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 70	95.00 87	96.51 47	98.10 74	94.07 18	92.46 164	98.13 40	90.69 130	93.75 181	96.25 145	98.03 2	97.02 275	92.08 91	95.55 273	98.45 113

pmmvs6		96.80 13	97.36 9	95.15 94	99.12 7	87.82 123	96.68 22	97.86 78	96.10 25	98.14 23	99.28 3	97.94 3	98.21 205	91.38 113	99.69 15	99.42 19

UniMVSNet_ETH3D		97.13 6	97.72 3	95.35 82	99.51 2	87.38 127	97.70 6	97.54 105	98.16 2	98.94 2	99.33 2	97.84 4	99.08 92	90.73 119	99.73 14	99.59 12

ACMH	88.36 12	96.59 27	97.43 5	94.07 134	98.56 36	85.33 170	96.33 39	98.30 20	94.66 39	98.72 8	98.30 30	97.51 5	98.00 222	94.87 14	99.59 26	98.86 72
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
HPM-MVS_fast		97.01 7	96.89 15	97.39 22	99.12 7	93.92 27	97.16 10	98.17 35	93.11 66	96.48 76	97.36 70	96.92 6	99.34 59	94.31 23	99.38 54	98.92 67

ACMH+	88.43 11	96.48 30	96.82 16	95.47 80	98.54 41	89.06 94	95.65 65	98.61 6	96.10 25	98.16 22	97.52 58	96.90 7	98.62 167	90.30 131	99.60 24	98.72 89

HPM-MVS		96.81 12	96.62 23	97.36 24	98.89 18	93.53 37	97.51 7	98.44 9	92.35 78	95.95 104	96.41 129	96.71 8	99.42 28	93.99 31	99.36 55	99.13 39
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 13	97.86 3	98.54 41	95.32 7	96.61 24	98.35 16	95.81 30	97.55 35	97.44 63	96.51 9	99.40 40	94.06 30	99.23 75	98.85 75

mvs_tets		96.83 9	96.71 19	97.17 27	98.83 21	92.51 48	96.58 26	97.61 100	87.57 193	98.80 7	98.90 9	96.50 10	99.59 12	96.15 7	99.47 38	99.40 21

SED-MVS		96.00 52	96.41 32	94.76 106	98.51 45	86.97 136	95.21 79	98.10 44	91.95 88	97.63 31	97.25 76	96.48 11	99.35 55	93.29 59	99.29 64	97.95 151

test_241102_ONE					98.51 45	86.97 136		98.10 44	91.85 94	97.63 31	97.03 89	96.48 11	98.95 115

LPG-MVS_test		96.38 40	96.23 38	96.84 40	98.36 60	92.13 52	95.33 75	98.25 24	91.78 101	97.07 51	97.22 79	96.38 13	99.28 70	92.07 92	99.59 26	99.11 41

LGP-MVS_train				96.84 40	98.36 60	92.13 52		98.25 24	91.78 101	97.07 51	97.22 79	96.38 13	99.28 70	92.07 92	99.59 26	99.11 41

ACMM	88.83 9	96.30 43	96.07 49	96.97 35	98.39 56	92.95 44	94.74 98	98.03 60	90.82 127	97.15 49	96.85 100	96.25 15	99.00 107	93.10 68	99.33 59	98.95 61
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
wuyk23d		87.83 260	90.79 199	78.96 337	90.46 324	88.63 103	92.72 153	90.67 300	91.65 109	98.68 11	97.64 52	96.06 16	77.53 357	59.84 352	99.41 51	70.73 354

ACMP	88.15 13	95.71 60	95.43 73	96.54 46	98.17 70	91.73 60	94.24 116	98.08 48	89.46 154	96.61 73	96.47 124	95.85 17	99.12 88	90.45 123	99.56 32	98.77 83
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
TransMVSNet (Re)		95.27 78	96.04 51	92.97 171	98.37 59	81.92 209	95.07 87	96.76 165	93.97 52	97.77 27	98.57 19	95.72 18	97.90 227	88.89 168	99.23 75	99.08 45

ZNCC-MVS		96.42 36	96.20 40	97.07 30	98.80 25	92.79 46	96.08 49	98.16 38	91.74 105	95.34 128	96.36 137	95.68 19	99.44 23	94.41 21	99.28 69	98.97 59

ACMMP_NAP		96.21 45	96.12 46	96.49 49	98.90 17	91.42 62	94.57 106	98.03 60	90.42 138	96.37 79	97.35 71	95.68 19	99.25 74	94.44 20	99.34 57	98.80 79

APD-MVS_3200maxsize		96.82 10	96.65 21	97.32 25	97.95 88	93.82 32	96.31 41	98.25 24	95.51 34	96.99 58	97.05 88	95.63 21	99.39 45	93.31 58	98.88 112	98.75 84

DVP-MVS		95.82 57	96.18 41	94.72 108	98.51 45	86.69 143	95.20 81	97.00 145	91.85 94	97.40 44	97.35 71	95.58 22	99.34 59	93.44 51	99.31 61	98.13 134
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
test0726					98.51 45	86.69 143	95.34 74	98.18 32	91.85 94	97.63 31	97.37 67	95.58 22

MP-MVS-pluss		96.08 49	95.92 56	96.57 45	99.06 9	91.21 64	93.25 141	98.32 17	87.89 184	96.86 62	97.38 66	95.55 24	99.39 45	95.47 10	99.47 38	99.11 41
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
COLMAP_ROB	91.06 5	96.75 16	96.62 23	97.13 28	98.38 57	94.31 15	96.79 20	98.32 17	96.69 16	96.86 62	97.56 55	95.48 25	98.77 147	90.11 139	99.44 44	98.31 121
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 79	95.73 65	93.55 153	96.62 152	88.88 100	94.67 100	98.05 55	91.26 117	97.25 48	96.40 130	95.42 26	94.36 330	92.72 80	99.19 79	97.40 195
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
RE-MVS-def			96.66 20		98.07 76	95.27 8	96.37 36	98.12 41	95.66 32	97.00 56	97.03 89	95.40 27		93.49 44	98.84 117	98.00 143

test_241102_TWO								98.10 44	91.95 88	97.54 36	97.25 76	95.37 28	99.35 55	93.29 59	99.25 72	98.49 109

HFP-MVS		96.39 39	96.17 43	97.04 31	98.51 45	93.37 38	96.30 43	97.98 67	92.35 78	95.63 116	96.47 124	95.37 28	99.27 72	93.78 35	99.14 84	98.48 110

#test#		95.89 53	95.51 69	97.04 31	98.51 45	93.37 38	95.14 84	97.98 67	89.34 156	95.63 116	96.47 124	95.37 28	99.27 72	91.99 94	99.14 84	98.48 110

jajsoiax		96.59 27	96.42 29	97.12 29	98.76 26	92.49 49	96.44 33	97.42 113	86.96 202	98.71 10	98.72 17	95.36 31	99.56 16	95.92 8	99.45 42	99.32 26

TranMVSNet+NR-MVSNet		96.07 50	96.26 37	95.50 79	98.26 65	87.69 124	93.75 129	97.86 78	95.96 29	97.48 39	97.14 83	95.33 32	99.44 23	90.79 118	99.76 11	99.38 22

PMVS	87.21 14	94.97 84	95.33 76	93.91 142	98.97 14	97.16 2	95.54 69	95.85 204	96.47 20	93.40 191	97.46 62	95.31 33	95.47 314	86.18 215	98.78 129	89.11 341
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
pm-mvs1		95.43 68	95.94 54	93.93 140	98.38 57	85.08 172	95.46 72	97.12 139	91.84 97	97.28 46	98.46 25	95.30 34	97.71 247	90.17 137	99.42 46	98.99 53

PGM-MVS		96.32 41	95.94 54	97.43 19	98.59 35	93.84 31	95.33 75	98.30 20	91.40 114	95.76 110	96.87 99	95.26 35	99.45 22	92.77 76	99.21 77	99.00 51

PS-CasMVS		96.69 20	97.43 5	94.49 122	99.13 5	84.09 185	96.61 24	97.97 70	97.91 5	98.64 13	98.13 32	95.24 36	99.65 3	93.39 55	99.84 3	99.72 2

GST-MVS		96.24 44	95.99 53	97.00 34	98.65 28	92.71 47	95.69 64	98.01 64	92.08 86	95.74 112	96.28 142	95.22 37	99.42 28	93.17 65	99.06 90	98.88 71

LTVRE_ROB	93.87 1	97.93 2	98.16 2	97.26 26	98.81 23	93.86 30	99.07 2	98.98 3	97.01 12	98.92 4	98.78 14	95.22 37	98.61 168	96.85 2	99.77 10	99.31 27
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
DPE-MVS		95.89 53	95.88 57	95.92 62	97.93 89	89.83 82	93.46 137	98.30 20	92.37 76	97.75 28	96.95 92	95.14 39	99.51 18	91.74 102	99.28 69	98.41 116

nrg030		96.32 41	96.55 26	95.62 75	97.83 92	88.55 107	95.77 61	98.29 23	92.68 69	98.03 25	97.91 42	95.13 40	98.95 115	93.85 33	99.49 37	99.36 24

APDe-MVS		96.46 32	96.64 22	95.93 60	97.68 103	89.38 91	96.90 17	98.41 13	92.52 73	97.43 41	97.92 41	95.11 41	99.50 19	94.45 19	99.30 63	98.92 67

