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 44	99.69 2	99.57 3	99.02 15	99.62 10	99.36 14	98.53 7	99.52 172	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 82	97.89 13	99.47 3	99.32 7	99.08 10	97.87 135	99.67 2	96.47 83	99.92 4	97.88 23	99.98 2	99.85 3

TDRefinement		98.90 5	98.86 8	99.02 9	99.54 19	98.06 7	99.34 4	99.44 6	98.85 19	99.00 36	99.20 23	97.42 31	99.59 151	97.21 47	99.76 39	99.40 81

UniMVSNet_ETH3D		99.12 3	99.28 3	98.65 43	99.77 3	96.34 63	99.18 5	99.20 13	99.67 2	99.73 3	99.65 4	99.15 3	99.86 20	97.22 45	99.92 14	99.77 8

OurMVSNet-221017-0		98.61 16	98.61 23	98.63 45	99.77 3	96.35 62	99.17 6	99.05 40	98.05 40	99.61 11	99.52 5	93.72 173	99.88 18	98.72 9	99.88 23	99.65 23

pmmvs6		99.07 4	99.24 4	98.56 49	99.81 2	96.38 61	98.87 7	99.30 8	99.01 16	99.63 9	99.66 3	99.27 2	99.68 118	97.75 30	99.89 22	99.62 25

Anonymous20231211		98.55 17	98.76 13	97.94 93	98.79 105	94.37 138	98.84 8	99.15 21	99.37 3	99.67 6	99.43 11	95.61 116	99.72 80	98.12 16	99.86 25	99.73 15

MIMVSNet1		98.51 20	98.45 26	98.67 41	99.72 6	96.71 50	98.76 9	98.89 75	98.49 27	99.38 17	99.14 30	95.44 123	99.84 25	96.47 69	99.80 33	99.47 59

EPP-MVSNet		96.84 125	96.58 136	97.65 112	99.18 68	93.78 162	98.68 10	96.34 284	97.91 44	97.30 161	98.06 124	88.46 259	99.85 22	93.85 192	99.40 143	99.32 95

v7n		98.73 11	98.99 5	97.95 92	99.64 11	94.20 146	98.67 11	99.14 23	99.08 10	99.42 15	99.23 21	96.53 78	99.91 12	99.27 2	99.93 10	99.73 15

MVSFormer		96.14 163	96.36 150	95.49 238	97.68 238	87.81 277	98.67 11	99.02 49	96.50 95	94.48 273	96.15 268	86.90 274	99.92 4	98.73 7	99.13 192	98.74 204

test_djsdf		98.73 11	98.74 16	98.69 40	99.63 12	96.30 65	98.67 11	99.02 49	96.50 95	99.32 20	99.44 10	97.43 30	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 37	98.65 14	99.19 15	95.62 138	99.35 19	99.37 12	97.38 32	99.90 13	98.59 11	99.91 17	99.77 8

HPM-MVS		98.11 38	97.83 51	98.92 22	99.42 34	97.46 31	98.57 15	99.05 40	95.43 147	97.41 159	97.50 181	97.98 15	99.79 38	95.58 106	99.57 81	99.50 43
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
IS-MVSNet		96.93 119	96.68 132	97.70 108	99.25 51	94.00 152	98.57 15	96.74 280	98.36 30	98.14 103	97.98 133	88.23 262	99.71 95	93.10 211	99.72 48	99.38 85

WR-MVS_H		98.65 15	98.62 21	98.75 33	99.51 22	96.61 55	98.55 17	99.17 16	99.05 13	99.17 29	98.79 51	95.47 121	99.89 16	97.95 21	99.91 17	99.75 13

mvs_tets		98.90 5	98.94 6	98.75 33	99.69 8	96.48 59	98.54 18	99.22 10	96.23 106	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	97.36 143	99.76 5	96.28 66	98.51 19	99.10 28	98.76 22	96.79 192	99.34 17	96.61 73	98.82 295	96.38 71	99.50 108	96.98 304
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 17	95.47 97	98.49 20	99.13 24	99.22 8	99.22 27	98.96 42	97.35 33	99.92 4	97.79 28	99.93 10	99.79 7

3Dnovator	96.53 2	97.61 80	97.64 69	97.50 125	97.74 234	93.65 169	98.49 20	98.88 80	96.86 84	97.11 171	98.55 69	95.82 104	99.73 76	95.94 89	99.42 136	99.13 138

DTE-MVSNet		98.79 8	98.86 8	98.59 47	99.55 17	96.12 70	98.48 22	99.10 28	99.36 4	99.29 23	99.06 36	97.27 37	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 59	98.45 23	99.12 25	95.83 131	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 14	95.67 87	98.45 23	99.15 21	99.33 5	99.30 21	99.00 38	97.27 37	99.92 4	97.64 33	99.92 14	99.75 13

LS3D		97.77 71	97.50 84	98.57 48	96.24 300	97.58 24	98.45 23	98.85 88	98.58 26	97.51 147	97.94 139	95.74 112	99.63 135	95.19 128	98.97 210	98.51 224

FC-MVSNet-test		98.16 33	98.37 27	97.56 117	99.49 26	93.10 182	98.35 26	99.21 11	98.43 28	98.89 39	98.83 50	94.30 158	99.81 31	97.87 24	99.91 17	99.77 8

HPM-MVS_fast		98.32 27	98.13 33	98.88 24	99.54 19	97.48 30	98.35 26	99.03 47	95.88 126	97.88 132	98.22 104	98.15 12	99.74 70	96.50 68	99.62 65	99.42 78

ab-mvs		96.59 145	96.59 135	96.60 184	98.64 122	92.21 199	98.35 26	97.67 240	94.45 182	96.99 182	98.79 51	94.96 138	99.49 178	90.39 268	99.07 202	98.08 258

pm-mvs1		98.47 21	98.67 17	97.86 98	99.52 21	94.58 131	98.28 29	99.00 57	97.57 60	99.27 24	99.22 22	98.32 9	99.50 177	97.09 53	99.75 43	99.50 43

SixPastTwentyTwo		97.49 89	97.57 78	97.26 149	99.56 15	92.33 195	98.28 29	96.97 271	98.30 33	99.45 14	99.35 16	88.43 260	99.89 16	98.01 20	99.76 39	99.54 36

CP-MVSNet		98.42 23	98.46 24	98.30 67	99.46 28	95.22 110	98.27 31	98.84 93	99.05 13	99.01 35	98.65 63	95.37 124	99.90 13	97.57 35	99.91 17	99.77 8

GG-mvs-BLEND				90.60 329	91.00 359	84.21 326	98.23 32	72.63 365		82.76 358	84.11 358	56.14 365	96.79 353	72.20 355	92.09 347	90.78 354

GBi-Net		96.99 114	96.80 126	97.56 117	97.96 200	93.67 165	98.23 32	98.66 143	95.59 140	97.99 119	99.19 24	89.51 251	99.73 76	94.60 158	99.44 126	99.30 101

test1		96.99 114	96.80 126	97.56 117	97.96 200	93.67 165	98.23 32	98.66 143	95.59 140	97.99 119	99.19 24	89.51 251	99.73 76	94.60 158	99.44 126	99.30 101

FMVSNet1		97.95 49	98.08 35	97.56 117	99.14 80	93.67 165	98.23 32	98.66 143	97.41 70	99.00 36	99.19 24	95.47 121	99.73 76	95.83 93	99.76 39	99.30 101

ACMH	93.61 9	98.44 22	98.76 13	97.51 122	99.43 32	93.54 171	98.23 32	99.05 40	97.40 71	99.37 18	99.08 34	98.79 5	99.47 184	97.74 31	99.71 51	99.50 43
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
TransMVSNet (Re)		98.38 25	98.67 17	97.51 122	99.51 22	93.39 175	98.20 37	98.87 82	98.23 35	99.48 12	99.27 19	98.47 8	99.55 164	96.52 66	99.53 96	99.60 26

gg-mvs-nofinetune		88.28 321	86.96 326	92.23 321	92.84 355	84.44 323	98.19 38	74.60 362	99.08 10	87.01 355	99.47 8	56.93 362	98.23 339	78.91 347	95.61 330	94.01 345

QAPM		95.88 174	95.57 183	96.80 173	97.90 206	91.84 211	98.18 39	98.73 123	88.41 287	96.42 211	98.13 111	94.73 141	99.75 63	88.72 291	98.94 215	98.81 195

NR-MVSNet		97.96 46	97.86 48	98.26 69	98.73 111	95.54 92	98.14 40	98.73 123	97.79 45	99.42 15	97.83 151	94.40 156	99.78 42	95.91 91	99.76 39	99.46 61

MIMVSNet		93.42 264	92.86 263	95.10 251	98.17 178	88.19 266	98.13 41	93.69 314	92.07 245	95.04 259	98.21 105	80.95 303	99.03 277	81.42 342	98.06 271	98.07 260

PS-MVSNAJss		98.53 19	98.63 19	98.21 75	99.68 9	94.82 121	98.10 42	99.21 11	96.91 82	99.75 2	99.45 9	95.82 104	99.92 4	98.80 4	99.96 4	99.89 1

ACMMP		98.05 41	97.75 59	98.93 21	99.23 54	97.60 22	98.09 43	98.96 67	95.75 135	97.91 128	98.06 124	96.89 59	99.76 56	95.32 121	99.57 81	99.43 77
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	98.72 113	96.04 73	98.07 44	99.10 28	95.96 120	98.59 55	98.69 59	96.94 54	99.81 31	96.64 61	99.58 78	99.57 32

Vis-MVSNet		98.27 29	98.34 28	98.07 83	99.33 43	95.21 112	98.04 45	99.46 5	97.32 73	97.82 140	99.11 31	96.75 67	99.86 20	97.84 25	99.36 150	99.15 132
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
3Dnovator+	96.13 3	97.73 72	97.59 76	98.15 79	98.11 188	95.60 90	98.04 45	98.70 133	98.13 38	96.93 187	98.45 76	95.30 128	99.62 143	95.64 101	98.96 211	99.24 120

FIs		97.93 54	98.07 36	97.48 129	99.38 38	92.95 185	98.03 47	99.11 26	98.04 41	98.62 51	98.66 61	93.75 172	99.78 42	97.23 44	99.84 28	99.73 15

COLMAP_ROB	94.48 6	98.25 31	98.11 34	98.64 44	99.21 64	97.35 35	97.96 48	99.16 17	98.34 31	98.78 44	98.52 71	97.32 34	99.45 191	94.08 181	99.67 58	99.13 138
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
VDDNet		96.98 117	96.84 123	97.41 139	99.40 36	93.26 177	97.94 49	95.31 304	99.26 7	98.39 72	99.18 27	87.85 269	99.62 143	95.13 137	99.09 199	99.35 92

test_part1		96.77 133	96.53 142	97.47 130	98.04 190	92.92 186	97.93 50	98.85 88	98.83 20	99.30 21	99.07 35	79.25 308	99.79 38	97.59 34	99.93 10	99.69 20

