS	84.52 17	89.12 248	87.71 267	93.34 170	96.06 209	85.84 175	86.58 332	97.31 131	68.46 354	93.61 197	93.89 257	87.51 196	98.52 192	67.85 359	98.11 210	95.66 279
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Patchmatch-RL test		88.81 256	88.52 249	89.69 283	95.33 249	79.94 248	86.22 333	92.71 287	78.46 306	95.80 116	94.18 245	66.25 332	95.33 335	89.22 174	98.53 162	93.78 321

FPMVS		84.50 306	83.28 310	88.16 308	96.32 188	94.49 16	85.76 334	85.47 349	83.09 265	85.20 335	94.26 241	63.79 345	86.58 370	63.72 366	91.88 347	83.40 365

IB-MVS	77.21 19	83.11 312	81.05 323	89.29 289	91.15 331	75.85 310	85.66 335	86.00 343	79.70 291	82.02 358	86.61 354	48.26 373	98.39 201	77.84 310	92.22 342	93.63 325
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		91.04 202	90.88 204	91.55 229	94.68 269	80.16 239	85.49 336	92.14 300	90.41 145	94.93 157	95.79 174	85.10 227	96.93 294	85.15 239	94.19 320	97.57 196

new-patchmatchnet		88.97 252	90.79 208	83.50 343	94.28 278	55.83 377	85.34 337	93.56 272	86.18 225	95.47 129	95.73 179	83.10 239	96.51 306	85.40 236	98.06 214	98.16 139

miper_enhance_ethall		88.42 262	87.87 265	90.07 275	88.67 359	75.52 313	85.10 338	95.59 225	75.68 318	92.49 232	89.45 337	78.96 272	97.88 243	87.86 203	97.02 255	96.81 233

HyFIR lowres test		87.19 288	85.51 298	92.24 206	97.12 141	80.51 237	85.03 339	96.06 208	66.11 360	91.66 255	92.98 280	70.12 317	99.14 91	75.29 328	95.23 299	97.07 221

pmmvs3		80.83 330	78.96 337	86.45 323	87.23 365	77.48 290	84.87 340	82.31 363	63.83 365	85.03 336	89.50 336	49.66 371	93.10 355	73.12 340	95.10 301	88.78 360

test0.0.03 1		82.48 317	81.47 321	85.48 330	89.70 347	73.57 329	84.73 341	81.64 365	83.07 266	88.13 316	86.61 354	62.86 349	89.10 369	66.24 363	90.29 353	93.77 322

N_pmnet		88.90 254	87.25 274	93.83 156	94.40 276	93.81 36	84.73 341	87.09 334	79.36 297	93.26 209	92.43 295	79.29 271	91.68 361	77.50 315	97.22 250	96.00 263

GA-MVS		87.70 273	86.82 282	90.31 267	93.27 295	77.22 294	84.72 343	92.79 285	85.11 245	89.82 288	90.07 326	66.80 327	97.76 258	84.56 250	94.27 318	95.96 265

ppachtmachnet_test		88.61 260	88.64 248	88.50 302	91.76 323	70.99 345	84.59 344	92.98 280	79.30 299	92.38 239	93.53 268	79.57 269	97.45 273	86.50 226	97.17 251	97.07 221

CHOSEN 1792x2688		87.19 288	85.92 296	91.00 248	97.13 140	79.41 260	84.51 345	95.60 221	64.14 364	90.07 282	94.81 222	78.26 280	97.14 286	73.34 337	95.38 296	96.46 246

thisisatest0515		84.72 305	82.99 313	89.90 279	92.96 303	75.33 315	84.36 346	83.42 361	77.37 312	88.27 314	86.65 353	53.94 367	98.72 162	82.56 266	97.40 245	95.67 278

cascas		87.02 292	86.28 293	89.25 291	91.56 328	76.45 304	84.33 347	96.78 172	71.01 344	86.89 328	85.91 359	81.35 258	96.94 292	83.09 261	95.60 288	94.35 308