ACMMP		96.61 24	96.34 34	97.43 19	98.61 32	93.88 28	96.95 16	98.18 32	92.26 81	96.33 82	96.84 103	95.10 42	99.40 40	93.47 48	99.33 59	99.02 50
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
SR-MVS		96.70 19	96.42 29	97.54 11	98.05 78	94.69 11	96.13 47	98.07 51	95.17 36	96.82 64	96.73 111	95.09 43	99.43 27	92.99 73	98.71 134	98.50 108

OPM-MVS		95.61 63	95.45 71	96.08 53	98.49 53	91.00 67	92.65 157	97.33 123	90.05 143	96.77 67	96.85 100	95.04 44	98.56 176	92.77 76	99.06 90	98.70 90

DTE-MVSNet		96.74 17	97.43 5	94.67 109	99.13 5	84.68 175	96.51 28	97.94 76	98.14 3	98.67 12	98.32 29	95.04 44	99.69 2	93.27 61	99.82 8	99.62 10

region2R		96.41 37	96.09 47	97.38 23	98.62 30	93.81 34	96.32 40	97.96 71	92.26 81	95.28 132	96.57 121	95.02 46	99.41 35	93.63 39	99.11 88	98.94 62

PEN-MVS		96.69 20	97.39 8	94.61 111	99.16 3	84.50 176	96.54 27	98.05 55	98.06 4	98.64 13	98.25 31	95.01 47	99.65 3	92.95 74	99.83 6	99.68 4

SteuartSystems-ACMMP		96.40 38	96.30 35	96.71 42	98.63 29	91.96 55	95.70 62	98.01 64	93.34 64	96.64 71	96.57 121	94.99 48	99.36 54	93.48 47	99.34 57	98.82 77
Skip Steuart: Steuart Systems R&D Blog.
canonicalmvs		94.59 101	94.69 98	94.30 129	95.60 222	87.03 135	95.59 66	98.24 27	91.56 111	95.21 137	92.04 286	94.95 49	98.66 164	91.45 111	97.57 226	97.20 205

ACMMPR		96.46 32	96.14 44	97.41 21	98.60 33	93.82 32	96.30 43	97.96 71	92.35 78	95.57 119	96.61 119	94.93 50	99.41 35	93.78 35	99.15 83	99.00 51

test1172		96.79 15	96.52 27	97.60 9	98.03 81	94.87 10	96.07 50	98.06 54	95.76 31	96.89 60	96.85 100	94.85 51	99.42 28	93.35 57	98.81 125	98.53 106

SR-MVS-dyc-post		96.84 8	96.60 25	97.56 10	98.07 76	95.27 8	96.37 36	98.12 41	95.66 32	97.00 56	97.03 89	94.85 51	99.42 28	93.49 44	98.84 117	98.00 143

CP-MVS		96.44 35	96.08 48	97.54 11	98.29 62	94.62 13	96.80 19	98.08 48	92.67 71	95.08 142	96.39 134	94.77 53	99.42 28	93.17 65	99.44 44	98.58 104

test_0728_THIRD									93.26 65	97.40 44	97.35 71	94.69 54	99.34 59	93.88 32	99.42 46	98.89 69

9.14			94.81 92		97.49 114		94.11 119	98.37 14	87.56 194	95.38 126	96.03 153	94.66 55	99.08 92	90.70 120	98.97 105

TDRefinement		97.68 3	97.60 4	97.93 2	99.02 11	95.95 5	98.61 3	98.81 4	97.41 9	97.28 46	98.46 25	94.62 56	98.84 130	94.64 17	99.53 34	98.99 53

XVS		96.49 29	96.18 41	97.44 17	98.56 36	93.99 25	96.50 29	97.95 73	94.58 40	94.38 165	96.49 123	94.56 57	99.39 45	93.57 40	99.05 93	98.93 63

X-MVStestdata		90.70 200	88.45 240	97.44 17	98.56 36	93.99 25	96.50 29	97.95 73	94.58 40	94.38 165	26.89 358	94.56 57	99.39 45	93.57 40	99.05 93	98.93 63

mPP-MVS		96.46 32	96.05 50	97.69 5	98.62 30	94.65 12	96.45 31	97.74 91	92.59 72	95.47 122	96.68 114	94.50 59	99.42 28	93.10 68	99.26 71	98.99 53

DeepC-MVS	91.39 4	95.43 68	95.33 76	95.71 73	97.67 104	90.17 76	93.86 127	98.02 62	87.35 195	96.22 92	97.99 38	94.48 60	99.05 97	92.73 79	99.68 18	97.93 153
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 58	95.54 68	96.47 50	98.27 64	91.19 65	95.09 85	97.79 89	86.48 206	97.42 43	97.51 60	94.47 61	99.29 68	93.55 42	99.29 64	98.93 63
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
xxxxxxxxxxxxxcwj		95.03 81	94.93 88	95.33 84	97.46 117	88.05 117	92.04 185	98.42 12	87.63 191	96.36 80	96.68 114	94.37 62	99.32 65	92.41 86	99.05 93	98.64 95

SF-MVS		95.88 55	95.88 57	95.87 64	98.12 72	89.65 85	95.58 67	98.56 7	91.84 97	96.36 80	96.68 114	94.37 62	99.32 65	92.41 86	99.05 93	98.64 95

MP-MVS		96.14 47	95.68 66	97.51 13	98.81 23	94.06 19	96.10 48	97.78 90	92.73 68	93.48 188	96.72 112	94.23 64	99.42 28	91.99 94	99.29 64	99.05 48
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 84	94.03 24	96.97 15	97.61 100	87.68 190	98.45 18	98.77 15	94.20 65	99.50 19	96.70 3	99.40 52	99.53 14

test_0402		95.73 59	96.22 39	94.26 130	98.19 69	85.77 165	93.24 142	97.24 131	96.88 15	97.69 29	97.77 47	94.12 66	99.13 86	91.54 110	99.29 64	97.88 159

Effi-MVS+		92.79 154	92.74 153	92.94 174	95.10 235	83.30 194	94.00 123	97.53 107	91.36 115	89.35 281	90.65 308	94.01 67	98.66 164	87.40 196	95.30 281	96.88 215

OMC-MVS		94.22 117	93.69 129	95.81 65	97.25 124	91.27 63	92.27 176	97.40 114	87.10 201	94.56 160	95.42 185	93.74 68	98.11 214	86.62 206	98.85 116	98.06 137

LCM-MVSNet-Re		94.20 118	94.58 103	93.04 168	95.91 203	83.13 198	93.79 128	99.19 2	92.00 87	98.84 5	98.04 35	93.64 69	99.02 103	81.28 263	98.54 147	96.96 211

zzz-MVS		96.47 31	96.14 44	97.47 15	98.95 15	94.05 21	93.69 131	97.62 97	94.46 44	96.29 86	96.94 93	93.56 70	99.37 52	94.29 24	99.42 46	98.99 53

MTAPA		96.65 22	96.38 33	97.47 15	98.95 15	94.05 21	95.88 58	97.62 97	94.46 44	96.29 86	96.94 93	93.56 70	99.37 52	94.29 24	99.42 46	98.99 53

UA-Net		97.35 4	97.24 11	97.69 5	98.22 67	93.87 29	98.42 4	98.19 31	96.95 13	95.46 124	99.23 4	93.45 72	99.57 13	95.34 12	99.89 2	99.63 9

MVS_111021_HR		93.63 129	93.42 138	94.26 130	96.65 149	86.96 138	89.30 270	96.23 190	88.36 176	93.57 187	94.60 218	93.45 72	97.77 242	90.23 135	98.38 161	98.03 141

cdsmvs_eth3d_5k		23.35 329	31.13 332	0.00 345	0.00 366	0.00 367	0.00 357	95.58 215	0.00 362	0.00 363	91.15 297	93.43 74	0.00 363	0.00 361	0.00 361	0.00 359

APD-MVS		95.00 83	94.69 98	95.93 60	97.38 120	90.88 70	94.59 103	97.81 85	89.22 159	95.46 124	96.17 149	93.42 75	99.34 59	89.30 155	98.87 115	97.56 185
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ANet_high		94.83 93	96.28 36	90.47 251	96.65 149	73.16 317	94.33 114	98.74 5	96.39 22	98.09 24	98.93 8	93.37 76	98.70 159	90.38 126	99.68 18	99.53 14

casdiffmvs		94.32 112	94.80 93	92.85 178	96.05 192	81.44 216	92.35 172	98.05 55	91.53 112	95.75 111	96.80 104	93.35 77	98.49 182	91.01 116	98.32 170	98.64 95

test_djsdf		96.62 23	96.49 28	97.01 33	98.55 39	91.77 59	97.15 11	97.37 115	88.98 161	98.26 21	98.86 10	93.35 77	99.60 8	96.41 4	99.45 42	99.66 6

VPA-MVSNet		95.14 80	95.67 67	93.58 152	97.76 94	83.15 197	94.58 105	97.58 102	93.39 63	97.05 54	98.04 35	93.25 79	98.51 181	89.75 149	99.59 26	99.08 45

Anonymous20240529		95.50 66	95.83 61	94.50 120	97.33 123	85.93 162	95.19 83	96.77 164	96.64 18	97.61 34	98.05 34	93.23 80	98.79 139	88.60 175	99.04 98	98.78 81

baseline		94.26 115	94.80 93	92.64 184	96.08 190	80.99 222	93.69 131	98.04 59	90.80 128	94.89 150	96.32 139	93.19 81	98.48 186	91.68 105	98.51 151	98.43 114

DeepPCF-MVS	90.46 6	94.20 118	93.56 134	96.14 51	95.96 199	92.96 43	89.48 264	97.46 111	85.14 229	96.23 91	95.42 185	93.19 81	98.08 215	90.37 127	98.76 131	97.38 198