CP-MVS		97.92 55	97.56 79	98.99 13	98.99 93	97.82 15	97.93 50	98.96 67	96.11 110	96.89 190	97.45 185	96.85 63	99.78 42	95.19 128	99.63 64	99.38 85

ANet_high		98.31 28	98.94 6	96.41 198	99.33 43	89.64 241	97.92 52	99.56 4	99.27 6	99.66 8	99.50 6	97.67 25	99.83 28	97.55 36	99.98 2	99.77 8

nrg030		98.54 18	98.62 21	98.32 64	99.22 57	95.66 88	97.90 53	99.08 34	98.31 32	99.02 34	98.74 55	97.68 24	99.61 149	97.77 29	99.85 27	99.70 18

ambc				96.56 189	98.23 170	91.68 214	97.88 54	98.13 209		98.42 69	98.56 68	94.22 161	99.04 274	94.05 185	99.35 155	98.95 169

Anonymous20240529		97.96 46	98.04 39	97.71 106	98.69 120	94.28 143	97.86 55	98.31 187	98.79 21	99.23 26	98.86 49	95.76 111	99.61 149	95.49 107	99.36 150	99.23 121

canonicalmvs		97.23 108	97.21 103	97.30 146	97.65 242	94.39 136	97.84 56	99.05 40	97.42 67	96.68 199	93.85 319	97.63 26	99.33 229	96.29 73	98.47 257	98.18 255

tfpnnormal		97.72 73	97.97 41	96.94 164	99.26 48	92.23 198	97.83 57	98.45 164	98.25 34	99.13 30	98.66 61	96.65 70	99.69 112	93.92 190	99.62 65	98.91 180

Anonymous20240521		97.07 111	97.51 82	95.76 226	99.35 41	88.18 267	97.78 58	98.40 174	97.11 77	98.34 78	99.04 37	89.58 247	99.79 38	98.09 18	99.93 10	99.30 101

XVS		97.96 46	97.63 71	98.94 18	99.15 72	97.66 19	97.77 59	98.83 100	97.42 67	96.32 216	97.64 170	96.49 81	99.72 80	95.66 99	99.37 147	99.45 66

X-MVStestdata		92.86 273	90.83 298	98.94 18	99.15 72	97.66 19	97.77 59	98.83 100	97.42 67	96.32 216	36.50 360	96.49 81	99.72 80	95.66 99	99.37 147	99.45 66

VPA-MVSNet		98.27 29	98.46 24	97.70 108	99.06 87	93.80 160	97.76 61	99.00 57	98.40 29	99.07 33	98.98 40	96.89 59	99.75 63	97.19 50	99.79 35	99.55 35

UGNet		96.81 130	96.56 138	97.58 116	96.64 290	93.84 159	97.75 62	97.12 265	96.47 98	93.62 297	98.88 47	93.22 182	99.53 168	95.61 103	99.69 55	99.36 91
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 58	97.53 80	99.04 7	99.22 57	97.87 14	97.74 63	98.78 114	96.04 115	97.10 172	97.73 163	96.53 78	99.78 42	95.16 132	99.50 108	99.46 61

OpenMVS	94.22 8	95.48 188	95.20 189	96.32 201	97.16 277	91.96 208	97.74 63	98.84 93	87.26 297	94.36 275	98.01 130	93.95 167	99.67 123	90.70 258	98.75 237	97.35 297

abl_6		98.42 23	98.19 32	99.09 3	99.16 69	98.10 5	97.73 65	99.11 26	97.76 49	98.62 51	98.27 97	97.88 19	99.80 37	95.67 97	99.50 108	99.38 85

MSP-MVS		97.45 92	96.92 120	99.03 8	99.26 48	97.70 18	97.66 66	98.89 75	95.65 136	98.51 60	96.46 253	92.15 208	99.81 31	95.14 135	98.58 253	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		95.32 196	94.88 204	96.62 183	98.03 191	91.47 217	97.65 67	90.72 344	99.11 9	97.89 131	98.31 86	79.20 309	99.48 181	93.91 191	99.12 195	98.93 175

K. test v3		96.44 152	96.28 153	96.95 163	99.41 35	91.53 215	97.65 67	90.31 347	98.89 18	98.93 38	99.36 14	84.57 289	99.92 4	97.81 26	99.56 84	99.39 83

TSAR-MVS + MP.		97.42 94	97.23 101	98.00 90	99.38 38	95.00 116	97.63 69	98.20 197	93.00 230	98.16 99	98.06 124	95.89 99	99.72 80	95.67 97	99.10 198	99.28 109
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 55	97.59 76	98.92 22	99.22 57	97.55 26	97.60 70	98.84 93	96.00 118	97.22 163	97.62 172	96.87 62	99.76 56	95.48 110	99.43 133	99.46 61

HFP-MVS		97.94 51	97.64 69	98.83 26	99.15 72	97.50 28	97.59 71	98.84 93	96.05 113	97.49 150	97.54 176	97.07 47	99.70 104	95.61 103	99.46 121	99.30 101

ACMMPR		97.95 49	97.62 73	98.94 18	99.20 65	97.56 25	97.59 71	98.83 100	96.05 113	97.46 156	97.63 171	96.77 66	99.76 56	95.61 103	99.46 121	99.49 51

RPSCF		97.87 61	97.51 82	98.95 17	99.15 72	98.43 3	97.56 73	99.06 38	96.19 107	98.48 63	98.70 58	94.72 142	99.24 248	94.37 169	99.33 165	99.17 128

DIV-MVS_2432*1600		97.86 63	98.07 36	97.25 150	99.22 57	92.81 188	97.55 74	98.94 70	97.10 78	98.85 40	98.88 47	95.03 135	99.67 123	97.39 42	99.65 61	99.26 114

SR-MVS-dyc-post		98.14 34	97.84 49	99.02 9	98.81 102	98.05 8	97.55 74	98.86 84	97.77 46	98.20 94	98.07 119	96.60 75	99.76 56	95.49 107	99.20 181	99.26 114

RE-MVS-def			97.88 47		98.81 102	98.05 8	97.55 74	98.86 84	97.77 46	98.20 94	98.07 119	96.94 54		95.49 107	99.20 181	99.26 114

APD-MVS_3200maxsize		98.13 37	97.90 44	98.79 31	98.79 105	97.31 36	97.55 74	98.92 72	97.72 53	98.25 90	98.13 111	97.10 44	99.75 63	95.44 114	99.24 179	99.32 95

ACMH+	93.58 10	98.23 32	98.31 29	97.98 91	99.39 37	95.22 110	97.55 74	99.20 13	98.21 36	99.25 25	98.51 72	98.21 11	99.40 208	94.79 151	99.72 48	99.32 95

Vis-MVSNet (Re-imp)		95.11 204	94.85 205	95.87 223	99.12 81	89.17 249	97.54 79	94.92 306	96.50 95	96.58 203	97.27 203	83.64 293	99.48 181	88.42 296	99.67 58	98.97 167

MP-MVS		97.64 77	97.18 104	99.00 12	99.32 45	97.77 17	97.49 80	98.73 123	96.27 103	95.59 247	97.75 160	96.30 91	99.78 42	93.70 198	99.48 116	99.45 66
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
ZNCC-MVS		97.92 55	97.62 73	98.83 26	99.32 45	97.24 39	97.45 81	98.84 93	95.76 133	96.93 187	97.43 186	97.26 39	99.79 38	96.06 79	99.53 96	99.45 66

tttt0517		93.31 267	92.56 274	95.57 233	98.71 116	87.86 274	97.44 82	87.17 355	95.79 132	97.47 155	96.84 229	64.12 356	99.81 31	96.20 75	99.32 167	99.02 162

v10		97.55 84	97.97 41	96.31 202	98.60 130	89.64 241	97.44 82	99.02 49	96.60 90	98.72 49	99.16 29	93.48 177	99.72 80	98.76 6	99.92 14	99.58 28

v8		97.60 81	98.06 38	96.23 205	98.71 116	89.44 245	97.43 84	98.82 108	97.29 75	98.74 47	99.10 32	93.86 168	99.68 118	98.61 10	99.94 8	99.56 33

PMVS	89.60 17	96.71 139	96.97 116	95.95 218	99.51 22	97.81 16	97.42 85	97.49 252	97.93 43	95.95 233	98.58 65	96.88 61	96.91 351	89.59 279	99.36 150	93.12 349
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test1172		98.08 39	97.76 57	99.05 6	98.78 107	98.07 6	97.41 86	98.85 88	97.57 60	98.15 101	97.96 134	96.60 75	99.76 56	95.30 122	99.18 185	99.33 94

SR-MVS		98.00 45	97.66 64	99.01 11	98.77 109	97.93 10	97.38 87	98.83 100	97.32 73	98.06 113	97.85 149	96.65 70	99.77 52	95.00 144	99.11 196	99.32 95

FMVSNet5		93.39 265	92.35 276	96.50 191	95.83 315	90.81 227	97.31 88	98.27 188	92.74 238	96.27 220	98.28 93	62.23 358	99.67 123	90.86 248	99.36 150	99.03 160

HY-MVS	91.43 15	92.58 277	91.81 283	94.90 259	96.49 294	88.87 254	97.31 88	94.62 308	85.92 309	90.50 338	96.84 229	85.05 284	99.40 208	83.77 337	95.78 328	96.43 325

CSCG		97.40 96	97.30 94	97.69 110	98.95 95	94.83 120	97.28 90	98.99 60	96.35 102	98.13 104	95.95 280	95.99 97	99.66 129	94.36 172	99.73 45	98.59 219

MTAPA		98.14 34	97.84 49	99.06 4	99.44 30	97.90 11	97.25 91	98.73 123	97.69 56	97.90 129	97.96 134	95.81 108	99.82 29	96.13 77	99.61 71	99.45 66

CPTT-MVS		96.69 140	96.08 162	98.49 52	98.89 99	96.64 54	97.25 91	98.77 115	92.89 236	96.01 232	97.13 209	92.23 207	99.67 123	92.24 220	99.34 158	99.17 128

EU-MVSNet		94.25 240	94.47 226	93.60 292	98.14 183	82.60 332	97.24 93	92.72 327	85.08 320	98.48 63	98.94 43	82.59 296	98.76 302	97.47 39	99.53 96	99.44 76

XXY-MVS		97.54 85	97.70 60	97.07 158	99.46 28	92.21 199	97.22 94	99.00 57	94.93 168	98.58 56	98.92 45	97.31 35	99.41 206	94.44 164	99.43 133	99.59 27

GST-MVS		97.82 67	97.49 85	98.81 29	99.23 54	97.25 38	97.16 95	98.79 110	95.96 120	97.53 145	97.40 188	96.93 56	99.77 52	95.04 141	99.35 155	99.42 78

