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		99.86 1	99.86 1	99.87 1	99.99 1	99.77 1	99.77 1	99.80 1	99.97 1	99.97 1	99.95 1	99.74 1	99.98 1	99.56 1	100.00 1	99.85 3

LTVRE_ROB	96.88 1	99.18 2	99.34 2	98.72 38	99.71 7	96.99 45	99.69 2	99.57 4	99.02 15	99.62 10	99.36 14	98.53 7	99.52 182	98.58 12	99.95 5	99.66 22
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
UA-Net		98.88 7	98.76 13	99.22 2	99.11 83	97.89 14	99.47 3	99.32 10	99.08 10	97.87 141	99.67 2	96.47 88	99.92 4	97.88 23	99.98 2	99.85 3

TDRefinement		98.90 5	98.86 8	99.02 9	99.54 20	98.06 8	99.34 4	99.44 8	98.85 20	99.00 36	99.20 23	97.42 32	99.59 160	97.21 48	99.76 42	99.40 84

UniMVSNet_ETH3D		99.12 3	99.28 3	98.65 43	99.77 3	96.34 64	99.18 5	99.20 16	99.67 2	99.73 3	99.65 4	99.15 3	99.86 20	97.22 46	99.92 14	99.77 8

OurMVSNet-221017-0		98.61 16	98.61 23	98.63 45	99.77 3	96.35 63	99.17 6	99.05 43	98.05 41	99.61 11	99.52 5	93.72 179	99.88 18	98.72 9	99.88 23	99.65 23

DVP-MVS++		97.96 46	97.90 45	98.12 83	97.75 242	95.40 101	99.03 7	98.89 79	96.62 92	98.62 52	98.30 93	96.97 56	99.75 65	95.70 104	99.25 186	99.21 129

FOURS1					99.59 14	98.20 4	99.03 7	99.25 12	98.96 18	98.87 40

pmmvs6		99.07 4	99.24 4	98.56 49	99.81 2	96.38 62	98.87 9	99.30 11	99.01 16	99.63 9	99.66 3	99.27 2	99.68 125	97.75 30	99.89 22	99.62 25

Anonymous20231211		98.55 17	98.76 13	97.94 96	98.79 111	94.37 146	98.84 10	99.15 24	99.37 3	99.67 6	99.43 11	95.61 120	99.72 86	98.12 16	99.86 25	99.73 15

MIMVSNet1		98.51 20	98.45 26	98.67 41	99.72 6	96.71 51	98.76 11	98.89 79	98.49 28	99.38 17	99.14 33	95.44 128	99.84 25	96.47 71	99.80 36	99.47 62

EPP-MVSNet		96.84 128	96.58 140	97.65 118	99.18 69	93.78 170	98.68 12	96.34 292	97.91 45	97.30 168	98.06 129	88.46 265	99.85 22	93.85 203	99.40 150	99.32 101

v7n		98.73 11	98.99 5	97.95 95	99.64 11	94.20 154	98.67 13	99.14 26	99.08 10	99.42 15	99.23 21	96.53 83	99.91 12	99.27 2	99.93 10	99.73 15

MVSFormer		96.14 167	96.36 154	95.49 244	97.68 249	87.81 286	98.67 13	99.02 52	96.50 100	94.48 283	96.15 277	86.90 280	99.92 4	98.73 7	99.13 203	98.74 212

test_djsdf		98.73 11	98.74 16	98.69 40	99.63 12	96.30 66	98.67 13	99.02 52	96.50 100	99.32 20	99.44 10	97.43 31	99.92 4	98.73 7	99.95 5	99.86 2

anonymousdsp		98.72 14	98.63 19	98.99 13	99.62 13	97.29 38	98.65 16	99.19 18	95.62 147	99.35 19	99.37 12	97.38 33	99.90 13	98.59 11	99.91 17	99.77 8

HPM-MVS		98.11 38	97.83 53	98.92 22	99.42 35	97.46 32	98.57 17	99.05 43	95.43 157	97.41 166	97.50 187	97.98 15	99.79 39	95.58 116	99.57 85	99.50 45
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
IS-MVSNet		96.93 122	96.68 135	97.70 114	99.25 52	94.00 160	98.57 17	96.74 288	98.36 31	98.14 108	97.98 138	88.23 268	99.71 100	93.10 221	99.72 51	99.38 88

WR-MVS_H		98.65 15	98.62 21	98.75 33	99.51 23	96.61 56	98.55 19	99.17 19	99.05 13	99.17 29	98.79 54	95.47 126	99.89 16	97.95 21	99.91 17	99.75 13

test2506		89.86 318	89.16 323	91.97 331	98.95 98	76.83 364	98.54 20	61.07 378	96.20 113	97.07 185	99.16 30	55.19 377	99.69 117	96.43 73	99.83 31	99.38 88

mvs_tets		98.90 5	98.94 6	98.75 33	99.69 8	96.48 60	98.54 20	99.22 13	96.23 112	99.71 4	99.48 7	98.77 6	99.93 2	98.89 3	99.95 5	99.84 5

Gipuma		98.07 40	98.31 29	97.36 149	99.76 5	96.28 67	98.51 22	99.10 31	98.76 23	96.79 201	99.34 17	96.61 78	98.82 305	96.38 74	99.50 114	96.98 314
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PS-CasMVS		98.73 11	98.85 10	98.39 59	99.55 18	95.47 100	98.49 23	99.13 27	99.22 8	99.22 27	98.96 45	97.35 34	99.92 4	97.79 28	99.93 10	99.79 7

3Dnovator	96.53 2	97.61 83	97.64 72	97.50 131	97.74 245	93.65 177	98.49 23	98.88 85	96.86 87	97.11 179	98.55 72	95.82 108	99.73 81	95.94 95	99.42 143	99.13 146

DTE-MVSNet		98.79 8	98.86 8	98.59 47	99.55 18	96.12 71	98.48 25	99.10 31	99.36 4	99.29 23	99.06 39	97.27 38	99.93 2	97.71 32	99.91 17	99.70 18

jajsoiax		98.77 9	98.79 12	98.74 35	99.66 10	96.48 60	98.45 26	99.12 28	95.83 139	99.67 6	99.37 12	98.25 10	99.92 4	98.77 5	99.94 8	99.82 6

PEN-MVS		98.75 10	98.85 10	98.44 55	99.58 15	95.67 88	98.45 26	99.15 24	99.33 5	99.30 21	99.00 41	97.27 38	99.92 4	97.64 34	99.92 14	99.75 13

LS3D		97.77 73	97.50 87	98.57 48	96.24 312	97.58 25	98.45 26	98.85 94	98.58 27	97.51 154	97.94 144	95.74 116	99.63 143	95.19 139	98.97 221	98.51 232

FC-MVSNet-test		98.16 33	98.37 27	97.56 123	99.49 27	93.10 190	98.35 29	99.21 14	98.43 29	98.89 39	98.83 53	94.30 164	99.81 32	97.87 24	99.91 17	99.77 8

HPM-MVS_fast		98.32 27	98.13 33	98.88 24	99.54 20	97.48 31	98.35 29	99.03 50	95.88 134	97.88 138	98.22 109	98.15 12	99.74 75	96.50 70	99.62 69	99.42 81

ab-mvs		96.59 149	96.59 138	96.60 190	98.64 129	92.21 207	98.35 29	97.67 248	94.45 192	96.99 191	98.79 54	94.96 143	99.49 188	90.39 279	99.07 213	98.08 268

test1111		94.53 239	94.81 214	93.72 298	99.06 88	81.94 347	98.31 32	83.87 370	96.37 105	98.49 65	99.17 29	81.49 306	99.73 81	96.64 62	99.86 25	99.49 53

ECVR-MVS		94.37 245	94.48 232	94.05 295	98.95 98	83.10 339	98.31 32	82.48 371	96.20 113	98.23 97	99.16 30	81.18 309	99.66 136	95.95 94	99.83 31	99.38 88

DROMVSNet		97.90 60	97.94 44	97.79 106	98.66 128	95.14 119	98.31 32	99.66 2	97.57 61	95.95 242	97.01 228	96.99 55	99.82 29	97.66 33	99.64 66	98.39 240

pm-mvs1		98.47 21	98.67 17	97.86 102	99.52 22	94.58 138	98.28 35	99.00 60	97.57 61	99.27 24	99.22 22	98.32 9	99.50 187	97.09 54	99.75 46	99.50 45

SixPastTwentyTwo		97.49 92	97.57 81	97.26 155	99.56 16	92.33 203	98.28 35	96.97 279	98.30 34	99.45 14	99.35 16	88.43 266	99.89 16	98.01 20	99.76 42	99.54 38

CP-MVSNet		98.42 23	98.46 24	98.30 67	99.46 29	95.22 116	98.27 37	98.84 99	99.05 13	99.01 35	98.65 66	95.37 129	99.90 13	97.57 36	99.91 17	99.77 8

GG-mvs-BLEND				90.60 339	91.00 371	84.21 335	98.23 38	72.63 377		82.76 369	84.11 369	56.14 375	96.79 363	72.20 367	92.09 358	90.78 366

GBi-Net		96.99 117	96.80 129	97.56 123	97.96 207	93.67 173	98.23 38	98.66 149	95.59 150	97.99 125	99.19 24	89.51 257	99.73 81	94.60 169	99.44 132	99.30 107

test1		96.99 117	96.80 129	97.56 123	97.96 207	93.67 173	98.23 38	98.66 149	95.59 150	97.99 125	99.19 24	89.51 257	99.73 81	94.60 169	99.44 132	99.30 107

FMVSNet1		97.95 50	98.08 35	97.56 123	99.14 81	93.67 173	98.23 38	98.66 149	97.41 72	99.00 36	99.19 24	95.47 126	99.73 81	95.83 102	99.76 42	99.30 107

ACMH	93.61 9	98.44 22	98.76 13	97.51 128	99.43 33	93.54 179	98.23 38	99.05 43	97.40 73	99.37 18	99.08 37	98.79 5	99.47 194	97.74 31	99.71 54	99.50 45
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
TransMVSNet (Re)		98.38 25	98.67 17	97.51 128	99.51 23	93.39 183	98.20 43	98.87 87	98.23 36	99.48 12	99.27 19	98.47 8	99.55 173	96.52 68	99.53 100	99.60 26

gg-mvs-nofinetune		88.28 329	86.96 334	92.23 330	92.84 367	84.44 332	98.19 44	74.60 374	99.08 10	87.01 366	99.47 8	56.93 372	98.23 349	78.91 358	95.61 341	94.01 356

QAPM		95.88 179	95.57 187	96.80 179	97.90 213	91.84 219	98.18 45	98.73 129	88.41 297	96.42 220	98.13 116	94.73 147	99.75 65	88.72 302	98.94 226	98.81 203

NR-MVSNet		97.96 46	97.86 50	98.26 69	98.73 117	95.54 93	98.14 46	98.73 129	97.79 46	99.42 15	97.83 156	94.40 162	99.78 43	95.91 97	99.76 42	99.46 64

MIMVSNet		93.42 271	92.86 270	95.10 258	98.17 185	88.19 275	98.13 47	93.69 324	92.07 255	95.04 269	98.21 110	80.95 312	99.03 287	81.42 353	98.06 281	98.07 270

PS-MVSNAJss		98.53 19	98.63 19	98.21 77	99.68 9	94.82 128	98.10 48	99.21 14	96.91 85	99.75 2	99.45 9	95.82 108	99.92 4	98.80 4	99.96 4	99.89 1