Anonymous20231211		96.60 25	97.13 12	95.00 98	97.46 117	86.35 154	97.11 14	98.24 27	97.58 7	98.72 8	98.97 7	93.15 83	99.15 83	93.18 64	99.74 13	99.50 16

OPU-MVS				95.15 94	96.84 142	89.43 88	95.21 79				95.66 171	93.12 84	98.06 216	86.28 214	98.61 141	97.95 151

LS3D		96.11 48	95.83 61	96.95 37	94.75 245	94.20 17	97.34 9	97.98 67	97.31 10	95.32 129	96.77 105	93.08 85	99.20 79	91.79 100	98.16 189	97.44 191

DP-MVS		95.62 62	95.84 60	94.97 99	97.16 129	88.62 104	94.54 110	97.64 96	96.94 14	96.58 74	97.32 74	93.07 86	98.72 153	90.45 123	98.84 117	97.57 183

EG-PatchMatch MVS		94.54 104	94.67 100	94.14 132	97.87 91	86.50 146	92.00 188	96.74 166	88.16 179	96.93 59	97.61 53	93.04 87	97.90 227	91.60 107	98.12 194	98.03 141

Fast-Effi-MVS+		91.28 192	90.86 196	92.53 191	95.45 226	82.53 204	89.25 273	96.52 178	85.00 234	89.91 270	88.55 328	92.94 88	98.84 130	84.72 233	95.44 277	96.22 239

v7n		96.82 10	97.31 10	95.33 84	98.54 41	86.81 140	96.83 18	98.07 51	96.59 19	98.46 17	98.43 27	92.91 89	99.52 17	96.25 6	99.76 11	99.65 8

XVG-ACMP-BASELINE		95.68 61	95.34 75	96.69 43	98.40 55	93.04 41	94.54 110	98.05 55	90.45 137	96.31 84	96.76 107	92.91 89	98.72 153	91.19 114	99.42 46	98.32 119

testgi		90.38 209	91.34 187	87.50 301	97.49 114	71.54 326	89.43 265	95.16 225	88.38 175	94.54 161	94.68 217	92.88 91	93.09 340	71.60 332	97.85 213	97.88 159

MVS_111021_LR		93.66 128	93.28 142	94.80 104	96.25 178	90.95 68	90.21 242	95.43 219	87.91 182	93.74 183	94.40 223	92.88 91	96.38 296	90.39 125	98.28 174	97.07 206

CNVR-MVS		94.58 102	94.29 113	95.46 81	96.94 137	89.35 92	91.81 203	96.80 161	89.66 150	93.90 178	95.44 184	92.80 93	98.72 153	92.74 78	98.52 149	98.32 119

ZD-MVS					97.23 125	90.32 75		97.54 105	84.40 241	94.78 154	95.79 164	92.76 94	99.39 45	88.72 173	98.40 157

XXY-MVS		92.58 162	93.16 145	90.84 244	97.75 95	79.84 239	91.87 197	96.22 192	85.94 216	95.53 121	97.68 49	92.69 95	94.48 326	83.21 244	97.51 227	98.21 128

CDPH-MVS		92.67 159	91.83 173	95.18 93	96.94 137	88.46 110	90.70 228	97.07 142	77.38 297	92.34 228	95.08 198	92.67 96	98.88 122	85.74 217	98.57 143	98.20 129

ETH3D-3000-0.1		94.86 90	94.55 104	95.81 65	97.61 107	89.72 83	94.05 121	98.37 14	88.09 180	95.06 143	95.85 159	92.58 97	99.10 91	90.33 130	98.99 100	98.62 99

Fast-Effi-MVS+-dtu		92.77 156	92.16 165	94.58 118	94.66 253	88.25 112	92.05 184	96.65 170	89.62 151	90.08 266	91.23 296	92.56 98	98.60 170	86.30 213	96.27 261	96.90 213

AllTest		94.88 89	94.51 107	96.00 55	98.02 82	92.17 50	95.26 78	98.43 10	90.48 135	95.04 144	96.74 109	92.54 99	97.86 233	85.11 226	98.98 101	97.98 147

TestCases				96.00 55	98.02 82	92.17 50		98.43 10	90.48 135	95.04 144	96.74 109	92.54 99	97.86 233	85.11 226	98.98 101	97.98 147

TinyColmap		92.00 176	92.76 152	89.71 268	95.62 221	77.02 283	90.72 227	96.17 195	87.70 189	95.26 133	96.29 141	92.54 99	96.45 293	81.77 258	98.77 130	95.66 263

Regformer-2		94.86 90	94.55 104	95.77 69	92.83 288	89.98 78	91.87 197	96.40 182	94.38 46	96.19 96	95.04 200	92.47 102	99.04 100	93.49 44	98.31 171	98.28 123

ETV-MVS		92.99 149	92.74 153	93.72 148	95.86 206	86.30 155	92.33 173	97.84 82	91.70 108	92.81 211	86.17 343	92.22 103	99.19 80	88.03 184	97.73 216	95.66 263

CLD-MVS		91.82 178	91.41 185	93.04 168	96.37 163	83.65 191	86.82 310	97.29 127	84.65 240	92.27 230	89.67 318	92.20 104	97.85 235	83.95 238	99.47 38	97.62 181
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
segment_acmp												92.14 105

Regformer-4		94.90 87	94.67 100	95.59 76	92.78 290	89.02 95	92.39 169	95.91 201	94.50 42	96.41 77	95.56 178	92.10 106	99.01 105	94.23 26	98.14 191	98.74 86

Vis-MVSNet		95.50 66	95.48 70	95.56 78	98.11 73	89.40 90	95.35 73	98.22 29	92.36 77	94.11 169	98.07 33	92.02 107	99.44 23	93.38 56	97.67 222	97.85 163
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CS-MVS		92.54 165	92.31 163	93.23 165	95.89 205	84.07 186	93.58 134	98.48 8	88.60 171	90.41 260	86.23 342	92.00 108	99.35 55	87.54 192	98.06 199	96.26 237

Regformer-1		94.55 103	94.33 112	95.19 92	92.83 288	88.54 108	91.87 197	95.84 205	93.99 50	95.95 104	95.04 200	92.00 108	98.79 139	93.14 67	98.31 171	98.23 126

ITE_SJBPF				95.95 57	97.34 122	93.36 40		96.55 177	91.93 90	94.82 152	95.39 188	91.99 110	97.08 273	85.53 219	97.96 207	97.41 192

CP-MVSNet		96.19 46	96.80 17	94.38 128	98.99 13	83.82 189	96.31 41	97.53 107	97.60 6	98.34 19	97.52 58	91.98 111	99.63 6	93.08 70	99.81 9	99.70 3

CSCG		94.69 98	94.75 95	94.52 119	97.55 111	87.87 121	95.01 90	97.57 103	92.68 69	96.20 94	93.44 254	91.92 112	98.78 143	89.11 163	99.24 74	96.92 212

TSAR-MVS + MP.		94.96 85	94.75 95	95.57 77	98.86 20	88.69 101	96.37 36	96.81 160	85.23 226	94.75 155	97.12 84	91.85 113	99.40 40	93.45 49	98.33 168	98.62 99
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
Gipuma		95.31 75	95.80 63	93.81 147	97.99 87	90.91 69	96.42 34	97.95 73	96.69 16	91.78 239	98.85 12	91.77 114	95.49 313	91.72 103	99.08 89	95.02 277
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
WR-MVS_H		96.60 25	97.05 14	95.24 90	99.02 11	86.44 150	96.78 21	98.08 48	97.42 8	98.48 16	97.86 44	91.76 115	99.63 6	94.23 26	99.84 3	99.66 6

testtj		94.81 94	94.42 108	96.01 54	97.23 125	90.51 74	94.77 97	97.85 81	91.29 116	94.92 149	95.66 171	91.71 116	99.40 40	88.07 183	98.25 179	98.11 136

ETH3D cwj APD-0.16		93.99 123	93.38 139	95.80 67	96.82 143	89.92 79	92.72 153	98.02 62	84.73 239	93.65 185	95.54 180	91.68 117	99.22 77	88.78 170	98.49 154	98.26 125

AdaColmap		91.63 182	91.36 186	92.47 194	95.56 223	86.36 153	92.24 179	96.27 187	88.88 165	89.90 271	92.69 271	91.65 118	98.32 196	77.38 300	97.64 223	92.72 324

PHI-MVS		94.34 111	93.80 124	95.95 57	95.65 218	91.67 61	94.82 95	97.86 78	87.86 185	93.04 206	94.16 232	91.58 119	98.78 143	90.27 133	98.96 107	97.41 192

xiu_mvs_v1_base_debu		91.47 186	91.52 180	91.33 224	95.69 215	81.56 213	89.92 253	96.05 198	83.22 248	91.26 245	90.74 303	91.55 120	98.82 132	89.29 156	95.91 266	93.62 310

xiu_mvs_v1_base		91.47 186	91.52 180	91.33 224	95.69 215	81.56 213	89.92 253	96.05 198	83.22 248	91.26 245	90.74 303	91.55 120	98.82 132	89.29 156	95.91 266	93.62 310

xiu_mvs_v1_base_debi		91.47 186	91.52 180	91.33 224	95.69 215	81.56 213	89.92 253	96.05 198	83.22 248	91.26 245	90.74 303	91.55 120	98.82 132	89.29 156	95.91 266	93.62 310

tfpnnormal		94.27 114	94.87 91	92.48 193	97.71 99	80.88 224	94.55 109	95.41 220	93.70 57	96.67 70	97.72 48	91.40 123	98.18 209	87.45 194	99.18 81	98.36 117