SteuartSystems-ACMMP		98.02 43	97.76 57	98.79 31	99.43 32	97.21 41	97.15 96	98.90 74	96.58 92	98.08 111	97.87 148	97.02 52	99.76 56	95.25 125	99.59 76	99.40 81
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FMVSNet2		96.72 137	96.67 133	96.87 169	97.96 200	91.88 209	97.15 96	98.06 219	95.59 140	98.50 62	98.62 64	89.51 251	99.65 130	94.99 145	99.60 74	99.07 154

AllTest		97.20 109	96.92 120	98.06 85	99.08 84	96.16 68	97.14 98	99.16 17	94.35 186	97.78 141	98.07 119	95.84 101	99.12 263	91.41 235	99.42 136	98.91 180

DP-MVS		97.87 61	97.89 46	97.81 101	98.62 127	94.82 121	97.13 99	98.79 110	98.98 17	98.74 47	98.49 73	95.80 110	99.49 178	95.04 141	99.44 126	99.11 147

PGM-MVS		97.88 60	97.52 81	98.96 16	99.20 65	97.62 21	97.09 100	99.06 38	95.45 145	97.55 144	97.94 139	97.11 43	99.78 42	94.77 154	99.46 121	99.48 56

LPG-MVS_test		97.94 51	97.67 63	98.74 35	99.15 72	97.02 42	97.09 100	99.02 49	95.15 157	98.34 78	98.23 101	97.91 17	99.70 104	94.41 166	99.73 45	99.50 43

SF-MVS		97.60 81	97.39 89	98.22 74	98.93 96	95.69 84	97.05 102	99.10 28	95.32 150	97.83 138	97.88 146	96.44 85	99.72 80	94.59 161	99.39 144	99.25 118

VDD-MVS		97.37 98	97.25 98	97.74 104	98.69 120	94.50 134	97.04 103	95.61 299	98.59 25	98.51 60	98.72 56	92.54 201	99.58 153	96.02 84	99.49 112	99.12 143

wuyk23d		93.25 269	95.20 189	87.40 341	96.07 310	95.38 99	97.04 103	94.97 305	95.33 149	99.70 5	98.11 115	98.14 13	91.94 358	77.76 351	99.68 57	74.89 357

MVS_0304		95.50 185	95.05 197	96.84 171	96.28 299	93.12 181	97.00 105	96.16 286	95.03 163	89.22 346	97.70 166	90.16 242	99.48 181	94.51 163	99.34 158	97.93 274

LCM-MVSNet-Re		97.33 101	97.33 93	97.32 145	98.13 186	93.79 161	96.99 106	99.65 2	96.74 87	99.47 13	98.93 44	96.91 58	99.84 25	90.11 271	99.06 205	98.32 240

MAR-MVS		94.21 243	93.03 260	97.76 102	96.94 285	97.44 33	96.97 107	97.15 263	87.89 295	92.00 329	92.73 333	92.14 209	99.12 263	83.92 334	97.51 296	96.73 318
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
hse-mvs3		96.29 156	95.63 179	98.26 69	98.50 143	96.11 71	96.90 108	97.09 266	96.58 92	97.21 165	98.19 106	84.14 290	99.78 42	95.89 92	96.17 323	98.89 184

test0726					99.24 52	95.51 94	96.89 109	98.89 75	95.92 123	98.64 50	98.31 86	97.06 49

baseline		97.44 93	97.78 56	96.43 195	98.52 139	90.75 228	96.84 110	99.03 47	96.51 94	97.86 136	98.02 128	96.67 69	99.36 221	97.09 53	99.47 118	99.19 125

API-MVS		95.09 206	95.01 198	95.31 244	96.61 291	94.02 151	96.83 111	97.18 262	95.60 139	95.79 239	94.33 314	94.54 152	98.37 334	85.70 320	98.52 254	93.52 346

#test#		97.62 79	97.22 102	98.83 26	99.15 72	97.50 28	96.81 112	98.84 93	94.25 190	97.49 150	97.54 176	97.07 47	99.70 104	94.37 169	99.46 121	99.30 101

SED-MVS		97.94 51	97.90 44	98.07 83	99.22 57	95.35 102	96.79 113	98.83 100	96.11 110	99.08 31	98.24 99	97.87 20	99.72 80	95.44 114	99.51 106	99.14 135

OPU-MVS				97.64 113	98.01 194	95.27 105	96.79 113				97.35 197	96.97 53	98.51 325	91.21 241	99.25 178	99.14 135

PHI-MVS		96.96 118	96.53 142	98.25 72	97.48 253	96.50 58	96.76 115	98.85 88	93.52 209	96.19 225	96.85 228	95.94 98	99.42 197	93.79 194	99.43 133	98.83 193

DVP-MVS		97.78 70	97.65 66	98.16 76	99.24 52	95.51 94	96.74 116	98.23 193	95.92 123	98.40 70	98.28 93	97.06 49	99.71 95	95.48 110	99.52 101	99.26 114
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 54	95.49 96	96.74 116	98.89 75					99.75 63	95.48 110	99.52 101	99.53 39

Anonymous202405211		96.34 155	95.98 167	97.43 137	98.25 167	93.85 158	96.74 116	94.41 311	97.72 53	98.37 73	98.03 127	87.15 273	99.53 168	94.06 182	99.07 202	98.92 179

SMA-MVS		97.48 90	97.11 107	98.60 46	98.83 101	96.67 52	96.74 116	98.73 123	91.61 253	98.48 63	98.36 81	96.53 78	99.68 118	95.17 130	99.54 93	99.45 66
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 86	95.87 78	96.73 120	99.05 40	98.67 23	98.84 41	98.45 76	97.58 27	99.88 18	96.45 70	99.86 25	99.54 36

test_0402		97.84 64	97.97 41	97.47 130	99.19 67	94.07 149	96.71 121	98.73 123	98.66 24	98.56 57	98.41 78	96.84 64	99.69 112	94.82 149	99.81 30	98.64 213

ACMM	93.33 11	98.05 41	97.79 53	98.85 25	99.15 72	97.55 26	96.68 122	98.83 100	95.21 153	98.36 75	98.13 111	98.13 14	99.62 143	96.04 82	99.54 93	99.39 83
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
baseline1		93.14 271	92.64 272	94.62 271	97.34 267	87.20 289	96.67 123	93.02 322	94.71 174	96.51 208	95.83 283	81.64 298	98.60 318	90.00 274	88.06 353	98.07 260

MTMP							96.55 124	74.60 362

SD-MVS		97.37 98	97.70 60	96.35 199	98.14 183	95.13 113	96.54 125	98.92 72	95.94 122	99.19 28	98.08 117	97.74 22	95.06 356	95.24 126	99.54 93	98.87 190
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		96.66 143	96.33 152	97.68 111	98.70 118	94.29 140	96.50 126	98.75 119	96.36 100	96.16 226	96.77 235	91.91 219	99.46 187	92.59 217	99.20 181	99.28 109

plane_prior2							96.50 126		96.36 100

testtj		96.69 140	96.13 158	98.36 61	98.46 150	96.02 75	96.44 128	98.70 133	94.26 189	96.79 192	97.13 209	94.07 164	99.75 63	90.53 263	98.80 232	99.31 100

CS-MVS		95.86 175	95.59 182	96.69 180	97.85 208	93.14 180	96.42 129	99.25 9	94.17 194	93.56 301	90.76 352	96.05 96	99.72 80	93.28 205	98.91 219	97.21 298

Effi-MVS+-dtu		96.81 130	96.09 161	98.99 13	96.90 287	98.69 2	96.42 129	98.09 212	95.86 128	95.15 255	95.54 291	94.26 159	99.81 31	94.06 182	98.51 256	98.47 227

thres100view900		91.76 292	91.26 291	93.26 298	98.21 171	84.50 322	96.39 131	90.39 345	96.87 83	96.33 215	93.08 326	73.44 340	99.42 197	78.85 348	97.74 282	95.85 330

XVG-ACMP-BASELINE		97.58 83	97.28 97	98.49 52	99.16 69	96.90 46	96.39 131	98.98 63	95.05 162	98.06 113	98.02 128	95.86 100	99.56 160	94.37 169	99.64 63	99.00 163

Patchmtry		95.03 209	94.59 221	96.33 200	94.83 332	90.82 225	96.38 133	97.20 260	96.59 91	97.49 150	98.57 66	77.67 316	99.38 216	92.95 214	99.62 65	98.80 196

ACMMP_NAP		97.89 59	97.63 71	98.67 41	99.35 41	96.84 47	96.36 134	98.79 110	95.07 161	97.88 132	98.35 82	97.24 41	99.72 80	96.05 81	99.58 78	99.45 66

VNet		96.84 125	96.83 124	96.88 168	98.06 189	92.02 206	96.35 135	97.57 251	97.70 55	97.88 132	97.80 156	92.40 205	99.54 167	94.73 156	98.96 211	99.08 152

V42		97.04 112	97.16 105	96.68 182	98.59 132	91.05 220	96.33 136	98.36 179	94.60 177	97.99 119	98.30 90	93.32 179	99.62 143	97.40 41	99.53 96	99.38 85

APD-MVS		97.00 113	96.53 142	98.41 57	98.55 136	96.31 64	96.32 137	98.77 115	92.96 235	97.44 158	97.58 175	95.84 101	99.74 70	91.96 222	99.35 155	99.19 125
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
VPNet		97.26 105	97.49 85	96.59 185	99.47 27	90.58 230	96.27 138	98.53 157	97.77 46	98.46 66	98.41 78	94.59 149	99.68 118	94.61 157	99.29 173	99.52 40

thres600view7		92.03 288	91.43 286	93.82 288	98.19 173	84.61 321	96.27 138	90.39 345	96.81 85	96.37 214	93.11 322	73.44 340	99.49 178	80.32 344	97.95 274	97.36 295

EPNet		93.72 256	92.62 273	97.03 161	87.61 364	92.25 197	96.27 138	91.28 338	96.74 87	87.65 352	97.39 192	85.00 285	99.64 133	92.14 221	99.48 116	99.20 124
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DSMNet-mixed		92.19 285	91.83 282	93.25 299	96.18 305	83.68 329	96.27 138	93.68 316	76.97 354	92.54 325	99.18 27	89.20 256	98.55 322	83.88 335	98.60 252	97.51 291

ACMP	92.54 13	97.47 91	97.10 108	98.55 50	99.04 90	96.70 51	96.24 142	98.89 75	93.71 206	97.97 123	97.75 160	97.44 29	99.63 135	93.22 208	99.70 54	99.32 95
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
DeepC-MVS	95.41 4	97.82 67	97.70 60	98.16 76	98.78 107	95.72 82	96.23 143	99.02 49	93.92 202	98.62 51	98.99 39	97.69 23	99.62 143	96.18 76	99.87 24	99.15 132
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PM-MVS		97.36 100	97.10 108	98.14 80	98.91 98	96.77 49	96.20 144	98.63 149	93.82 203	98.54 58	98.33 84	93.98 166	99.05 273	95.99 87	99.45 125	98.61 218