bset_n11_16_dypcd		89.99 235	89.15 238	92.53 200	94.75 262	81.34 228	84.19 348	87.56 331	85.13 243	93.77 191	92.46 291	72.82 307	99.01 113	92.46 91	99.21 83	97.23 218

new_pmnet		81.22 326	81.01 325	81.86 347	90.92 335	70.15 348	84.03 349	80.25 370	70.83 345	85.97 332	89.78 332	67.93 323	84.65 371	67.44 360	91.90 346	90.78 353

PAPM		81.91 323	80.11 333	87.31 318	93.87 288	72.32 339	84.02 350	93.22 277	69.47 351	76.13 370	89.84 328	72.15 310	97.23 283	53.27 372	89.02 355	92.37 343

our_test_3		87.55 278	87.59 269	87.44 317	91.76 323	70.48 346	83.83 351	90.55 315	79.79 289	92.06 250	92.17 299	78.63 277	95.63 325	84.77 247	94.73 308	96.22 255

miper_lstm_enhance		89.90 237	89.80 229	90.19 274	91.37 330	77.50 289	83.82 352	95.00 239	84.84 250	93.05 217	94.96 216	76.53 297	95.20 338	89.96 157	98.67 150	97.86 173

test-LLR		83.58 310	83.17 311	84.79 336	89.68 348	66.86 359	83.08 353	84.52 356	83.07 266	82.85 351	84.78 362	62.86 349	93.49 353	82.85 262	94.86 304	94.03 314

TESTMET0.1,1		79.09 337	78.04 339	82.25 346	87.52 362	64.03 371	83.08 353	80.62 368	70.28 348	80.16 364	83.22 365	44.13 377	90.56 364	79.95 292	93.36 326	92.15 344

test-mter		81.21 327	80.01 334	84.79 336	89.68 348	66.86 359	83.08 353	84.52 356	73.85 330	82.85 351	84.78 362	43.66 378	93.49 353	82.85 262	94.86 304	94.03 314

test123		9.49 344	12.01 347	1.91 359	2.87 382	1.30 383	82.38 356	1.34 384	1.36 377	2.84 378	6.56 376	2.45 382	0.97 378	2.73 376	5.56 376	3.47 374

PMMVS		83.00 314	81.11 322	88.66 300	83.81 376	86.44 161	82.24 357	85.65 346	61.75 368	82.07 356	85.64 360	79.75 268	91.59 362	75.99 325	93.09 332	87.94 361

KD-MVS_2432*1600		82.17 320	80.75 327	86.42 324	82.04 377	70.09 349	81.75 358	90.80 312	82.56 270	90.37 276	89.30 338	42.90 379	96.11 318	74.47 331	92.55 339	93.06 333

miper_refine_blended		82.17 320	80.75 327	86.42 324	82.04 377	70.09 349	81.75 358	90.80 312	82.56 270	90.37 276	89.30 338	42.90 379	96.11 318	74.47 331	92.55 339	93.06 333

YYNet1		88.17 266	88.24 257	87.93 311	92.21 314	73.62 328	80.75 360	88.77 320	82.51 273	94.99 155	95.11 208	82.70 245	93.70 351	83.33 258	93.83 322	96.48 245

MDA-MVSNet_test_wron		88.16 267	88.23 258	87.93 311	92.22 313	73.71 327	80.71 361	88.84 319	82.52 272	94.88 160	95.14 206	82.70 245	93.61 352	83.28 259	93.80 323	96.46 246

testmvs		9.02 345	11.42 348	1.81 360	2.77 383	1.13 384	79.44 362	1.90 383	1.18 378	2.65 379	6.80 375	1.95 383	0.87 379	2.62 377	3.45 377	3.44 375

