High-res multi-view results

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

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

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

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

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

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

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

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

XVG-OURS-SEG-HR		95.38 66	95.00 90	96.51 43	98.10 69	94.07 15	92.46 174	98.13 32	90.69 132	93.75 178	96.25 147	98.03 2	97.02 274	92.08 98	95.55 275	98.45 117

pmmvs6		96.80 13	97.36 8	95.15 85	99.12 6	87.82 111	96.68 23	97.86 58	96.10 25	98.14 25	99.28 2	97.94 3	98.21 205	91.38 119	99.69 15	99.42 27

ACMH	88.36 12	96.59 26	97.43 4	94.07 123	98.56 34	85.33 151	96.33 39	98.30 15	94.66 36	98.72 8	98.30 36	97.51 4	98.00 218	94.87 21	99.59 34	98.86 86
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pcd1.5k->3k		41.03 341	43.65 344	33.18 354	98.74 25	0.00 373	0.00 364	97.57 83	0.00 368	0.00 370	0.00 370	97.01 5	0.00 370	0.00 367	99.52 45	99.53 16

HPM-MVS_fast		97.01 6	96.89 17	97.39 18	99.12 6	93.92 24	97.16 11	98.17 27	93.11 64	96.48 78	97.36 81	96.92 6	99.34 48	94.31 33	99.38 64	98.92 83

ACMH+	88.43 11	96.48 30	96.82 18	95.47 74	98.54 38	89.06 83	95.65 61	98.61 6	96.10 25	98.16 24	97.52 69	96.90 7	98.62 162	90.30 132	99.60 32	98.72 102

HPM-MVS		96.81 12	96.62 24	97.36 20	98.89 17	93.53 34	97.51 8	98.44 7	92.35 84	95.95 105	96.41 133	96.71 8	99.42 27	93.99 42	99.36 65	99.13 50
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
abl_6		97.31 5	97.12 14	97.86 3	98.54 38	95.32 8	96.61 25	98.35 11	95.81 30	97.55 40	97.44 75	96.51 9	99.40 35	94.06 41	99.23 80	98.85 89

mvs_tets		96.83 9	96.71 21	97.17 25	98.83 20	92.51 43	96.58 27	97.61 79	87.57 203	98.80 7	98.90 9	96.50 10	99.59 12	96.15 9	99.47 48	99.40 31

LPG-MVS_test		96.38 39	96.23 36	96.84 36	98.36 52	92.13 47	95.33 70	98.25 19	91.78 106	97.07 56	97.22 86	96.38 11	99.28 55	92.07 99	99.59 34	99.11 52

LGP-MVS_train				96.84 36	98.36 52	92.13 47		98.25 19	91.78 106	97.07 56	97.22 86	96.38 11	99.28 55	92.07 99	99.59 34	99.11 52

ACMM	88.83 9	96.30 42	96.07 46	96.97 31	98.39 48	92.95 41	94.74 94	98.03 41	90.82 130	97.15 54	96.85 107	96.25 13	99.00 93	93.10 72	99.33 68	98.95 77
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
wuyk23d		87.83 257	90.79 203	78.96 344	90.46 330	88.63 92	92.72 160	90.67 290	91.65 112	98.68 11	97.64 63	96.06 14	77.53 364	59.84 354	99.41 60	70.73 361

wuykxyi23d		96.76 15	96.57 26	97.34 21	97.75 86	96.73 3	94.37 111	96.48 169	91.00 124	99.72 2	98.99 5	96.06 14	98.21 205	94.86 22	99.90 2	97.09 196

ACMP	88.15 13	95.71 55	95.43 75	96.54 42	98.17 64	91.73 55	94.24 115	98.08 33	89.46 156	96.61 75	96.47 128	95.85 16	99.12 75	90.45 124	99.56 41	98.77 97
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
TransMVSNet (Re)		95.27 74	96.04 48	92.97 160	98.37 51	81.92 187	95.07 80	96.76 156	93.97 49	97.77 34	98.57 21	95.72 17	97.90 221	88.89 161	99.23 80	99.08 59

ACMMP_Plus		96.21 43	96.12 42	96.49 45	98.90 16	91.42 57	94.57 103	98.03 41	90.42 141	96.37 81	97.35 82	95.68 18	99.25 59	94.44 31	99.34 66	98.80 93

APD-MVS_3200maxsize		96.82 10	96.65 22	97.32 22	97.95 79	93.82 29	96.31 41	98.25 19	95.51 31	96.99 62	97.05 96	95.63 19	99.39 39	93.31 63	98.88 109	98.75 98

MP-MVS-pluss		96.08 47	95.92 53	96.57 41	99.06 8	91.21 59	93.25 146	98.32 12	87.89 197	96.86 64	97.38 78	95.55 20	99.39 39	95.47 13	99.47 48	99.11 52
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
COLMAP_ROB	91.06 5	96.75 16	96.62 24	97.13 26	98.38 49	94.31 12	96.79 21	98.32 12	96.69 15	96.86 64	97.56 66	95.48 21	98.77 141	90.11 138	99.44 54	98.31 123
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
SD-MVS		95.19 75	95.73 64	93.55 139	96.62 147	88.88 89	94.67 96	98.05 38	91.26 118	97.25 53	96.40 134	95.42 22	94.36 329	92.72 82	99.19 83	97.40 183

HFP-MVS		96.39 38	96.17 39	97.04 28	98.51 42	93.37 35	96.30 43	97.98 46	92.35 84	95.63 120	96.47 128	95.37 23	99.27 57	93.78 45	99.14 88	98.48 114

#test#		95.89 50	95.51 69	97.04 28	98.51 42	93.37 35	95.14 76	97.98 46	89.34 158	95.63 120	96.47 128	95.37 23	99.27 57	91.99 101	99.14 88	98.48 114

jajsoiax		96.59 26	96.42 29	97.12 27	98.76 24	92.49 44	96.44 35	97.42 97	86.96 213	98.71 10	98.72 17	95.36 25	99.56 16	95.92 10	99.45 52	99.32 37

TranMVSNet+NR-MVSNet		96.07 48	96.26 35	95.50 73	98.26 58	87.69 112	93.75 130	97.86 58	95.96 29	97.48 43	97.14 90	95.33 26	99.44 25	90.79 122	99.76 12	99.38 32

PMVS	87.21 14	94.97 83	95.33 78	93.91 130	98.97 13	97.16 2	95.54 65	95.85 197	96.47 19	93.40 188	97.46 74	95.31 27	95.47 313	86.18 202	98.78 126	89.11 347
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
pm-mvs1		95.43 63	95.94 51	93.93 129	98.38 49	85.08 153	95.46 67	97.12 127	91.84 102	97.28 50	98.46 28	95.30 28	97.71 246	90.17 136	99.42 56	98.99 70

PGM-MVS		96.32 40	95.94 51	97.43 15	98.59 33	93.84 28	95.33 70	98.30 15	91.40 116	95.76 116	96.87 106	95.26 29	99.45 24	92.77 78	99.21 82	99.00 68

PS-CasMVS		96.69 19	97.43 4	94.49 110	99.13 4	84.09 164	96.61 25	97.97 49	97.91 4	98.64 13	98.13 40	95.24 30	99.65 3	93.39 60	99.84 5	99.72 2

LTVRE_ROB	93.87 1	97.93 2	98.16 2	97.26 23	98.81 22	93.86 27	99.07 2	98.98 3	97.01 11	98.92 4	98.78 14	95.22 31	98.61 163	96.85 4	99.77 11	99.31 38
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
ESAPD		95.89 50	95.88 55	95.92 57	97.93 80	89.83 73	93.46 136	98.30 15	92.37 81	97.75 36	96.95 97	95.14 32	99.51 18	91.74 108	99.28 75	98.41 118

nrg030		96.32 40	96.55 27	95.62 69	97.83 83	88.55 96	95.77 58	98.29 18	92.68 72	98.03 27	97.91 53	95.13 33	98.95 101	93.85 43	99.49 47	99.36 35

APDe-MVS		96.46 32	96.64 23	95.93 55	97.68 95	89.38 80	96.90 18	98.41 10	92.52 78	97.43 46	97.92 51	95.11 34	99.50 19	94.45 30	99.30 70	98.92 83

ACMMP		96.61 23	96.34 32	97.43 15	98.61 30	93.88 25	96.95 17	98.18 26	92.26 87	96.33 82	96.84 109	95.10 35	99.40 35	93.47 56	99.33 68	99.02 67
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
OPM-MVS		95.61 58	95.45 72	96.08 49	98.49 45	91.00 62	92.65 163	97.33 111	90.05 146	96.77 68	96.85 107	95.04 36	98.56 171	92.77 78	99.06 93	98.70 103

DTE-MVSNet		96.74 17	97.43 4	94.67 97	99.13 4	84.68 156	96.51 30	97.94 55	98.14 2	98.67 12	98.32 35	95.04 36	99.69 2	93.27 64	99.82 9	99.62 10

region2R		96.41 36	96.09 44	97.38 19	98.62 28	93.81 31	96.32 40	97.96 50	92.26 87	95.28 133	96.57 123	95.02 38	99.41 31	93.63 49	99.11 91	98.94 78

PEN-MVS		96.69 19	97.39 7	94.61 99	99.16 2	84.50 157	96.54 29	98.05 38	98.06 3	98.64 13	98.25 38	95.01 39	99.65 3	92.95 76	99.83 7	99.68 4

SteuartSystems-ACMMP		96.40 37	96.30 33	96.71 38	98.63 27	91.96 50	95.70 59	98.01 44	93.34 62	96.64 73	96.57 123	94.99 40	99.36 46	93.48 55	99.34 66	98.82 91
Skip Steuart: Steuart Systems R&D Blog.
canonicalmvs		94.59 102	94.69 96	94.30 118	95.60 228	87.03 121	95.59 62	98.24 22	91.56 114	95.21 138	92.04 285	94.95 41	98.66 159	91.45 117	97.57 214	97.20 194

ACMMPR		96.46 32	96.14 40	97.41 17	98.60 31	93.82 29	96.30 43	97.96 50	92.35 84	95.57 123	96.61 121	94.93 42	99.41 31	93.78 45	99.15 87	99.00 68

CP-MVS		96.44 35	96.08 45	97.54 9	98.29 55	94.62 10	96.80 20	98.08 33	92.67 74	95.08 144	96.39 138	94.77 43	99.42 27	93.17 70	99.44 54	98.58 112

TDRefinement		97.68 3	97.60 3	97.93 2	99.02 10	95.95 6	98.61 3	98.81 4	97.41 8	97.28 50	98.46 28	94.62 44	98.84 123	94.64 26	99.53 43	98.99 70

XVS		96.49 29	96.18 38	97.44 13	98.56 34	93.99 22	96.50 31	97.95 52	94.58 37	94.38 162	96.49 125	94.56 45	99.39 39	93.57 50	99.05 95	98.93 79

X-MVStestdata		90.70 203	88.45 233	97.44 13	98.56 34	93.99 22	96.50 31	97.95 52	94.58 37	94.38 162	26.89 365	94.56 45	99.39 39	93.57 50	99.05 95	98.93 79

mPP-MVS		96.46 32	96.05 47	97.69 5	98.62 28	94.65 9	96.45 33	97.74 69	92.59 77	95.47 125	96.68 118	94.50 47	99.42 27	93.10 72	99.26 76	98.99 70