MVS_Test		96.27 157	96.79 128	94.73 268	96.94 285	86.63 296	96.18 145	98.33 184	94.94 166	96.07 229	98.28 93	95.25 129	99.26 245	97.21 47	97.90 277	98.30 243

CR-MVSNet		93.29 268	92.79 266	94.78 266	95.44 324	88.15 268	96.18 145	97.20 260	84.94 324	94.10 280	98.57 66	77.67 316	99.39 213	95.17 130	95.81 325	96.81 315

RPMNet		94.68 226	94.60 219	94.90 259	95.44 324	88.15 268	96.18 145	98.86 84	97.43 66	94.10 280	98.49 73	79.40 307	99.76 56	95.69 96	95.81 325	96.81 315

EIA-MVS		96.04 167	95.77 175	96.85 170	97.80 220	92.98 184	96.12 148	99.16 17	94.65 175	93.77 291	91.69 344	95.68 113	99.67 123	94.18 177	98.85 228	97.91 275

Effi-MVS+		96.19 161	96.01 164	96.71 178	97.43 259	92.19 202	96.12 148	99.10 28	95.45 145	93.33 310	94.71 306	97.23 42	99.56 160	93.21 209	97.54 294	98.37 233

alignmvs		96.01 169	95.52 184	97.50 125	97.77 231	94.71 125	96.07 150	96.84 274	97.48 65	96.78 196	94.28 316	85.50 282	99.40 208	96.22 74	98.73 241	98.40 230

PatchT		93.75 255	93.57 251	94.29 284	95.05 330	87.32 287	96.05 151	92.98 323	97.54 63	94.25 276	98.72 56	75.79 329	99.24 248	95.92 90	95.81 325	96.32 326

Patchmatch-test		93.60 261	93.25 257	94.63 270	96.14 309	87.47 283	96.04 152	94.50 310	93.57 208	96.47 209	96.97 221	76.50 324	98.61 316	90.67 259	98.41 259	97.81 281

thisisatest0530		92.71 276	91.76 284	95.56 235	98.42 152	88.23 265	96.03 153	87.35 354	94.04 198	96.56 205	95.47 293	64.03 357	99.77 52	94.78 153	99.11 196	98.68 212

9.14			96.69 131		98.53 138		96.02 154	98.98 63	93.23 219	97.18 166	97.46 184	96.47 83	99.62 143	92.99 212	99.32 167

DeepC-MVS_fast	94.34 7	96.74 134	96.51 145	97.44 136	97.69 237	94.15 147	96.02 154	98.43 167	93.17 225	97.30 161	97.38 194	95.48 120	99.28 242	93.74 195	99.34 158	98.88 188
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
114514_t		93.96 251	93.22 258	96.19 208	99.06 87	90.97 223	95.99 156	98.94 70	73.88 357	93.43 307	96.93 224	92.38 206	99.37 219	89.09 286	99.28 174	98.25 249

FMVSNet3		95.26 199	94.94 199	96.22 207	96.53 293	90.06 234	95.99 156	97.66 242	94.11 196	97.99 119	97.91 143	80.22 306	99.63 135	94.60 158	99.44 126	98.96 168

HPM-MVS++		96.99 114	96.38 149	98.81 29	98.64 122	97.59 23	95.97 158	98.20 197	95.51 143	95.06 256	96.53 249	94.10 163	99.70 104	94.29 173	99.15 187	99.13 138

ETH3D-3000-0.1		96.89 124	96.46 147	98.16 76	98.62 127	95.69 84	95.96 159	98.98 63	93.36 214	97.04 178	97.31 201	94.93 139	99.63 135	92.60 215	99.34 158	99.17 128

testgi		96.07 165	96.50 146	94.80 265	99.26 48	87.69 280	95.96 159	98.58 154	95.08 160	98.02 118	96.25 264	97.92 16	97.60 348	88.68 293	98.74 238	99.11 147

EG-PatchMatch MVS		97.69 75	97.79 53	97.40 140	99.06 87	93.52 172	95.96 159	98.97 66	94.55 181	98.82 42	98.76 54	97.31 35	99.29 240	97.20 49	99.44 126	99.38 85

PAPM_NR		94.61 230	94.17 237	95.96 216	98.36 156	91.23 218	95.93 162	97.95 222	92.98 231	93.42 308	94.43 313	90.53 233	98.38 332	87.60 306	96.29 321	98.27 247

UniMVSNet (Re)		97.83 65	97.65 66	98.35 63	98.80 104	95.86 79	95.92 163	99.04 46	97.51 64	98.22 93	97.81 155	94.68 145	99.78 42	97.14 52	99.75 43	99.41 80

1314		92.38 281	92.30 277	92.64 313	95.42 326	85.15 314	95.86 164	96.97 271	85.40 318	90.62 335	93.06 327	91.12 226	97.80 346	86.74 314	95.49 332	94.97 341

1121		94.26 239	93.26 256	97.27 147	98.26 166	94.73 123	95.86 164	97.71 238	77.96 351	94.53 270	96.71 239	91.93 217	99.40 208	87.71 302	98.64 248	97.69 285

MVS		90.02 306	89.20 313	92.47 316	94.71 333	86.90 293	95.86 164	96.74 280	64.72 359	90.62 335	92.77 331	92.54 201	98.39 331	79.30 346	95.56 331	92.12 350

casdiffmvs		97.50 88	97.81 52	96.56 189	98.51 140	91.04 221	95.83 167	99.09 33	97.23 76	98.33 82	98.30 90	97.03 51	99.37 219	96.58 64	99.38 146	99.28 109

tpmvs		90.79 302	90.87 296	90.57 330	92.75 356	76.30 353	95.79 168	93.64 317	91.04 263	91.91 330	96.26 263	77.19 322	98.86 294	89.38 283	89.85 351	96.56 323

RRT_test8_iter05		92.46 279	92.52 275	92.29 320	95.33 327	77.43 350	95.73 169	98.55 156	94.41 183	97.46 156	97.72 165	57.44 361	99.74 70	96.92 58	99.14 188	99.69 20

MSLP-MVS++		96.42 154	96.71 130	95.57 233	97.82 215	90.56 232	95.71 170	98.84 93	94.72 173	96.71 198	97.39 192	94.91 140	98.10 343	95.28 123	99.02 207	98.05 267

tfpn200view9		91.55 294	91.00 293	93.21 301	98.02 192	84.35 324	95.70 171	90.79 342	96.26 104	95.90 237	92.13 339	73.62 338	99.42 197	78.85 348	97.74 282	95.85 330

Anonymous20231206		95.27 198	95.06 196	95.88 222	98.72 113	89.37 246	95.70 171	97.85 228	88.00 293	96.98 184	97.62 172	91.95 215	99.34 226	89.21 284	99.53 96	98.94 171

thres400		91.68 293	91.00 293	93.71 290	98.02 192	84.35 324	95.70 171	90.79 342	96.26 104	95.90 237	92.13 339	73.62 338	99.42 197	78.85 348	97.74 282	97.36 295

test20.03		96.58 146	96.61 134	96.48 193	98.49 144	91.72 213	95.68 174	97.69 239	96.81 85	98.27 89	97.92 142	94.18 162	98.71 306	90.78 252	99.66 60	99.00 163

zzz-MVS		98.01 44	97.66 64	99.06 4	99.44 30	97.90 11	95.66 175	98.73 123	97.69 56	97.90 129	97.96 134	95.81 108	99.82 29	96.13 77	99.61 71	99.45 66

UniMVSNet_NR-MVSNet		97.83 65	97.65 66	98.37 60	98.72 113	95.78 80	95.66 175	99.02 49	98.11 39	98.31 85	97.69 168	94.65 147	99.85 22	97.02 56	99.71 51	99.48 56

DU-MVS		97.79 69	97.60 75	98.36 61	98.73 111	95.78 80	95.65 177	98.87 82	97.57 60	98.31 85	97.83 151	94.69 143	99.85 22	97.02 56	99.71 51	99.46 61

EPMVS		89.26 314	88.55 318	91.39 324	92.36 357	79.11 344	95.65 177	79.86 360	88.60 286	93.12 312	96.53 249	70.73 349	98.10 343	90.75 253	89.32 352	96.98 304

MVP-Stereo		95.69 178	95.28 188	96.92 165	98.15 182	93.03 183	95.64 179	98.20 197	90.39 268	96.63 202	97.73 163	91.63 222	99.10 268	91.84 228	97.31 303	98.63 215
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
F-COLMAP		95.30 197	94.38 230	98.05 88	98.64 122	96.04 73	95.61 180	98.66 143	89.00 281	93.22 311	96.40 257	92.90 189	99.35 224	87.45 310	97.53 295	98.77 202

AUN-MVS		93.95 253	92.69 270	97.74 104	97.80 220	95.38 99	95.57 181	95.46 303	91.26 260	92.64 322	96.10 273	74.67 332	99.55 164	93.72 197	96.97 306	98.30 243

v144192		96.69 140	96.90 122	96.03 213	98.25 167	88.92 252	95.49 182	98.77 115	93.05 228	98.09 109	98.29 92	92.51 203	99.70 104	98.11 17	99.56 84	99.47 59

Fast-Effi-MVS+-dtu		96.44 152	96.12 159	97.39 141	97.18 276	94.39 136	95.46 183	98.73 123	96.03 117	94.72 264	94.92 303	96.28 93	99.69 112	93.81 193	97.98 273	98.09 257

Baseline_NR-MVSNet		97.72 73	97.79 53	97.50 125	99.56 15	93.29 176	95.44 184	98.86 84	98.20 37	98.37 73	99.24 20	94.69 143	99.55 164	95.98 88	99.79 35	99.65 23

LF4IMVS		96.07 165	95.63 179	97.36 143	98.19 173	95.55 91	95.44 184	98.82 108	92.29 244	95.70 245	96.55 247	92.63 197	98.69 308	91.75 231	99.33 165	97.85 277

v1921920		96.72 137	96.96 118	95.99 214	98.21 171	88.79 257	95.42 186	98.79 110	93.22 220	98.19 97	98.26 98	92.68 194	99.70 104	98.34 15	99.55 90	99.49 51

plane_prior						94.29 140	95.42 186		94.31 188						98.93 217

v1144		96.84 125	97.08 110	96.13 211	98.42 152	89.28 248	95.41 188	98.67 141	94.21 191	97.97 123	98.31 86	93.06 184	99.65 130	98.06 19	99.62 65	99.45 66

ETV-MVS		96.13 164	95.90 171	96.82 172	97.76 232	93.89 155	95.40 189	98.95 69	95.87 127	95.58 248	91.00 349	96.36 90	99.72 80	93.36 202	98.83 230	96.85 311

v1240		96.74 134	97.02 115	95.91 221	98.18 176	88.52 260	95.39 190	98.88 80	93.15 226	98.46 66	98.40 80	92.80 191	99.71 95	98.45 13	99.49 112	99.49 51