Regformer-3		94.28 113	94.23 118	94.46 124	92.78 290	86.28 156	92.39 169	94.70 239	93.69 60	95.97 102	95.56 178	91.34 124	98.48 186	93.45 49	98.14 191	98.62 99

3Dnovator+	92.74 2	95.86 56	95.77 64	96.13 52	96.81 145	90.79 72	96.30 43	97.82 84	96.13 24	94.74 156	97.23 78	91.33 125	99.16 82	93.25 62	98.30 173	98.46 112

TEST9					96.45 161	89.46 86	90.60 230	96.92 152	79.09 286	90.49 257	94.39 224	91.31 126	98.88 122

agg_prior1		92.60 161	91.76 176	95.10 96	96.20 180	88.89 98	90.37 237	96.88 156	79.67 278	90.21 263	94.41 222	91.30 127	98.78 143	88.46 176	98.37 166	97.64 180

DeepC-MVS_fast	89.96 7	93.73 127	93.44 137	94.60 115	96.14 185	87.90 120	93.36 140	97.14 136	85.53 223	93.90 178	95.45 183	91.30 127	98.59 172	89.51 152	98.62 140	97.31 201
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	94.28 114	94.61 111	92.55 292	85.98 161	92.44 165	94.69 240	93.70 57	96.12 99	95.81 163	91.24 129	98.86 127	93.76 38	98.22 184	98.98 58

MCST-MVS		92.91 151	92.51 159	94.10 133	97.52 112	85.72 166	91.36 214	97.13 138	80.33 272	92.91 210	94.24 228	91.23 130	98.72 153	89.99 143	97.93 209	97.86 161

RPSCF		95.58 64	94.89 90	97.62 8	97.58 109	96.30 4	95.97 54	97.53 107	92.42 74	93.41 189	97.78 45	91.21 131	97.77 242	91.06 115	97.06 239	98.80 79

train_agg		92.71 158	91.83 173	95.35 82	96.45 161	89.46 86	90.60 230	96.92 152	79.37 281	90.49 257	94.39 224	91.20 132	98.88 122	88.66 174	98.43 156	97.72 174

test_8					96.37 163	89.14 93	90.51 233	96.89 155	79.37 281	90.42 259	94.36 226	91.20 132	98.82 132

EI-MVSNet-UG-set		94.35 110	94.27 116	94.59 116	92.46 293	85.87 163	92.42 167	94.69 240	93.67 61	96.13 98	95.84 162	91.20 132	98.86 127	93.78 35	98.23 182	99.03 49

EIA-MVS		92.35 169	92.03 168	93.30 163	95.81 209	83.97 187	92.80 152	98.17 35	87.71 188	89.79 275	87.56 332	91.17 135	99.18 81	87.97 185	97.27 234	96.77 218

xiu_mvs_v2_base		89.00 240	89.19 225	88.46 290	94.86 240	74.63 305	86.97 304	95.60 210	80.88 268	87.83 304	88.62 327	91.04 136	98.81 137	82.51 252	94.38 298	91.93 330

HPM-MVS++		95.02 82	94.39 109	96.91 38	97.88 90	93.58 36	94.09 120	96.99 147	91.05 122	92.40 223	95.22 192	91.03 137	99.25 74	92.11 89	98.69 137	97.90 157

TAPA-MVS	88.58 10	92.49 166	91.75 177	94.73 107	96.50 158	89.69 84	92.91 149	97.68 94	78.02 295	92.79 212	94.10 233	90.85 138	97.96 226	84.76 232	98.16 189	96.54 222
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
pcd_1.5k_mvsjas		7.56 332	10.09 335	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	90.77 139	0.00 363	0.00 361	0.00 361	0.00 359

PS-MVSNAJss		96.01 51	96.04 51	95.89 63	98.82 22	88.51 109	95.57 68	97.88 77	88.72 167	98.81 6	98.86 10	90.77 139	99.60 8	95.43 11	99.53 34	99.57 13

PS-MVSNAJ		88.86 244	88.99 231	88.48 289	94.88 238	74.71 303	86.69 313	95.60 210	80.88 268	87.83 304	87.37 335	90.77 139	98.82 132	82.52 251	94.37 299	91.93 330

MVS_Test		92.57 164	93.29 140	90.40 254	93.53 275	75.85 298	92.52 160	96.96 148	88.73 166	92.35 226	96.70 113	90.77 139	98.37 194	92.53 83	95.49 275	96.99 210

MIMVSNet1		95.52 65	95.45 71	95.72 72	99.14 4	89.02 95	96.23 46	96.87 158	93.73 56	97.87 26	98.49 24	90.73 143	99.05 97	86.43 211	99.60 24	99.10 44

ab-mvs		92.40 167	92.62 157	91.74 213	97.02 134	81.65 212	95.84 59	95.50 218	86.95 203	92.95 209	97.56 55	90.70 144	97.50 255	79.63 281	97.43 230	96.06 245

Test By Simon												90.61 145

3Dnovator	92.54 3	94.80 95	94.90 89	94.47 123	95.47 225	87.06 133	96.63 23	97.28 129	91.82 100	94.34 167	97.41 64	90.60 146	98.65 166	92.47 84	98.11 195	97.70 175

NCCC		94.08 121	93.54 135	95.70 74	96.49 159	89.90 81	92.39 169	96.91 154	90.64 132	92.33 229	94.60 218	90.58 147	98.96 113	90.21 136	97.70 220	98.23 126

UniMVSNet_NR-MVSNet		95.35 71	95.21 81	95.76 70	97.69 102	88.59 105	92.26 177	97.84 82	94.91 37	96.80 65	95.78 167	90.42 148	99.41 35	91.60 107	99.58 30	99.29 28

test_prior3		93.29 136	92.85 149	94.61 111	95.95 200	87.23 129	90.21 242	97.36 120	89.33 157	90.77 252	94.81 210	90.41 149	98.68 162	88.21 177	98.55 144	97.93 153

test_prior2							90.21 242		89.33 157	90.77 252	94.81 210	90.41 149		88.21 177	98.55 144

DIV-MVS_2432*1600		94.10 120	94.73 97	92.19 199	97.66 105	79.49 248	94.86 94	97.12 139	89.59 153	96.87 61	97.65 51	90.40 151	98.34 195	89.08 164	99.35 56	98.75 84

MSLP-MVS++		93.25 141	93.88 122	91.37 223	96.34 169	82.81 202	93.11 143	97.74 91	89.37 155	94.08 171	95.29 191	90.40 151	96.35 298	90.35 128	98.25 179	94.96 278

UniMVSNet (Re)		95.32 73	95.15 83	95.80 67	97.79 93	88.91 97	92.91 149	98.07 51	93.46 62	96.31 84	95.97 156	90.14 153	99.34 59	92.11 89	99.64 22	99.16 36

Effi-MVS+-dtu		93.90 125	92.60 158	97.77 4	94.74 247	96.67 3	94.00 123	95.41 220	89.94 144	91.93 237	92.13 284	90.12 154	98.97 112	87.68 190	97.48 228	97.67 178

mvs-test1		93.07 147	91.80 175	96.89 39	94.74 247	95.83 6	92.17 180	95.41 220	89.94 144	89.85 272	90.59 309	90.12 154	98.88 122	87.68 190	95.66 271	95.97 248

FMVSNet1		94.84 92	95.13 84	93.97 137	97.60 108	84.29 178	95.99 51	96.56 174	92.38 75	97.03 55	98.53 21	90.12 154	98.98 108	88.78 170	99.16 82	98.65 91

DU-MVS		95.28 76	95.12 85	95.75 71	97.75 95	88.59 105	92.58 158	97.81 85	93.99 50	96.80 65	95.90 157	90.10 157	99.41 35	91.60 107	99.58 30	99.26 29

NR-MVSNet		95.28 76	95.28 79	95.26 89	97.75 95	87.21 131	95.08 86	97.37 115	93.92 54	97.65 30	95.90 157	90.10 157	99.33 64	90.11 139	99.66 20	99.26 29

Baseline_NR-MVSNet		94.47 106	95.09 86	92.60 188	98.50 52	80.82 225	92.08 183	96.68 168	93.82 55	96.29 86	98.56 20	90.10 157	97.75 245	90.10 141	99.66 20	99.24 31

API-MVS		91.52 185	91.61 178	91.26 227	94.16 262	86.26 157	94.66 101	94.82 234	91.17 120	92.13 233	91.08 299	90.03 160	97.06 274	79.09 288	97.35 233	90.45 339

test12				94.43 126	95.95 200	86.75 141		96.24 189		89.76 276		89.79 161	98.79 139		97.95 208	97.75 173

旧先验1					96.20 180	84.17 183		94.82 234			95.57 177	89.57 162			97.89 211	96.32 234

DELS-MVS		92.05 175	92.16 165	91.72 214	94.44 257	80.13 231	87.62 291	97.25 130	87.34 196	92.22 231	93.18 261	89.54 163	98.73 152	89.67 150	98.20 187	96.30 235
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
VPNet		93.08 145	93.76 126	91.03 235	98.60 33	75.83 300	91.51 209	95.62 209	91.84 97	95.74 112	97.10 85	89.31 164	98.32 196	85.07 228	99.06 90	98.93 63

QAPM		92.88 152	92.77 151	93.22 166	95.82 207	83.31 193	96.45 31	97.35 122	83.91 244	93.75 181	96.77 105	89.25 165	98.88 122	84.56 234	97.02 241	97.49 188