DeepC-MVS	91.39 4	95.43 63	95.33 78	95.71 67	97.67 96	90.17 68	93.86 128	98.02 43	87.35 205	96.22 92	97.99 48	94.48 48	99.05 83	92.73 81	99.68 18	97.93 146
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SMA-MVS		95.77 53	95.54 68	96.47 46	98.27 57	91.19 60	95.09 78	97.79 67	86.48 218	97.42 48	97.51 71	94.47 49	99.29 53	93.55 52	99.29 72	98.93 79

MP-MVS		96.14 45	95.68 65	97.51 10	98.81 22	94.06 16	96.10 47	97.78 68	92.73 71	93.48 185	96.72 116	94.23 50	99.42 27	91.99 101	99.29 72	99.05 63
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
anonymousdsp		96.74 17	96.42 29	97.68 7	98.00 75	94.03 21	96.97 16	97.61 79	87.68 202	98.45 21	98.77 15	94.20 51	99.50 19	96.70 5	99.40 61	99.53 16

test_0402		95.73 54	96.22 37	94.26 119	98.19 63	85.77 146	93.24 147	97.24 119	96.88 14	97.69 37	97.77 59	94.12 52	99.13 73	91.54 116	99.29 72	97.88 152

Effi-MVS+		92.79 160	92.74 157	92.94 163	95.10 245	83.30 172	94.00 120	97.53 88	91.36 117	89.35 283	90.65 309	94.01 53	98.66 159	87.40 184	95.30 283	96.88 208

OMC-MVS		94.22 115	93.69 132	95.81 61	97.25 111	91.27 58	92.27 184	97.40 99	87.10 211	94.56 158	95.42 186	93.74 54	98.11 213	86.62 194	98.85 113	98.06 136

LCM-MVSNet-Re		94.20 116	94.58 100	93.04 154	95.91 211	83.13 176	93.79 129	99.19 2	92.00 96	98.84 5	98.04 43	93.64 55	99.02 90	81.28 250	98.54 141	96.96 202

zzz-MVS		96.47 31	96.14 40	97.47 11	98.95 14	94.05 18	93.69 132	97.62 76	94.46 41	96.29 86	96.94 99	93.56 56	99.37 44	94.29 35	99.42 56	98.99 70

MTAPA		96.65 21	96.38 31	97.47 11	98.95 14	94.05 18	95.88 55	97.62 76	94.46 41	96.29 86	96.94 99	93.56 56	99.37 44	94.29 35	99.42 56	98.99 70

UA-Net		97.35 4	97.24 12	97.69 5	98.22 60	93.87 26	98.42 4	98.19 25	96.95 12	95.46 127	99.23 3	93.45 58	99.57 13	95.34 17	99.89 4	99.63 9

MVS_111021_HR		93.63 127	93.42 142	94.26 119	96.65 141	86.96 122	89.30 284	96.23 185	88.36 187	93.57 183	94.60 216	93.45 58	97.77 241	90.23 134	98.38 155	98.03 138

cdsmvs_eth3d_5k		23.35 344	31.13 345	0.00 358	0.00 373	0.00 373	0.00 364	95.58 208	0.00 368	0.00 370	91.15 296	93.43 60	0.00 370	0.00 367	0.00 368	0.00 368

APD-MVS		95.00 82	94.69 96	95.93 55	97.38 107	90.88 65	94.59 100	97.81 63	89.22 162	95.46 127	96.17 157	93.42 61	99.34 48	89.30 151	98.87 112	97.56 176
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
v52		96.93 7	97.29 10	95.86 59	98.12 66	88.48 99	97.69 6	97.74 69	94.90 34	98.55 15	98.72 17	93.39 62	99.49 22	96.92 2	99.62 29	99.61 11

V4		96.93 7	97.29 10	95.86 59	98.11 67	88.47 100	97.69 6	97.74 69	94.91 32	98.55 15	98.72 17	93.37 63	99.49 22	96.92 2	99.62 29	99.61 11

ANet_high		94.83 92	96.28 34	90.47 243	96.65 141	73.16 316	94.33 113	98.74 5	96.39 21	98.09 26	98.93 8	93.37 63	98.70 155	90.38 127	99.68 18	99.53 16

test_djsdf		96.62 22	96.49 28	97.01 30	98.55 37	91.77 54	97.15 12	97.37 101	88.98 164	98.26 23	98.86 10	93.35 65	99.60 8	96.41 6	99.45 52	99.66 6

v748		96.51 28	97.05 15	94.89 91	98.35 54	85.82 145	96.58 27	97.47 94	96.25 22	98.46 19	98.35 33	93.27 66	99.33 51	95.13 19	99.59 34	99.52 19

VPA-MVSNet		95.14 78	95.67 66	93.58 138	97.76 85	83.15 175	94.58 102	97.58 82	93.39 61	97.05 60	98.04 43	93.25 67	98.51 180	89.75 145	99.59 34	99.08 59

Anonymous20240529		95.50 61	95.83 59	94.50 108	97.33 110	85.93 142	95.19 75	96.77 155	96.64 17	97.61 39	98.05 42	93.23 68	98.79 133	88.60 168	99.04 98	98.78 95

DeepPCF-MVS	90.46 6	94.20 116	93.56 137	96.14 47	95.96 207	92.96 40	89.48 278	97.46 95	85.14 234	96.23 91	95.42 186	93.19 69	98.08 214	90.37 128	98.76 128	97.38 186

Anonymous20231211		96.60 24	97.13 13	95.00 88	97.46 106	86.35 134	97.11 15	98.24 22	97.58 6	98.72 8	98.97 7	93.15 70	99.15 68	93.18 69	99.74 14	99.50 21

LS3D		96.11 46	95.83 59	96.95 33	94.75 255	94.20 14	97.34 10	97.98 46	97.31 9	95.32 130	96.77 110	93.08 71	99.20 64	91.79 107	98.16 181	97.44 180

DP-MVS		95.62 57	95.84 58	94.97 89	97.16 115	88.62 93	94.54 107	97.64 75	96.94 13	96.58 76	97.32 83	93.07 72	98.72 148	90.45 124	98.84 114	97.57 174

EG-PatchMatch MVS		94.54 105	94.67 98	94.14 121	97.87 82	86.50 126	92.00 193	96.74 157	88.16 193	96.93 63	97.61 64	93.04 73	97.90 221	91.60 113	98.12 186	98.03 138

Fast-Effi-MVS+		91.28 197	90.86 200	92.53 188	95.45 233	82.53 182	89.25 287	96.52 167	85.00 238	89.91 272	88.55 326	92.94 74	98.84 123	84.72 220	95.44 280	96.22 235

v7n		96.82 10	97.31 9	95.33 78	98.54 38	86.81 124	96.83 19	98.07 36	96.59 18	98.46 19	98.43 32	92.91 75	99.52 17	96.25 8	99.76 12	99.65 8

XVG-ACMP-BASELINE		95.68 56	95.34 77	96.69 39	98.40 47	93.04 38	94.54 107	98.05 38	90.45 139	96.31 84	96.76 112	92.91 75	98.72 148	91.19 120	99.42 56	98.32 121

testgi		90.38 209	91.34 191	87.50 304	97.49 104	71.54 326	89.43 279	95.16 216	88.38 186	94.54 159	94.68 215	92.88 77	93.09 339	71.60 328	97.85 203	97.88 152

MVS_111021_LR		93.66 125	93.28 145	94.80 94	96.25 182	90.95 63	90.21 254	95.43 212	87.91 195	93.74 180	94.40 222	92.88 77	96.38 297	90.39 126	98.28 168	97.07 197

CNVR-MVS		94.58 103	94.29 109	95.46 75	96.94 126	89.35 81	91.81 211	96.80 151	89.66 154	93.90 176	95.44 185	92.80 79	98.72 148	92.74 80	98.52 143	98.32 121

XXY-MVS		92.58 168	93.16 148	90.84 238	97.75 86	79.84 223	91.87 202	96.22 187	85.94 225	95.53 124	97.68 61	92.69 80	94.48 325	83.21 231	97.51 216	98.21 129

CDPH-MVS		92.67 165	91.83 176	95.18 84	96.94 126	88.46 101	90.70 238	97.07 128	77.38 301	92.34 220	95.08 197	92.67 81	98.88 111	85.74 204	98.57 138	98.20 130

Fast-Effi-MVS+-dtu		92.77 162	92.16 170	94.58 106	94.66 263	88.25 103	92.05 191	96.65 160	89.62 155	90.08 265	91.23 295	92.56 82	98.60 165	86.30 201	96.27 263	96.90 206

AllTest		94.88 89	94.51 102	96.00 50	98.02 73	92.17 45	95.26 73	98.43 8	90.48 137	95.04 145	96.74 114	92.54 83	97.86 232	85.11 213	98.98 102	97.98 142

TestCases				96.00 50	98.02 73	92.17 45		98.43 8	90.48 137	95.04 145	96.74 114	92.54 83	97.86 232	85.11 213	98.98 102	97.98 142

TinyColmap		92.00 183	92.76 156	89.71 259	95.62 227	77.02 275	90.72 237	96.17 189	87.70 201	95.26 134	96.29 144	92.54 83	96.45 293	81.77 245	98.77 127	95.66 256

Regformer-2		94.86 90	94.55 101	95.77 63	92.83 298	89.98 70	91.87 202	96.40 173	94.38 43	96.19 96	95.04 199	92.47 86	99.04 86	93.49 54	98.31 164	98.28 125

CLD-MVS		91.82 184	91.41 188	93.04 154	96.37 164	83.65 168	86.82 317	97.29 115	84.65 243	92.27 222	89.67 319	92.20 87	97.85 235	83.95 225	99.47 48	97.62 172
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
segment_acmp												92.14 88

Regformer-4		94.90 87	94.67 98	95.59 70	92.78 300	89.02 84	92.39 178	95.91 194	94.50 39	96.41 79	95.56 180	92.10 89	99.01 92	94.23 37	98.14 183	98.74 99

Vis-MVSNet		95.50 61	95.48 70	95.56 72	98.11 67	89.40 79	95.35 69	98.22 24	92.36 82	94.11 170	98.07 41	92.02 90	99.44 25	93.38 61	97.67 210	97.85 155
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
Regformer-1		94.55 104	94.33 108	95.19 83	92.83 298	88.54 97	91.87 202	95.84 198	93.99 47	95.95 105	95.04 199	92.00 91	98.79 133	93.14 71	98.31 164	98.23 127

ITE_SJBPF				95.95 52	97.34 109	93.36 37		96.55 166	91.93 97	94.82 151	95.39 189	91.99 92	97.08 272	85.53 206	97.96 197	97.41 181

CP-MVSNet		96.19 44	96.80 19	94.38 116	98.99 12	83.82 166	96.31 41	97.53 88	97.60 5	98.34 22	97.52 69	91.98 93	99.63 6	93.08 74	99.81 10	99.70 3

CSCG		94.69 98	94.75 94	94.52 107	97.55 101	87.87 109	95.01 84	97.57 83	92.68 72	96.20 94	93.44 252	91.92 94	98.78 137	89.11 159	99.24 78	96.92 204

TSAR-MVS + MP.		94.96 84	94.75 94	95.57 71	98.86 19	88.69 90	96.37 38	96.81 150	85.23 232	94.75 153	97.12 92	91.85 95	99.40 35	93.45 57	98.33 162	98.62 108