MP-MVS-pluss		97.69 75	97.36 91	98.70 39	99.50 25	96.84 47	95.38 191	98.99 60	92.45 242	98.11 105	98.31 86	97.25 40	99.77 52	96.60 62	99.62 65	99.48 56
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
v1192		96.83 128	97.06 112	96.15 210	98.28 162	89.29 247	95.36 192	98.77 115	93.73 205	98.11 105	98.34 83	93.02 188	99.67 123	98.35 14	99.58 78	99.50 43

v2v482		96.78 132	97.06 112	95.95 218	98.57 134	88.77 258	95.36 192	98.26 190	95.18 156	97.85 137	98.23 101	92.58 198	99.63 135	97.80 27	99.69 55	99.45 66

EI-MVSNet-Vis-set		97.32 102	97.39 89	97.11 155	97.36 262	92.08 205	95.34 194	97.65 244	97.74 50	98.29 88	98.11 115	95.05 132	99.68 118	97.50 38	99.50 108	99.56 33

EI-MVSNet-UG-set		97.32 102	97.40 88	97.09 157	97.34 267	92.01 207	95.33 195	97.65 244	97.74 50	98.30 87	98.14 110	95.04 134	99.69 112	97.55 36	99.52 101	99.58 28

CostFormer		89.75 311	89.25 310	91.26 326	94.69 334	78.00 348	95.32 196	91.98 332	81.50 337	90.55 337	96.96 223	71.06 347	98.89 290	88.59 294	92.63 345	96.87 309

PVSNet_Blended_VisFu		95.95 171	95.80 173	96.42 196	99.28 47	90.62 229	95.31 197	99.08 34	88.40 288	96.97 185	98.17 109	92.11 210	99.78 42	93.64 199	99.21 180	98.86 191

UnsupCasMVSNet_eth		95.91 172	95.73 176	96.44 194	98.48 146	91.52 216	95.31 197	98.45 164	95.76 133	97.48 153	97.54 176	89.53 250	98.69 308	94.43 165	94.61 338	99.13 138

EI-MVSNet		96.63 144	96.93 119	95.74 227	97.26 272	88.13 270	95.29 199	97.65 244	96.99 79	97.94 126	98.19 106	92.55 199	99.58 153	96.91 59	99.56 84	99.50 43

CVMVSNet		92.33 283	92.79 266	90.95 327	97.26 272	75.84 355	95.29 199	92.33 330	81.86 334	96.27 220	98.19 106	81.44 299	98.46 327	94.23 176	98.29 263	98.55 223

RRT_MVS		94.90 212	94.07 239	97.39 141	93.18 349	93.21 179	95.26 201	97.49 252	93.94 201	98.25 90	97.85 149	72.96 342	99.84 25	97.90 22	99.78 38	99.14 135

Regformer-3		97.25 106	97.29 95	97.11 155	97.35 263	92.32 196	95.26 201	97.62 249	97.67 58	98.17 98	97.89 144	95.05 132	99.56 160	97.16 51	99.42 136	99.46 61

Regformer-4		97.53 87	97.47 87	97.71 106	97.35 263	93.91 154	95.26 201	98.14 207	97.97 42	98.34 78	97.89 144	95.49 119	99.71 95	97.41 40	99.42 136	99.51 42

OPM-MVS		97.54 85	97.25 98	98.41 57	99.11 82	96.61 55	95.24 204	98.46 163	94.58 180	98.10 108	98.07 119	97.09 46	99.39 213	95.16 132	99.44 126	99.21 123

TAPA-MVS	93.32 12	94.93 211	94.23 233	97.04 160	98.18 176	94.51 132	95.22 205	98.73 123	81.22 339	96.25 222	95.95 280	93.80 171	98.98 282	89.89 275	98.87 224	97.62 287
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
DPE-MVS		97.64 77	97.35 92	98.50 51	98.85 100	96.18 67	95.21 206	98.99 60	95.84 130	98.78 44	98.08 117	96.84 64	99.81 31	93.98 188	99.57 81	99.52 40
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MVSTER		94.21 243	93.93 246	95.05 253	95.83 315	86.46 297	95.18 207	97.65 244	92.41 243	97.94 126	98.00 132	72.39 343	99.58 153	96.36 72	99.56 84	99.12 143

PatchmatchNet		91.98 289	91.87 281	92.30 319	94.60 335	79.71 343	95.12 208	93.59 318	89.52 276	93.61 298	97.02 219	77.94 314	99.18 254	90.84 249	94.57 340	98.01 271
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
DWT-MVSNet_test		87.92 324	86.77 328	91.39 324	93.18 349	78.62 345	95.10 209	91.42 336	85.58 313	88.00 350	88.73 355	60.60 359	98.90 288	90.60 260	87.70 354	96.65 319

IterMVS-LS		96.92 120	97.29 95	95.79 225	98.51 140	88.13 270	95.10 209	98.66 143	96.99 79	98.46 66	98.68 60	92.55 199	99.74 70	96.91 59	99.79 35	99.50 43
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v148		96.58 146	96.97 116	95.42 241	98.63 126	87.57 281	95.09 211	97.90 225	95.91 125	98.24 92	97.96 134	93.42 178	99.39 213	96.04 82	99.52 101	99.29 108

tpm2		88.47 319	87.69 323	90.79 328	94.98 331	77.34 351	95.09 211	91.83 333	77.51 353	89.40 344	96.41 255	67.83 353	98.73 304	83.58 339	92.60 346	96.29 327

OpenMVS_ROB	91.80 14	93.64 260	93.05 259	95.42 241	97.31 271	91.21 219	95.08 213	96.68 282	81.56 336	96.88 191	96.41 255	90.44 235	99.25 247	85.39 325	97.67 289	95.80 332

mvs-test1		96.20 160	95.50 185	98.32 64	96.90 287	98.16 4	95.07 214	98.09 212	95.86 128	93.63 296	94.32 315	94.26 159	99.71 95	94.06 182	97.27 305	97.07 301

TAMVS		95.49 186	94.94 199	97.16 152	98.31 158	93.41 174	95.07 214	96.82 276	91.09 262	97.51 147	97.82 154	89.96 243	99.42 197	88.42 296	99.44 126	98.64 213

tpmrst		90.31 304	90.61 302	89.41 334	94.06 343	72.37 361	95.06 216	93.69 314	88.01 292	92.32 327	96.86 227	77.45 318	98.82 295	91.04 243	87.01 355	97.04 303

ADS-MVSNet2		91.47 295	90.51 303	94.36 281	95.51 322	85.63 305	95.05 217	95.70 295	83.46 330	92.69 319	96.84 229	79.15 310	99.41 206	85.66 322	90.52 348	98.04 268

ADS-MVSNet		90.95 301	90.26 305	93.04 304	95.51 322	82.37 333	95.05 217	93.41 319	83.46 330	92.69 319	96.84 229	79.15 310	98.70 307	85.66 322	90.52 348	98.04 268

tpm		91.08 299	90.85 297	91.75 322	95.33 327	78.09 346	95.03 219	91.27 339	88.75 284	93.53 302	97.40 188	71.24 345	99.30 236	91.25 240	93.87 341	97.87 276

NCCC		96.52 148	95.99 166	98.10 81	97.81 216	95.68 86	95.00 220	98.20 197	95.39 148	95.40 251	96.36 260	93.81 170	99.45 191	93.55 201	98.42 258	99.17 128

test_post1							94.98 221				10.37 364	76.21 327	99.04 274	89.47 281

ETH3D cwj APD-0.16		96.23 159	95.61 181	98.09 82	97.91 204	95.65 89	94.94 222	98.74 121	91.31 259	96.02 231	97.08 214	94.05 165	99.69 112	91.51 234	98.94 215	98.93 175

AdaColmap		95.11 204	94.62 218	96.58 186	97.33 269	94.45 135	94.92 223	98.08 214	93.15 226	93.98 287	95.53 292	94.34 157	99.10 268	85.69 321	98.61 250	96.20 328

MDTV_nov1_ep13_2view						57.28 365	94.89 224		80.59 341	94.02 284		78.66 312		85.50 324		97.82 279

CNVR-MVS		96.92 120	96.55 139	98.03 89	98.00 198	95.54 92	94.87 225	98.17 203	94.60 177	96.38 213	97.05 217	95.67 114	99.36 221	95.12 138	99.08 200	99.19 125

OMC-MVS		96.48 150	96.00 165	97.91 95	98.30 159	96.01 76	94.86 226	98.60 151	91.88 250	97.18 166	97.21 207	96.11 94	99.04 274	90.49 267	99.34 158	98.69 210

Regformer-1		97.27 104	97.16 105	97.61 115	97.21 274	93.86 157	94.85 227	98.04 221	97.62 59	98.03 117	97.50 181	95.34 125	99.63 135	96.52 66	99.31 169	99.35 92

Regformer-2		97.41 95	97.24 100	97.93 94	97.21 274	94.72 124	94.85 227	98.27 188	97.74 50	98.11 105	97.50 181	95.58 117	99.69 112	96.57 65	99.31 169	99.37 90

EPNet_dtu		91.39 296	90.75 299	93.31 297	90.48 361	82.61 331	94.80 229	92.88 324	93.39 213	81.74 360	94.90 304	81.36 300	99.11 266	88.28 298	98.87 224	98.21 252
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MDTV_nov1_ep13			91.28 289		94.31 337	73.51 359	94.80 229	93.16 321	86.75 304	93.45 306	97.40 188	76.37 325	98.55 322	88.85 289	96.43 318

pmmvs-eth3d		96.49 149	96.18 157	97.42 138	98.25 167	94.29 140	94.77 231	98.07 218	89.81 274	97.97 123	98.33 84	93.11 183	99.08 270	95.46 113	99.84 28	98.89 184

test_yl		94.40 236	94.00 242	95.59 231	96.95 283	89.52 243	94.75 232	95.55 301	96.18 108	96.79 192	96.14 270	81.09 301	99.18 254	90.75 253	97.77 279	98.07 260

DCV-MVSNet		94.40 236	94.00 242	95.59 231	96.95 283	89.52 243	94.75 232	95.55 301	96.18 108	96.79 192	96.14 270	81.09 301	99.18 254	90.75 253	97.77 279	98.07 260

MCST-MVS		96.24 158	95.80 173	97.56 117	98.75 110	94.13 148	94.66 234	98.17 203	90.17 271	96.21 224	96.10 273	95.14 131	99.43 196	94.13 180	98.85 228	99.13 138

XVG-OURS-SEG-HR		97.38 97	97.07 111	98.30 67	99.01 92	97.41 34	94.66 234	99.02 49	95.20 154	98.15 101	97.52 179	98.83 4	98.43 328	94.87 147	96.41 319	99.07 154

mvs_anonymous		95.36 194	96.07 163	93.21 301	96.29 298	81.56 337	94.60 236	97.66 242	93.30 217	96.95 186	98.91 46	93.03 187	99.38 216	96.60 62	97.30 304	98.69 210