ACMMP		98.05 41	97.75 61	98.93 21	99.23 55	97.60 23	98.09 49	98.96 71	95.75 143	97.91 134	98.06 129	96.89 64	99.76 58	95.32 132	99.57 85	99.43 80
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		98.14 34	98.03 40	98.47 54	98.72 119	96.04 74	98.07 50	99.10 31	95.96 128	98.59 57	98.69 62	96.94 58	99.81 32	96.64 62	99.58 82	99.57 32

Vis-MVSNet		98.27 29	98.34 28	98.07 86	99.33 44	95.21 118	98.04 51	99.46 7	97.32 75	97.82 146	99.11 34	96.75 72	99.86 20	97.84 25	99.36 158	99.15 140
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
3Dnovator+	96.13 3	97.73 75	97.59 79	98.15 81	98.11 195	95.60 91	98.04 51	98.70 139	98.13 39	96.93 196	98.45 79	95.30 133	99.62 151	95.64 111	98.96 222	99.24 126

FIs		97.93 55	98.07 36	97.48 135	99.38 39	92.95 193	98.03 53	99.11 29	98.04 42	98.62 52	98.66 64	93.75 178	99.78 43	97.23 45	99.84 29	99.73 15

COLMAP_ROB	94.48 6	98.25 31	98.11 34	98.64 44	99.21 65	97.35 36	97.96 54	99.16 20	98.34 32	98.78 45	98.52 74	97.32 35	99.45 201	94.08 192	99.67 61	99.13 146
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
VDDNet		96.98 120	96.84 126	97.41 145	99.40 37	93.26 185	97.94 55	95.31 314	99.26 7	98.39 75	99.18 27	87.85 275	99.62 151	95.13 148	99.09 210	99.35 98

test_part1		96.77 136	96.53 146	97.47 136	98.04 197	92.92 194	97.93 56	98.85 94	98.83 21	99.30 21	99.07 38	79.25 317	99.79 39	97.59 35	99.93 10	99.69 20

CP-MVS		97.92 56	97.56 82	98.99 13	98.99 96	97.82 16	97.93 56	98.96 71	96.11 118	96.89 199	97.45 191	96.85 68	99.78 43	95.19 139	99.63 68	99.38 88

ANet_high		98.31 28	98.94 6	96.41 204	99.33 44	89.64 250	97.92 58	99.56 5	99.27 6	99.66 8	99.50 6	97.67 25	99.83 28	97.55 37	99.98 2	99.77 8

nrg030		98.54 18	98.62 21	98.32 64	99.22 58	95.66 89	97.90 59	99.08 37	98.31 33	99.02 34	98.74 58	97.68 24	99.61 158	97.77 29	99.85 28	99.70 18

ambc				96.56 195	98.23 177	91.68 222	97.88 60	98.13 217		98.42 72	98.56 71	94.22 167	99.04 284	94.05 196	99.35 163	98.95 177

Anonymous20240529		97.96 46	98.04 39	97.71 112	98.69 126	94.28 151	97.86 61	98.31 193	98.79 22	99.23 26	98.86 52	95.76 115	99.61 158	95.49 117	99.36 158	99.23 127

canonicalmvs		97.23 111	97.21 106	97.30 152	97.65 253	94.39 144	97.84 62	99.05 43	97.42 69	96.68 208	93.85 330	97.63 26	99.33 239	96.29 77	98.47 267	98.18 265

tfpnnormal		97.72 76	97.97 41	96.94 170	99.26 49	92.23 206	97.83 63	98.45 170	98.25 35	99.13 30	98.66 64	96.65 75	99.69 117	93.92 201	99.62 69	98.91 188

Anonymous20240521		97.07 114	97.51 85	95.76 232	99.35 42	88.18 276	97.78 64	98.40 180	97.11 80	98.34 82	99.04 40	89.58 253	99.79 39	98.09 18	99.93 10	99.30 107

XVS		97.96 46	97.63 74	98.94 18	99.15 73	97.66 20	97.77 65	98.83 106	97.42 69	96.32 225	97.64 175	96.49 86	99.72 86	95.66 109	99.37 155	99.45 69

X-MVStestdata		92.86 280	90.83 305	98.94 18	99.15 73	97.66 20	97.77 65	98.83 106	97.42 69	96.32 225	36.50 371	96.49 86	99.72 86	95.66 109	99.37 155	99.45 69

VPA-MVSNet		98.27 29	98.46 24	97.70 114	99.06 88	93.80 168	97.76 67	99.00 60	98.40 30	99.07 33	98.98 43	96.89 64	99.75 65	97.19 51	99.79 38	99.55 37

UGNet		96.81 133	96.56 142	97.58 122	96.64 302	93.84 167	97.75 68	97.12 273	96.47 103	93.62 309	98.88 50	93.22 188	99.53 178	95.61 113	99.69 58	99.36 96
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
mPP-MVS		97.91 59	97.53 83	99.04 7	99.22 58	97.87 15	97.74 69	98.78 120	96.04 123	97.10 180	97.73 168	96.53 83	99.78 43	95.16 143	99.50 114	99.46 64

OpenMVS	94.22 8	95.48 193	95.20 193	96.32 207	97.16 289	91.96 216	97.74 69	98.84 99	87.26 307	94.36 285	98.01 135	93.95 173	99.67 130	90.70 269	98.75 247	97.35 307

abl_6		98.42 23	98.19 32	99.09 3	99.16 70	98.10 6	97.73 71	99.11 29	97.76 50	98.62 52	98.27 102	97.88 19	99.80 38	95.67 107	99.50 114	99.38 88

MSP-MVS		97.45 95	96.92 123	99.03 8	99.26 49	97.70 19	97.66 72	98.89 79	95.65 145	98.51 62	96.46 261	92.15 214	99.81 32	95.14 146	98.58 263	99.58 28
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
LFMVS		95.32 201	94.88 209	96.62 189	98.03 198	91.47 225	97.65 73	90.72 354	99.11 9	97.89 137	98.31 89	79.20 318	99.48 191	93.91 202	99.12 206	98.93 183

K. test v3		96.44 156	96.28 157	96.95 169	99.41 36	91.53 223	97.65 73	90.31 357	98.89 19	98.93 38	99.36 14	84.57 295	99.92 4	97.81 26	99.56 88	99.39 86

TSAR-MVS + MP.		97.42 97	97.23 104	98.00 93	99.38 39	95.00 123	97.63 75	98.20 203	93.00 240	98.16 104	98.06 129	95.89 103	99.72 86	95.67 107	99.10 209	99.28 115
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
region2R		97.92 56	97.59 79	98.92 22	99.22 58	97.55 27	97.60 76	98.84 99	96.00 126	97.22 170	97.62 177	96.87 67	99.76 58	95.48 120	99.43 140	99.46 64

HFP-MVS		97.94 52	97.64 72	98.83 26	99.15 73	97.50 29	97.59 77	98.84 99	96.05 121	97.49 157	97.54 182	97.07 48	99.70 109	95.61 113	99.46 127	99.30 107

ACMMPR		97.95 50	97.62 76	98.94 18	99.20 66	97.56 26	97.59 77	98.83 106	96.05 121	97.46 163	97.63 176	96.77 71	99.76 58	95.61 113	99.46 127	99.49 53

RPSCF		97.87 63	97.51 85	98.95 17	99.15 73	98.43 3	97.56 79	99.06 41	96.19 115	98.48 66	98.70 61	94.72 148	99.24 258	94.37 180	99.33 173	99.17 136

KD-MVS_self_test		97.86 65	98.07 36	97.25 156	99.22 58	92.81 196	97.55 80	98.94 74	97.10 81	98.85 41	98.88 50	95.03 140	99.67 130	97.39 43	99.65 64	99.26 120

SR-MVS-dyc-post		98.14 34	97.84 51	99.02 9	98.81 108	98.05 9	97.55 80	98.86 90	97.77 47	98.20 99	98.07 124	96.60 80	99.76 58	95.49 117	99.20 191	99.26 120

RE-MVS-def			97.88 49		98.81 108	98.05 9	97.55 80	98.86 90	97.77 47	98.20 99	98.07 124	96.94 58		95.49 117	99.20 191	99.26 120

APD-MVS_3200maxsize		98.13 37	97.90 45	98.79 31	98.79 111	97.31 37	97.55 80	98.92 76	97.72 54	98.25 94	98.13 116	97.10 45	99.75 65	95.44 124	99.24 189	99.32 101

ACMH+	93.58 10	98.23 32	98.31 29	97.98 94	99.39 38	95.22 116	97.55 80	99.20 16	98.21 37	99.25 25	98.51 75	98.21 11	99.40 218	94.79 162	99.72 51	99.32 101

Vis-MVSNet (Re-imp)		95.11 209	94.85 210	95.87 229	99.12 82	89.17 258	97.54 85	94.92 316	96.50 100	96.58 212	97.27 210	83.64 300	99.48 191	88.42 307	99.67 61	98.97 175

MP-MVS		97.64 80	97.18 107	99.00 12	99.32 46	97.77 18	97.49 86	98.73 129	96.27 109	95.59 257	97.75 165	96.30 96	99.78 43	93.70 209	99.48 122	99.45 69
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
ZNCC-MVS		97.92 56	97.62 76	98.83 26	99.32 46	97.24 40	97.45 87	98.84 99	95.76 141	96.93 196	97.43 193	97.26 40	99.79 39	96.06 84	99.53 100	99.45 69

tttt0517		93.31 274	92.56 281	95.57 239	98.71 122	87.86 283	97.44 88	87.17 365	95.79 140	97.47 162	96.84 237	64.12 366	99.81 32	96.20 79	99.32 175	99.02 170

v10		97.55 87	97.97 41	96.31 208	98.60 137	89.64 250	97.44 88	99.02 52	96.60 94	98.72 50	99.16 30	93.48 183	99.72 86	98.76 6	99.92 14	99.58 28

v8		97.60 84	98.06 38	96.23 211	98.71 122	89.44 254	97.43 90	98.82 114	97.29 77	98.74 48	99.10 35	93.86 174	99.68 125	98.61 10	99.94 8	99.56 35

PMVS	89.60 17	96.71 142	96.97 119	95.95 224	99.51 23	97.81 17	97.42 91	97.49 260	97.93 44	95.95 242	98.58 68	96.88 66	96.91 361	89.59 290	99.36 158	93.12 361
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test1172		98.08 39	97.76 59	99.05 6	98.78 113	98.07 7	97.41 92	98.85 94	97.57 61	98.15 106	97.96 139	96.60 80	99.76 58	95.30 133	99.18 196	99.33 100

SR-MVS		98.00 45	97.66 67	99.01 11	98.77 115	97.93 11	97.38 93	98.83 106	97.32 75	98.06 118	97.85 154	96.65 75	99.77 53	95.00 155	99.11 207	99.32 101

FMVSNet5		93.39 272	92.35 283	96.50 197	95.83 327	90.81 235	97.31 94	98.27 194	92.74 248	96.27 229	98.28 98	62.23 368	99.67 130	90.86 259	99.36 158	99.03 168

HY-MVS	91.43 15	92.58 284	91.81 290	94.90 266	96.49 306	88.87 263	97.31 94	94.62 318	85.92 320	90.50 349	96.84 237	85.05 290	99.40 218	83.77 348	95.78 339	96.43 335