Gipuma		95.31 70	95.80 61	93.81 134	97.99 78	90.91 64	96.42 36	97.95 52	96.69 15	91.78 231	98.85 12	91.77 96	95.49 312	91.72 109	99.08 92	95.02 273
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
WR-MVS_H		96.60 24	97.05 15	95.24 81	99.02 10	86.44 130	96.78 22	98.08 33	97.42 7	98.48 18	97.86 56	91.76 97	99.63 6	94.23 37	99.84 5	99.66 6

AdaColmap		91.63 186	91.36 190	92.47 190	95.56 229	86.36 133	92.24 187	96.27 182	88.88 168	89.90 273	92.69 267	91.65 98	98.32 196	77.38 293	97.64 211	92.72 323

PHI-MVS		94.34 111	93.80 124	95.95 52	95.65 223	91.67 56	94.82 91	97.86 58	87.86 198	93.04 202	94.16 231	91.58 99	98.78 137	90.27 133	98.96 105	97.41 181

xiu_mvs_v1_base_debu		91.47 191	91.52 183	91.33 223	95.69 220	81.56 191	89.92 266	96.05 191	83.22 251	91.26 238	90.74 304	91.55 100	98.82 126	89.29 152	95.91 268	93.62 308

xiu_mvs_v1_base		91.47 191	91.52 183	91.33 223	95.69 220	81.56 191	89.92 266	96.05 191	83.22 251	91.26 238	90.74 304	91.55 100	98.82 126	89.29 152	95.91 268	93.62 308

xiu_mvs_v1_base_debi		91.47 191	91.52 183	91.33 223	95.69 220	81.56 191	89.92 266	96.05 191	83.22 251	91.26 238	90.74 304	91.55 100	98.82 126	89.29 152	95.91 268	93.62 308

tfpnnormal		94.27 113	94.87 93	92.48 189	97.71 91	80.88 199	94.55 106	95.41 213	93.70 54	96.67 72	97.72 60	91.40 103	98.18 210	87.45 182	99.18 85	98.36 119

Regformer-3		94.28 112	94.23 114	94.46 112	92.78 300	86.28 135	92.39 178	94.70 227	93.69 57	95.97 103	95.56 180	91.34 104	98.48 184	93.45 57	98.14 183	98.62 108

3Dnovator+	92.74 2	95.86 52	95.77 62	96.13 48	96.81 134	90.79 67	96.30 43	97.82 62	96.13 24	94.74 154	97.23 85	91.33 105	99.16 66	93.25 65	98.30 167	98.46 116

TEST9					96.45 161	89.46 75	90.60 241	96.92 141	79.09 291	90.49 259	94.39 223	91.31 106	98.88 111

agg_prior1		92.60 167	91.76 179	95.10 86	96.20 184	88.89 87	90.37 249	96.88 146	79.67 283	90.21 262	94.41 220	91.30 107	98.78 137	88.46 170	98.37 160	97.64 171

DeepC-MVS_fast	89.96 7	93.73 124	93.44 141	94.60 103	96.14 189	87.90 108	93.36 139	97.14 124	85.53 231	93.90 176	95.45 184	91.30 107	98.59 167	89.51 148	98.62 135	97.31 189
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
EI-MVSNet-Vis-set		94.36 109	94.28 110	94.61 99	92.55 303	85.98 141	92.44 175	94.69 228	93.70 54	96.12 99	95.81 169	91.24 109	98.86 120	93.76 48	98.22 176	98.98 75

MCST-MVS		92.91 157	92.51 164	94.10 122	97.52 102	85.72 147	91.36 222	97.13 126	80.33 276	92.91 206	94.24 227	91.23 110	98.72 148	89.99 142	97.93 199	97.86 154

RPSCF		95.58 59	94.89 92	97.62 8	97.58 99	96.30 5	95.97 51	97.53 88	92.42 79	93.41 186	97.78 57	91.21 111	97.77 241	91.06 121	97.06 235	98.80 93

train_agg		92.71 164	91.83 176	95.35 76	96.45 161	89.46 75	90.60 241	96.92 141	79.37 286	90.49 259	94.39 223	91.20 112	98.88 111	88.66 166	98.43 151	97.72 164

test_8					96.37 164	89.14 82	90.51 245	96.89 145	79.37 286	90.42 261	94.36 225	91.20 112	98.82 126

EI-MVSNet-UG-set		94.35 110	94.27 112	94.59 104	92.46 304	85.87 143	92.42 177	94.69 228	93.67 58	96.13 98	95.84 168	91.20 112	98.86 120	93.78 45	98.23 174	99.03 66

testing_2		94.03 119	94.38 105	93.00 158	96.79 136	81.41 194	92.87 157	96.96 136	85.88 227	97.06 59	97.92 51	91.18 115	98.71 154	91.72 109	99.04 98	98.87 85

xiu_mvs_v2_base		89.00 233	89.19 219	88.46 294	94.86 250	74.63 300	86.97 314	95.60 204	80.88 272	87.83 307	88.62 325	91.04 116	98.81 131	82.51 239	94.38 300	91.93 334

HPM-MVS++		95.02 81	94.39 104	96.91 34	97.88 81	93.58 33	94.09 118	96.99 134	91.05 123	92.40 215	95.22 192	91.03 117	99.25 59	92.11 96	98.69 133	97.90 150

TAPA-MVS	88.58 10	92.49 173	91.75 180	94.73 96	96.50 154	89.69 74	92.91 155	97.68 73	78.02 298	92.79 207	94.10 233	90.85 118	97.96 220	84.76 219	98.16 181	96.54 215
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
pcd_1.5k_mvsjas		7.56 347	10.09 348	0.00 358	0.00 373	0.00 373	0.00 364	0.00 375	0.00 368	0.00 370	0.00 370	90.77 119	0.00 370	0.00 367	0.00 368	0.00 368

PS-MVSNAJss		96.01 49	96.04 48	95.89 58	98.82 21	88.51 98	95.57 63	97.88 57	88.72 176	98.81 6	98.86 10	90.77 119	99.60 8	95.43 14	99.53 43	99.57 14

PS-MVSNAJ		88.86 237	88.99 225	88.48 293	94.88 248	74.71 298	86.69 318	95.60 204	80.88 272	87.83 307	87.37 338	90.77 119	98.82 126	82.52 238	94.37 301	91.93 334

MVS_Test		92.57 170	93.29 143	90.40 245	93.53 288	75.85 286	92.52 168	96.96 136	88.73 175	92.35 218	96.70 117	90.77 119	98.37 195	92.53 90	95.49 277	96.99 201

MIMVSNet1		95.52 60	95.45 72	95.72 66	99.14 3	89.02 84	96.23 46	96.87 148	93.73 53	97.87 32	98.49 26	90.73 123	99.05 83	86.43 199	99.60 32	99.10 55

agg_prior3		92.56 171	91.62 181	95.35 76	96.39 163	89.45 77	90.61 240	96.82 149	78.82 294	90.03 267	94.14 232	90.72 124	98.88 111	88.66 166	98.43 151	97.72 164

ab-mvs		92.40 174	92.62 161	91.74 210	97.02 122	81.65 190	95.84 56	95.50 211	86.95 214	92.95 205	97.56 66	90.70 125	97.50 253	79.63 269	97.43 223	96.06 241

Test By Simon												90.61 126

3Dnovator	92.54 3	94.80 93	94.90 91	94.47 111	95.47 232	87.06 120	96.63 24	97.28 117	91.82 105	94.34 165	97.41 76	90.60 127	98.65 161	92.47 91	98.11 187	97.70 166

NCCC		94.08 118	93.54 138	95.70 68	96.49 155	89.90 72	92.39 178	96.91 144	90.64 134	92.33 221	94.60 216	90.58 128	98.96 99	90.21 135	97.70 208	98.23 127

UniMVSNet_NR-MVSNet		95.35 67	95.21 84	95.76 64	97.69 94	88.59 94	92.26 185	97.84 61	94.91 32	96.80 66	95.78 172	90.42 129	99.41 31	91.60 113	99.58 39	99.29 39

test_prior3		93.29 140	92.85 153	94.61 99	95.95 208	87.23 116	90.21 254	97.36 107	89.33 159	90.77 252	94.81 206	90.41 130	98.68 157	88.21 171	98.55 139	97.93 146

test_prior2							90.21 254		89.33 159	90.77 252	94.81 206	90.41 130		88.21 171	98.55 139

MSLP-MVS++		93.25 145	93.88 122	91.37 222	96.34 173	82.81 179	93.11 148	97.74 69	89.37 157	94.08 172	95.29 191	90.40 132	96.35 299	90.35 130	98.25 172	94.96 274

UniMVSNet (Re)		95.32 68	95.15 86	95.80 62	97.79 84	88.91 86	92.91 155	98.07 36	93.46 59	96.31 84	95.97 163	90.14 133	99.34 48	92.11 96	99.64 26	99.16 47

Effi-MVS+-dtu		93.90 122	92.60 162	97.77 4	94.74 256	96.67 4	94.00 120	95.41 213	89.94 148	91.93 230	92.13 283	90.12 134	98.97 98	87.68 179	97.48 221	97.67 169

mvs-test1		93.07 151	91.80 178	96.89 35	94.74 256	95.83 7	92.17 188	95.41 213	89.94 148	89.85 274	90.59 310	90.12 134	98.88 111	87.68 179	95.66 273	95.97 243

FMVSNet1		94.84 91	95.13 87	93.97 126	97.60 98	84.29 158	95.99 48	96.56 163	92.38 80	97.03 61	98.53 23	90.12 134	98.98 94	88.78 163	99.16 86	98.65 104

DU-MVS		95.28 72	95.12 88	95.75 65	97.75 86	88.59 94	92.58 164	97.81 63	93.99 47	96.80 66	95.90 164	90.10 137	99.41 31	91.60 113	99.58 39	99.26 40

NR-MVSNet		95.28 72	95.28 81	95.26 80	97.75 86	87.21 118	95.08 79	97.37 101	93.92 51	97.65 38	95.90 164	90.10 137	99.33 51	90.11 138	99.66 23	99.26 40

Baseline_NR-MVSNet		94.47 107	95.09 89	92.60 182	98.50 44	80.82 200	92.08 190	96.68 159	93.82 52	96.29 86	98.56 22	90.10 137	97.75 244	90.10 140	99.66 23	99.24 42

API-MVS		91.52 189	91.61 182	91.26 227	94.16 274	86.26 136	94.66 97	94.82 222	91.17 121	92.13 225	91.08 298	90.03 140	97.06 273	79.09 274	97.35 228	90.45 344

diffmvs1		92.93 156	93.48 140	91.27 226	92.73 302	79.03 250	92.35 181	96.79 152	90.94 125	91.04 249	96.92 104	89.99 141	97.48 256	93.20 68	97.32 229	97.31 189

test12				94.43 114	95.95 208	86.75 125		96.24 184		89.76 277		89.79 142	98.79 133		97.95 198	97.75 163

diffmvs		92.17 180	92.73 158	90.49 242	92.22 307	77.47 270	92.53 167	95.74 201	90.43 140	88.32 300	96.48 126	89.76 143	97.38 264	92.63 85	96.50 260	96.63 214

v13		95.39 65	96.12 42	93.18 150	97.22 112	80.81 201	95.55 64	97.57 83	93.42 60	98.02 29	98.49 26	89.62 144	99.18 65	95.54 12	99.68 18	99.54 15