DP-MVS Recon		95.55 184	95.13 192	96.80 173	98.51 140	93.99 153	94.60 236	98.69 136	90.20 270	95.78 241	96.21 267	92.73 193	98.98 282	90.58 262	98.86 226	97.42 294

ETH3 D test6400		94.77 218	93.87 247	97.47 130	98.12 187	93.73 163	94.56 238	98.70 133	85.45 317	94.70 266	95.93 282	91.77 221	99.63 135	86.45 316	99.14 188	99.05 158

xxxxxxxxxxxxxcwj		97.24 107	97.03 114	97.89 96	98.48 146	94.71 125	94.53 239	99.07 37	95.02 164	97.83 138	97.88 146	96.44 85	99.72 80	94.59 161	99.39 144	99.25 118

save fliter					98.48 146	94.71 125	94.53 239	98.41 172	95.02 164

tpm cat1		88.01 323	87.33 324	90.05 333	94.48 336	76.28 354	94.47 241	94.35 312	73.84 358	89.26 345	95.61 290	73.64 337	98.30 337	84.13 333	86.20 356	95.57 337

CANet		95.86 175	95.65 178	96.49 192	96.41 296	90.82 225	94.36 242	98.41 172	94.94 166	92.62 324	96.73 238	92.68 194	99.71 95	95.12 138	99.60 74	98.94 171

WR-MVS		96.90 122	96.81 125	97.16 152	98.56 135	92.20 201	94.33 243	98.12 210	97.34 72	98.20 94	97.33 199	92.81 190	99.75 63	94.79 151	99.81 30	99.54 36

HQP-NCC					97.85 208		94.26 244		93.18 222	92.86 316

ACMP_Plane					97.85 208		94.26 244		93.18 222	92.86 316

HQP-MVS		95.17 203	94.58 222	96.92 165	97.85 208	92.47 193	94.26 244	98.43 167	93.18 222	92.86 316	95.08 297	90.33 236	99.23 250	90.51 265	98.74 238	99.05 158

PLC	91.02 16	94.05 250	92.90 262	97.51 122	98.00 198	95.12 114	94.25 247	98.25 191	86.17 306	91.48 332	95.25 295	91.01 227	99.19 253	85.02 329	96.69 314	98.22 251
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
1112_ss		94.12 246	93.42 253	96.23 205	98.59 132	90.85 224	94.24 248	98.85 88	85.49 314	92.97 314	94.94 301	86.01 279	99.64 133	91.78 229	97.92 275	98.20 253

MS-PatchMatch		94.83 215	94.91 203	94.57 275	96.81 289	87.10 290	94.23 249	97.34 257	88.74 285	97.14 168	97.11 212	91.94 216	98.23 339	92.99 212	97.92 275	98.37 233

Fast-Effi-MVS+		95.49 186	95.07 194	96.75 176	97.67 241	92.82 187	94.22 250	98.60 151	91.61 253	93.42 308	92.90 329	96.73 68	99.70 104	92.60 215	97.89 278	97.74 282

CMPMVS	73.10 23	92.74 275	91.39 287	96.77 175	93.57 348	94.67 129	94.21 251	97.67 240	80.36 343	93.61 298	96.60 245	82.85 295	97.35 349	84.86 330	98.78 234	98.29 246
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
dp		88.08 322	88.05 320	88.16 340	92.85 354	68.81 363	94.17 252	92.88 324	85.47 315	91.38 333	96.14 270	68.87 352	98.81 297	86.88 313	83.80 358	96.87 309

JIA-IIPM		91.79 291	90.69 300	95.11 250	93.80 345	90.98 222	94.16 253	91.78 334	96.38 99	90.30 340	99.30 18	72.02 344	98.90 288	88.28 298	90.17 350	95.45 338

D2MVS		95.18 201	95.17 191	95.21 247	97.76 232	87.76 279	94.15 254	97.94 223	89.77 275	96.99 182	97.68 169	87.45 271	99.14 261	95.03 143	99.81 30	98.74 204

TSAR-MVS + GP.		96.47 151	96.12 159	97.49 128	97.74 234	95.23 107	94.15 254	96.90 273	93.26 218	98.04 116	96.70 240	94.41 155	98.89 290	94.77 154	99.14 188	98.37 233

PVSNet_BlendedMVS		95.02 210	94.93 201	95.27 245	97.79 226	87.40 285	94.14 256	98.68 138	88.94 282	94.51 271	98.01 130	93.04 185	99.30 236	89.77 277	99.49 112	99.11 147

TinyColmap		96.00 170	96.34 151	94.96 256	97.90 206	87.91 273	94.13 257	98.49 161	94.41 183	98.16 99	97.76 157	96.29 92	98.68 311	90.52 264	99.42 136	98.30 243

CNLPA		95.04 207	94.47 226	96.75 176	97.81 216	95.25 106	94.12 258	97.89 226	94.41 183	94.57 268	95.69 285	90.30 239	98.35 335	86.72 315	98.76 236	96.64 320

BH-untuned		94.69 224	94.75 211	94.52 277	97.95 203	87.53 282	94.07 259	97.01 269	93.99 199	97.10 172	95.65 287	92.65 196	98.95 287	87.60 306	96.74 313	97.09 300

pmmvs5		94.63 229	94.34 231	95.50 237	97.63 244	88.34 264	94.02 260	97.13 264	87.15 299	95.22 254	97.15 208	87.50 270	99.27 244	93.99 187	99.26 177	98.88 188

thres200		91.00 300	90.42 304	92.77 311	97.47 257	83.98 327	94.01 261	91.18 340	95.12 159	95.44 249	91.21 347	73.93 334	99.31 233	77.76 351	97.63 292	95.01 340

xiu_mvs_v1_base_debu		95.62 181	95.96 168	94.60 272	98.01 194	88.42 261	93.99 262	98.21 194	92.98 231	95.91 234	94.53 309	96.39 87	99.72 80	95.43 117	98.19 265	95.64 334

xiu_mvs_v1_base		95.62 181	95.96 168	94.60 272	98.01 194	88.42 261	93.99 262	98.21 194	92.98 231	95.91 234	94.53 309	96.39 87	99.72 80	95.43 117	98.19 265	95.64 334

xiu_mvs_v1_base_debi		95.62 181	95.96 168	94.60 272	98.01 194	88.42 261	93.99 262	98.21 194	92.98 231	95.91 234	94.53 309	96.39 87	99.72 80	95.43 117	98.19 265	95.64 334

CDS-MVSNet		94.88 214	94.12 238	97.14 154	97.64 243	93.57 170	93.96 265	97.06 268	90.05 272	96.30 219	96.55 247	86.10 278	99.47 184	90.10 272	99.31 169	98.40 230
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
CANet_DTU		94.65 228	94.21 235	95.96 216	95.90 312	89.68 240	93.92 266	97.83 232	93.19 221	90.12 341	95.64 288	88.52 258	99.57 159	93.27 207	99.47 118	98.62 216

WTY-MVS		93.55 262	93.00 261	95.19 248	97.81 216	87.86 274	93.89 267	96.00 289	89.02 280	94.07 282	95.44 294	86.27 277	99.33 229	87.69 304	96.82 310	98.39 232

sss		94.22 241	93.72 249	95.74 227	97.71 236	89.95 237	93.84 268	96.98 270	88.38 289	93.75 292	95.74 284	87.94 264	98.89 290	91.02 244	98.10 269	98.37 233

baseline2		89.65 312	88.44 319	93.25 299	95.62 320	82.71 330	93.82 269	85.94 357	88.89 283	87.35 354	92.54 335	71.23 346	99.33 229	86.01 317	94.60 339	97.72 283

XVG-OURS		97.12 110	96.74 129	98.26 69	98.99 93	97.45 32	93.82 269	99.05 40	95.19 155	98.32 83	97.70 166	95.22 130	98.41 329	94.27 174	98.13 268	98.93 175

MVS_111021_LR		96.82 129	96.55 139	97.62 114	98.27 164	95.34 104	93.81 271	98.33 184	94.59 179	96.56 205	96.63 244	96.61 73	98.73 304	94.80 150	99.34 158	98.78 199

BH-RMVSNet		94.56 232	94.44 229	94.91 257	97.57 246	87.44 284	93.78 272	96.26 285	93.69 207	96.41 212	96.50 252	92.10 211	99.00 278	85.96 318	97.71 285	98.31 241

CDPH-MVS		95.45 191	94.65 214	97.84 100	98.28 162	94.96 117	93.73 273	98.33 184	85.03 322	95.44 249	96.60 245	95.31 127	99.44 194	90.01 273	99.13 192	99.11 147

PatchMatch-RL		94.61 230	93.81 248	97.02 162	98.19 173	95.72 82	93.66 274	97.23 259	88.17 291	94.94 261	95.62 289	91.43 223	98.57 319	87.36 311	97.68 288	96.76 317

TEST9					97.84 213	95.23 107	93.62 275	98.39 175	86.81 302	93.78 289	95.99 275	94.68 145	99.52 172

train_agg		95.46 190	94.66 213	97.88 97	97.84 213	95.23 107	93.62 275	98.39 175	87.04 300	93.78 289	95.99 275	94.58 150	99.52 172	91.76 230	98.90 220	98.89 184

test_prior4						95.38 99	93.61 277

test_8					97.81 216	95.07 115	93.54 278	98.38 177	87.04 300	93.71 293	95.96 279	94.58 150	99.52 172

TR-MVS		92.54 278	92.20 278	93.57 293	96.49 294	86.66 295	93.51 279	94.73 307	89.96 273	94.95 260	93.87 318	90.24 241	98.61 316	81.18 343	94.88 335	95.45 338

新几何2							93.43 280

diffmvs		96.04 167	96.23 154	95.46 240	97.35 263	88.03 272	93.42 281	99.08 34	94.09 197	96.66 200	96.93 224	93.85 169	99.29 240	96.01 86	98.67 243	99.06 156

MVS_111021_HR		96.73 136	96.54 141	97.27 147	98.35 157	93.66 168	93.42 281	98.36 179	94.74 172	96.58 203	96.76 237	96.54 77	98.99 280	94.87 147	99.27 176	99.15 132

agg_prior1		95.39 193	94.60 219	97.75 103	97.80 220	94.96 117	93.39 283	98.36 179	87.20 298	93.49 303	95.97 278	94.65 147	99.53 168	91.69 232	98.86 226	98.77 202

UnsupCasMVSNet_bld		94.72 223	94.26 232	96.08 212	98.62 127	90.54 233	93.38 284	98.05 220	90.30 269	97.02 180	96.80 234	89.54 248	99.16 259	88.44 295	96.18 322	98.56 221

旧先验2							93.35 285		77.95 352	95.77 243			98.67 312	90.74 256

test_prior3		95.91 172	95.39 186	97.46 133	97.79 226	94.26 144	93.33 286	98.42 170	94.21 191	94.02 284	96.25 264	93.64 174	99.34 226	91.90 224	98.96 211	98.79 197

test_prior2							93.33 286		94.21 191	94.02 284	96.25 264	93.64 174		91.90 224	98.96 211