CSCG		97.40 99	97.30 97	97.69 116	98.95 98	94.83 127	97.28 96	98.99 63	96.35 108	98.13 109	95.95 289	95.99 101	99.66 136	94.36 183	99.73 48	98.59 227

MTAPA		98.14 34	97.84 51	99.06 4	99.44 31	97.90 12	97.25 97	98.73 129	97.69 57	97.90 135	97.96 139	95.81 112	99.82 29	96.13 81	99.61 75	99.45 69

CPTT-MVS		96.69 143	96.08 167	98.49 52	98.89 104	96.64 55	97.25 97	98.77 121	92.89 246	96.01 241	97.13 216	92.23 213	99.67 130	92.24 230	99.34 166	99.17 136

EU-MVSNet		94.25 247	94.47 233	93.60 301	98.14 190	82.60 342	97.24 99	92.72 337	85.08 331	98.48 66	98.94 46	82.59 303	98.76 312	97.47 40	99.53 100	99.44 79

XXY-MVS		97.54 88	97.70 62	97.07 164	99.46 29	92.21 207	97.22 100	99.00 60	94.93 178	98.58 58	98.92 48	97.31 36	99.41 216	94.44 175	99.43 140	99.59 27

CS-MVS-test		96.62 148	96.59 138	96.69 186	97.88 215	93.16 188	97.21 101	99.53 6	95.61 148	93.72 304	95.33 304	95.49 123	99.69 117	95.37 131	99.19 195	97.22 308

GST-MVS		97.82 69	97.49 88	98.81 29	99.23 55	97.25 39	97.16 102	98.79 116	95.96 128	97.53 152	97.40 195	96.93 60	99.77 53	95.04 152	99.35 163	99.42 81

SteuartSystems-ACMMP		98.02 43	97.76 59	98.79 31	99.43 33	97.21 42	97.15 103	98.90 78	96.58 96	98.08 116	97.87 153	97.02 53	99.76 58	95.25 136	99.59 80	99.40 84
Skip Steuart: Steuart Systems R&D Blog.
FMVSNet2		96.72 140	96.67 136	96.87 175	97.96 207	91.88 217	97.15 103	98.06 227	95.59 150	98.50 64	98.62 67	89.51 257	99.65 138	94.99 156	99.60 78	99.07 162

AllTest		97.20 112	96.92 123	98.06 88	99.08 85	96.16 69	97.14 105	99.16 20	94.35 196	97.78 147	98.07 124	95.84 105	99.12 273	91.41 246	99.42 143	98.91 188

DP-MVS		97.87 63	97.89 48	97.81 105	98.62 134	94.82 128	97.13 106	98.79 116	98.98 17	98.74 48	98.49 76	95.80 114	99.49 188	95.04 152	99.44 132	99.11 155

GeoE		97.75 74	97.70 62	97.89 99	98.88 105	94.53 139	97.10 107	98.98 66	95.75 143	97.62 149	97.59 179	97.61 27	99.77 53	96.34 76	99.44 132	99.36 96

PGM-MVS		97.88 62	97.52 84	98.96 16	99.20 66	97.62 22	97.09 108	99.06 41	95.45 155	97.55 151	97.94 144	97.11 44	99.78 43	94.77 165	99.46 127	99.48 59

LPG-MVS_test		97.94 52	97.67 66	98.74 35	99.15 73	97.02 43	97.09 108	99.02 52	95.15 167	98.34 82	98.23 106	97.91 17	99.70 109	94.41 177	99.73 48	99.50 45

SF-MVS		97.60 84	97.39 92	98.22 74	98.93 101	95.69 85	97.05 110	99.10 31	95.32 160	97.83 144	97.88 151	96.44 90	99.72 86	94.59 172	99.39 152	99.25 124

VDD-MVS		97.37 101	97.25 101	97.74 110	98.69 126	94.50 142	97.04 111	95.61 308	98.59 26	98.51 62	98.72 59	92.54 207	99.58 162	96.02 89	99.49 118	99.12 151

wuyk23d		93.25 276	95.20 193	87.40 351	96.07 322	95.38 103	97.04 111	94.97 315	95.33 159	99.70 5	98.11 120	98.14 13	91.94 369	77.76 362	99.68 60	74.89 369

MVS_0304		95.50 190	95.05 202	96.84 177	96.28 311	93.12 189	97.00 113	96.16 294	95.03 173	89.22 357	97.70 171	90.16 248	99.48 191	94.51 174	99.34 166	97.93 284

LCM-MVSNet-Re		97.33 104	97.33 96	97.32 151	98.13 193	93.79 169	96.99 114	99.65 3	96.74 90	99.47 13	98.93 47	96.91 63	99.84 25	90.11 282	99.06 216	98.32 249

MAR-MVS		94.21 250	93.03 267	97.76 108	96.94 297	97.44 34	96.97 115	97.15 271	87.89 305	92.00 340	92.73 344	92.14 215	99.12 273	83.92 345	97.51 306	96.73 328
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
h-mvs33		96.29 160	95.63 184	98.26 69	98.50 150	96.11 72	96.90 116	97.09 274	96.58 96	97.21 172	98.19 111	84.14 296	99.78 43	95.89 98	96.17 334	98.89 192

test0726					99.24 53	95.51 95	96.89 117	98.89 79	95.92 131	98.64 51	98.31 89	97.06 50

baseline		97.44 96	97.78 58	96.43 201	98.52 146	90.75 236	96.84 118	99.03 50	96.51 99	97.86 142	98.02 133	96.67 74	99.36 231	97.09 54	99.47 124	99.19 133

API-MVS		95.09 211	95.01 203	95.31 250	96.61 303	94.02 159	96.83 119	97.18 270	95.60 149	95.79 249	94.33 325	94.54 158	98.37 344	85.70 331	98.52 264	93.52 358

#test#		97.62 82	97.22 105	98.83 26	99.15 73	97.50 29	96.81 120	98.84 99	94.25 200	97.49 157	97.54 182	97.07 48	99.70 109	94.37 180	99.46 127	99.30 107

SED-MVS		97.94 52	97.90 45	98.07 86	99.22 58	95.35 106	96.79 121	98.83 106	96.11 118	99.08 31	98.24 104	97.87 20	99.72 86	95.44 124	99.51 110	99.14 143

OPU-MVS				97.64 119	98.01 201	95.27 111	96.79 121				97.35 204	96.97 56	98.51 335	91.21 252	99.25 186	99.14 143

PHI-MVS		96.96 121	96.53 146	98.25 72	97.48 264	96.50 59	96.76 123	98.85 94	93.52 219	96.19 234	96.85 236	95.94 102	99.42 207	93.79 205	99.43 140	98.83 201

DVP-MVS		97.78 72	97.65 69	98.16 78	99.24 53	95.51 95	96.74 124	98.23 199	95.92 131	98.40 73	98.28 98	97.06 50	99.71 100	95.48 120	99.52 105	99.26 120
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				98.25 72	99.23 55	95.49 99	96.74 124	98.89 79					99.75 65	95.48 120	99.52 105	99.53 41

Anonymous202405211		96.34 159	95.98 172	97.43 143	98.25 174	93.85 166	96.74 124	94.41 321	97.72 54	98.37 76	98.03 132	87.15 279	99.53 178	94.06 193	99.07 213	98.92 187

SMA-MVS		97.48 93	97.11 110	98.60 46	98.83 107	96.67 53	96.74 124	98.73 129	91.61 263	98.48 66	98.36 84	96.53 83	99.68 125	95.17 141	99.54 97	99.45 69
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
TranMVSNet+NR-MVSNet		98.33 26	98.30 31	98.43 56	99.07 87	95.87 79	96.73 128	99.05 43	98.67 24	98.84 42	98.45 79	97.58 28	99.88 18	96.45 72	99.86 25	99.54 38

test_0402		97.84 66	97.97 41	97.47 136	99.19 68	94.07 157	96.71 129	98.73 129	98.66 25	98.56 59	98.41 81	96.84 69	99.69 117	94.82 160	99.81 33	98.64 221

ACMM	93.33 11	98.05 41	97.79 55	98.85 25	99.15 73	97.55 27	96.68 130	98.83 106	95.21 163	98.36 79	98.13 116	98.13 14	99.62 151	96.04 87	99.54 97	99.39 86
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
baseline1		93.14 278	92.64 279	94.62 278	97.34 278	87.20 298	96.67 131	93.02 332	94.71 184	96.51 217	95.83 292	81.64 305	98.60 328	90.00 285	88.06 364	98.07 270

MTMP							96.55 132	74.60 374

SD-MVS		97.37 101	97.70 62	96.35 205	98.14 190	95.13 120	96.54 133	98.92 76	95.94 130	99.19 28	98.08 122	97.74 22	95.06 367	95.24 137	99.54 97	98.87 198
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
HQP_MVS		96.66 146	96.33 156	97.68 117	98.70 124	94.29 148	96.50 134	98.75 125	96.36 106	96.16 235	96.77 243	91.91 225	99.46 197	92.59 227	99.20 191	99.28 115

plane_prior2							96.50 134		96.36 106

testtj		96.69 143	96.13 163	98.36 61	98.46 157	96.02 76	96.44 136	98.70 139	94.26 199	96.79 201	97.13 216	94.07 170	99.75 65	90.53 274	98.80 242	99.31 106

Effi-MVS+-dtu		96.81 133	96.09 166	98.99 13	96.90 299	98.69 2	96.42 137	98.09 220	95.86 136	95.15 265	95.54 300	94.26 165	99.81 32	94.06 193	98.51 266	98.47 235

thres100view900		91.76 299	91.26 298	93.26 307	98.21 178	84.50 331	96.39 138	90.39 355	96.87 86	96.33 224	93.08 337	73.44 350	99.42 207	78.85 359	97.74 292	95.85 341

XVG-ACMP-BASELINE		97.58 86	97.28 100	98.49 52	99.16 70	96.90 47	96.39 138	98.98 66	95.05 172	98.06 118	98.02 133	95.86 104	99.56 169	94.37 180	99.64 66	99.00 171

Patchmtry		95.03 214	94.59 227	96.33 206	94.83 344	90.82 233	96.38 140	97.20 268	96.59 95	97.49 157	98.57 69	77.67 325	99.38 226	92.95 224	99.62 69	98.80 204

ACMMP_NAP		97.89 61	97.63 74	98.67 41	99.35 42	96.84 48	96.36 141	98.79 116	95.07 171	97.88 138	98.35 85	97.24 42	99.72 86	96.05 86	99.58 82	99.45 69

VNet		96.84 128	96.83 127	96.88 174	98.06 196	92.02 214	96.35 142	97.57 259	97.70 56	97.88 138	97.80 161	92.40 211	99.54 176	94.73 167	98.96 222	99.08 160

V42		97.04 115	97.16 108	96.68 188	98.59 139	91.05 228	96.33 143	98.36 185	94.60 187	97.99 125	98.30 93	93.32 185	99.62 151	97.40 42	99.53 100	99.38 88

APD-MVS		97.00 116	96.53 146	98.41 57	98.55 143	96.31 65	96.32 144	98.77 121	92.96 245	97.44 165	97.58 181	95.84 105	99.74 75	91.96 233	99.35 163	99.19 133
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
VPNet		97.26 108	97.49 88	96.59 191	99.47 28	90.58 238	96.27 145	98.53 163	97.77 47	98.46 69	98.41 81	94.59 155	99.68 125	94.61 168	99.29 181	99.52 42

thres600view7		92.03 295	91.43 293	93.82 296	98.19 180	84.61 330	96.27 145	90.39 355	96.81 88	96.37 223	93.11 333	73.44 350	99.49 188	80.32 355	97.95 284	97.36 305