旧先验1					96.20 184	84.17 162		94.82 222			95.57 179	89.57 145			97.89 201	96.32 231

DELS-MVS		92.05 182	92.16 170	91.72 211	94.44 269	80.13 212	87.62 304	97.25 118	87.34 206	92.22 223	93.18 259	89.54 146	98.73 147	89.67 146	98.20 179	96.30 232
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
v12		95.29 71	96.02 50	93.10 152	97.14 118	80.63 202	95.39 68	97.55 87	93.19 63	97.98 30	98.44 30	89.40 147	99.16 66	95.38 16	99.67 21	99.52 19

VPNet		93.08 149	93.76 127	91.03 233	98.60 31	75.83 288	91.51 217	95.62 203	91.84 102	95.74 117	97.10 93	89.31 148	98.32 196	85.07 215	99.06 93	98.93 79

casdiffmvs1		93.02 153	93.00 149	93.07 153	95.65 223	82.54 181	94.79 93	97.35 109	80.09 278	92.18 224	97.51 71	89.25 149	98.84 123	92.65 84	97.52 215	97.83 156

QAPM		92.88 158	92.77 155	93.22 149	95.82 213	83.31 171	96.45 33	97.35 109	83.91 247	93.75 178	96.77 110	89.25 149	98.88 111	84.56 221	97.02 237	97.49 178

MSDG		90.82 200	90.67 206	91.26 227	94.16 274	83.08 177	86.63 320	96.19 188	90.60 136	91.94 229	91.89 286	89.16 151	95.75 308	80.96 257	94.51 299	94.95 275

V9		95.17 77	95.89 54	93.02 156	97.04 121	80.42 204	95.22 74	97.53 88	92.92 70	97.90 31	98.35 33	89.15 152	99.14 71	95.21 18	99.65 25	99.50 21

v11		95.10 79	95.88 55	92.76 173	96.98 124	79.64 233	95.12 77	97.60 81	92.64 75	98.03 27	98.44 30	89.06 153	99.15 68	95.42 15	99.67 21	99.50 21

CPTT-MVS		94.74 96	94.12 115	96.60 40	98.15 65	93.01 39	95.84 56	97.66 74	89.21 163	93.28 192	95.46 183	88.89 154	98.98 94	89.80 144	98.82 120	97.80 160

V14		95.05 80	95.75 63	92.94 163	96.94 126	80.21 207	95.03 82	97.50 92	92.62 76	97.84 33	98.28 37	88.87 155	99.13 73	95.03 20	99.64 26	99.48 24

v15		94.93 85	95.62 67	92.86 168	96.83 132	80.01 220	94.84 90	97.48 93	92.36 82	97.76 35	98.20 39	88.61 156	99.11 76	94.86 22	99.62 29	99.46 25

DP-MVS Recon		92.31 176	91.88 175	93.60 137	97.18 114	86.87 123	91.10 228	97.37 101	84.92 240	92.08 226	94.08 234	88.59 157	98.20 207	83.50 228	98.14 183	95.73 252

FC-MVSNet-test		95.32 68	95.88 55	93.62 136	98.49 45	81.77 188	95.90 54	98.32 12	93.93 50	97.53 41	97.56 66	88.48 158	99.40 35	92.91 77	99.83 7	99.68 4

OpenMVS	89.45 8	92.27 178	92.13 172	92.68 178	94.53 268	84.10 163	95.70 59	97.03 130	82.44 263	91.14 248	96.42 132	88.47 159	98.38 192	85.95 203	97.47 222	95.55 263

v17		94.80 93	95.46 71	92.83 169	96.76 137	80.02 218	94.85 88	97.40 99	92.23 89	97.45 45	98.04 43	88.46 160	99.06 81	94.56 27	99.40 61	99.41 28

F-COLMAP		92.28 177	91.06 197	95.95 52	97.52 102	91.90 51	93.53 134	97.18 122	83.98 246	88.70 295	94.04 235	88.41 161	98.55 177	80.17 263	95.99 267	97.39 184

v16		94.79 95	95.44 74	92.83 169	96.73 138	80.03 216	94.85 88	97.41 98	92.23 89	97.41 49	98.04 43	88.40 162	99.06 81	94.56 27	99.30 70	99.41 28

ambc				92.98 159	96.88 130	83.01 178	95.92 53	96.38 176		96.41 79	97.48 73	88.26 163	97.80 238	89.96 143	98.93 106	98.12 135

casdiffmvs		92.55 172	92.40 168	93.01 157	94.72 260	83.36 170	94.54 107	97.04 129	83.00 257	89.97 270	96.95 97	88.23 164	98.76 142	93.22 66	93.95 307	96.92 204

v7		93.66 125	93.97 117	92.73 176	96.55 151	80.15 209	92.54 165	96.99 134	87.36 204	95.99 102	96.48 126	88.18 165	98.94 104	93.35 62	98.31 164	99.09 56

v10		94.68 99	95.27 82	92.90 166	96.57 150	80.15 209	94.65 98	97.57 83	90.68 133	97.43 46	98.00 47	88.18 165	99.15 68	94.84 24	99.55 42	99.41 28

v8		94.65 100	95.29 80	92.74 174	96.65 141	79.77 228	94.59 100	97.17 123	91.86 101	97.47 44	97.93 50	88.16 167	99.08 78	94.32 32	99.47 48	99.38 32

v18		94.63 101	95.26 83	92.74 174	96.60 148	79.81 226	94.64 99	97.37 101	91.87 100	97.26 52	97.91 53	88.13 168	99.04 86	94.30 34	99.24 78	99.38 32

v6		93.59 128	93.93 118	92.56 184	96.65 141	79.77 228	92.50 171	96.40 173	88.55 181	95.94 107	96.23 150	88.13 168	98.87 117	92.46 92	98.50 147	99.06 62

v1neww		93.58 129	93.92 120	92.56 184	96.64 145	79.77 228	92.50 171	96.41 171	88.55 181	95.93 108	96.24 148	88.08 170	98.87 117	92.45 93	98.50 147	99.05 63

v7new		93.58 129	93.92 120	92.56 184	96.64 145	79.77 228	92.50 171	96.41 171	88.55 181	95.93 108	96.24 148	88.08 170	98.87 117	92.45 93	98.50 147	99.05 63

TSAR-MVS + GP.		93.07 151	92.41 167	95.06 87	95.82 213	90.87 66	90.97 230	92.61 269	88.04 194	94.61 157	93.79 243	88.08 170	97.81 237	89.41 150	98.39 154	96.50 224

OurMVSNet-221017-0		96.80 13	96.75 20	96.96 32	99.03 9	91.85 52	97.98 5	98.01 44	94.15 45	98.93 3	99.07 4	88.07 173	99.57 13	95.86 11	99.69 15	99.46 25

原ACMM1				92.87 167	96.91 129	84.22 161		97.01 131	76.84 306	89.64 279	94.46 219	88.00 174	98.70 155	81.53 248	98.01 195	95.70 254

VDD-MVS		94.37 108	94.37 106	94.40 115	97.49 104	86.07 139	93.97 122	93.28 253	94.49 40	96.24 90	97.78 57	87.99 175	98.79 133	88.92 160	99.14 88	98.34 120

XVG-OURS		94.72 97	94.12 115	96.50 44	98.00 75	94.23 13	91.48 218	98.17 27	90.72 131	95.30 131	96.47 128	87.94 176	96.98 275	91.41 118	97.61 213	98.30 124

CANet		92.38 175	91.99 174	93.52 143	93.82 284	83.46 169	91.14 226	97.00 132	89.81 152	86.47 318	94.04 235	87.90 177	99.21 63	89.50 149	98.27 169	97.90 150

BH-untuned		90.68 204	90.90 198	90.05 255	95.98 206	79.57 236	90.04 262	94.94 220	87.91 195	94.07 173	93.00 260	87.76 178	97.78 240	79.19 273	95.17 286	92.80 321

FIs		94.90 87	95.35 76	93.55 139	98.28 56	81.76 189	95.33 70	98.14 29	93.05 65	97.07 56	97.18 88	87.65 179	99.29 53	91.72 109	99.69 15	99.61 11

v1144		93.50 131	93.81 123	92.57 183	96.28 178	79.61 235	91.86 206	96.96 136	86.95 214	95.91 111	96.32 143	87.65 179	98.96 99	93.51 53	98.88 109	99.13 50

mvs_anonymous		90.37 210	91.30 192	87.58 303	92.17 311	68.00 337	89.84 271	94.73 226	83.82 249	93.22 198	97.40 77	87.54 181	97.40 261	87.94 176	95.05 288	97.34 187

PCF-MVS	84.52 17	89.12 231	87.71 250	93.34 145	96.06 193	85.84 144	86.58 321	97.31 112	68.46 344	93.61 182	93.89 240	87.51 182	98.52 179	67.85 341	98.11 187	95.66 256
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
v1		93.43 134	93.77 126	92.41 191	96.37 164	79.24 242	91.84 207	96.38 176	88.33 188	95.87 112	96.22 153	87.45 183	98.89 107	92.61 87	98.83 117	99.09 56

VNet		92.67 165	92.96 150	91.79 209	96.27 179	80.15 209	91.95 194	94.98 218	92.19 92	94.52 160	96.07 159	87.43 184	97.39 262	84.83 217	98.38 155	97.83 156

v1141		93.42 136	93.76 127	92.40 193	96.37 164	79.24 242	91.84 207	96.38 176	88.33 188	95.86 113	96.23 150	87.41 185	98.89 107	92.61 87	98.82 120	99.08 59

divwei89l23v2f112		93.42 136	93.76 127	92.41 191	96.37 164	79.24 242	91.84 207	96.38 176	88.33 188	95.86 113	96.23 150	87.41 185	98.89 107	92.61 87	98.83 117	99.09 56

v148		92.87 159	93.29 143	91.62 214	96.25 182	77.72 267	91.28 223	95.05 217	89.69 153	95.93 108	96.04 160	87.34 187	98.38 192	90.05 141	97.99 196	98.78 95

V42		93.43 134	93.58 136	92.97 160	95.34 239	81.22 195	92.67 162	96.49 168	87.25 207	96.20 94	96.37 141	87.32 188	98.85 122	92.39 95	98.21 177	98.85 89

v1192		93.49 132	93.78 125	92.62 181	96.16 188	79.62 234	91.83 210	97.22 121	86.07 223	96.10 100	96.38 140	87.22 189	99.02 90	94.14 40	98.88 109	99.22 43

WR-MVS		93.49 132	93.72 130	92.80 172	97.57 100	80.03 216	90.14 258	95.68 202	93.70 54	96.62 74	95.39 189	87.21 190	99.04 86	87.50 181	99.64 26	99.33 36

IterMVS-LS		93.78 123	94.28 110	92.27 195	96.27 179	79.21 247	91.87 202	96.78 153	91.77 108	96.57 77	97.07 94	87.15 191	98.74 146	91.99 101	99.03 100	98.86 86
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		92.99 154	93.26 147	92.19 198	92.12 312	79.21 247	92.32 182	94.67 230	91.77 108	95.24 136	95.85 166	87.14 192	98.49 181	91.99 101	98.26 170	98.86 86

v144192		93.20 148	93.54 138	92.16 200	96.05 194	78.26 261	91.95 194	97.14 124	84.98 239	95.96 104	96.11 158	87.08 193	99.04 86	93.79 44	98.84 114	99.17 46