SCA		93.38 266	93.52 252	92.96 308	96.24 300	81.40 338	93.24 288	94.00 313	91.58 255	94.57 268	96.97 221	87.94 264	99.42 197	89.47 281	97.66 290	98.06 264

无先验							93.20 289	97.91 224	80.78 340				99.40 208	87.71 302		97.94 273

MG-MVS		94.08 249	94.00 242	94.32 282	97.09 279	85.89 304	93.19 290	95.96 291	92.52 239	94.93 262	97.51 180	89.54 248	98.77 300	87.52 309	97.71 285	98.31 241

MVS-HIRNet		88.40 320	90.20 306	82.99 342	97.01 281	60.04 364	93.11 291	85.61 358	84.45 328	88.72 348	99.09 33	84.72 288	98.23 339	82.52 340	96.59 317	90.69 355

new-patchmatchnet		95.67 180	96.58 136	92.94 309	97.48 253	80.21 342	92.96 292	98.19 202	94.83 170	98.82 42	98.79 51	93.31 180	99.51 176	95.83 93	99.04 206	99.12 143

MDA-MVSNet-bldmvs		95.69 178	95.67 177	95.74 227	98.48 146	88.76 259	92.84 293	97.25 258	96.00 118	97.59 143	97.95 138	91.38 224	99.46 187	93.16 210	96.35 320	98.99 166

原ACMM2							92.82 294

testdata1							92.77 295		93.78 204

Test_1112_low_res		93.53 263	92.86 263	95.54 236	98.60 130	88.86 255	92.75 296	98.69 136	82.66 333	92.65 321	96.92 226	84.75 287	99.56 160	90.94 246	97.76 281	98.19 254

USDC		94.56 232	94.57 224	94.55 276	97.78 230	86.43 299	92.75 296	98.65 148	85.96 308	96.91 189	97.93 141	90.82 230	98.74 303	90.71 257	99.59 76	98.47 227

test222					98.17 178	93.24 178	92.74 298	97.61 250	75.17 355	94.65 267	96.69 241	90.96 229			98.66 245	97.66 286

jason		94.39 238	94.04 241	95.41 243	98.29 160	87.85 276	92.74 298	96.75 279	85.38 319	95.29 252	96.15 268	88.21 263	99.65 130	94.24 175	99.34 158	98.74 204
jason: jason.
Patchmatch-RL test		94.66 227	94.49 225	95.19 248	98.54 137	88.91 253	92.57 300	98.74 121	91.46 256	98.32 83	97.75 160	77.31 321	98.81 297	96.06 79	99.61 71	97.85 277

DeepPCF-MVS	94.58 5	96.90 122	96.43 148	98.31 66	97.48 253	97.23 40	92.56 301	98.60 151	92.84 237	98.54 58	97.40 188	96.64 72	98.78 299	94.40 168	99.41 142	98.93 175

N_pmnet		95.18 201	94.23 233	98.06 85	97.85 208	96.55 57	92.49 302	91.63 335	89.34 277	98.09 109	97.41 187	90.33 236	99.06 272	91.58 233	99.31 169	98.56 221

BH-w/o		92.14 286	91.94 280	92.73 312	97.13 278	85.30 310	92.46 303	95.64 296	89.33 278	94.21 277	92.74 332	89.60 246	98.24 338	81.68 341	94.66 337	94.66 342

IterMVS-SCA-FT		95.86 175	96.19 156	94.85 262	97.68 238	85.53 307	92.42 304	97.63 248	96.99 79	98.36 75	98.54 70	87.94 264	99.75 63	97.07 55	99.08 200	99.27 113

IterMVS		95.42 192	95.83 172	94.20 285	97.52 251	83.78 328	92.41 305	97.47 255	95.49 144	98.06 113	98.49 73	87.94 264	99.58 153	96.02 84	99.02 207	99.23 121
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DELS-MVS		96.17 162	96.23 154	95.99 214	97.55 250	90.04 235	92.38 306	98.52 158	94.13 195	96.55 207	97.06 216	94.99 137	99.58 153	95.62 102	99.28 174	98.37 233
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		92.34 282	91.69 285	94.32 282	96.23 302	89.16 250	92.27 307	92.88 324	84.39 329	95.29 252	96.35 261	85.66 281	96.74 354	84.53 332	97.56 293	97.05 302

CHOSEN 1792x2688		94.10 247	93.41 254	96.18 209	99.16 69	90.04 235	92.15 308	98.68 138	79.90 344	96.22 223	97.83 151	87.92 268	99.42 197	89.18 285	99.65 61	99.08 152

xiu_mvs_v2_base		94.22 241	94.63 217	92.99 307	97.32 270	84.84 319	92.12 309	97.84 230	91.96 248	94.17 278	93.43 320	96.07 95	99.71 95	91.27 238	97.48 297	94.42 343

lupinMVS		93.77 254	93.28 255	95.24 246	97.68 238	87.81 277	92.12 309	96.05 288	84.52 326	94.48 273	95.06 299	86.90 274	99.63 135	93.62 200	99.13 192	98.27 247

pmmvs4		94.82 216	94.19 236	96.70 179	97.42 260	92.75 190	92.09 311	96.76 278	86.80 303	95.73 244	97.22 206	89.28 254	98.89 290	93.28 205	99.14 188	98.46 229

PAPR		92.22 284	91.27 290	95.07 252	95.73 319	88.81 256	91.97 312	97.87 227	85.80 311	90.91 334	92.73 333	91.16 225	98.33 336	79.48 345	95.76 329	98.08 258

PS-MVSNAJ		94.10 247	94.47 226	93.00 306	97.35 263	84.88 318	91.86 313	97.84 230	91.96 248	94.17 278	92.50 336	95.82 104	99.71 95	91.27 238	97.48 297	94.40 344

cl_fuxian		95.20 200	95.32 187	94.83 264	96.19 304	86.43 299	91.83 314	98.35 183	93.47 211	97.36 160	97.26 204	88.69 257	99.28 242	95.41 120	99.36 150	98.78 199

test0.0.03 1		90.11 305	89.21 312	92.83 310	93.89 344	86.87 294	91.74 315	88.74 352	92.02 246	94.71 265	91.14 348	73.92 335	94.48 357	83.75 338	92.94 343	97.16 299

FPMVS		89.92 310	88.63 317	93.82 288	98.37 155	96.94 45	91.58 316	93.34 320	88.00 293	90.32 339	97.10 213	70.87 348	91.13 359	71.91 356	96.16 324	93.39 348

ET-MVSNet_ETH3D		91.12 297	89.67 309	95.47 239	96.41 296	89.15 251	91.54 317	90.23 348	89.07 279	86.78 356	92.84 330	69.39 351	99.44 194	94.16 178	96.61 316	97.82 279

PVSNet_Blended		93.96 251	93.65 250	94.91 257	97.79 226	87.40 285	91.43 318	98.68 138	84.50 327	94.51 271	94.48 312	93.04 185	99.30 236	89.77 277	98.61 250	98.02 270

CLD-MVS		95.47 189	95.07 194	96.69 180	98.27 164	92.53 192	91.36 319	98.67 141	91.22 261	95.78 241	94.12 317	95.65 115	98.98 282	90.81 250	99.72 48	98.57 220
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		94.89 213	94.93 201	94.75 267	95.99 311	86.12 302	91.35 320	98.49 161	93.40 212	97.12 170	97.25 205	86.87 276	99.35 224	95.08 140	98.82 231	98.78 199

cl-mvsnet_		94.73 219	94.64 215	95.01 254	95.85 314	87.00 291	91.33 321	98.08 214	93.34 215	97.10 172	97.33 199	84.01 292	99.30 236	95.14 135	99.56 84	98.71 209

cl-mvsnet1		94.73 219	94.64 215	95.01 254	95.86 313	87.00 291	91.33 321	98.08 214	93.34 215	97.10 172	97.34 198	84.02 291	99.31 233	95.15 134	99.55 90	98.72 207

miper_ehance_all_eth		94.69 224	94.70 212	94.64 269	95.77 317	86.22 301	91.32 323	98.24 192	91.67 252	97.05 177	96.65 243	88.39 261	99.22 252	94.88 146	98.34 260	98.49 226

pmmvs3		90.00 307	88.90 316	93.32 296	94.20 342	85.34 309	91.25 324	92.56 329	78.59 348	93.82 288	95.17 296	67.36 354	98.69 308	89.08 287	98.03 272	95.92 329

HyFIR lowres test		93.72 256	92.65 271	96.91 167	98.93 96	91.81 212	91.23 325	98.52 158	82.69 332	96.46 210	96.52 251	80.38 305	99.90 13	90.36 269	98.79 233	99.03 160

DPM-MVS		93.68 258	92.77 269	96.42 196	97.91 204	92.54 191	91.17 326	97.47 255	84.99 323	93.08 313	94.74 305	89.90 244	99.00 278	87.54 308	98.09 270	97.72 283

CL-MVSNet_2432*1600		95.04 207	94.79 210	95.82 224	97.51 252	89.79 239	91.14 327	96.82 276	93.05 228	96.72 197	96.40 257	90.82 230	99.16 259	91.95 223	98.66 245	98.50 225

miper_lstm_enhance		94.81 217	94.80 209	94.85 262	96.16 306	86.45 298	91.14 327	98.20 197	93.49 210	97.03 179	97.37 196	84.97 286	99.26 245	95.28 123	99.56 84	98.83 193

cl-mvsnet2		93.25 269	92.84 265	94.46 278	94.30 338	86.00 303	91.09 329	96.64 283	90.74 264	95.79 239	96.31 262	78.24 313	98.77 300	94.15 179	98.34 260	98.62 216

MSDG		95.33 195	95.13 192	95.94 220	97.40 261	91.85 210	91.02 330	98.37 178	95.30 151	96.31 218	95.99 275	94.51 153	98.38 332	89.59 279	97.65 291	97.60 289

IB-MVS	85.98 20	88.63 318	86.95 327	93.68 291	95.12 329	84.82 320	90.85 331	90.17 349	87.55 296	88.48 349	91.34 346	58.01 360	99.59 151	87.24 312	93.80 342	96.63 322
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
test123		12.59 332	15.49 335	3.87 345	6.07 366	2.55 367	90.75 332	2.59 368	2.52 362	5.20 364	13.02 362	4.96 368	1.85 364	5.20 361	9.09 361	7.23 359

ppachtmachnet_test		94.49 235	94.84 206	93.46 295	96.16 306	82.10 334	90.59 333	97.48 254	90.53 267	97.01 181	97.59 174	91.01 227	99.36 221	93.97 189	99.18 185	98.94 171