EPNet		93.72 263	92.62 280	97.03 167	87.61 376	92.25 205	96.27 145	91.28 348	96.74 90	87.65 363	97.39 199	85.00 291	99.64 141	92.14 231	99.48 122	99.20 132
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DSMNet-mixed		92.19 292	91.83 289	93.25 308	96.18 317	83.68 338	96.27 145	93.68 326	76.97 365	92.54 336	99.18 27	89.20 262	98.55 332	83.88 346	98.60 262	97.51 301

ACMP	92.54 13	97.47 94	97.10 111	98.55 50	99.04 93	96.70 52	96.24 149	98.89 79	93.71 216	97.97 129	97.75 165	97.44 30	99.63 143	93.22 218	99.70 57	99.32 101
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
DeepC-MVS	95.41 4	97.82 69	97.70 62	98.16 78	98.78 113	95.72 83	96.23 150	99.02 52	93.92 211	98.62 52	98.99 42	97.69 23	99.62 151	96.18 80	99.87 24	99.15 140
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PM-MVS		97.36 103	97.10 111	98.14 82	98.91 103	96.77 50	96.20 151	98.63 155	93.82 213	98.54 60	98.33 87	93.98 172	99.05 283	95.99 92	99.45 131	98.61 226

MVS_Test		96.27 161	96.79 131	94.73 275	96.94 297	86.63 305	96.18 152	98.33 190	94.94 176	96.07 238	98.28 98	95.25 134	99.26 255	97.21 48	97.90 287	98.30 253

CR-MVSNet		93.29 275	92.79 273	94.78 273	95.44 336	88.15 277	96.18 152	97.20 268	84.94 335	94.10 291	98.57 69	77.67 325	99.39 223	95.17 141	95.81 336	96.81 325

RPMNet		94.68 231	94.60 225	94.90 266	95.44 336	88.15 277	96.18 152	98.86 90	97.43 68	94.10 291	98.49 76	79.40 316	99.76 58	95.69 106	95.81 336	96.81 325

EIA-MVS		96.04 171	95.77 180	96.85 176	97.80 228	92.98 192	96.12 155	99.16 20	94.65 185	93.77 302	91.69 355	95.68 117	99.67 130	94.18 188	98.85 238	97.91 285

Effi-MVS+		96.19 165	96.01 169	96.71 184	97.43 270	92.19 210	96.12 155	99.10 31	95.45 155	93.33 321	94.71 317	97.23 43	99.56 169	93.21 219	97.54 304	98.37 242

alignmvs		96.01 173	95.52 188	97.50 131	97.77 239	94.71 132	96.07 157	96.84 282	97.48 67	96.78 205	94.28 327	85.50 288	99.40 218	96.22 78	98.73 251	98.40 238

PatchT		93.75 262	93.57 258	94.29 291	95.05 342	87.32 296	96.05 158	92.98 333	97.54 65	94.25 287	98.72 59	75.79 338	99.24 258	95.92 96	95.81 336	96.32 336

Patchmatch-test		93.60 268	93.25 264	94.63 277	96.14 321	87.47 292	96.04 159	94.50 320	93.57 218	96.47 218	96.97 229	76.50 333	98.61 326	90.67 270	98.41 269	97.81 291

thisisatest0530		92.71 283	91.76 291	95.56 241	98.42 159	88.23 274	96.03 160	87.35 364	94.04 207	96.56 214	95.47 302	64.03 367	99.77 53	94.78 164	99.11 207	98.68 220

9.14			96.69 134		98.53 145		96.02 161	98.98 66	93.23 229	97.18 174	97.46 190	96.47 88	99.62 151	92.99 222	99.32 175

DeepC-MVS_fast	94.34 7	96.74 137	96.51 149	97.44 142	97.69 248	94.15 155	96.02 161	98.43 173	93.17 235	97.30 168	97.38 201	95.48 125	99.28 252	93.74 206	99.34 166	98.88 196
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
114514_t		93.96 258	93.22 265	96.19 214	99.06 88	90.97 231	95.99 163	98.94 74	73.88 368	93.43 318	96.93 232	92.38 212	99.37 229	89.09 297	99.28 182	98.25 259

FMVSNet3		95.26 204	94.94 204	96.22 213	96.53 305	90.06 242	95.99 163	97.66 250	94.11 205	97.99 125	97.91 148	80.22 315	99.63 143	94.60 169	99.44 132	98.96 176

HPM-MVS++		96.99 117	96.38 153	98.81 29	98.64 129	97.59 24	95.97 165	98.20 203	95.51 153	95.06 266	96.53 257	94.10 169	99.70 109	94.29 184	99.15 198	99.13 146

ETH3D-3000-0.1		96.89 127	96.46 151	98.16 78	98.62 134	95.69 85	95.96 166	98.98 66	93.36 224	97.04 187	97.31 208	94.93 144	99.63 143	92.60 225	99.34 166	99.17 136

testgi		96.07 169	96.50 150	94.80 272	99.26 49	87.69 289	95.96 166	98.58 160	95.08 170	98.02 124	96.25 272	97.92 16	97.60 358	88.68 304	98.74 248	99.11 155

EG-PatchMatch MVS		97.69 78	97.79 55	97.40 146	99.06 88	93.52 180	95.96 166	98.97 70	94.55 191	98.82 43	98.76 57	97.31 36	99.29 250	97.20 50	99.44 132	99.38 88

PAPM_NR		94.61 235	94.17 244	95.96 222	98.36 163	91.23 226	95.93 169	97.95 230	92.98 241	93.42 319	94.43 324	90.53 239	98.38 342	87.60 317	96.29 332	98.27 257

UniMVSNet (Re)		97.83 67	97.65 69	98.35 63	98.80 110	95.86 80	95.92 170	99.04 49	97.51 66	98.22 98	97.81 160	94.68 151	99.78 43	97.14 53	99.75 46	99.41 83

1314		92.38 288	92.30 284	92.64 322	95.42 338	85.15 323	95.86 171	96.97 279	85.40 329	90.62 346	93.06 338	91.12 232	97.80 356	86.74 325	95.49 343	94.97 352

1121		94.26 246	93.26 263	97.27 153	98.26 173	94.73 130	95.86 171	97.71 246	77.96 362	94.53 280	96.71 247	91.93 223	99.40 218	87.71 313	98.64 258	97.69 295

MVS		90.02 313	89.20 320	92.47 325	94.71 345	86.90 302	95.86 171	96.74 288	64.72 370	90.62 346	92.77 342	92.54 207	98.39 341	79.30 357	95.56 342	92.12 362

CS-MVS		95.98 175	96.24 158	95.20 254	97.26 283	89.88 246	95.84 174	99.39 9	93.89 212	94.28 286	95.15 307	94.81 146	99.62 151	96.11 83	99.40 150	96.10 339

casdiffmvs		97.50 91	97.81 54	96.56 195	98.51 147	91.04 229	95.83 175	99.09 36	97.23 78	98.33 86	98.30 93	97.03 52	99.37 229	96.58 66	99.38 154	99.28 115

tpmvs		90.79 309	90.87 303	90.57 340	92.75 368	76.30 365	95.79 176	93.64 327	91.04 273	91.91 341	96.26 271	77.19 331	98.86 304	89.38 294	89.85 362	96.56 333

RRT_test8_iter05		92.46 286	92.52 282	92.29 329	95.33 339	77.43 361	95.73 177	98.55 162	94.41 193	97.46 163	97.72 170	57.44 371	99.74 75	96.92 59	99.14 199	99.69 20

MSLP-MVS++		96.42 158	96.71 133	95.57 239	97.82 223	90.56 240	95.71 178	98.84 99	94.72 183	96.71 207	97.39 199	94.91 145	98.10 353	95.28 134	99.02 218	98.05 277

tfpn200view9		91.55 301	91.00 300	93.21 310	98.02 199	84.35 333	95.70 179	90.79 352	96.26 110	95.90 247	92.13 350	73.62 347	99.42 207	78.85 359	97.74 292	95.85 341

Anonymous20231206		95.27 203	95.06 201	95.88 228	98.72 119	89.37 255	95.70 179	97.85 236	88.00 303	96.98 193	97.62 177	91.95 221	99.34 236	89.21 295	99.53 100	98.94 179

thres400		91.68 300	91.00 300	93.71 299	98.02 199	84.35 333	95.70 179	90.79 352	96.26 110	95.90 247	92.13 350	73.62 347	99.42 207	78.85 359	97.74 292	97.36 305

test20.03		96.58 150	96.61 137	96.48 199	98.49 151	91.72 221	95.68 182	97.69 247	96.81 88	98.27 93	97.92 147	94.18 168	98.71 316	90.78 263	99.66 63	99.00 171

hse-mvs2		95.77 182	95.09 198	97.79 106	97.84 220	95.51 95	95.66 183	95.43 313	96.58 96	97.21 172	96.16 276	84.14 296	99.54 176	95.89 98	96.92 317	98.32 249

zzz-MVS		98.01 44	97.66 67	99.06 4	99.44 31	97.90 12	95.66 183	98.73 129	97.69 57	97.90 135	97.96 139	95.81 112	99.82 29	96.13 81	99.61 75	99.45 69

UniMVSNet_NR-MVSNet		97.83 67	97.65 69	98.37 60	98.72 119	95.78 81	95.66 183	99.02 52	98.11 40	98.31 89	97.69 173	94.65 153	99.85 22	97.02 57	99.71 54	99.48 59

DU-MVS		97.79 71	97.60 78	98.36 61	98.73 117	95.78 81	95.65 186	98.87 87	97.57 61	98.31 89	97.83 156	94.69 149	99.85 22	97.02 57	99.71 54	99.46 64

EPMVS		89.26 322	88.55 326	91.39 334	92.36 369	79.11 355	95.65 186	79.86 372	88.60 296	93.12 323	96.53 257	70.73 359	98.10 353	90.75 264	89.32 363	96.98 314

MVP-Stereo		95.69 183	95.28 192	96.92 171	98.15 189	93.03 191	95.64 188	98.20 203	90.39 278	96.63 211	97.73 168	91.63 228	99.10 278	91.84 239	97.31 313	98.63 223
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
F-COLMAP		95.30 202	94.38 237	98.05 91	98.64 129	96.04 74	95.61 189	98.66 149	89.00 291	93.22 322	96.40 265	92.90 195	99.35 234	87.45 321	97.53 305	98.77 210

AUN-MVS		93.95 260	92.69 277	97.74 110	97.80 228	95.38 103	95.57 190	95.46 312	91.26 270	92.64 333	96.10 282	74.67 341	99.55 173	93.72 208	96.97 316	98.30 253

v144192		96.69 143	96.90 125	96.03 219	98.25 174	88.92 261	95.49 191	98.77 121	93.05 238	98.09 114	98.29 97	92.51 209	99.70 109	98.11 17	99.56 88	99.47 62

Fast-Effi-MVS+-dtu		96.44 156	96.12 164	97.39 147	97.18 288	94.39 144	95.46 192	98.73 129	96.03 125	94.72 274	94.92 314	96.28 98	99.69 117	93.81 204	97.98 283	98.09 267

Baseline_NR-MVSNet		97.72 76	97.79 55	97.50 131	99.56 16	93.29 184	95.44 193	98.86 90	98.20 38	98.37 76	99.24 20	94.69 149	99.55 173	95.98 93	99.79 38	99.65 23