MVS_0304		92.99 154	92.54 163	94.35 117	94.67 262	86.06 140	91.16 225	97.92 56	90.01 147	88.33 299	94.41 220	87.02 194	99.22 62	90.36 129	99.00 101	97.76 162

114514_t		90.51 205	89.80 214	92.63 180	98.00 75	82.24 184	93.40 138	97.29 115	65.84 352	89.40 282	94.80 209	86.99 195	98.75 143	83.88 226	98.61 136	96.89 207

新几何1				93.17 151	97.16 115	87.29 115		94.43 232	67.95 345	91.29 237	94.94 203	86.97 196	98.23 204	81.06 255	97.75 204	93.98 298

HQP_MVS		94.26 114	93.93 118	95.23 82	97.71 91	88.12 105	94.56 104	97.81 63	91.74 110	93.31 189	95.59 175	86.93 197	98.95 101	89.26 155	98.51 145	98.60 110

plane_prior6					97.21 113	88.23 104						86.93 197

1121		90.26 214	89.23 218	93.34 145	97.15 117	87.40 114	91.94 196	94.39 233	67.88 346	91.02 250	94.91 204	86.91 199	98.59 167	81.17 253	97.71 207	94.02 297

UGNet		93.08 149	92.50 165	94.79 95	93.87 282	87.99 107	95.07 80	94.26 237	90.64 134	87.33 313	97.67 62	86.89 200	98.49 181	88.10 175	98.71 131	97.91 149
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
LF4IMVS		92.72 163	92.02 173	94.84 93	95.65 223	91.99 49	92.92 154	96.60 162	85.08 237	92.44 214	93.62 245	86.80 201	96.35 299	86.81 189	98.25 172	96.18 237

v1921920		93.26 143	93.61 135	92.19 198	96.04 197	78.31 260	91.88 201	97.24 119	85.17 233	96.19 96	96.19 155	86.76 202	99.05 83	94.18 39	98.84 114	99.22 43

v1240		93.29 140	93.71 131	92.06 203	96.01 198	77.89 265	91.81 211	97.37 101	85.12 235	96.69 71	96.40 134	86.67 203	99.07 80	94.51 29	98.76 128	99.22 43

test_normal		91.49 190	91.44 187	91.62 214	95.21 242	79.44 237	90.08 261	93.84 244	82.60 259	94.37 164	94.74 212	86.66 204	98.46 187	88.58 169	96.92 240	96.95 203

MAR-MVS		90.32 213	88.87 229	94.66 98	94.82 251	91.85 52	94.22 116	94.75 225	80.91 271	87.52 312	88.07 330	86.63 205	97.87 231	76.67 297	96.21 265	94.25 290
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
HSP-MVS		95.18 76	94.49 103	97.23 24	98.67 26	94.05 18	96.41 37	97.00 132	91.26 118	95.12 139	95.15 193	86.60 206	99.50 19	93.43 59	96.81 244	98.13 134

DI_MVS_plusplus_test		91.42 194	91.41 188	91.46 219	95.34 239	79.06 249	90.58 243	93.74 246	82.59 260	94.69 156	94.76 211	86.54 207	98.44 189	87.93 177	96.49 261	96.87 209

BH-RMVSNet		90.47 206	90.44 207	90.56 241	95.21 242	78.65 258	89.15 288	93.94 243	88.21 191	92.74 208	94.22 228	86.38 208	97.88 229	78.67 282	95.39 281	95.14 270

CNLPA		91.72 185	91.20 194	93.26 148	96.17 187	91.02 61	91.14 226	95.55 209	90.16 145	90.87 251	93.56 248	86.31 209	94.40 328	79.92 268	97.12 233	94.37 288

PVSNet_BlendedMVS		90.35 211	89.96 212	91.54 218	94.81 252	78.80 256	90.14 258	96.93 139	79.43 284	88.68 296	95.06 198	86.27 210	98.15 211	80.27 260	98.04 193	97.68 168

PVSNet_Blended		88.74 240	88.16 241	90.46 244	94.81 252	78.80 256	86.64 319	96.93 139	74.67 311	88.68 296	89.18 323	86.27 210	98.15 211	80.27 260	96.00 266	94.44 287

PAPR		87.65 262	86.77 269	90.27 248	92.85 297	77.38 271	88.56 298	96.23 185	76.82 307	84.98 326	89.75 318	86.08 212	97.16 270	72.33 322	93.35 314	96.26 234

v2v482		93.29 140	93.63 134	92.29 194	96.35 172	78.82 254	91.77 213	96.28 181	88.45 184	95.70 119	96.26 146	86.02 213	98.90 105	93.02 75	98.81 123	99.14 49

test20.03		90.80 201	90.85 201	90.63 240	95.63 226	79.24 242	89.81 272	92.87 260	89.90 150	94.39 161	96.40 134	85.77 214	95.27 320	73.86 313	99.05 95	97.39 184

PLC	85.34 15	90.40 208	88.92 226	94.85 92	96.53 153	90.02 69	91.58 215	96.48 169	80.16 277	86.14 320	92.18 281	85.73 215	98.25 203	76.87 296	94.61 298	96.30 232
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MVS		84.98 298	84.30 297	87.01 308	91.03 320	77.69 268	91.94 196	94.16 238	59.36 360	84.23 332	87.50 337	85.66 216	96.80 282	71.79 325	93.05 321	86.54 352

testdata				91.03 233	96.87 131	82.01 185		94.28 236	71.55 329	92.46 213	95.42 186	85.65 217	97.38 264	82.64 236	97.27 230	93.70 306

PM-MVS		93.33 139	92.67 160	95.33 78	96.58 149	94.06 16	92.26 185	92.18 274	85.92 226	96.22 92	96.61 121	85.64 218	95.99 305	90.35 130	98.23 174	95.93 245

MDA-MVSNet-bldmvs		91.04 198	90.88 199	91.55 217	94.68 261	80.16 208	85.49 326	92.14 277	90.41 142	94.93 149	95.79 170	85.10 219	96.93 277	85.15 210	94.19 306	97.57 174

PAPM_NR		91.03 199	90.81 202	91.68 213	96.73 138	81.10 197	93.72 131	96.35 180	88.19 192	88.77 293	92.12 284	85.09 220	97.25 267	82.40 240	93.90 308	96.68 213

HQP2-MVS												84.76 221

HQP-MVS		92.09 181	91.49 186	93.88 131	96.36 169	84.89 154	91.37 219	97.31 112	87.16 208	88.81 289	93.40 253	84.76 221	98.60 165	86.55 196	97.73 205	98.14 133

test222					96.95 125	85.27 152	88.83 294	93.61 247	65.09 354	90.74 254	94.85 205	84.62 223			97.36 227	93.91 299

VDDNet		94.03 119	94.27 112	93.31 147	98.87 18	82.36 183	95.51 66	91.78 282	97.19 10	96.32 83	98.60 20	84.24 224	98.75 143	87.09 187	98.83 117	98.81 92

PVSNet_Blended_VisFu		91.63 186	91.20 194	92.94 163	97.73 90	83.95 165	92.14 189	97.46 95	78.85 293	92.35 218	94.98 202	84.16 225	99.08 78	86.36 200	96.77 246	95.79 250

BH-w/o		87.21 273	87.02 264	87.79 302	94.77 254	77.27 273	87.90 302	93.21 257	81.74 268	89.99 269	88.39 328	83.47 226	96.93 277	71.29 330	92.43 326	89.15 346

PatchMatch-RL		89.18 229	88.02 244	92.64 179	95.90 212	92.87 42	88.67 297	91.06 287	80.34 275	90.03 267	91.67 290	83.34 227	94.42 327	76.35 300	94.84 292	90.64 343

OpenMVS_ROB	85.12 16	89.52 226	89.05 222	90.92 237	94.58 267	81.21 196	91.10 228	93.41 252	77.03 305	93.41 186	93.99 239	83.23 228	97.80 238	79.93 267	94.80 293	93.74 305

new-patchmatchnet		88.97 234	90.79 203	83.50 333	94.28 273	55.83 363	85.34 327	93.56 249	86.18 221	95.47 125	95.73 173	83.10 229	96.51 290	85.40 207	98.06 191	98.16 131

1314		86.46 289	86.33 277	86.87 310	91.65 317	74.54 301	91.94 196	94.10 239	74.28 315	84.78 328	87.33 339	83.03 230	95.00 322	78.72 281	91.16 337	91.06 340

IS-MVSNet		94.49 106	94.35 107	94.92 90	98.25 59	86.46 129	97.13 14	94.31 235	96.24 23	96.28 89	96.36 142	82.88 231	99.35 47	88.19 173	99.52 45	98.96 76

MG-MVS		89.54 225	89.80 214	88.76 283	94.88 248	72.47 323	89.60 275	92.44 272	85.82 228	89.48 281	95.98 162	82.85 232	97.74 245	81.87 244	95.27 284	96.08 240

TR-MVS		87.70 259	87.17 259	89.27 274	94.11 276	79.26 241	88.69 296	91.86 280	81.94 267	90.69 255	89.79 316	82.82 233	97.42 259	72.65 321	91.98 332	91.14 339

YYNet1		88.17 251	88.24 237	87.93 299	92.21 309	73.62 308	80.75 349	88.77 297	82.51 262	94.99 147	95.11 196	82.70 234	93.70 334	83.33 229	93.83 309	96.48 225

MDA-MVSNet_test_wron		88.16 252	88.23 238	87.93 299	92.22 307	73.71 307	80.71 350	88.84 296	82.52 261	94.88 150	95.14 194	82.70 234	93.61 335	83.28 230	93.80 310	96.46 226

pmmvs-eth3d		91.54 188	90.73 205	93.99 124	95.76 217	87.86 110	90.83 234	93.98 242	78.23 297	94.02 174	96.22 153	82.62 236	96.83 281	86.57 195	98.33 162	97.29 191

Anonymous20231206		88.77 239	88.29 235	90.20 253	96.31 176	78.81 255	89.56 277	93.49 251	74.26 316	92.38 216	95.58 178	82.21 237	95.43 315	72.07 323	98.75 130	96.34 230

USDC		89.02 232	89.08 221	88.84 282	95.07 246	74.50 303	88.97 291	96.39 175	73.21 322	93.27 193	96.28 145	82.16 238	96.39 296	77.55 290	98.80 124	95.62 258

EPP-MVSNet		93.91 121	93.68 133	94.59 104	98.08 70	85.55 149	97.44 9	94.03 240	94.22 44	94.94 148	96.19 155	82.07 239	99.57 13	87.28 186	98.89 107	98.65 104

UnsupCasMVSNet_eth		90.33 212	90.34 208	90.28 247	94.64 264	80.24 206	89.69 274	95.88 195	85.77 229	93.94 175	95.69 174	81.99 240	92.98 340	84.21 223	91.30 335	97.62 172

alignmvs		93.26 143	92.85 153	94.50 108	95.70 219	87.45 113	93.45 137	95.76 199	91.58 113	95.25 135	92.42 277	81.96 241	98.72 148	91.61 112	97.87 202	97.33 188

TAMVS		90.16 216	89.05 222	93.49 144	96.49 155	86.37 132	90.34 251	92.55 270	80.84 274	92.99 203	94.57 218	81.94 242	98.20 207	73.51 314	98.21 177	95.90 248