PMMVS		92.39 280	91.08 292	96.30 203	93.12 352	92.81 188	90.58 334	95.96 291	79.17 347	91.85 331	92.27 337	90.29 240	98.66 313	89.85 276	96.68 315	97.43 293

our_test_3		94.20 245	94.58 222	93.07 303	96.16 306	81.20 339	90.42 335	96.84 274	90.72 265	97.14 168	97.13 209	90.47 234	99.11 266	94.04 186	98.25 264	98.91 180

YYNet1		94.73 219	94.84 206	94.41 280	97.47 257	85.09 316	90.29 336	95.85 294	92.52 239	97.53 145	97.76 157	91.97 214	99.18 254	93.31 204	96.86 309	98.95 169

MDA-MVSNet_test_wron		94.73 219	94.83 208	94.42 279	97.48 253	85.15 314	90.28 337	95.87 293	92.52 239	97.48 153	97.76 157	91.92 218	99.17 258	93.32 203	96.80 312	98.94 171

GA-MVS		92.83 274	92.15 279	94.87 261	96.97 282	87.27 288	90.03 338	96.12 287	91.83 251	94.05 283	94.57 307	76.01 328	98.97 286	92.46 219	97.34 302	98.36 238

miper_enhance_ethall		93.14 271	92.78 268	94.20 285	93.65 346	85.29 311	89.97 339	97.85 228	85.05 321	96.15 228	94.56 308	85.74 280	99.14 261	93.74 195	98.34 260	98.17 256

test-LLR		89.97 309	89.90 307	90.16 331	94.24 340	74.98 356	89.89 340	89.06 350	92.02 246	89.97 342	90.77 350	73.92 335	98.57 319	91.88 226	97.36 300	96.92 306

TESTMET0.1,1		87.20 327	86.57 329	89.07 335	93.62 347	72.84 360	89.89 340	87.01 356	85.46 316	89.12 347	90.20 353	56.00 366	97.72 347	90.91 247	96.92 307	96.64 320

test-mter		87.92 324	87.17 325	90.16 331	94.24 340	74.98 356	89.89 340	89.06 350	86.44 305	89.97 342	90.77 350	54.96 367	98.57 319	91.88 226	97.36 300	96.92 306

PCF-MVS	89.43 18	92.12 287	90.64 301	96.57 188	97.80 220	93.48 173	89.88 343	98.45 164	74.46 356	96.04 230	95.68 286	90.71 232	99.31 233	73.73 353	99.01 209	96.91 308
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
thisisatest0515		90.43 303	89.18 315	94.17 287	97.07 280	85.44 308	89.75 344	87.58 353	88.28 290	93.69 295	91.72 343	65.27 355	99.58 153	90.59 261	98.67 243	97.50 292

KD-MVS_2432*1600		88.93 316	87.74 321	92.49 314	88.04 362	81.99 335	89.63 345	95.62 297	91.35 257	95.06 256	93.11 322	56.58 363	98.63 314	85.19 326	95.07 333	96.85 311

miper_refine_blended		88.93 316	87.74 321	92.49 314	88.04 362	81.99 335	89.63 345	95.62 297	91.35 257	95.06 256	93.11 322	56.58 363	98.63 314	85.19 326	95.07 333	96.85 311

testmvs		12.33 333	15.23 336	3.64 346	5.77 367	2.23 368	88.99 347	3.62 367	2.30 363	5.29 363	13.09 361	4.52 369	1.95 363	5.16 362	8.32 362	6.75 360

cascas		91.89 290	91.35 288	93.51 294	94.27 339	85.60 306	88.86 348	98.61 150	79.32 346	92.16 328	91.44 345	89.22 255	98.12 342	90.80 251	97.47 299	96.82 314

bset_n11_16_dypcd		94.53 234	93.95 245	96.25 204	97.56 248	89.85 238	88.52 349	91.32 337	94.90 169	97.51 147	96.38 259	82.34 297	99.78 42	97.22 45	99.80 33	99.12 143

PAPM		87.64 326	85.84 331	93.04 304	96.54 292	84.99 317	88.42 350	95.57 300	79.52 345	83.82 357	93.05 328	80.57 304	98.41 329	62.29 359	92.79 344	95.71 333

PVSNet	86.72 19	91.10 298	90.97 295	91.49 323	97.56 248	78.04 347	87.17 351	94.60 309	84.65 325	92.34 326	92.20 338	87.37 272	98.47 326	85.17 328	97.69 287	97.96 272

PMMVS2		93.66 259	94.07 239	92.45 317	97.57 246	80.67 341	86.46 352	96.00 289	93.99 199	97.10 172	97.38 194	89.90 244	97.82 345	88.76 290	99.47 118	98.86 191

CHOSEN 280x420		89.98 308	89.19 314	92.37 318	95.60 321	81.13 340	86.22 353	97.09 266	81.44 338	87.44 353	93.15 321	73.99 333	99.47 184	88.69 292	99.07 202	96.52 324

tmp_tt		57.23 330	62.50 333	41.44 344	34.77 365	49.21 366	83.93 354	60.22 366	15.31 361	71.11 362	79.37 359	70.09 350	44.86 362	64.76 358	82.93 359	30.25 358

PVSNet_0	81.89 21	84.49 329	83.21 332	88.34 338	95.76 318	74.97 358	83.49 355	92.70 328	78.47 349	87.94 351	86.90 357	83.38 294	96.63 355	73.44 354	66.86 360	93.40 347

E-PMN		89.52 313	89.78 308	88.73 336	93.14 351	77.61 349	83.26 356	92.02 331	94.82 171	93.71 293	93.11 322	75.31 330	96.81 352	85.81 319	96.81 311	91.77 352

EMVS		89.06 315	89.22 311	88.61 337	93.00 353	77.34 351	82.91 357	90.92 341	94.64 176	92.63 323	91.81 342	76.30 326	97.02 350	83.83 336	96.90 308	91.48 353

MVE	73.61 22	86.48 328	85.92 330	88.18 339	96.23 302	85.28 312	81.78 358	75.79 361	86.01 307	82.53 359	91.88 341	92.74 192	87.47 360	71.42 357	94.86 336	91.78 351
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
uanet_test		0.00 336	0.00 339	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	0.00 365	0.00 370	0.00 365	0.00 363	0.00 363	0.00 361

cdsmvs_eth3d_5k		24.22 331	32.30 334	0.00 347	0.00 368	0.00 369	0.00 359	98.10 211	0.00 364	0.00 365	95.06 299	97.54 28	0.00 365	0.00 363	0.00 363	0.00 361

pcd_1.5k_mvsjas		7.98 334	10.65 337	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	0.00 365	95.82 104	0.00 365	0.00 363	0.00 363	0.00 361

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

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

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

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

ab-mvs-re		7.91 335	10.55 338	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	94.94 301	0.00 370	0.00 365	0.00 363	0.00 363	0.00 361

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

ZD-MVS					98.43 151	95.94 77		98.56 155	90.72 265	96.66 200	97.07 215	95.02 136	99.74 70	91.08 242	98.93 217

IU-MVS					99.22 57	95.40 98		98.14 207	85.77 312	98.36 75				95.23 127	99.51 106	99.49 51

test_241102_TWO								98.83 100	96.11 110	98.62 51	98.24 99	96.92 57	99.72 80	95.44 114	99.49 112	99.49 51

test_241102_ONE					99.22 57	95.35 102		98.83 100	96.04 115	99.08 31	98.13 111	97.87 20	99.33 229

test_0728_THIRD									96.62 89	98.40 70	98.28 93	97.10 44	99.71 95	95.70 95	99.62 65	99.58 28

GSMVS																98.06 264

test_part2					99.03 91	96.07 72				98.08 111

sam_mvs1												77.80 315				98.06 264

sam_mvs												77.38 319

MTGPA								98.73 123

test_post											10.87 363	76.83 323	99.07 271

patchmatchnet-post											96.84 229	77.36 320	99.42 197

gm-plane-assit					91.79 358	71.40 362			81.67 335		90.11 354		98.99 280	84.86 330

test9_res														91.29 237	98.89 223	99.00 163

agg_prior2														90.34 270	98.90 220	99.10 151

agg_prior					97.80 220	94.96 117		98.36 179		93.49 303			99.53 168

TestCases				98.06 85	99.08 84	96.16 68		99.16 17	94.35 186	97.78 141	98.07 119	95.84 101	99.12 263	91.41 235	99.42 136	98.91 180

test_prior				97.46 133	97.79 226	94.26 144		98.42 170					99.34 226			98.79 197

新几何1				97.25 150	98.29 160	94.70 128		97.73 236	77.98 350	94.83 263	96.67 242	92.08 212	99.45 191	88.17 300	98.65 247	97.61 288

旧先验1					97.80 220	93.87 156		97.75 235			97.04 218	93.57 176			98.68 242	98.72 207

原ACMM1				96.58 186	98.16 180	92.12 203		98.15 206	85.90 310	93.49 303	96.43 254	92.47 204	99.38 216	87.66 305	98.62 249	98.23 250

testdata2													99.46 187	87.84 301

segment_acmp												95.34 125

testdata				95.70 230	98.16 180	90.58 230		97.72 237	80.38 342	95.62 246	97.02 219	92.06 213	98.98 282	89.06 288	98.52 254	97.54 290

test12				97.46 133	97.61 245	94.07 149		97.78 234		93.57 300		93.31 180	99.42 197		98.78 234	98.89 184

plane_prior7					98.70 118	94.67 129

plane_prior6					98.38 154	94.37 138						91.91 219

plane_prior5								98.75 119					99.46 187	92.59 217	99.20 181	99.28 109

plane_prior4											96.77 235

plane_prior3						94.51 132			95.29 152	96.16 226

plane_prior1					98.49 144

n2								0.00 369

nn								0.00 369

door-mid								98.17 203

lessismore_v0				97.05 159	99.36 40	92.12 203		84.07 359		98.77 46	98.98 40	85.36 283	99.74 70	97.34 43	99.37 147	99.30 101

LGP-MVS_train				98.74 35	99.15 72	97.02 42		99.02 49	95.15 157	98.34 78	98.23 101	97.91 17	99.70 104	94.41 166	99.73 45	99.50 43

test11								98.08 214

door								97.81 233

HQP5-MVS						92.47 193

BP-MVS														90.51 265

HQP4-MVS										92.87 315			99.23 250			99.06 156

HQP3-MVS								98.43 167							98.74 238

HQP2-MVS												90.33 236

NP-MVS					98.14 183	93.72 164					95.08 297

ACMMP++_ref															99.52 101

ACMMP++															99.55 90

Test By Simon												94.51 153

ITE_SJBPF				97.85 99	98.64 122	96.66 53		98.51 160	95.63 137	97.22 163	97.30 202	95.52 118	98.55 322	90.97 245	98.90 220	98.34 239

DeepMVS_CX				77.17 343	90.94 360	85.28 312		74.08 364	52.51 360	80.87 361	88.03 356	75.25 331	70.63 361	59.23 360	84.94 357	75.62 356

High-resolution multi-view benchmark