PVSNet	76.22 20	82.89 315	82.37 315	84.48 338	93.96 285	64.38 370	78.60 363	88.61 321	71.50 341	84.43 342	86.36 357	74.27 302	94.60 341	69.87 356	93.69 325	94.46 305

PVSNet_0	70.34 21	74.58 339	72.96 342	79.47 351	90.63 337	66.24 363	73.26 364	83.40 362	63.67 366	78.02 367	78.35 370	72.53 308	89.59 367	56.68 370	60.05 374	82.57 368

E-PMN		80.72 332	80.86 326	80.29 350	85.11 372	68.77 354	72.96 365	81.97 364	87.76 200	83.25 350	83.01 366	62.22 352	89.17 368	77.15 318	94.31 317	82.93 366

CHOSEN 280x420		80.04 335	77.97 340	86.23 327	90.13 343	74.53 320	72.87 366	89.59 318	66.38 359	76.29 369	85.32 361	56.96 362	95.36 333	69.49 357	94.72 309	88.79 359

EMVS		80.35 334	80.28 332	80.54 349	84.73 374	69.07 353	72.54 367	80.73 367	87.80 199	81.66 360	81.73 367	62.89 348	89.84 366	75.79 327	94.65 311	82.71 367

PMMVS2		81.31 325	83.44 309	74.92 354	90.52 339	46.49 379	69.19 368	85.23 354	84.30 255	87.95 318	94.71 229	76.95 292	84.36 372	64.07 365	98.09 212	93.89 319

tmp_tt		37.97 342	44.33 345	18.88 358	11.80 381	21.54 381	63.51 369	45.66 382	4.23 375	51.34 375	50.48 373	59.08 359	22.11 377	44.50 374	68.35 373	13.00 373

MVE	59.87 23	73.86 340	72.65 343	77.47 353	87.00 368	74.35 322	61.37 370	60.93 378	67.27 357	69.69 373	86.49 356	81.24 262	72.33 374	56.45 371	83.45 365	85.74 363
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		50.44 341	48.94 344	54.93 356	39.68 380	12.38 382	28.59 371	90.09 316	6.82 374	41.10 376	78.41 369	54.41 366	70.69 375	50.12 373	51.26 375	81.72 369

test_blank		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

uanet_test		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

DCPMVS		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

cdsmvs_eth3d_5k		23.35 343	31.13 346	0.00 361	0.00 384	0.00 385	0.00 372	95.58 227	0.00 379	0.00 380	91.15 313	93.43 79	0.00 380	0.00 378	0.00 378	0.00 376

pcd_1.5k_mvsjas		7.56 346	10.09 349	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	90.77 147	0.00 380	0.00 378	0.00 378	0.00 376

sosnet-low-res		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

sosnet		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

uncertanet		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

Regformer		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

ab-mvs-re		7.56 346	10.08 350	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	90.69 322	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

uanet		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

MSC_two_6792asdad				95.90 66	96.54 171	89.57 92		96.87 166					99.41 38	94.06 30	99.30 66	98.72 93

PC_three_1452									75.31 323	95.87 114	95.75 178	92.93 95	96.34 315	87.18 213	98.68 148	98.04 149

No_MVS				95.90 66	96.54 171	89.57 92		96.87 166					99.41 38	94.06 30	99.30 66	98.72 93

test_one_0601					98.26 67	87.14 141		98.18 37	94.25 48	96.99 61	97.36 75	95.13 40

eth-test2					0.00 384

eth-test					0.00 384

ZD-MVS					97.23 132	90.32 81		97.54 111	84.40 254	94.78 163	95.79 174	92.76 101	99.39 50	88.72 186	98.40 171

IU-MVS					98.51 47	86.66 155		96.83 169	72.74 336	95.83 115				93.00 77	99.29 69	98.64 103

test_241102_TWO								98.10 50	91.95 94	97.54 38	97.25 83	95.37 28	99.35 61	93.29 64	99.25 77	98.49 117

test_241102_ONE					98.51 47	86.97 146		98.10 50	91.85 100	97.63 33	97.03 96	96.48 11	98.95 123

test_0728_THIRD									93.26 70	97.40 47	97.35 78	94.69 55	99.34 64	93.88 34	99.42 48	98.89 72