Anonymous202405211		92.58 168	92.50 165	92.83 169	96.55 151	83.22 173	92.43 176	91.64 283	94.10 46	95.59 122	96.64 119	81.88 243	97.50 253	85.12 212	98.52 143	97.77 161

no-one		87.84 256	87.21 258	89.74 258	93.58 287	78.64 259	81.28 348	92.69 266	74.36 314	92.05 228	97.14 90	81.86 244	96.07 303	72.03 324	99.90 2	94.52 284

SixPastTwentyTwo		94.91 86	95.21 84	93.98 125	98.52 41	83.19 174	95.93 52	94.84 221	94.86 35	98.49 17	98.74 16	81.45 245	99.60 8	94.69 25	99.39 63	99.15 48

cascas		87.02 279	86.28 278	89.25 275	91.56 318	76.45 279	84.33 336	96.78 153	71.01 333	86.89 317	85.91 346	81.35 246	96.94 276	83.09 232	95.60 274	94.35 289

Test4		91.41 195	91.25 193	91.89 206	95.35 238	80.32 205	90.97 230	96.92 141	81.96 266	95.11 140	93.81 242	81.34 247	98.48 184	88.71 165	97.08 234	96.87 209

GBi-Net		93.21 146	92.96 150	93.97 126	95.40 234	84.29 158	95.99 48	96.56 163	88.63 177	95.10 141	98.53 23	81.31 248	98.98 94	86.74 190	98.38 155	98.65 104

test1		93.21 146	92.96 150	93.97 126	95.40 234	84.29 158	95.99 48	96.56 163	88.63 177	95.10 141	98.53 23	81.31 248	98.98 94	86.74 190	98.38 155	98.65 104

FMVSNet2		92.78 161	92.73 158	92.95 162	95.40 234	81.98 186	94.18 117	95.53 210	88.63 177	96.05 101	97.37 79	81.31 248	98.81 131	87.38 185	98.67 134	98.06 136

MVE	59.87 23	73.86 338	72.65 339	77.47 346	87.00 358	74.35 304	61.37 363	60.93 369	67.27 348	69.69 365	86.49 343	81.24 251	72.33 365	56.45 358	83.45 352	85.74 353
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmv		88.46 244	88.11 242	89.48 263	96.00 199	76.14 282	86.20 323	93.75 245	84.48 244	93.57 183	95.52 182	80.91 252	95.09 321	63.97 350	98.61 136	97.22 193

MVP-Stereo		90.07 220	88.92 226	93.54 141	96.31 176	86.49 127	90.93 232	95.59 207	79.80 279	91.48 233	95.59 175	80.79 253	97.39 262	78.57 283	91.19 336	96.76 212
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
UnsupCasMVSNet_bld		88.50 243	88.03 243	89.90 256	95.52 231	78.88 253	87.39 309	94.02 241	79.32 289	93.06 201	94.02 237	80.72 254	94.27 330	75.16 310	93.08 320	96.54 215

MS-PatchMatch		88.05 253	87.75 249	88.95 280	93.28 290	77.93 263	87.88 303	92.49 271	75.42 310	92.57 212	93.59 247	80.44 255	94.24 332	81.28 250	92.75 323	94.69 281

CANet_DTU		89.85 221	89.17 220	91.87 207	92.20 310	80.02 218	90.79 235	95.87 196	86.02 224	82.53 342	91.77 288	80.01 256	98.57 170	85.66 205	97.70 208	97.01 200

PMMVS		83.00 308	81.11 317	88.66 286	83.81 367	86.44 130	82.24 345	85.65 323	61.75 359	82.07 345	85.64 347	79.75 257	91.59 346	75.99 302	93.09 319	87.94 351

ppachtmachnet_test		88.61 242	88.64 231	88.50 292	91.76 315	70.99 329	84.59 333	92.98 258	79.30 290	92.38 216	93.53 249	79.57 258	97.45 257	86.50 198	97.17 232	97.07 197

N_pmnet		88.90 236	87.25 257	93.83 133	94.40 271	93.81 31	84.73 330	87.09 312	79.36 288	93.26 194	92.43 276	79.29 259	91.68 345	77.50 292	97.22 231	96.00 242

EPNet		89.80 223	88.25 236	94.45 113	83.91 366	86.18 137	93.87 127	87.07 313	91.16 122	80.64 353	94.72 213	78.83 260	98.89 107	85.17 208	98.89 107	98.28 125
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
sss		87.23 272	86.82 267	88.46 294	93.96 279	77.94 262	86.84 316	92.78 264	77.59 299	87.61 311	91.83 287	78.75 261	91.92 344	77.84 287	94.20 305	95.52 264

Patchmatch-test1		87.28 270	87.30 256	87.22 307	92.01 314	71.98 325	89.43 279	88.11 305	82.26 265	88.71 294	92.20 280	78.65 262	95.81 307	80.99 256	93.30 315	93.87 302

our_test_3		87.55 264	87.59 252	87.44 305	91.76 315	70.48 330	83.83 339	90.55 293	79.79 280	92.06 227	92.17 282	78.63 263	95.63 309	84.77 218	94.73 294	96.22 235

jason		89.17 230	88.32 234	91.70 212	95.73 218	80.07 213	88.10 301	93.22 255	71.98 328	90.09 264	92.79 263	78.53 264	98.56 171	87.43 183	97.06 235	96.46 226
jason: jason.
IterMVS		90.18 215	90.16 210	90.21 252	93.15 293	75.98 285	87.56 307	92.97 259	86.43 220	94.09 171	96.40 134	78.32 265	97.43 258	87.87 178	94.69 296	97.23 192
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CHOSEN 1792x2688		87.19 275	85.92 288	91.00 236	97.13 119	79.41 238	84.51 334	95.60 204	64.14 355	90.07 266	94.81 206	78.26 266	97.14 271	73.34 315	95.38 282	96.46 226

WTY-MVS		86.93 282	86.50 276	88.24 296	94.96 247	74.64 299	87.19 312	92.07 279	78.29 296	88.32 300	91.59 293	78.06 267	94.27 330	74.88 311	93.15 318	95.80 249

pmmvs4		88.95 235	87.70 251	92.70 177	94.30 272	85.60 148	87.22 311	92.16 276	74.62 312	89.75 278	94.19 229	77.97 268	96.41 295	82.71 235	96.36 262	96.09 239

DSMNet-mixed		82.21 314	81.56 313	84.16 330	89.57 339	70.00 333	90.65 239	77.66 364	54.99 363	83.30 338	97.57 65	77.89 269	90.50 351	66.86 344	95.54 276	91.97 333

lessismore_v0				93.87 132	98.05 71	83.77 167		80.32 361		97.13 55	97.91 53	77.49 270	99.11 76	92.62 86	98.08 190	98.74 99

HY-MVS	82.50 18	86.81 284	85.93 287	89.47 264	93.63 286	77.93 263	94.02 119	91.58 284	75.68 308	83.64 335	93.64 244	77.40 271	97.42 259	71.70 327	92.07 331	93.05 317

1112_ss		88.42 245	87.41 254	91.45 220	96.69 140	80.99 198	89.72 273	96.72 158	73.37 321	87.00 316	90.69 307	77.38 272	98.20 207	81.38 249	93.72 311	95.15 269

semantic-postprocess				91.94 205	93.89 281	79.22 246		93.51 250	91.53 115	95.37 129	96.62 120	77.17 273	98.90 105	91.89 106	94.95 289	97.70 166

CDS-MVSNet		89.55 224	88.22 239	93.53 142	95.37 237	86.49 127	89.26 285	93.59 248	79.76 281	91.15 247	92.31 279	77.12 274	98.38 192	77.51 291	97.92 200	95.71 253
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MVSFormer		92.18 179	92.23 169	92.04 204	94.74 256	80.06 214	97.15 12	97.37 101	88.98 164	88.83 287	92.79 263	77.02 275	99.60 8	96.41 6	96.75 247	96.46 226

lupinMVS		88.34 246	87.31 255	91.45 220	94.74 256	80.06 214	87.23 310	92.27 273	71.10 332	88.83 287	91.15 296	77.02 275	98.53 178	86.67 193	96.75 247	95.76 251

PMMVS2		81.31 320	83.44 302	74.92 348	90.52 328	46.49 365	69.19 361	85.23 333	84.30 245	87.95 306	94.71 214	76.95 277	84.36 362	64.07 349	98.09 189	93.89 300

pmmvs5		87.87 255	87.14 260	90.07 254	93.26 292	76.97 277	88.89 293	92.18 274	73.71 320	88.36 298	93.89 240	76.86 278	96.73 284	80.32 259	96.81 244	96.51 217

testus		82.09 316	81.78 311	83.03 335	92.35 305	64.37 354	79.44 351	93.27 254	73.08 323	87.06 315	85.21 349	76.80 279	89.27 355	53.30 359	95.48 278	95.46 265

K. test v3		93.37 138	93.27 146	93.66 135	98.05 71	82.62 180	94.35 112	86.62 315	96.05 27	97.51 42	98.85 12	76.59 280	99.65 3	93.21 67	98.20 179	98.73 101

Test_1112_low_res		87.50 266	86.58 271	90.25 249	96.80 135	77.75 266	87.53 308	96.25 183	69.73 340	86.47 318	93.61 246	75.67 281	97.88 229	79.95 265	93.20 316	95.11 271

Vis-MVSNet (Re-imp)		90.42 207	90.16 210	91.20 231	97.66 97	77.32 272	94.33 113	87.66 308	91.20 120	92.99 203	95.13 195	75.40 282	98.28 198	77.86 286	99.19 83	97.99 141

PVSNet	76.22 20	82.89 309	82.37 308	84.48 328	93.96 279	64.38 353	78.60 353	88.61 298	71.50 330	84.43 331	86.36 345	74.27 283	94.60 324	69.87 338	93.69 312	94.46 286

0601test		90.11 217	89.73 216	91.26 227	94.09 277	79.82 224	90.44 246	92.65 267	90.90 126	93.19 199	93.30 255	73.90 284	98.03 215	82.23 241	96.87 242	95.93 245

Anonymous20240521		90.11 217	89.73 216	91.26 227	94.09 277	79.82 224	90.44 246	92.65 267	90.90 126	93.19 199	93.30 255	73.90 284	98.03 215	82.23 241	96.87 242	95.93 245

CMPMVS	68.83 22	87.28 270	85.67 289	92.09 202	88.77 347	85.42 150	90.31 252	94.38 234	70.02 339	88.00 305	93.30 255	73.78 286	94.03 333	75.96 303	96.54 255	96.83 211
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
PVSNet_0	70.34 21	74.58 336	72.96 338	79.47 343	90.63 326	66.24 346	73.26 356	83.40 346	63.67 357	78.02 358	78.35 361	72.53 287	89.59 354	56.68 357	60.05 363	82.57 358

MIMVSNet		87.13 277	86.54 273	88.89 281	96.05 194	76.11 283	94.39 110	88.51 299	81.37 270	88.27 302	96.75 113	72.38 288	95.52 311	65.71 348	95.47 279	95.03 272

PAPM		81.91 317	80.11 327	87.31 306	93.87 282	72.32 324	84.02 338	93.22 255	69.47 341	76.13 361	89.84 313	72.15 289	97.23 268	53.27 360	89.02 341	92.37 326

LFMVS		91.33 196	91.16 196	91.82 208	96.27 179	79.36 239	95.01 84	85.61 325	96.04 28	94.82 151	97.06 95	72.03 290	98.46 187	84.96 216	98.70 132	97.65 170