LF4IMVS		96.07 169	95.63 184	97.36 149	98.19 180	95.55 92	95.44 193	98.82 114	92.29 254	95.70 255	96.55 255	92.63 203	98.69 318	91.75 242	99.33 173	97.85 287

v1921920		96.72 140	96.96 121	95.99 220	98.21 178	88.79 266	95.42 195	98.79 116	93.22 230	98.19 102	98.26 103	92.68 200	99.70 109	98.34 15	99.55 94	99.49 53

plane_prior						94.29 148	95.42 195		94.31 198						98.93 228

v1144		96.84 128	97.08 113	96.13 217	98.42 159	89.28 257	95.41 197	98.67 147	94.21 201	97.97 129	98.31 89	93.06 190	99.65 138	98.06 19	99.62 69	99.45 69

ETV-MVS		96.13 168	95.90 176	96.82 178	97.76 240	93.89 163	95.40 198	98.95 73	95.87 135	95.58 258	91.00 361	96.36 95	99.72 86	93.36 213	98.83 240	96.85 321

v1240		96.74 137	97.02 118	95.91 227	98.18 183	88.52 269	95.39 199	98.88 85	93.15 236	98.46 69	98.40 83	92.80 197	99.71 100	98.45 13	99.49 118	99.49 53

MP-MVS-pluss		97.69 78	97.36 94	98.70 39	99.50 26	96.84 48	95.38 200	98.99 63	92.45 252	98.11 110	98.31 89	97.25 41	99.77 53	96.60 64	99.62 69	99.48 59
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
v1192		96.83 131	97.06 115	96.15 216	98.28 169	89.29 256	95.36 201	98.77 121	93.73 215	98.11 110	98.34 86	93.02 194	99.67 130	98.35 14	99.58 82	99.50 45

v2v482		96.78 135	97.06 115	95.95 224	98.57 141	88.77 267	95.36 201	98.26 196	95.18 166	97.85 143	98.23 106	92.58 204	99.63 143	97.80 27	99.69 58	99.45 69

EI-MVSNet-Vis-set		97.32 105	97.39 92	97.11 161	97.36 273	92.08 213	95.34 203	97.65 252	97.74 51	98.29 92	98.11 120	95.05 137	99.68 125	97.50 39	99.50 114	99.56 35

EI-MVSNet-UG-set		97.32 105	97.40 91	97.09 163	97.34 278	92.01 215	95.33 204	97.65 252	97.74 51	98.30 91	98.14 115	95.04 139	99.69 117	97.55 37	99.52 105	99.58 28

CostFormer		89.75 319	89.25 317	91.26 336	94.69 346	78.00 359	95.32 205	91.98 342	81.50 348	90.55 348	96.96 231	71.06 357	98.89 300	88.59 305	92.63 356	96.87 319

PVSNet_Blended_VisFu		95.95 176	95.80 178	96.42 202	99.28 48	90.62 237	95.31 206	99.08 37	88.40 298	96.97 194	98.17 114	92.11 216	99.78 43	93.64 210	99.21 190	98.86 199

UnsupCasMVSNet_eth		95.91 177	95.73 181	96.44 200	98.48 153	91.52 224	95.31 206	98.45 170	95.76 141	97.48 160	97.54 182	89.53 256	98.69 318	94.43 176	94.61 349	99.13 146

EI-MVSNet		96.63 147	96.93 122	95.74 233	97.26 283	88.13 279	95.29 208	97.65 252	96.99 82	97.94 132	98.19 111	92.55 205	99.58 162	96.91 60	99.56 88	99.50 45

CVMVSNet		92.33 290	92.79 273	90.95 337	97.26 283	75.84 367	95.29 208	92.33 340	81.86 345	96.27 229	98.19 111	81.44 307	98.46 337	94.23 187	98.29 273	98.55 231

RRT_MVS		94.90 217	94.07 246	97.39 147	93.18 361	93.21 187	95.26 210	97.49 260	93.94 210	98.25 94	97.85 154	72.96 352	99.84 25	97.90 22	99.78 41	99.14 143

Regformer-3		97.25 109	97.29 98	97.11 161	97.35 274	92.32 204	95.26 210	97.62 257	97.67 59	98.17 103	97.89 149	95.05 137	99.56 169	97.16 52	99.42 143	99.46 64

Regformer-4		97.53 90	97.47 90	97.71 112	97.35 274	93.91 162	95.26 210	98.14 215	97.97 43	98.34 82	97.89 149	95.49 123	99.71 100	97.41 41	99.42 143	99.51 44

OPM-MVS		97.54 88	97.25 101	98.41 57	99.11 83	96.61 56	95.24 213	98.46 169	94.58 190	98.10 113	98.07 124	97.09 47	99.39 223	95.16 143	99.44 132	99.21 129
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
TAPA-MVS	93.32 12	94.93 216	94.23 240	97.04 166	98.18 183	94.51 140	95.22 214	98.73 129	81.22 350	96.25 231	95.95 289	93.80 177	98.98 292	89.89 286	98.87 234	97.62 297
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
DPE-MVS		97.64 80	97.35 95	98.50 51	98.85 106	96.18 68	95.21 215	98.99 63	95.84 138	98.78 45	98.08 122	96.84 69	99.81 32	93.98 199	99.57 85	99.52 42
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MVSTER		94.21 250	93.93 253	95.05 260	95.83 327	86.46 306	95.18 216	97.65 252	92.41 253	97.94 132	98.00 137	72.39 353	99.58 162	96.36 75	99.56 88	99.12 151

PatchmatchNet		91.98 296	91.87 288	92.30 328	94.60 347	79.71 354	95.12 217	93.59 328	89.52 286	93.61 310	97.02 226	77.94 323	99.18 264	90.84 260	94.57 351	98.01 281
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
DWT-MVSNet_test		87.92 332	86.77 336	91.39 334	93.18 361	78.62 356	95.10 218	91.42 346	85.58 324	88.00 361	88.73 366	60.60 369	98.90 298	90.60 271	87.70 365	96.65 329

IterMVS-LS		96.92 123	97.29 98	95.79 231	98.51 147	88.13 279	95.10 218	98.66 149	96.99 82	98.46 69	98.68 63	92.55 205	99.74 75	96.91 60	99.79 38	99.50 45
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v148		96.58 150	96.97 119	95.42 247	98.63 133	87.57 290	95.09 220	97.90 233	95.91 133	98.24 96	97.96 139	93.42 184	99.39 223	96.04 87	99.52 105	99.29 114

tpm2		88.47 327	87.69 331	90.79 338	94.98 343	77.34 362	95.09 220	91.83 343	77.51 364	89.40 355	96.41 263	67.83 363	98.73 314	83.58 350	92.60 357	96.29 337

OpenMVS_ROB	91.80 14	93.64 267	93.05 266	95.42 247	97.31 282	91.21 227	95.08 222	96.68 290	81.56 347	96.88 200	96.41 263	90.44 241	99.25 257	85.39 336	97.67 299	95.80 343

mvs-test1		96.20 164	95.50 189	98.32 64	96.90 299	98.16 5	95.07 223	98.09 220	95.86 136	93.63 308	94.32 326	94.26 165	99.71 100	94.06 193	97.27 315	97.07 311

TAMVS		95.49 191	94.94 204	97.16 158	98.31 165	93.41 182	95.07 223	96.82 284	91.09 272	97.51 154	97.82 159	89.96 249	99.42 207	88.42 307	99.44 132	98.64 221

tpmrst		90.31 311	90.61 309	89.41 344	94.06 355	72.37 373	95.06 225	93.69 324	88.01 302	92.32 338	96.86 235	77.45 327	98.82 305	91.04 254	87.01 366	97.04 313

ADS-MVSNet2		91.47 302	90.51 310	94.36 288	95.51 334	85.63 314	95.05 226	95.70 304	83.46 341	92.69 330	96.84 237	79.15 319	99.41 216	85.66 333	90.52 359	98.04 278

ADS-MVSNet		90.95 308	90.26 312	93.04 313	95.51 334	82.37 343	95.05 226	93.41 329	83.46 341	92.69 330	96.84 237	79.15 319	98.70 317	85.66 333	90.52 359	98.04 278

tpm		91.08 306	90.85 304	91.75 332	95.33 339	78.09 357	95.03 228	91.27 349	88.75 294	93.53 313	97.40 195	71.24 355	99.30 246	91.25 251	93.87 352	97.87 286

NCCC		96.52 152	95.99 171	98.10 84	97.81 224	95.68 87	95.00 229	98.20 203	95.39 158	95.40 261	96.36 268	93.81 176	99.45 201	93.55 212	98.42 268	99.17 136

test_post1							94.98 230				10.37 375	76.21 336	99.04 284	89.47 292

ETH3D cwj APD-0.16		96.23 163	95.61 186	98.09 85	97.91 211	95.65 90	94.94 231	98.74 127	91.31 269	96.02 240	97.08 221	94.05 171	99.69 117	91.51 245	98.94 226	98.93 183

AdaColmap		95.11 209	94.62 224	96.58 192	97.33 280	94.45 143	94.92 232	98.08 222	93.15 236	93.98 298	95.53 301	94.34 163	99.10 278	85.69 332	98.61 260	96.20 338

MDTV_nov1_ep13_2view						57.28 377	94.89 233		80.59 352	94.02 295		78.66 321		85.50 335		97.82 289

CNVR-MVS		96.92 123	96.55 143	98.03 92	98.00 205	95.54 93	94.87 234	98.17 209	94.60 187	96.38 222	97.05 224	95.67 118	99.36 231	95.12 149	99.08 211	99.19 133

OMC-MVS		96.48 154	96.00 170	97.91 98	98.30 166	96.01 77	94.86 235	98.60 157	91.88 260	97.18 174	97.21 214	96.11 99	99.04 284	90.49 278	99.34 166	98.69 218

Regformer-1		97.27 107	97.16 108	97.61 121	97.21 286	93.86 165	94.85 236	98.04 229	97.62 60	98.03 122	97.50 187	95.34 130	99.63 143	96.52 68	99.31 177	99.35 98

Regformer-2		97.41 98	97.24 103	97.93 97	97.21 286	94.72 131	94.85 236	98.27 194	97.74 51	98.11 110	97.50 187	95.58 121	99.69 117	96.57 67	99.31 177	99.37 95

EPNet_dtu		91.39 303	90.75 306	93.31 306	90.48 373	82.61 341	94.80 238	92.88 334	93.39 223	81.74 371	94.90 315	81.36 308	99.11 276	88.28 309	98.87 234	98.21 262
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MDTV_nov1_ep13			91.28 296		94.31 349	73.51 371	94.80 238	93.16 331	86.75 315	93.45 317	97.40 195	76.37 334	98.55 332	88.85 300	96.43 329

pmmvs-eth3d		96.49 153	96.18 162	97.42 144	98.25 174	94.29 148	94.77 240	98.07 226	89.81 284	97.97 129	98.33 87	93.11 189	99.08 280	95.46 123	99.84 29	98.89 192

test_yl		94.40 242	94.00 249	95.59 237	96.95 295	89.52 252	94.75 241	95.55 310	96.18 116	96.79 201	96.14 279	81.09 310	99.18 264	90.75 264	97.77 289	98.07 270

DCV-MVSNet		94.40 242	94.00 249	95.59 237	96.95 295	89.52 252	94.75 241	95.55 310	96.18 116	96.79 201	96.14 279	81.09 310	99.18 264	90.75 264	97.77 289	98.07 270