MSDG		90.82 196	90.67 202	91.26 227	94.16 262	83.08 199	86.63 315	96.19 193	90.60 134	91.94 236	91.89 287	89.16 166	95.75 308	80.96 269	94.51 297	94.95 279

CPTT-MVS		94.74 96	94.12 119	96.60 44	98.15 71	93.01 42	95.84 59	97.66 95	89.21 160	93.28 195	95.46 182	88.89 167	98.98 108	89.80 146	98.82 123	97.80 168

ETH3 D test6400		91.91 177	91.25 189	93.89 143	96.59 153	84.41 177	92.10 182	97.72 93	78.52 291	91.82 238	93.78 247	88.70 168	99.13 86	83.61 240	98.39 159	98.14 132

DP-MVS Recon		92.31 170	91.88 172	93.60 151	97.18 128	86.87 139	91.10 219	97.37 115	84.92 236	92.08 234	94.08 234	88.59 169	98.20 206	83.50 241	98.14 191	95.73 259

FC-MVSNet-test		95.32 73	95.88 57	93.62 150	98.49 53	81.77 210	95.90 57	98.32 17	93.93 53	97.53 37	97.56 55	88.48 170	99.40 40	92.91 75	99.83 6	99.68 4

OpenMVS	89.45 8	92.27 172	92.13 167	92.68 183	94.53 256	84.10 184	95.70 62	97.03 143	82.44 260	91.14 249	96.42 128	88.47 171	98.38 191	85.95 216	97.47 229	95.55 267

F-COLMAP		92.28 171	91.06 193	95.95 57	97.52 112	91.90 56	93.53 135	97.18 134	83.98 243	88.70 293	94.04 235	88.41 172	98.55 178	80.17 274	95.99 265	97.39 196

ambc				92.98 170	96.88 140	83.01 200	95.92 56	96.38 184		96.41 77	97.48 61	88.26 173	97.80 238	89.96 144	98.93 109	98.12 135

v10		94.68 99	95.27 80	92.90 176	96.57 155	80.15 229	94.65 102	97.57 103	90.68 131	97.43 41	98.00 37	88.18 174	99.15 83	94.84 15	99.55 33	99.41 20

v8		94.65 100	95.29 78	92.74 181	96.65 149	79.77 243	94.59 103	97.17 135	91.86 93	97.47 40	97.93 40	88.16 175	99.08 92	94.32 22	99.47 38	99.38 22

TSAR-MVS + GP.		93.07 147	92.41 162	95.06 97	95.82 207	90.87 71	90.97 221	92.61 278	88.04 181	94.61 159	93.79 246	88.08 176	97.81 237	89.41 154	98.39 159	96.50 227

OurMVSNet-221017-0		96.80 13	96.75 18	96.96 36	99.03 10	91.85 57	97.98 5	98.01 64	94.15 48	98.93 3	99.07 5	88.07 177	99.57 13	95.86 9	99.69 15	99.46 18

diffmvs		91.74 179	91.93 171	91.15 233	93.06 283	78.17 268	88.77 281	97.51 110	86.28 210	92.42 222	93.96 240	88.04 178	97.46 258	90.69 121	96.67 254	97.82 166

原ACMM1				92.87 177	96.91 139	84.22 181		97.01 144	76.84 302	89.64 278	94.46 221	88.00 179	98.70 159	81.53 261	98.01 205	95.70 261

VDD-MVS		94.37 108	94.37 110	94.40 127	97.49 114	86.07 160	93.97 125	93.28 263	94.49 43	96.24 90	97.78 45	87.99 180	98.79 139	88.92 166	99.14 84	98.34 118

XVG-OURS		94.72 97	94.12 119	96.50 48	98.00 84	94.23 16	91.48 210	98.17 35	90.72 129	95.30 130	96.47 124	87.94 181	96.98 276	91.41 112	97.61 225	98.30 122

CANet		92.38 168	91.99 170	93.52 157	93.82 273	83.46 192	91.14 217	97.00 145	89.81 148	86.47 314	94.04 235	87.90 182	99.21 78	89.50 153	98.27 175	97.90 157

BH-untuned		90.68 201	90.90 194	90.05 265	95.98 198	79.57 247	90.04 249	94.94 230	87.91 182	94.07 172	93.00 263	87.76 183	97.78 241	79.19 287	95.17 284	92.80 322

FIs		94.90 87	95.35 74	93.55 153	98.28 63	81.76 211	95.33 75	98.14 39	93.05 67	97.07 51	97.18 81	87.65 184	99.29 68	91.72 103	99.69 15	99.61 11

v1144		93.50 130	93.81 123	92.57 189	96.28 174	79.61 246	91.86 201	96.96 148	86.95 203	95.91 107	96.32 139	87.65 184	98.96 113	93.51 43	98.88 112	99.13 39

mvs_anonymous		90.37 210	91.30 188	87.58 300	92.17 299	68.00 339	89.84 257	94.73 238	83.82 245	93.22 200	97.40 65	87.54 186	97.40 263	87.94 186	95.05 286	97.34 199

PCF-MVS	84.52 17	89.12 237	87.71 256	93.34 160	96.06 191	85.84 164	86.58 318	97.31 124	68.46 339	93.61 186	93.89 243	87.51 187	98.52 180	67.85 343	98.11 195	95.66 263
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
VNet		92.67 159	92.96 146	91.79 211	96.27 175	80.15 229	91.95 189	94.98 228	92.19 84	94.52 162	96.07 151	87.43 188	97.39 264	84.83 230	98.38 161	97.83 164

v148		92.87 153	93.29 140	91.62 217	96.25 178	77.72 275	91.28 215	95.05 226	89.69 149	95.93 106	96.04 152	87.34 189	98.38 191	90.05 142	97.99 206	98.78 81

V42		93.43 133	93.58 133	92.97 171	95.34 231	81.22 219	92.67 156	96.49 179	87.25 197	96.20 94	96.37 136	87.32 190	98.85 129	92.39 88	98.21 185	98.85 75

v1192		93.49 131	93.78 125	92.62 187	96.16 184	79.62 245	91.83 202	97.22 133	86.07 214	96.10 100	96.38 135	87.22 191	99.02 103	94.14 29	98.88 112	99.22 32

WR-MVS		93.49 131	93.72 127	92.80 180	97.57 110	80.03 235	90.14 246	95.68 208	93.70 57	96.62 72	95.39 188	87.21 192	99.04 100	87.50 193	99.64 22	99.33 25

IterMVS-LS		93.78 126	94.28 114	92.27 196	96.27 175	79.21 255	91.87 197	96.78 162	91.77 103	96.57 75	97.07 86	87.15 193	98.74 151	91.99 94	99.03 99	98.86 72
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		92.99 149	93.26 144	92.19 199	92.12 300	79.21 255	92.32 174	94.67 242	91.77 103	95.24 135	95.85 159	87.14 194	98.49 182	91.99 94	98.26 176	98.86 72

v144192		93.20 144	93.54 135	92.16 203	96.05 192	78.26 267	91.95 189	97.14 136	84.98 235	95.96 103	96.11 150	87.08 195	99.04 100	93.79 34	98.84 117	99.17 35

114514_t		90.51 204	89.80 218	92.63 186	98.00 84	82.24 206	93.40 139	97.29 127	65.84 346	89.40 280	94.80 213	86.99 196	98.75 148	83.88 239	98.61 141	96.89 214

新几何1				93.17 167	97.16 129	87.29 128		94.43 244	67.95 340	91.29 244	94.94 205	86.97 197	98.23 204	81.06 268	97.75 215	93.98 301

HQP_MVS		94.26 115	93.93 121	95.23 91	97.71 99	88.12 115	94.56 107	97.81 85	91.74 105	93.31 192	95.59 173	86.93 198	98.95 115	89.26 159	98.51 151	98.60 102

plane_prior6					97.21 127	88.23 113						86.93 198

1121		90.26 215	89.23 224	93.34 160	97.15 131	87.40 126	91.94 191	94.39 245	67.88 341	91.02 250	94.91 206	86.91 200	98.59 172	81.17 266	97.71 219	94.02 300

UGNet		93.08 145	92.50 160	94.79 105	93.87 271	87.99 119	95.07 87	94.26 249	90.64 132	87.33 310	97.67 50	86.89 201	98.49 182	88.10 182	98.71 134	97.91 156
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		92.72 157	92.02 169	94.84 103	95.65 218	91.99 54	92.92 148	96.60 172	85.08 233	92.44 221	93.62 249	86.80 202	96.35 298	86.81 201	98.25 179	96.18 241

v1921920		93.26 139	93.61 132	92.19 199	96.04 196	78.31 266	91.88 196	97.24 131	85.17 228	96.19 96	96.19 147	86.76 203	99.05 97	94.18 28	98.84 117	99.22 32

v1240		93.29 136	93.71 128	92.06 206	96.01 197	77.89 272	91.81 203	97.37 115	85.12 231	96.69 69	96.40 130	86.67 204	99.07 96	94.51 18	98.76 131	99.22 32

MAR-MVS		90.32 213	88.87 235	94.66 110	94.82 241	91.85 57	94.22 117	94.75 237	80.91 267	87.52 308	88.07 331	86.63 205	97.87 232	76.67 304	96.21 262	94.25 294
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
MSP-MVS		95.34 72	94.63 102	97.48 14	98.67 27	94.05 21	96.41 35	98.18 32	91.26 117	95.12 138	95.15 193	86.60 206	99.50 19	93.43 53	96.81 249	98.89 69
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
BH-RMVSNet		90.47 206	90.44 206	90.56 250	95.21 234	78.65 264	89.15 274	93.94 256	88.21 177	92.74 213	94.22 229	86.38 207	97.88 229	78.67 290	95.39 279	95.14 274