GSMVS																94.75 299

test_part2					98.21 71	89.41 97				96.72 72

sam_mvs1												66.64 330				94.75 299

sam_mvs												66.41 331

MTGPA								97.62 103

test_post											6.07 377	65.74 335	95.84 323

patchmatchnet-post											91.71 306	66.22 333	97.59 265

gm-plane-assit					87.08 367	59.33 374			71.22 342		83.58 364		97.20 284	73.95 334

test9_res														88.16 195	98.40 171	97.83 176

agg_prior2														87.06 216	98.36 182	97.98 158

agg_prior					96.20 197	88.89 106		96.88 164		90.21 278			98.78 152

TestCases				96.00 57	98.02 86	92.17 52		98.43 14	90.48 141	95.04 153	96.74 118	92.54 106	97.86 247	85.11 242	98.98 111	97.98 158

test_prior				94.61 120	95.95 218	87.23 138		97.36 126					98.68 172			97.93 164

新几何1				93.17 176	97.16 137	87.29 137		94.43 256	67.95 355	91.29 259	94.94 217	86.97 206	98.23 216	81.06 284	97.75 229	93.98 317

旧先验1					96.20 197	84.17 195		94.82 246			95.57 188	89.57 171			97.89 225	96.32 251

原ACMM1				92.87 186	96.91 149	84.22 193		97.01 152	76.84 316	89.64 293	94.46 235	88.00 188	98.70 168	81.53 277	98.01 219	95.70 277

testdata2													98.03 230	80.24 289

segment_acmp												92.14 113

testdata				91.03 245	96.87 151	82.01 218		94.28 260	71.55 340	92.46 234	95.42 197	85.65 225	97.38 280	82.64 265	97.27 248	93.70 324

test12				94.43 135	95.95 218	86.75 151		96.24 200		89.76 291		89.79 170	98.79 148		97.95 222	97.75 185

plane_prior7					97.71 105	88.68 110

plane_prior6					97.21 135	88.23 121						86.93 207

plane_prior5								97.81 91					98.95 123	89.26 172	98.51 165	98.60 110

plane_prior4											95.59 184

plane_prior3						88.43 119			90.35 146	93.31 204

plane_prior1					97.38 127

n2								0.00 385

nn								0.00 385

door-mid								92.13 301

lessismore_v0				93.87 155	98.05 81	83.77 201		80.32 369		97.13 53	97.91 46	77.49 284	99.11 97	92.62 87	98.08 213	98.74 90

LGP-MVS_train				96.84 40	98.36 62	92.13 54		98.25 28	91.78 107	97.07 54	97.22 86	96.38 13	99.28 75	92.07 98	99.59 27	99.11 43

test11								96.65 180

door								91.26 309

HQP5-MVS						84.89 185

BP-MVS														86.55 224

HQP4-MVS										88.81 302			98.61 180			98.15 140

HQP3-MVS								97.31 131							97.73 230

HQP2-MVS												84.76 229

NP-MVS					96.82 155	87.10 142					93.40 270

ACMMP++_ref															98.82 133

ACMMP++															99.25 77

Test By Simon												90.61 153

ITE_SJBPF				95.95 60	97.34 129	93.36 42		96.55 188	91.93 96	94.82 161	95.39 200	91.99 117	97.08 288	85.53 235	97.96 221	97.41 207

DeepMVS_CX				53.83 357	70.38 379	64.56 369		48.52 381	33.01 373	65.50 374	74.21 372	56.19 364	46.64 376	38.45 375	70.07 372	50.30 372

High-resolution multi-view benchmark