MCST-MVS		96.24 162	95.80 178	97.56 123	98.75 116	94.13 156	94.66 243	98.17 209	90.17 281	96.21 233	96.10 282	95.14 136	99.43 206	94.13 191	98.85 238	99.13 146

XVG-OURS-SEG-HR		97.38 100	97.07 114	98.30 67	99.01 95	97.41 35	94.66 243	99.02 52	95.20 164	98.15 106	97.52 185	98.83 4	98.43 338	94.87 158	96.41 330	99.07 162

mvs_anonymous		95.36 199	96.07 168	93.21 310	96.29 310	81.56 348	94.60 245	97.66 250	93.30 227	96.95 195	98.91 49	93.03 193	99.38 226	96.60 64	97.30 314	98.69 218

DP-MVS Recon		95.55 189	95.13 196	96.80 179	98.51 147	93.99 161	94.60 245	98.69 142	90.20 280	95.78 251	96.21 275	92.73 199	98.98 292	90.58 273	98.86 236	97.42 304

ETH3 D test6400		94.77 223	93.87 254	97.47 136	98.12 194	93.73 171	94.56 247	98.70 139	85.45 328	94.70 276	95.93 291	91.77 227	99.63 143	86.45 327	99.14 199	99.05 166

xxxxxxxxxxxxxcwj		97.24 110	97.03 117	97.89 99	98.48 153	94.71 132	94.53 248	99.07 40	95.02 174	97.83 144	97.88 151	96.44 90	99.72 86	94.59 172	99.39 152	99.25 124

save fliter					98.48 153	94.71 132	94.53 248	98.41 178	95.02 174

tpm cat1		88.01 331	87.33 332	90.05 343	94.48 348	76.28 366	94.47 250	94.35 322	73.84 369	89.26 356	95.61 299	73.64 346	98.30 347	84.13 344	86.20 367	95.57 348

CANet		95.86 180	95.65 183	96.49 198	96.41 308	90.82 233	94.36 251	98.41 178	94.94 176	92.62 335	96.73 246	92.68 200	99.71 100	95.12 149	99.60 78	98.94 179

WR-MVS		96.90 125	96.81 128	97.16 158	98.56 142	92.20 209	94.33 252	98.12 218	97.34 74	98.20 99	97.33 206	92.81 196	99.75 65	94.79 162	99.81 33	99.54 38

HQP-NCC					97.85 216		94.26 253		93.18 232	92.86 327

ACMP_Plane					97.85 216		94.26 253		93.18 232	92.86 327

HQP-MVS		95.17 208	94.58 228	96.92 171	97.85 216	92.47 201	94.26 253	98.43 173	93.18 232	92.86 327	95.08 308	90.33 242	99.23 260	90.51 276	98.74 248	99.05 166

PLC	91.02 16	94.05 257	92.90 269	97.51 128	98.00 205	95.12 121	94.25 256	98.25 197	86.17 317	91.48 343	95.25 305	91.01 233	99.19 263	85.02 340	96.69 325	98.22 261
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
1112_ss		94.12 253	93.42 260	96.23 211	98.59 139	90.85 232	94.24 257	98.85 94	85.49 325	92.97 325	94.94 312	86.01 285	99.64 141	91.78 240	97.92 285	98.20 263

MS-PatchMatch		94.83 220	94.91 208	94.57 282	96.81 301	87.10 299	94.23 258	97.34 265	88.74 295	97.14 176	97.11 219	91.94 222	98.23 349	92.99 222	97.92 285	98.37 242

Fast-Effi-MVS+		95.49 191	95.07 199	96.75 182	97.67 252	92.82 195	94.22 259	98.60 157	91.61 263	93.42 319	92.90 340	96.73 73	99.70 109	92.60 225	97.89 288	97.74 292

CMPMVS	73.10 23	92.74 282	91.39 294	96.77 181	93.57 360	94.67 136	94.21 260	97.67 248	80.36 354	93.61 310	96.60 253	82.85 302	97.35 359	84.86 341	98.78 244	98.29 256
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
dp		88.08 330	88.05 328	88.16 350	92.85 366	68.81 375	94.17 261	92.88 334	85.47 326	91.38 344	96.14 279	68.87 362	98.81 307	86.88 324	83.80 369	96.87 319

JIA-IIPM		91.79 298	90.69 307	95.11 257	93.80 357	90.98 230	94.16 262	91.78 344	96.38 104	90.30 351	99.30 18	72.02 354	98.90 298	88.28 309	90.17 361	95.45 349

D2MVS		95.18 206	95.17 195	95.21 253	97.76 240	87.76 288	94.15 263	97.94 231	89.77 285	96.99 191	97.68 174	87.45 277	99.14 271	95.03 154	99.81 33	98.74 212

TSAR-MVS + GP.		96.47 155	96.12 164	97.49 134	97.74 245	95.23 113	94.15 263	96.90 281	93.26 228	98.04 121	96.70 248	94.41 161	98.89 300	94.77 165	99.14 199	98.37 242

PVSNet_BlendedMVS		95.02 215	94.93 206	95.27 251	97.79 234	87.40 294	94.14 265	98.68 144	88.94 292	94.51 281	98.01 135	93.04 191	99.30 246	89.77 288	99.49 118	99.11 155

TinyColmap		96.00 174	96.34 155	94.96 263	97.90 213	87.91 282	94.13 266	98.49 167	94.41 193	98.16 104	97.76 162	96.29 97	98.68 321	90.52 275	99.42 143	98.30 253

CNLPA		95.04 212	94.47 233	96.75 182	97.81 224	95.25 112	94.12 267	97.89 234	94.41 193	94.57 278	95.69 294	90.30 245	98.35 345	86.72 326	98.76 246	96.64 330

BH-untuned		94.69 229	94.75 217	94.52 284	97.95 210	87.53 291	94.07 268	97.01 277	93.99 208	97.10 180	95.65 296	92.65 202	98.95 297	87.60 317	96.74 324	97.09 310

pmmvs5		94.63 234	94.34 238	95.50 243	97.63 255	88.34 273	94.02 269	97.13 272	87.15 310	95.22 264	97.15 215	87.50 276	99.27 254	93.99 198	99.26 185	98.88 196

thres200		91.00 307	90.42 311	92.77 320	97.47 268	83.98 336	94.01 270	91.18 350	95.12 169	95.44 259	91.21 359	73.93 343	99.31 243	77.76 362	97.63 302	95.01 351

xiu_mvs_v1_base_debu		95.62 186	95.96 173	94.60 279	98.01 201	88.42 270	93.99 271	98.21 200	92.98 241	95.91 244	94.53 320	96.39 92	99.72 86	95.43 127	98.19 275	95.64 345

xiu_mvs_v1_base		95.62 186	95.96 173	94.60 279	98.01 201	88.42 270	93.99 271	98.21 200	92.98 241	95.91 244	94.53 320	96.39 92	99.72 86	95.43 127	98.19 275	95.64 345

xiu_mvs_v1_base_debi		95.62 186	95.96 173	94.60 279	98.01 201	88.42 270	93.99 271	98.21 200	92.98 241	95.91 244	94.53 320	96.39 92	99.72 86	95.43 127	98.19 275	95.64 345

CDS-MVSNet		94.88 219	94.12 245	97.14 160	97.64 254	93.57 178	93.96 274	97.06 276	90.05 282	96.30 228	96.55 255	86.10 284	99.47 194	90.10 283	99.31 177	98.40 238
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
CANet_DTU		94.65 233	94.21 242	95.96 222	95.90 324	89.68 249	93.92 275	97.83 240	93.19 231	90.12 352	95.64 297	88.52 264	99.57 168	93.27 217	99.47 124	98.62 224

WTY-MVS		93.55 269	93.00 268	95.19 255	97.81 224	87.86 283	93.89 276	96.00 298	89.02 290	94.07 293	95.44 303	86.27 283	99.33 239	87.69 315	96.82 321	98.39 240

sss		94.22 248	93.72 256	95.74 233	97.71 247	89.95 245	93.84 277	96.98 278	88.38 299	93.75 303	95.74 293	87.94 270	98.89 300	91.02 255	98.10 279	98.37 242

baseline2		89.65 320	88.44 327	93.25 308	95.62 332	82.71 340	93.82 278	85.94 367	88.89 293	87.35 365	92.54 346	71.23 356	99.33 239	86.01 328	94.60 350	97.72 293

XVG-OURS		97.12 113	96.74 132	98.26 69	98.99 96	97.45 33	93.82 278	99.05 43	95.19 165	98.32 87	97.70 171	95.22 135	98.41 339	94.27 185	98.13 278	98.93 183

MVS_111021_LR		96.82 132	96.55 143	97.62 120	98.27 171	95.34 108	93.81 280	98.33 190	94.59 189	96.56 214	96.63 252	96.61 78	98.73 314	94.80 161	99.34 166	98.78 207

BH-RMVSNet		94.56 237	94.44 236	94.91 264	97.57 257	87.44 293	93.78 281	96.26 293	93.69 217	96.41 221	96.50 260	92.10 217	99.00 288	85.96 329	97.71 295	98.31 251

CDPH-MVS		95.45 196	94.65 220	97.84 104	98.28 169	94.96 124	93.73 282	98.33 190	85.03 333	95.44 259	96.60 253	95.31 132	99.44 204	90.01 284	99.13 203	99.11 155

PatchMatch-RL		94.61 235	93.81 255	97.02 168	98.19 180	95.72 83	93.66 283	97.23 267	88.17 301	94.94 271	95.62 298	91.43 229	98.57 329	87.36 322	97.68 298	96.76 327

TEST9					97.84 220	95.23 113	93.62 284	98.39 181	86.81 313	93.78 300	95.99 284	94.68 151	99.52 182

train_agg		95.46 195	94.66 219	97.88 101	97.84 220	95.23 113	93.62 284	98.39 181	87.04 311	93.78 300	95.99 284	94.58 156	99.52 182	91.76 241	98.90 230	98.89 192

test_prior4						95.38 103	93.61 286

test_8					97.81 224	95.07 122	93.54 287	98.38 183	87.04 311	93.71 305	95.96 288	94.58 156	99.52 182

TR-MVS		92.54 285	92.20 285	93.57 302	96.49 306	86.66 304	93.51 288	94.73 317	89.96 283	94.95 270	93.87 329	90.24 247	98.61 326	81.18 354	94.88 346	95.45 349

新几何2							93.43 289

diffmvs		96.04 171	96.23 159	95.46 246	97.35 274	88.03 281	93.42 290	99.08 37	94.09 206	96.66 209	96.93 232	93.85 175	99.29 250	96.01 91	98.67 253	99.06 164

MVS_111021_HR		96.73 139	96.54 145	97.27 153	98.35 164	93.66 176	93.42 290	98.36 185	94.74 182	96.58 212	96.76 245	96.54 82	98.99 290	94.87 158	99.27 184	99.15 140

agg_prior1		95.39 198	94.60 225	97.75 109	97.80 228	94.96 124	93.39 292	98.36 185	87.20 309	93.49 314	95.97 287	94.65 153	99.53 178	91.69 243	98.86 236	98.77 210

UnsupCasMVSNet_bld		94.72 228	94.26 239	96.08 218	98.62 134	90.54 241	93.38 293	98.05 228	90.30 279	97.02 189	96.80 242	89.54 254	99.16 269	88.44 306	96.18 333	98.56 229