CNLPA		91.72 180	91.20 190	93.26 164	96.17 183	91.02 66	91.14 217	95.55 216	90.16 142	90.87 251	93.56 252	86.31 208	94.40 329	79.92 280	97.12 238	94.37 291

PVSNet_BlendedMVS		90.35 211	89.96 215	91.54 220	94.81 242	78.80 262	90.14 246	96.93 150	79.43 280	88.68 294	95.06 199	86.27 209	98.15 212	80.27 271	98.04 202	97.68 177

PVSNet_Blended		88.74 247	88.16 251	90.46 253	94.81 242	78.80 262	86.64 314	96.93 150	74.67 309	88.68 294	89.18 324	86.27 209	98.15 212	80.27 271	96.00 264	94.44 290

PAPR		87.65 265	86.77 273	90.27 257	92.85 287	77.38 279	88.56 286	96.23 190	76.82 303	84.98 322	89.75 317	86.08 211	97.16 271	72.33 327	93.35 311	96.26 237

v2v482		93.29 136	93.63 131	92.29 195	96.35 168	78.82 260	91.77 205	96.28 186	88.45 173	95.70 115	96.26 144	86.02 212	98.90 119	93.02 71	98.81 125	99.14 38

test20.03		90.80 197	90.85 197	90.63 248	95.63 220	79.24 253	89.81 258	92.87 269	89.90 146	94.39 164	96.40 130	85.77 213	95.27 321	73.86 319	99.05 93	97.39 196

PLC	85.34 15	90.40 208	88.92 232	94.85 102	96.53 157	90.02 77	91.58 208	96.48 180	80.16 273	86.14 316	92.18 282	85.73 214	98.25 203	76.87 303	94.61 296	96.30 235
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MVS		84.98 292	84.30 292	87.01 304	91.03 315	77.69 276	91.94 191	94.16 250	59.36 354	84.23 328	87.50 334	85.66 215	96.80 283	71.79 329	93.05 318	86.54 346

testdata				91.03 235	96.87 141	82.01 207		94.28 248	71.55 325	92.46 220	95.42 185	85.65 216	97.38 266	82.64 249	97.27 234	93.70 308

PM-MVS		93.33 135	92.67 156	95.33 84	96.58 154	94.06 19	92.26 177	92.18 284	85.92 217	96.22 92	96.61 119	85.64 217	95.99 306	90.35 128	98.23 182	95.93 250

MDA-MVSNet-bldmvs		91.04 193	90.88 195	91.55 219	94.68 252	80.16 228	85.49 322	92.14 287	90.41 139	94.93 148	95.79 164	85.10 218	96.93 279	85.15 223	94.19 304	97.57 183

PAPM_NR		91.03 194	90.81 198	91.68 216	96.73 147	81.10 221	93.72 130	96.35 185	88.19 178	88.77 291	92.12 285	85.09 219	97.25 268	82.40 253	93.90 305	96.68 221

HQP2-MVS												84.76 220

HQP-MVS		92.09 174	91.49 183	93.88 144	96.36 165	84.89 173	91.37 211	97.31 124	87.16 198	88.81 287	93.40 255	84.76 220	98.60 170	86.55 208	97.73 216	98.14 132

test222					96.95 136	85.27 171	88.83 279	93.61 257	65.09 348	90.74 254	94.85 209	84.62 222			97.36 232	93.91 302

VDDNet		94.03 122	94.27 116	93.31 162	98.87 19	82.36 205	95.51 71	91.78 292	97.19 11	96.32 83	98.60 18	84.24 223	98.75 148	87.09 199	98.83 122	98.81 78

PVSNet_Blended_VisFu		91.63 182	91.20 190	92.94 174	97.73 98	83.95 188	92.14 181	97.46 111	78.85 290	92.35 226	94.98 203	84.16 224	99.08 92	86.36 212	96.77 251	95.79 257

CL-MVSNet_2432*1600		90.04 223	89.90 217	90.47 251	95.24 233	77.81 273	86.60 317	92.62 277	85.64 222	93.25 199	93.92 241	83.84 225	96.06 304	79.93 278	98.03 203	97.53 187

BH-w/o		87.21 275	87.02 269	87.79 299	94.77 244	77.27 281	87.90 289	93.21 266	81.74 265	89.99 269	88.39 330	83.47 226	96.93 279	71.29 333	92.43 325	89.15 340

PatchMatch-RL		89.18 235	88.02 253	92.64 184	95.90 204	92.87 45	88.67 285	91.06 296	80.34 271	90.03 268	91.67 291	83.34 227	94.42 328	76.35 307	94.84 290	90.64 338

DPM-MVS		89.35 233	88.40 241	92.18 202	96.13 188	84.20 182	86.96 305	96.15 196	75.40 308	87.36 309	91.55 294	83.30 228	98.01 221	82.17 256	96.62 255	94.32 293

OpenMVS_ROB	85.12 16	89.52 232	89.05 229	90.92 240	94.58 255	81.21 220	91.10 219	93.41 262	77.03 301	93.41 189	93.99 239	83.23 229	97.80 238	79.93 278	94.80 291	93.74 307

new-patchmatchnet		88.97 241	90.79 199	83.50 328	94.28 261	55.83 360	85.34 323	93.56 259	86.18 212	95.47 122	95.73 168	83.10 230	96.51 291	85.40 220	98.06 199	98.16 130

1314		86.46 284	86.33 281	86.87 306	91.65 308	74.54 306	91.94 191	94.10 251	74.28 311	84.78 324	87.33 336	83.03 231	95.00 323	78.72 289	91.16 334	91.06 336

IS-MVSNet		94.49 105	94.35 111	94.92 100	98.25 66	86.46 149	97.13 13	94.31 247	96.24 23	96.28 89	96.36 137	82.88 232	99.35 55	88.19 179	99.52 36	98.96 60

MG-MVS		89.54 231	89.80 218	88.76 283	94.88 238	72.47 323	89.60 261	92.44 281	85.82 218	89.48 279	95.98 155	82.85 233	97.74 246	81.87 257	95.27 282	96.08 244

TR-MVS		87.70 262	87.17 265	89.27 276	94.11 264	79.26 252	88.69 283	91.86 291	81.94 264	90.69 255	89.79 315	82.82 234	97.42 261	72.65 326	91.98 329	91.14 335

cl_fuxian		91.32 191	91.42 184	91.00 238	92.29 295	76.79 289	87.52 297	96.42 181	85.76 220	94.72 158	93.89 243	82.73 235	98.16 211	90.93 117	98.55 144	98.04 140

YYNet1		88.17 255	88.24 246	87.93 296	92.21 297	73.62 314	80.75 346	88.77 305	82.51 259	94.99 146	95.11 196	82.70 236	93.70 335	83.33 242	93.83 306	96.48 228

MDA-MVSNet_test_wron		88.16 256	88.23 247	87.93 296	92.22 296	73.71 313	80.71 347	88.84 304	82.52 258	94.88 151	95.14 194	82.70 236	93.61 336	83.28 243	93.80 307	96.46 229

pmmvs-eth3d		91.54 184	90.73 201	93.99 135	95.76 212	87.86 122	90.83 224	93.98 255	78.23 294	94.02 176	96.22 146	82.62 238	96.83 282	86.57 207	98.33 168	97.29 202

MVS_0304		90.96 195	90.15 212	93.37 159	93.17 280	87.06 133	93.62 133	92.43 282	89.60 152	82.25 339	95.50 181	82.56 239	97.83 236	84.41 236	97.83 214	95.22 271

Anonymous20231206		88.77 246	88.29 244	90.20 261	96.31 172	78.81 261	89.56 263	93.49 261	74.26 312	92.38 224	95.58 176	82.21 240	95.43 316	72.07 328	98.75 133	96.34 233

miper_ehance_all_eth		90.48 205	90.42 207	90.69 246	91.62 309	76.57 291	86.83 309	96.18 194	83.38 246	94.06 173	92.66 273	82.20 241	98.04 217	89.79 147	97.02 241	97.45 190

USDC		89.02 238	89.08 228	88.84 282	95.07 236	74.50 308	88.97 276	96.39 183	73.21 318	93.27 196	96.28 142	82.16 242	96.39 295	77.55 297	98.80 127	95.62 266

EPP-MVSNet		93.91 124	93.68 130	94.59 116	98.08 75	85.55 168	97.44 8	94.03 252	94.22 47	94.94 147	96.19 147	82.07 243	99.57 13	87.28 198	98.89 110	98.65 91

UnsupCasMVSNet_eth		90.33 212	90.34 208	90.28 256	94.64 254	80.24 227	89.69 260	95.88 202	85.77 219	93.94 177	95.69 169	81.99 244	92.98 341	84.21 237	91.30 332	97.62 181

alignmvs		93.26 139	92.85 149	94.50 120	95.70 214	87.45 125	93.45 138	95.76 206	91.58 110	95.25 134	92.42 280	81.96 245	98.72 153	91.61 106	97.87 212	97.33 200

TAMVS		90.16 217	89.05 229	93.49 158	96.49 159	86.37 152	90.34 239	92.55 279	80.84 270	92.99 207	94.57 220	81.94 246	98.20 206	73.51 320	98.21 185	95.90 253

Anonymous202405211		92.58 162	92.50 160	92.83 179	96.55 156	83.22 195	92.43 166	91.64 293	94.10 49	95.59 118	96.64 117	81.88 247	97.50 255	85.12 225	98.52 149	97.77 170