MVS-HIRNet		78.83 333	80.60 322	73.51 349	93.07 294	47.37 364	87.10 313	78.00 363	68.94 342	77.53 359	97.26 84	71.45 291	94.62 323	63.28 352	88.74 342	78.55 360

EPNet_dtu		85.63 294	84.37 296	89.40 271	86.30 359	74.33 305	91.64 214	88.26 301	84.84 242	72.96 364	89.85 312	71.27 292	97.69 247	76.60 298	97.62 212	96.18 237
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test1235678		84.54 300	83.85 301	86.59 311	93.81 285	73.41 310	82.38 343	91.79 281	79.43 284	89.50 280	91.61 292	70.59 293	92.94 341	58.14 356	97.40 225	93.44 312

LP		86.29 290	85.35 291	89.10 277	87.80 349	76.21 281	89.92 266	90.99 288	84.86 241	87.66 309	92.32 278	70.40 294	96.48 291	81.94 243	82.24 356	94.63 282

HyFIR lowres test		87.19 275	85.51 290	92.24 196	97.12 120	80.51 203	85.03 328	96.06 190	66.11 351	91.66 232	92.98 261	70.12 295	99.14 71	75.29 309	95.23 285	97.07 197

FMVSNet3		90.78 202	90.32 209	92.16 200	93.03 295	79.92 222	92.54 165	94.95 219	86.17 222	95.10 141	96.01 161	69.97 296	98.75 143	86.74 190	98.38 155	97.82 159

RPMNet		89.30 228	89.00 224	90.22 250	91.01 321	78.93 251	92.52 168	87.85 307	91.91 98	89.10 284	96.89 105	68.84 297	97.64 249	90.17 136	92.70 324	94.08 292

ADS-MVSNet2		84.01 304	82.20 310	89.41 270	89.04 344	76.37 280	87.57 305	90.98 289	72.71 326	84.46 329	92.45 273	68.08 298	96.48 291	70.58 336	83.97 349	95.38 266

ADS-MVSNet		82.25 313	81.55 314	84.34 329	89.04 344	65.30 347	87.57 305	85.13 334	72.71 326	84.46 329	92.45 273	68.08 298	92.33 343	70.58 336	83.97 349	95.38 266

CVMVSNet		85.16 296	84.72 294	86.48 312	92.12 312	70.19 331	92.32 182	88.17 304	56.15 362	90.64 256	95.85 166	67.97 300	96.69 285	88.78 163	90.52 339	92.56 324

new_pmnet		81.22 321	81.01 320	81.86 339	90.92 323	70.15 332	84.03 337	80.25 362	70.83 335	85.97 321	89.78 317	67.93 301	84.65 361	67.44 342	91.90 333	90.78 341

CR-MVSNet		87.89 254	87.12 261	90.22 250	91.01 321	78.93 251	92.52 168	92.81 261	73.08 323	89.10 284	96.93 101	67.11 302	97.64 249	88.80 162	92.70 324	94.08 292

Patchmtry		90.11 217	89.92 213	90.66 239	90.35 332	77.00 276	92.96 153	92.81 261	90.25 144	94.74 154	96.93 101	67.11 302	97.52 252	85.17 208	98.98 102	97.46 179

PatchmatchNet		85.22 295	84.64 295	86.98 309	89.51 340	69.83 334	90.52 244	87.34 311	78.87 292	87.22 314	92.74 265	66.91 304	96.53 288	81.77 245	86.88 347	94.58 283
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
GA-MVS		87.70 259	86.82 267	90.31 246	93.27 291	77.22 274	84.72 332	92.79 263	85.11 236	89.82 275	90.07 311	66.80 305	97.76 243	84.56 221	94.27 304	95.96 244

MDTV_nov1_ep13_2view						42.48 368	88.45 299		67.22 349	83.56 336		66.80 305		72.86 320		94.06 294

tpmrst		82.85 310	82.93 307	82.64 337	87.65 350	58.99 360	90.14 258	87.90 306	75.54 309	83.93 333	91.63 291	66.79 307	95.36 316	81.21 252	81.54 357	93.57 311

sam_mvs1												66.64 308				94.75 279

sam_mvs												66.41 309

Patchmatch-RL test		88.81 238	88.52 232	89.69 262	95.33 241	79.94 221	86.22 322	92.71 265	78.46 295	95.80 115	94.18 230	66.25 310	95.33 318	89.22 157	98.53 142	93.78 303

patchmatchnet-post											91.71 289	66.22 311	97.59 251

test_post											6.07 368	65.74 312	95.84 306

test_post1							90.21 254				5.85 369	65.36 313	96.00 304	79.61 270

MDTV_nov1_ep13			83.88 300		89.42 341	61.52 357	88.74 295	87.41 310	73.99 318	84.96 327	94.01 238	65.25 314	95.53 310	78.02 285	93.16 317

Patchmatch-test		86.10 291	86.01 286	86.38 314	90.63 326	74.22 306	89.57 276	86.69 314	85.73 230	89.81 276	92.83 262	65.24 315	91.04 347	77.82 289	95.78 272	93.88 301

tpmvs		84.22 303	83.97 299	84.94 324	87.09 356	65.18 348	91.21 224	88.35 300	82.87 258	85.21 323	90.96 300	65.24 315	96.75 283	79.60 271	85.25 348	92.90 319

EU-MVSNet		87.39 268	86.71 270	89.44 269	93.40 289	76.11 283	94.93 87	90.00 294	57.17 361	95.71 118	97.37 79	64.77 317	97.68 248	92.67 83	94.37 301	94.52 284

thres200		85.85 292	85.18 292	87.88 301	94.44 269	72.52 322	89.08 289	86.21 317	88.57 180	91.44 235	88.40 327	64.22 318	98.00 218	68.35 340	95.88 271	93.12 316

PatchT		87.51 265	88.17 240	85.55 318	90.64 325	66.91 341	92.02 192	86.09 318	92.20 91	89.05 286	97.16 89	64.15 319	96.37 298	89.21 158	92.98 322	93.37 314

tfpn200view9		87.05 278	86.52 274	88.67 285	95.77 215	72.94 320	91.89 199	86.00 320	90.84 128	92.61 210	89.80 314	63.93 320	98.28 198	71.27 331	96.54 255	94.79 277

thres400		87.20 274	86.52 274	89.24 276	95.77 215	72.94 320	91.89 199	86.00 320	90.84 128	92.61 210	89.80 314	63.93 320	98.28 198	71.27 331	96.54 255	96.51 217

FPMVS		84.50 301	83.28 303	88.16 297	96.32 175	94.49 11	85.76 324	85.47 326	83.09 254	85.20 324	94.26 226	63.79 322	86.58 360	63.72 351	91.88 334	83.40 355

tfpn111		87.60 263	87.12 261	89.04 278	96.14 189	73.09 317	93.00 150	85.31 328	92.13 93	93.26 194	90.96 300	63.42 323	98.48 184	72.87 319	96.98 239	95.56 259

conf200view11		87.41 267	86.89 265	88.97 279	96.14 189	73.09 317	93.00 150	85.31 328	92.13 93	93.26 194	90.96 300	63.42 323	98.28 198	71.27 331	96.54 255	95.56 259

thres100view900		87.35 269	86.89 265	88.72 284	96.14 189	73.09 317	93.00 150	85.31 328	92.13 93	93.26 194	90.96 300	63.42 323	98.28 198	71.27 331	96.54 255	94.79 277

thres600view7		87.66 261	87.10 263	89.36 272	96.05 194	73.17 315	92.72 160	85.31 328	91.89 99	93.29 191	90.97 299	63.42 323	98.39 190	73.23 316	96.99 238	96.51 217

view600		88.32 247	87.94 245	89.46 265	96.49 155	73.31 311	93.95 123	84.46 338	93.02 66	94.18 166	92.68 268	63.33 327	98.56 171	75.87 304	97.50 217	96.51 217

view800		88.32 247	87.94 245	89.46 265	96.49 155	73.31 311	93.95 123	84.46 338	93.02 66	94.18 166	92.68 268	63.33 327	98.56 171	75.87 304	97.50 217	96.51 217

conf0.05thres1000		88.32 247	87.94 245	89.46 265	96.49 155	73.31 311	93.95 123	84.46 338	93.02 66	94.18 166	92.68 268	63.33 327	98.56 171	75.87 304	97.50 217	96.51 217

tfpn		88.32 247	87.94 245	89.46 265	96.49 155	73.31 311	93.95 123	84.46 338	93.02 66	94.18 166	92.68 268	63.33 327	98.56 171	75.87 304	97.50 217	96.51 217

EMVS		80.35 329	80.28 326	80.54 341	84.73 365	69.07 335	72.54 359	80.73 359	87.80 199	81.66 349	81.73 357	62.89 331	89.84 353	75.79 308	94.65 297	82.71 357

test-LLR		83.58 305	83.17 304	84.79 326	89.68 337	66.86 343	83.08 340	84.52 336	83.07 255	82.85 340	84.78 350	62.86 332	93.49 336	82.85 233	94.86 290	94.03 295

test0.0.03 1		82.48 312	81.47 315	85.48 319	89.70 336	73.57 309	84.73 330	81.64 357	83.07 255	88.13 304	86.61 341	62.86 332	89.10 357	66.24 347	90.29 340	93.77 304

tpm cat1		80.61 327	79.46 329	84.07 331	88.78 346	65.06 351	89.26 285	88.23 302	62.27 358	81.90 348	89.66 320	62.70 334	95.29 319	71.72 326	80.60 358	91.86 336

E-PMN		80.72 326	80.86 321	80.29 342	85.11 363	68.77 336	72.96 357	81.97 356	87.76 200	83.25 339	83.01 356	62.22 335	89.17 356	77.15 295	94.31 303	82.93 356

CostFormer		83.09 307	82.21 309	85.73 317	89.27 343	67.01 340	90.35 250	86.47 316	70.42 337	83.52 337	93.23 258	61.18 336	96.85 280	77.21 294	88.26 345	93.34 315

MVSTER		89.32 227	88.75 230	91.03 233	90.10 334	76.62 278	90.85 233	94.67 230	82.27 264	95.24 136	95.79 170	61.09 337	98.49 181	90.49 123	98.26 170	97.97 145

tpm		84.38 302	84.08 298	85.30 323	90.47 329	63.43 356	89.34 282	85.63 324	77.24 304	87.62 310	95.03 201	61.00 338	97.30 266	79.26 272	91.09 338	95.16 268

PatchFormer-LS_test		82.62 311	81.71 312	85.32 322	87.92 348	67.31 339	89.03 290	88.20 303	77.58 300	83.79 334	80.50 360	60.96 339	96.42 294	83.86 227	83.59 351	92.23 331