旧先验2							93.35 294		77.95 363	95.77 253			98.67 322	90.74 267

test_prior3		95.91 177	95.39 190	97.46 139	97.79 234	94.26 152	93.33 295	98.42 176	94.21 201	94.02 295	96.25 272	93.64 180	99.34 236	91.90 235	98.96 222	98.79 205

test_prior2							93.33 295		94.21 201	94.02 295	96.25 272	93.64 180		91.90 235	98.96 222

SCA		93.38 273	93.52 259	92.96 317	96.24 312	81.40 349	93.24 297	94.00 323	91.58 265	94.57 278	96.97 229	87.94 270	99.42 207	89.47 292	97.66 300	98.06 274

无先验							93.20 298	97.91 232	80.78 351				99.40 218	87.71 313		97.94 283

MG-MVS		94.08 256	94.00 249	94.32 289	97.09 291	85.89 313	93.19 299	95.96 300	92.52 249	94.93 272	97.51 186	89.54 254	98.77 310	87.52 320	97.71 295	98.31 251

MVS-HIRNet		88.40 328	90.20 313	82.99 352	97.01 293	60.04 376	93.11 300	85.61 368	84.45 339	88.72 359	99.09 36	84.72 294	98.23 349	82.52 351	96.59 328	90.69 367

new-patchmatchnet		95.67 185	96.58 140	92.94 318	97.48 264	80.21 353	92.96 301	98.19 208	94.83 180	98.82 43	98.79 54	93.31 186	99.51 186	95.83 102	99.04 217	99.12 151

MDA-MVSNet-bldmvs		95.69 183	95.67 182	95.74 233	98.48 153	88.76 268	92.84 302	97.25 266	96.00 126	97.59 150	97.95 143	91.38 230	99.46 197	93.16 220	96.35 331	98.99 174

原ACMM2							92.82 303

testdata1							92.77 304		93.78 214

Test_1112_low_res		93.53 270	92.86 270	95.54 242	98.60 137	88.86 264	92.75 305	98.69 142	82.66 344	92.65 332	96.92 234	84.75 293	99.56 169	90.94 257	97.76 291	98.19 264

USDC		94.56 237	94.57 230	94.55 283	97.78 238	86.43 308	92.75 305	98.65 154	85.96 319	96.91 198	97.93 146	90.82 236	98.74 313	90.71 268	99.59 80	98.47 235

test222					98.17 185	93.24 186	92.74 307	97.61 258	75.17 366	94.65 277	96.69 249	90.96 235			98.66 255	97.66 296

jason		94.39 244	94.04 248	95.41 249	98.29 167	87.85 285	92.74 307	96.75 287	85.38 330	95.29 262	96.15 277	88.21 269	99.65 138	94.24 186	99.34 166	98.74 212
jason: jason.
Patchmatch-RL test		94.66 232	94.49 231	95.19 255	98.54 144	88.91 262	92.57 309	98.74 127	91.46 266	98.32 87	97.75 165	77.31 330	98.81 307	96.06 84	99.61 75	97.85 287

DeepPCF-MVS	94.58 5	96.90 125	96.43 152	98.31 66	97.48 264	97.23 41	92.56 310	98.60 157	92.84 247	98.54 60	97.40 195	96.64 77	98.78 309	94.40 179	99.41 149	98.93 183

N_pmnet		95.18 206	94.23 240	98.06 88	97.85 216	96.55 58	92.49 311	91.63 345	89.34 287	98.09 114	97.41 194	90.33 242	99.06 282	91.58 244	99.31 177	98.56 229

BH-w/o		92.14 293	91.94 287	92.73 321	97.13 290	85.30 319	92.46 312	95.64 305	89.33 288	94.21 288	92.74 343	89.60 252	98.24 348	81.68 352	94.66 348	94.66 353

IterMVS-SCA-FT		95.86 180	96.19 161	94.85 269	97.68 249	85.53 316	92.42 313	97.63 256	96.99 82	98.36 79	98.54 73	87.94 270	99.75 65	97.07 56	99.08 211	99.27 119

IterMVS		95.42 197	95.83 177	94.20 292	97.52 262	83.78 337	92.41 314	97.47 263	95.49 154	98.06 118	98.49 76	87.94 270	99.58 162	96.02 89	99.02 218	99.23 127
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DELS-MVS		96.17 166	96.23 159	95.99 220	97.55 261	90.04 243	92.38 315	98.52 164	94.13 204	96.55 216	97.06 223	94.99 142	99.58 162	95.62 112	99.28 182	98.37 242
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
new_pmnet		92.34 289	91.69 292	94.32 289	96.23 314	89.16 259	92.27 316	92.88 334	84.39 340	95.29 262	96.35 269	85.66 287	96.74 365	84.53 343	97.56 303	97.05 312

CHOSEN 1792x2688		94.10 254	93.41 261	96.18 215	99.16 70	90.04 243	92.15 317	98.68 144	79.90 355	96.22 232	97.83 156	87.92 274	99.42 207	89.18 296	99.65 64	99.08 160

xiu_mvs_v2_base		94.22 248	94.63 223	92.99 316	97.32 281	84.84 328	92.12 318	97.84 238	91.96 258	94.17 289	93.43 331	96.07 100	99.71 100	91.27 249	97.48 307	94.42 354

lupinMVS		93.77 261	93.28 262	95.24 252	97.68 249	87.81 286	92.12 318	96.05 296	84.52 337	94.48 283	95.06 310	86.90 280	99.63 143	93.62 211	99.13 203	98.27 257

pmmvs4		94.82 221	94.19 243	96.70 185	97.42 271	92.75 198	92.09 320	96.76 286	86.80 314	95.73 254	97.22 213	89.28 260	98.89 300	93.28 216	99.14 199	98.46 237

PAPR		92.22 291	91.27 297	95.07 259	95.73 331	88.81 265	91.97 321	97.87 235	85.80 322	90.91 345	92.73 344	91.16 231	98.33 346	79.48 356	95.76 340	98.08 268

PS-MVSNAJ		94.10 254	94.47 233	93.00 315	97.35 274	84.88 327	91.86 322	97.84 238	91.96 258	94.17 289	92.50 347	95.82 108	99.71 100	91.27 249	97.48 307	94.40 355

c3_l		95.20 205	95.32 191	94.83 271	96.19 316	86.43 308	91.83 323	98.35 189	93.47 221	97.36 167	97.26 211	88.69 263	99.28 252	95.41 130	99.36 158	98.78 207

test0.0.03 1		90.11 312	89.21 319	92.83 319	93.89 356	86.87 303	91.74 324	88.74 362	92.02 256	94.71 275	91.14 360	73.92 344	94.48 368	83.75 349	92.94 354	97.16 309

FPMVS		89.92 317	88.63 325	93.82 296	98.37 162	96.94 46	91.58 325	93.34 330	88.00 303	90.32 350	97.10 220	70.87 358	91.13 370	71.91 368	96.16 335	93.39 360

ET-MVSNet_ETH3D		91.12 304	89.67 316	95.47 245	96.41 308	89.15 260	91.54 326	90.23 358	89.07 289	86.78 367	92.84 341	69.39 361	99.44 204	94.16 189	96.61 327	97.82 289

PVSNet_Blended		93.96 258	93.65 257	94.91 264	97.79 234	87.40 294	91.43 327	98.68 144	84.50 338	94.51 281	94.48 323	93.04 191	99.30 246	89.77 288	98.61 260	98.02 280

CLD-MVS		95.47 194	95.07 199	96.69 186	98.27 171	92.53 200	91.36 328	98.67 147	91.22 271	95.78 251	94.12 328	95.65 119	98.98 292	90.81 261	99.72 51	98.57 228
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
eth_miper_zixun_eth		94.89 218	94.93 206	94.75 274	95.99 323	86.12 311	91.35 329	98.49 167	93.40 222	97.12 178	97.25 212	86.87 282	99.35 234	95.08 151	98.82 241	98.78 207

cl____		94.73 224	94.64 221	95.01 261	95.85 326	87.00 300	91.33 330	98.08 222	93.34 225	97.10 180	97.33 206	84.01 299	99.30 246	95.14 146	99.56 88	98.71 217

DIV-MVS_self_test		94.73 224	94.64 221	95.01 261	95.86 325	87.00 300	91.33 330	98.08 222	93.34 225	97.10 180	97.34 205	84.02 298	99.31 243	95.15 145	99.55 94	98.72 215

miper_ehance_all_eth		94.69 229	94.70 218	94.64 276	95.77 329	86.22 310	91.32 332	98.24 198	91.67 262	97.05 186	96.65 251	88.39 267	99.22 262	94.88 157	98.34 270	98.49 234

pmmvs3		90.00 314	88.90 324	93.32 305	94.20 354	85.34 318	91.25 333	92.56 339	78.59 359	93.82 299	95.17 306	67.36 364	98.69 318	89.08 298	98.03 282	95.92 340

HyFIR lowres test		93.72 263	92.65 278	96.91 173	98.93 101	91.81 220	91.23 334	98.52 164	82.69 343	96.46 219	96.52 259	80.38 314	99.90 13	90.36 280	98.79 243	99.03 168

DPM-MVS		93.68 265	92.77 276	96.42 202	97.91 211	92.54 199	91.17 335	97.47 263	84.99 334	93.08 324	94.74 316	89.90 250	99.00 288	87.54 319	98.09 280	97.72 293

CL-MVSNet_self_test		95.04 212	94.79 216	95.82 230	97.51 263	89.79 248	91.14 336	96.82 284	93.05 238	96.72 206	96.40 265	90.82 236	99.16 269	91.95 234	98.66 255	98.50 233

miper_lstm_enhance		94.81 222	94.80 215	94.85 269	96.16 318	86.45 307	91.14 336	98.20 203	93.49 220	97.03 188	97.37 203	84.97 292	99.26 255	95.28 134	99.56 88	98.83 201

cl22		93.25 276	92.84 272	94.46 285	94.30 350	86.00 312	91.09 338	96.64 291	90.74 274	95.79 249	96.31 270	78.24 322	98.77 310	94.15 190	98.34 270	98.62 224

MSDG		95.33 200	95.13 196	95.94 226	97.40 272	91.85 218	91.02 339	98.37 184	95.30 161	96.31 227	95.99 284	94.51 159	98.38 342	89.59 290	97.65 301	97.60 299

IB-MVS	85.98 20	88.63 326	86.95 335	93.68 300	95.12 341	84.82 329	90.85 340	90.17 359	87.55 306	88.48 360	91.34 358	58.01 370	99.59 160	87.24 323	93.80 353	96.63 332
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
test123		12.59 341	15.49 344	3.87 356	6.07 379	2.55 380	90.75 341	2.59 381	2.52 374	5.20 376	13.02 373	4.96 379	1.85 376	5.20 373	9.09 373	7.23 371

ppachtmachnet_test		94.49 241	94.84 211	93.46 304	96.16 318	82.10 344	90.59 342	97.48 262	90.53 277	97.01 190	97.59 179	91.01 233	99.36 231	93.97 200	99.18 196	98.94 179

PMMVS		92.39 287	91.08 299	96.30 209	93.12 364	92.81 196	90.58 343	95.96 300	79.17 358	91.85 342	92.27 348	90.29 246	98.66 323	89.85 287	96.68 326	97.43 303

our_test_3		94.20 252	94.58 228	93.07 312	96.16 318	81.20 350	90.42 344	96.84 282	90.72 275	97.14 176	97.13 216	90.47 240	99.11 276	94.04 197	98.25 274	98.91 188