SixPastTwentyTwo		94.91 86	95.21 81	93.98 136	98.52 44	83.19 196	95.93 55	94.84 233	94.86 38	98.49 15	98.74 16	81.45 248	99.60 8	94.69 16	99.39 53	99.15 37

cascas		87.02 281	86.28 282	89.25 277	91.56 311	76.45 292	84.33 333	96.78 162	71.01 329	86.89 313	85.91 344	81.35 249	96.94 277	83.09 245	95.60 272	94.35 292

GBi-Net		93.21 142	92.96 146	93.97 137	95.40 227	84.29 178	95.99 51	96.56 174	88.63 168	95.10 139	98.53 21	81.31 250	98.98 108	86.74 202	98.38 161	98.65 91

test1		93.21 142	92.96 146	93.97 137	95.40 227	84.29 178	95.99 51	96.56 174	88.63 168	95.10 139	98.53 21	81.31 250	98.98 108	86.74 202	98.38 161	98.65 91

FMVSNet2		92.78 155	92.73 155	92.95 173	95.40 227	81.98 208	94.18 118	95.53 217	88.63 168	96.05 101	97.37 67	81.31 250	98.81 137	87.38 197	98.67 138	98.06 137

MVE	59.87 23	73.86 327	72.65 330	77.47 338	87.00 351	74.35 309	61.37 356	60.93 361	67.27 342	69.69 358	86.49 340	81.24 253	72.33 358	56.45 355	83.45 349	85.74 347
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
MVP-Stereo		90.07 221	88.92 232	93.54 155	96.31 172	86.49 147	90.93 222	95.59 213	79.80 274	91.48 241	95.59 173	80.79 254	97.39 264	78.57 291	91.19 333	96.76 219
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
UnsupCasMVSNet_bld		88.50 250	88.03 252	89.90 266	95.52 224	78.88 259	87.39 298	94.02 254	79.32 284	93.06 204	94.02 237	80.72 255	94.27 331	75.16 313	93.08 317	96.54 222

MS-PatchMatch		88.05 257	87.75 255	88.95 279	93.28 277	77.93 270	87.88 290	92.49 280	75.42 307	92.57 218	93.59 251	80.44 256	94.24 333	81.28 263	92.75 320	94.69 286

CANet_DTU		89.85 227	89.17 226	91.87 209	92.20 298	80.02 236	90.79 225	95.87 203	86.02 215	82.53 338	91.77 289	80.01 257	98.57 175	85.66 218	97.70 220	97.01 209

PMMVS		83.00 302	81.11 310	88.66 286	83.81 359	86.44 150	82.24 343	85.65 331	61.75 353	82.07 341	85.64 345	79.75 258	91.59 346	75.99 309	93.09 316	87.94 345

ppachtmachnet_test		88.61 249	88.64 237	88.50 288	91.76 306	70.99 329	84.59 330	92.98 267	79.30 285	92.38 224	93.53 253	79.57 259	97.45 259	86.50 210	97.17 237	97.07 206

eth_miper_zixun_eth		90.72 199	90.61 203	91.05 234	92.04 302	76.84 288	86.91 306	96.67 169	85.21 227	94.41 163	93.92 241	79.53 260	98.26 202	89.76 148	97.02 241	98.06 137

N_pmnet		88.90 243	87.25 263	93.83 146	94.40 259	93.81 34	84.73 327	87.09 319	79.36 283	93.26 197	92.43 279	79.29 261	91.68 345	77.50 299	97.22 236	96.00 247

miper_enhance_ethall		88.42 251	87.87 254	90.07 263	88.67 342	75.52 301	85.10 324	95.59 213	75.68 304	92.49 219	89.45 321	78.96 262	97.88 229	87.86 188	97.02 241	96.81 217

EPNet		89.80 229	88.25 245	94.45 125	83.91 358	86.18 158	93.87 126	87.07 320	91.16 121	80.64 348	94.72 215	78.83 263	98.89 121	85.17 221	98.89 110	98.28 123
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
sss		87.23 274	86.82 271	88.46 290	93.96 268	77.94 269	86.84 308	92.78 273	77.59 296	87.61 307	91.83 288	78.75 264	91.92 344	77.84 294	94.20 303	95.52 268

IterMVS-SCA-FT		91.65 181	91.55 179	91.94 208	93.89 270	79.22 254	87.56 294	93.51 260	91.53 112	95.37 127	96.62 118	78.65 265	98.90 119	91.89 99	94.95 287	97.70 175

SCA		87.43 270	87.21 264	88.10 295	92.01 303	71.98 325	89.43 265	88.11 313	82.26 262	88.71 292	92.83 266	78.65 265	97.59 251	79.61 282	93.30 312	94.75 283

our_test_3		87.55 267	87.59 258	87.44 302	91.76 306	70.48 330	83.83 337	90.55 301	79.79 275	92.06 235	92.17 283	78.63 267	95.63 309	84.77 231	94.73 292	96.22 239

jason		89.17 236	88.32 242	91.70 215	95.73 213	80.07 232	88.10 288	93.22 264	71.98 324	90.09 265	92.79 268	78.53 268	98.56 176	87.43 195	97.06 239	96.46 229
jason: jason.
IterMVS		90.18 216	90.16 210	90.21 260	93.15 281	75.98 297	87.56 294	92.97 268	86.43 208	94.09 170	96.40 130	78.32 269	97.43 260	87.87 187	94.69 294	97.23 203
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CHOSEN 1792x2688		87.19 277	85.92 285	91.00 238	97.13 132	79.41 249	84.51 331	95.60 210	64.14 349	90.07 267	94.81 210	78.26 270	97.14 272	73.34 321	95.38 280	96.46 229

WTY-MVS		86.93 282	86.50 280	88.24 293	94.96 237	74.64 304	87.19 301	92.07 289	78.29 293	88.32 298	91.59 293	78.06 271	94.27 331	74.88 314	93.15 315	95.80 256

pmmvs4		88.95 242	87.70 257	92.70 182	94.30 260	85.60 167	87.22 300	92.16 286	74.62 310	89.75 277	94.19 230	77.97 272	96.41 294	82.71 248	96.36 260	96.09 243

DSMNet-mixed		82.21 307	81.56 306	84.16 325	89.57 333	70.00 335	90.65 229	77.66 357	54.99 357	83.30 334	97.57 54	77.89 273	90.50 349	66.86 346	95.54 274	91.97 329

lessismore_v0				93.87 145	98.05 78	83.77 190		80.32 354		97.13 50	97.91 42	77.49 274	99.11 89	92.62 82	98.08 198	98.74 86

HY-MVS	82.50 18	86.81 283	85.93 284	89.47 270	93.63 274	77.93 270	94.02 122	91.58 294	75.68 304	83.64 331	93.64 248	77.40 275	97.42 261	71.70 331	92.07 328	93.05 319

1112_ss		88.42 251	87.41 260	91.45 221	96.69 148	80.99 222	89.72 259	96.72 167	73.37 317	87.00 312	90.69 306	77.38 276	98.20 206	81.38 262	93.72 308	95.15 273

cl-mvsnet1		90.65 202	90.56 204	90.91 242	91.85 304	76.99 285	86.75 311	95.36 223	85.52 225	94.06 173	94.89 207	77.37 277	97.99 224	90.28 132	98.97 105	97.76 171

cl-mvsnet_		90.65 202	90.56 204	90.91 242	91.85 304	76.98 286	86.75 311	95.36 223	85.53 223	94.06 173	94.89 207	77.36 278	97.98 225	90.27 133	98.98 101	97.76 171

CDS-MVSNet		89.55 230	88.22 248	93.53 156	95.37 230	86.49 147	89.26 271	93.59 258	79.76 276	91.15 248	92.31 281	77.12 279	98.38 191	77.51 298	97.92 210	95.71 260
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MVSFormer		92.18 173	92.23 164	92.04 207	94.74 247	80.06 233	97.15 11	97.37 115	88.98 161	88.83 285	92.79 268	77.02 280	99.60 8	96.41 4	96.75 252	96.46 229

lupinMVS		88.34 253	87.31 261	91.45 221	94.74 247	80.06 233	87.23 299	92.27 283	71.10 328	88.83 285	91.15 297	77.02 280	98.53 179	86.67 205	96.75 252	95.76 258

PMMVS2		81.31 313	83.44 297	74.92 339	90.52 322	46.49 362	69.19 354	85.23 339	84.30 242	87.95 303	94.71 216	76.95 282	84.36 356	64.07 349	98.09 197	93.89 303

pmmvs5		87.87 259	87.14 266	90.07 263	93.26 279	76.97 287	88.89 278	92.18 284	73.71 316	88.36 297	93.89 243	76.86 283	96.73 285	80.32 270	96.81 249	96.51 224

K. test v3		93.37 134	93.27 143	93.66 149	98.05 78	82.62 203	94.35 113	86.62 322	96.05 27	97.51 38	98.85 12	76.59 284	99.65 3	93.21 63	98.20 187	98.73 88

miper_lstm_enhance		89.90 226	89.80 218	90.19 262	91.37 313	77.50 277	83.82 338	95.00 227	84.84 237	93.05 205	94.96 204	76.53 285	95.20 322	89.96 144	98.67 138	97.86 161

test_part1		94.39 107	94.55 104	93.92 141	96.14 185	82.86 201	95.54 69	98.09 47	95.36 35	98.27 20	98.36 28	75.91 286	99.44 23	93.41 54	99.84 3	99.47 17