EPMVS		81.17 323	80.37 324	83.58 332	85.58 362	65.08 350	90.31 252	71.34 366	77.31 303	85.80 322	91.30 294	59.38 340	92.70 342	79.99 264	82.34 355	92.96 318

tmp_tt		37.97 343	44.33 343	18.88 355	11.80 370	21.54 370	63.51 362	45.66 372	4.23 365	51.34 367	50.48 364	59.08 341	22.11 367	44.50 363	68.35 362	13.00 364

conf0.01		86.95 280	86.04 280	89.70 260	95.99 200	75.66 289	93.28 140	82.70 348	88.81 169	91.26 238	88.01 331	58.77 342	97.89 223	78.93 275	96.60 249	95.56 259

conf0.002		86.95 280	86.04 280	89.70 260	95.99 200	75.66 289	93.28 140	82.70 348	88.81 169	91.26 238	88.01 331	58.77 342	97.89 223	78.93 275	96.60 249	95.56 259

thresconf0.02		86.69 285	86.04 280	88.64 287	95.99 200	75.66 289	93.28 140	82.70 348	88.81 169	91.26 238	88.01 331	58.77 342	97.89 223	78.93 275	96.60 249	92.36 327

tfpn_n400		86.69 285	86.04 280	88.64 287	95.99 200	75.66 289	93.28 140	82.70 348	88.81 169	91.26 238	88.01 331	58.77 342	97.89 223	78.93 275	96.60 249	92.36 327

tfpnconf		86.69 285	86.04 280	88.64 287	95.99 200	75.66 289	93.28 140	82.70 348	88.81 169	91.26 238	88.01 331	58.77 342	97.89 223	78.93 275	96.60 249	92.36 327

tfpnview11		86.69 285	86.04 280	88.64 287	95.99 200	75.66 289	93.28 140	82.70 348	88.81 169	91.26 238	88.01 331	58.77 342	97.89 223	78.93 275	96.60 249	92.36 327

tpm2		81.46 319	80.35 325	84.80 325	89.90 335	65.14 349	90.44 246	85.36 327	65.82 353	82.05 346	92.44 275	57.94 348	96.69 285	70.71 335	88.49 344	92.56 324

tfpn_ndepth		85.85 292	85.15 293	87.98 298	95.19 244	75.36 295	92.79 159	83.18 347	86.97 212	89.92 271	86.43 344	57.44 349	97.85 235	78.18 284	96.22 264	90.72 342

tfpn1000		86.83 283	86.23 279	88.64 287	95.53 230	75.25 297	93.57 133	82.28 355	89.27 161	91.46 234	89.24 322	57.22 350	97.86 232	80.63 258	96.88 241	92.81 320

CHOSEN 280x420		80.04 330	77.97 334	86.23 316	90.13 333	74.53 302	72.87 358	89.59 295	66.38 350	76.29 360	85.32 348	56.96 351	95.36 316	69.49 339	94.72 295	88.79 349

JIA-IIPM		85.08 297	83.04 305	91.19 232	87.56 351	86.14 138	89.40 281	84.44 342	88.98 164	82.20 344	97.95 49	56.82 352	96.15 301	76.55 299	83.45 352	91.30 338

tpmp4_e23		81.87 318	80.41 323	86.27 315	89.29 342	67.84 338	91.58 215	87.61 309	67.42 347	78.60 357	92.71 266	56.42 353	96.87 279	71.44 329	88.63 343	94.10 291

DeepMVS_CX				53.83 352	70.38 369	64.56 352		48.52 371	33.01 364	65.50 366	74.21 363	56.19 354	46.64 366	38.45 364	70.07 361	50.30 363

dp		79.28 331	78.62 332	81.24 340	85.97 361	56.45 362	86.91 315	85.26 332	72.97 325	81.45 350	89.17 324	56.01 355	95.45 314	73.19 317	76.68 360	91.82 337

thisisatest0515		84.72 299	82.99 306	89.90 256	92.96 296	75.33 296	84.36 335	83.42 345	77.37 302	88.27 302	86.65 340	53.94 356	98.72 148	82.56 237	97.40 225	95.67 255

tttt0517		89.81 222	88.90 228	92.55 187	97.00 123	79.73 232	95.03 82	83.65 344	89.88 151	95.30 131	94.79 210	53.64 357	99.39 39	91.99 101	98.79 125	98.54 113

thisisatest0530		88.69 241	87.52 253	92.20 197	96.33 174	79.36 239	92.81 158	84.01 343	86.44 219	93.67 181	92.68 268	53.62 358	99.25 59	89.65 147	98.45 150	98.00 140

FMVSNet5		87.82 258	86.56 272	91.62 214	92.31 306	79.81 226	93.49 135	94.81 224	83.26 250	91.36 236	96.93 101	52.77 359	97.49 255	76.07 301	98.03 194	97.55 177

test12356		76.35 334	77.41 335	73.19 350	90.70 324	38.86 369	74.56 355	91.14 286	74.55 313	80.54 354	88.18 329	52.36 360	90.49 352	52.38 361	92.26 328	90.21 345

pmmvs3		80.83 324	78.96 331	86.45 313	87.23 355	77.48 269	84.87 329	82.31 354	63.83 356	85.03 325	89.50 321	49.66 361	93.10 338	73.12 318	95.10 287	88.78 350

DWT-MVSNet_test		80.74 325	79.18 330	85.43 320	87.51 353	66.87 342	89.87 270	86.01 319	74.20 317	80.86 351	80.62 359	48.84 362	96.68 287	81.54 247	83.14 354	92.75 322

IB-MVS	77.21 19	83.11 306	81.05 318	89.29 273	91.15 319	75.85 286	85.66 325	86.00 320	79.70 282	82.02 347	86.61 341	48.26 363	98.39 190	77.84 287	92.22 329	93.63 307
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
gg-mvs-nofinetune		82.10 315	81.02 319	85.34 321	87.46 354	71.04 327	94.74 94	67.56 367	96.44 20	79.43 356	98.99 5	45.24 364	96.15 301	67.18 343	92.17 330	88.85 348

GG-mvs-BLEND				83.24 334	85.06 364	71.03 328	94.99 86	65.55 368		74.09 363	75.51 362	44.57 365	94.46 326	59.57 355	87.54 346	84.24 354

TESTMET0.1,1		79.09 332	78.04 333	82.25 338	87.52 352	64.03 355	83.08 340	80.62 360	70.28 338	80.16 355	83.22 355	44.13 366	90.56 350	79.95 265	93.36 313	92.15 332

test-mter		81.21 322	80.01 328	84.79 326	89.68 337	66.86 343	83.08 340	84.52 336	73.85 319	82.85 340	84.78 350	43.66 367	93.49 336	82.85 233	94.86 290	94.03 295

testpf		74.01 337	76.37 336	66.95 351	80.56 368	60.00 358	88.43 300	75.07 365	81.54 269	75.75 362	83.73 352	38.93 368	83.09 363	84.01 224	79.32 359	57.75 362

test2356		75.58 335	73.13 337	82.95 336	86.10 360	66.42 345	75.07 354	84.87 335	70.91 334	80.85 352	80.66 358	38.02 369	88.98 358	49.32 362	92.35 327	93.44 312

1111		80.36 328	81.32 316	77.48 345	94.61 265	44.56 366	81.59 346	90.66 291	86.78 216	90.60 257	93.52 250	30.37 370	90.67 348	66.36 345	97.42 224	97.20 194

.test1245		64.72 340	70.88 341	46.22 353	94.61 265	44.56 366	81.59 346	90.66 291	86.78 216	90.60 257	93.52 250	30.37 370	90.67 348	66.36 345	3.45 366	3.44 366

PNet_i23d		72.03 339	70.91 340	75.38 347	90.46 330	57.84 361	71.73 360	81.53 358	83.86 248	82.21 343	83.49 354	29.97 372	87.80 359	60.78 353	54.12 364	80.51 359

test123		9.49 345	12.01 346	1.91 356	2.87 371	1.30 371	82.38 343	1.34 374	1.36 366	2.84 368	6.56 367	2.45 373	0.97 368	2.73 365	5.56 365	3.47 365

testmvs		9.02 346	11.42 347	1.81 357	2.77 372	1.13 372	79.44 351	1.90 373	1.18 367	2.65 369	6.80 366	1.95 374	0.87 369	2.62 366	3.45 366	3.44 366

test_part1				0.00 358		0.00 373	0.00 364	98.14 29				0.00 375	0.00 370	0.00 367	0.00 368	0.00 368

v1.0		40.11 342	53.48 342	0.00 358	98.21 61	0.00 373	0.00 364	98.14 29	91.83 104	96.72 69	96.39 138	0.00 375	0.00 370	0.00 367	0.00 368	0.00 368

sosnet-low-res		0.00 349	0.00 350	0.00 358	0.00 373	0.00 373	0.00 364	0.00 375	0.00 368	0.00 370	0.00 370	0.00 375	0.00 370	0.00 367	0.00 368	0.00 368

sosnet		0.00 349	0.00 350	0.00 358	0.00 373	0.00 373	0.00 364	0.00 375	0.00 368	0.00 370	0.00 370	0.00 375	0.00 370	0.00 367	0.00 368	0.00 368

uncertanet		0.00 349	0.00 350	0.00 358	0.00 373	0.00 373	0.00 364	0.00 375	0.00 368	0.00 370	0.00 370	0.00 375	0.00 370	0.00 367	0.00 368	0.00 368

Regformer		0.00 349	0.00 350	0.00 358	0.00 373	0.00 373	0.00 364	0.00 375	0.00 368	0.00 370	0.00 370	0.00 375	0.00 370	0.00 367	0.00 368	0.00 368

ab-mvs-re		7.56 347	10.08 349	0.00 358	0.00 373	0.00 373	0.00 364	0.00 375	0.00 368	0.00 370	90.69 307	0.00 375	0.00 370	0.00 367	0.00 368	0.00 368

uanet		0.00 349	0.00 350	0.00 358	0.00 373	0.00 373	0.00 364	0.00 375	0.00 368	0.00 370	0.00 370	0.00 375	0.00 370	0.00 367	0.00 368	0.00 368

GSMVS																94.75 279

test_part2					98.21 61	89.41 78				96.72 69

MTGPA								97.62 76

MTMP							94.82 91	54.62 370

gm-plane-assit					87.08 357	59.33 359			71.22 331		83.58 353		97.20 269	73.95 312

test9_res														88.16 174	98.40 153	97.83 156

agg_prior2														87.06 188	98.36 161	97.98 142

agg_prior					96.20 184	88.89 87		96.88 146		90.21 262			98.78 137

test_prior4						89.91 71	90.74 236

test_prior				94.61 99	95.95 208	87.23 116		97.36 107					98.68 157			97.93 146

旧先验2							90.00 264		68.65 343	92.71 209			96.52 289	85.15 210

新几何2							90.02 263

无先验							89.94 265	95.75 200	70.81 336				98.59 167	81.17 253		94.81 276

原ACMM2							89.34 282

testdata2													98.03 215	80.24 262

testdata1							88.96 292		88.44 185

plane_prior7					97.71 91	88.68 91

plane_prior5								97.81 63					98.95 101	89.26 155	98.51 145	98.60 110

plane_prior4											95.59 175

plane_prior3						88.43 102			90.35 143	93.31 189

plane_prior2							94.56 104		91.74 110

plane_prior1					97.38 107

plane_prior						88.12 105	93.01 149		88.98 164						98.06 191

n2								0.00 375

nn								0.00 375

door-mid								92.13 278

test11								96.65 160

door								91.26 285

HQP5-MVS						84.89 154

HQP-NCC					96.36 169		91.37 219		87.16 208	88.81 289

ACMP_Plane					96.36 169		91.37 219		87.16 208	88.81 289

BP-MVS														86.55 196

HQP4-MVS										88.81 289			98.61 163			98.15 132

HQP3-MVS								97.31 112							97.73 205

NP-MVS					96.82 133	87.10 119					93.40 253

ACMMP++_ref															98.82 120

ACMMP++															99.25 77

High-resolution multi-view benchmark