YYNet1		94.73 224	94.84 211	94.41 287	97.47 268	85.09 325	90.29 345	95.85 303	92.52 249	97.53 152	97.76 162	91.97 220	99.18 264	93.31 215	96.86 320	98.95 177

MDA-MVSNet_test_wron		94.73 224	94.83 213	94.42 286	97.48 264	85.15 323	90.28 346	95.87 302	92.52 249	97.48 160	97.76 162	91.92 224	99.17 268	93.32 214	96.80 323	98.94 179

GA-MVS		92.83 281	92.15 286	94.87 268	96.97 294	87.27 297	90.03 347	96.12 295	91.83 261	94.05 294	94.57 318	76.01 337	98.97 296	92.46 229	97.34 312	98.36 247

miper_enhance_ethall		93.14 278	92.78 275	94.20 292	93.65 358	85.29 320	89.97 348	97.85 236	85.05 332	96.15 237	94.56 319	85.74 286	99.14 271	93.74 206	98.34 270	98.17 266

test-LLR		89.97 316	89.90 314	90.16 341	94.24 352	74.98 368	89.89 349	89.06 360	92.02 256	89.97 353	90.77 362	73.92 344	98.57 329	91.88 237	97.36 310	96.92 316

TESTMET0.1,1		87.20 335	86.57 337	89.07 345	93.62 359	72.84 372	89.89 349	87.01 366	85.46 327	89.12 358	90.20 364	56.00 376	97.72 357	90.91 258	96.92 317	96.64 330

test-mter		87.92 332	87.17 333	90.16 341	94.24 352	74.98 368	89.89 349	89.06 360	86.44 316	89.97 353	90.77 362	54.96 378	98.57 329	91.88 237	97.36 310	96.92 316

PCF-MVS	89.43 18	92.12 294	90.64 308	96.57 194	97.80 228	93.48 181	89.88 352	98.45 170	74.46 367	96.04 239	95.68 295	90.71 238	99.31 243	73.73 365	99.01 220	96.91 318
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
thisisatest0515		90.43 310	89.18 322	94.17 294	97.07 292	85.44 317	89.75 353	87.58 363	88.28 300	93.69 307	91.72 354	65.27 365	99.58 162	90.59 272	98.67 253	97.50 302

KD-MVS_2432*1600		88.93 324	87.74 329	92.49 323	88.04 374	81.99 345	89.63 354	95.62 306	91.35 267	95.06 266	93.11 333	56.58 373	98.63 324	85.19 337	95.07 344	96.85 321

miper_refine_blended		88.93 324	87.74 329	92.49 323	88.04 374	81.99 345	89.63 354	95.62 306	91.35 267	95.06 266	93.11 333	56.58 373	98.63 324	85.19 337	95.07 344	96.85 321

testmvs		12.33 342	15.23 345	3.64 357	5.77 380	2.23 381	88.99 356	3.62 380	2.30 375	5.29 375	13.09 372	4.52 380	1.95 375	5.16 374	8.32 374	6.75 372

cascas		91.89 297	91.35 295	93.51 303	94.27 351	85.60 315	88.86 357	98.61 156	79.32 357	92.16 339	91.44 357	89.22 261	98.12 352	90.80 262	97.47 309	96.82 324

bset_n11_16_dypcd		94.53 239	93.95 252	96.25 210	97.56 259	89.85 247	88.52 358	91.32 347	94.90 179	97.51 154	96.38 267	82.34 304	99.78 43	97.22 46	99.80 36	99.12 151

PAPM		87.64 334	85.84 339	93.04 313	96.54 304	84.99 326	88.42 359	95.57 309	79.52 356	83.82 368	93.05 339	80.57 313	98.41 339	62.29 371	92.79 355	95.71 344

PVSNet	86.72 19	91.10 305	90.97 302	91.49 333	97.56 259	78.04 358	87.17 360	94.60 319	84.65 336	92.34 337	92.20 349	87.37 278	98.47 336	85.17 339	97.69 297	97.96 282

PMMVS2		93.66 266	94.07 246	92.45 326	97.57 257	80.67 352	86.46 361	96.00 298	93.99 208	97.10 180	97.38 201	89.90 250	97.82 355	88.76 301	99.47 124	98.86 199

CHOSEN 280x420		89.98 315	89.19 321	92.37 327	95.60 333	81.13 351	86.22 362	97.09 274	81.44 349	87.44 364	93.15 332	73.99 342	99.47 194	88.69 303	99.07 213	96.52 334

tmp_tt		57.23 339	62.50 342	41.44 355	34.77 378	49.21 378	83.93 363	60.22 379	15.31 372	71.11 373	79.37 370	70.09 360	44.86 374	64.76 370	82.93 370	30.25 370

PVSNet_0	81.89 21	84.49 337	83.21 340	88.34 348	95.76 330	74.97 370	83.49 364	92.70 338	78.47 360	87.94 362	86.90 368	83.38 301	96.63 366	73.44 366	66.86 372	93.40 359

E-PMN		89.52 321	89.78 315	88.73 346	93.14 363	77.61 360	83.26 365	92.02 341	94.82 181	93.71 305	93.11 333	75.31 339	96.81 362	85.81 330	96.81 322	91.77 364

EMVS		89.06 323	89.22 318	88.61 347	93.00 365	77.34 362	82.91 366	90.92 351	94.64 186	92.63 334	91.81 353	76.30 335	97.02 360	83.83 347	96.90 319	91.48 365

MVE	73.61 22	86.48 336	85.92 338	88.18 349	96.23 314	85.28 321	81.78 367	75.79 373	86.01 318	82.53 370	91.88 352	92.74 198	87.47 372	71.42 369	94.86 347	91.78 363
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		66.88 338	66.13 341	69.11 354	62.68 377	25.73 379	49.76 368	96.04 297	14.32 373	64.27 374	91.69 355	73.45 349	88.05 371	76.06 364	66.94 371	93.54 357

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

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

cdsmvs_eth3d_5k		24.22 340	32.30 343	0.00 358	0.00 381	0.00 382	0.00 369	98.10 219	0.00 376	0.00 377	95.06 310	97.54 29	0.00 377	0.00 375	0.00 375	0.00 373

pcd_1.5k_mvsjas		7.98 343	10.65 346	0.00 358	0.00 381	0.00 382	0.00 369	0.00 382	0.00 376	0.00 377	0.00 376	95.82 108	0.00 377	0.00 375	0.00 375	0.00 373

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

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

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

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

ab-mvs-re		7.91 344	10.55 347	0.00 358	0.00 381	0.00 382	0.00 369	0.00 382	0.00 376	0.00 377	94.94 312	0.00 381	0.00 377	0.00 375	0.00 375	0.00 373

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

MSC_two_6792asdad				98.22 74	97.75 242	95.34 108		98.16 212					99.75 65	95.87 100	99.51 110	99.57 32

PC_three_1452									87.24 308	98.37 76	97.44 192	97.00 54	96.78 364	92.01 232	99.25 186	99.21 129

No_MVS				98.22 74	97.75 242	95.34 108		98.16 212					99.75 65	95.87 100	99.51 110	99.57 32

test_one_0601					99.05 92	95.50 98		98.87 87	97.21 79	98.03 122	98.30 93	96.93 60

eth-test2					0.00 381

eth-test					0.00 381

ZD-MVS					98.43 158	95.94 78		98.56 161	90.72 275	96.66 209	97.07 222	95.02 141	99.74 75	91.08 253	98.93 228

IU-MVS					99.22 58	95.40 101		98.14 215	85.77 323	98.36 79				95.23 138	99.51 110	99.49 53

test_241102_TWO								98.83 106	96.11 118	98.62 52	98.24 104	96.92 62	99.72 86	95.44 124	99.49 118	99.49 53

test_241102_ONE					99.22 58	95.35 106		98.83 106	96.04 123	99.08 31	98.13 116	97.87 20	99.33 239

test_0728_THIRD									96.62 92	98.40 73	98.28 98	97.10 45	99.71 100	95.70 104	99.62 69	99.58 28

GSMVS																98.06 274

test_part2					99.03 94	96.07 73				98.08 116

sam_mvs1												77.80 324				98.06 274

sam_mvs												77.38 328

MTGPA								98.73 129

test_post											10.87 374	76.83 332	99.07 281

patchmatchnet-post											96.84 237	77.36 329	99.42 207

gm-plane-assit					91.79 370	71.40 374			81.67 346		90.11 365		98.99 290	84.86 341

test9_res														91.29 248	98.89 233	99.00 171

agg_prior2														90.34 281	98.90 230	99.10 159

agg_prior					97.80 228	94.96 124		98.36 185		93.49 314			99.53 178

TestCases				98.06 88	99.08 85	96.16 69		99.16 20	94.35 196	97.78 147	98.07 124	95.84 105	99.12 273	91.41 246	99.42 143	98.91 188

test_prior				97.46 139	97.79 234	94.26 152		98.42 176					99.34 236			98.79 205

新几何1				97.25 156	98.29 167	94.70 135		97.73 244	77.98 361	94.83 273	96.67 250	92.08 218	99.45 201	88.17 311	98.65 257	97.61 298

旧先验1					97.80 228	93.87 164		97.75 243			97.04 225	93.57 182			98.68 252	98.72 215

原ACMM1				96.58 192	98.16 187	92.12 211		98.15 214	85.90 321	93.49 314	96.43 262	92.47 210	99.38 226	87.66 316	98.62 259	98.23 260

testdata2													99.46 197	87.84 312

segment_acmp												95.34 130

testdata				95.70 236	98.16 187	90.58 238		97.72 245	80.38 353	95.62 256	97.02 226	92.06 219	98.98 292	89.06 299	98.52 264	97.54 300

test12				97.46 139	97.61 256	94.07 157		97.78 242		93.57 312		93.31 186	99.42 207		98.78 244	98.89 192

plane_prior7					98.70 124	94.67 136

plane_prior6					98.38 161	94.37 146						91.91 225

plane_prior5								98.75 125					99.46 197	92.59 227	99.20 191	99.28 115

plane_prior4											96.77 243

plane_prior3						94.51 140			95.29 162	96.16 235

plane_prior1					98.49 151

n2								0.00 382

nn								0.00 382

door-mid								98.17 209

lessismore_v0				97.05 165	99.36 41	92.12 211		84.07 369		98.77 47	98.98 43	85.36 289	99.74 75	97.34 44	99.37 155	99.30 107

LGP-MVS_train				98.74 35	99.15 73	97.02 43		99.02 52	95.15 167	98.34 82	98.23 106	97.91 17	99.70 109	94.41 177	99.73 48	99.50 45

test11								98.08 222

door								97.81 241

HQP5-MVS						92.47 201

BP-MVS														90.51 276

HQP4-MVS										92.87 326			99.23 260			99.06 164

HQP3-MVS								98.43 173							98.74 248

HQP2-MVS												90.33 242

NP-MVS					98.14 190	93.72 172					95.08 308

ACMMP++_ref															99.52 105

ACMMP++															99.55 94

Test By Simon												94.51 159

ITE_SJBPF				97.85 103	98.64 129	96.66 54		98.51 166	95.63 146	97.22 170	97.30 209	95.52 122	98.55 332	90.97 256	98.90 230	98.34 248

DeepMVS_CX				77.17 353	90.94 372	85.28 321		74.08 376	52.51 371	80.87 372	88.03 367	75.25 340	70.63 373	59.23 372	84.94 368	75.62 368

High-resolution multi-view benchmark