Test_1112_low_res		87.50 269	86.58 275	90.25 258	96.80 146	77.75 274	87.53 296	96.25 188	69.73 335	86.47 314	93.61 250	75.67 287	97.88 229	79.95 276	93.20 313	95.11 275

Vis-MVSNet (Re-imp)		90.42 207	90.16 210	91.20 231	97.66 105	77.32 280	94.33 114	87.66 315	91.20 119	92.99 207	95.13 195	75.40 288	98.28 198	77.86 293	99.19 79	97.99 146

D2MVS		89.93 225	89.60 223	90.92 240	94.03 267	78.40 265	88.69 283	94.85 232	78.96 288	93.08 203	95.09 197	74.57 289	96.94 277	88.19 179	98.96 107	97.41 192

PVSNet	76.22 20	82.89 303	82.37 303	84.48 323	93.96 268	64.38 353	78.60 349	88.61 306	71.50 326	84.43 327	86.36 341	74.27 290	94.60 325	69.87 340	93.69 309	94.46 289

test_yl		90.11 218	89.73 221	91.26 227	94.09 265	79.82 240	90.44 234	92.65 275	90.90 123	93.19 201	93.30 257	73.90 291	98.03 218	82.23 254	96.87 247	95.93 250

DCV-MVSNet		90.11 218	89.73 221	91.26 227	94.09 265	79.82 240	90.44 234	92.65 275	90.90 123	93.19 201	93.30 257	73.90 291	98.03 218	82.23 254	96.87 247	95.93 250

CMPMVS	68.83 22	87.28 273	85.67 286	92.09 205	88.77 341	85.42 169	90.31 240	94.38 246	70.02 334	88.00 302	93.30 257	73.78 293	94.03 334	75.96 310	96.54 256	96.83 216
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
baseline1		87.62 266	87.31 261	88.54 287	94.71 251	74.27 311	93.10 144	88.20 311	86.20 211	92.18 232	93.04 262	73.21 294	95.52 311	79.32 285	85.82 345	95.83 255

bset_n11_16_dypcd		89.99 224	89.15 227	92.53 191	94.75 245	81.34 217	84.19 334	87.56 316	85.13 230	93.77 180	92.46 275	72.82 295	99.01 105	92.46 85	99.21 77	97.23 203

PVSNet_0	70.34 21	74.58 326	72.96 329	79.47 336	90.63 320	66.24 346	73.26 350	83.40 347	63.67 351	78.02 352	78.35 354	72.53 296	89.59 351	56.68 354	60.05 358	82.57 352

MIMVSNet		87.13 279	86.54 277	88.89 281	96.05 192	76.11 295	94.39 112	88.51 307	81.37 266	88.27 299	96.75 108	72.38 297	95.52 311	65.71 348	95.47 276	95.03 276

PAPM		81.91 311	80.11 321	87.31 303	93.87 271	72.32 324	84.02 336	93.22 264	69.47 336	76.13 355	89.84 312	72.15 298	97.23 269	53.27 356	89.02 339	92.37 327

cl-mvsnet2		89.02 238	88.50 239	90.59 249	89.76 329	76.45 292	86.62 316	94.03 252	82.98 254	92.65 215	92.49 274	72.05 299	97.53 253	88.93 165	97.02 241	97.78 169

LFMVS		91.33 190	91.16 192	91.82 210	96.27 175	79.36 250	95.01 90	85.61 333	96.04 28	94.82 152	97.06 87	72.03 300	98.46 188	84.96 229	98.70 136	97.65 179

MVS-HIRNet		78.83 325	80.60 317	73.51 340	93.07 282	47.37 361	87.10 303	78.00 356	68.94 337	77.53 353	97.26 75	71.45 301	94.62 324	63.28 351	88.74 340	78.55 353

EPNet_dtu		85.63 288	84.37 291	89.40 273	86.30 352	74.33 310	91.64 207	88.26 309	84.84 237	72.96 357	89.85 311	71.27 302	97.69 248	76.60 305	97.62 224	96.18 241
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
HyFIR lowres test		87.19 277	85.51 287	92.24 197	97.12 133	80.51 226	85.03 325	96.06 197	66.11 345	91.66 240	92.98 264	70.12 303	99.14 85	75.29 312	95.23 283	97.07 206

FMVSNet3		90.78 198	90.32 209	92.16 203	93.03 285	79.92 238	92.54 159	94.95 229	86.17 213	95.10 139	96.01 154	69.97 304	98.75 148	86.74 202	98.38 161	97.82 166

RPMNet		90.31 214	90.14 213	90.81 245	91.01 316	78.93 257	92.52 160	98.12 41	91.91 91	89.10 282	96.89 98	68.84 305	99.41 35	90.17 137	92.70 321	94.08 295

ADS-MVSNet2		84.01 297	82.20 305	89.41 272	89.04 338	76.37 294	87.57 292	90.98 297	72.71 322	84.46 325	92.45 276	68.08 306	96.48 292	70.58 338	83.97 347	95.38 269

ADS-MVSNet		82.25 306	81.55 307	84.34 324	89.04 338	65.30 347	87.57 292	85.13 340	72.71 322	84.46 325	92.45 276	68.08 306	92.33 343	70.58 338	83.97 347	95.38 269

CVMVSNet		85.16 290	84.72 289	86.48 307	92.12 300	70.19 331	92.32 174	88.17 312	56.15 356	90.64 256	95.85 159	67.97 308	96.69 286	88.78 170	90.52 336	92.56 325

new_pmnet		81.22 314	81.01 313	81.86 332	90.92 318	70.15 332	84.03 335	80.25 355	70.83 330	85.97 317	89.78 316	67.93 309	84.65 355	67.44 344	91.90 330	90.78 337

CR-MVSNet		87.89 258	87.12 267	90.22 259	91.01 316	78.93 257	92.52 160	92.81 270	73.08 319	89.10 282	96.93 95	67.11 310	97.64 250	88.80 169	92.70 321	94.08 295

Patchmtry		90.11 218	89.92 216	90.66 247	90.35 325	77.00 284	92.96 147	92.81 270	90.25 141	94.74 156	96.93 95	67.11 310	97.52 254	85.17 221	98.98 101	97.46 189

PatchmatchNet		85.22 289	84.64 290	86.98 305	89.51 334	69.83 336	90.52 232	87.34 318	78.87 289	87.22 311	92.74 270	66.91 312	96.53 289	81.77 258	86.88 344	94.58 287
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
GA-MVS		87.70 262	86.82 271	90.31 255	93.27 278	77.22 282	84.72 329	92.79 272	85.11 232	89.82 273	90.07 310	66.80 313	97.76 244	84.56 234	94.27 302	95.96 249

MDTV_nov1_ep13_2view						42.48 363	88.45 287		67.22 343	83.56 332		66.80 313		72.86 325		94.06 297

tpmrst		82.85 304	82.93 302	82.64 330	87.65 343	58.99 358	90.14 246	87.90 314	75.54 306	83.93 329	91.63 292	66.79 315	95.36 317	81.21 265	81.54 353	93.57 313

sam_mvs1												66.64 316				94.75 283

sam_mvs												66.41 317

Patchmatch-RL test		88.81 245	88.52 238	89.69 269	95.33 232	79.94 237	86.22 319	92.71 274	78.46 292	95.80 109	94.18 231	66.25 318	95.33 319	89.22 161	98.53 148	93.78 305

patchmatchnet-post											91.71 290	66.22 319	97.59 251

AUN-MVS		90.05 222	88.30 243	95.32 87	96.09 189	90.52 73	92.42 167	92.05 290	82.08 263	88.45 296	92.86 265	65.76 320	98.69 161	88.91 167	96.07 263	96.75 220

test_post											6.07 361	65.74 321	95.84 307

test_post1							90.21 242				5.85 362	65.36 322	96.00 305	79.61 282

MDTV_nov1_ep13			83.88 296		89.42 335	61.52 356	88.74 282	87.41 317	73.99 314	84.96 323	94.01 238	65.25 323	95.53 310	78.02 292	93.16 314

Patchmatch-test		86.10 286	86.01 283	86.38 311	90.63 320	74.22 312	89.57 262	86.69 321	85.73 221	89.81 274	92.83 266	65.24 324	91.04 347	77.82 296	95.78 270	93.88 304

tpmvs		84.22 296	83.97 295	84.94 319	87.09 349	65.18 348	91.21 216	88.35 308	82.87 255	85.21 319	90.96 301	65.24 324	96.75 284	79.60 284	85.25 346	92.90 321

EU-MVSNet		87.39 271	86.71 274	89.44 271	93.40 276	76.11 295	94.93 93	90.00 302	57.17 355	95.71 114	97.37 67	64.77 326	97.68 249	92.67 81	94.37 299	94.52 288

thres200		85.85 287	85.18 288	87.88 298	94.44 257	72.52 322	89.08 275	86.21 324	88.57 172	91.44 242	88.40 329	64.22 327	98.00 222	68.35 342	95.88 269	93.12 316

PatchT		87.51 268	88.17 249	85.55 314	90.64 319	66.91 341	92.02 187	86.09 326	92.20 83	89.05 284	97.16 82	64.15 328	96.37 297	89.21 162	92.98 319	93.37 314

tfpn200view9		87.05 280	86.52 278	88.67 285	95.77 210	72.94 319	91.89 194	86.00 328	90.84 125	92.61 216	89.80 313	63.93 329	98.28 198	71.27 334	96.54 256	94.79 281

thres400		87.20 276	86.52 278	89.24 278	95.77 210	72.94 319	91.89 194	86.00 328	90.84 125	92.61 216	89.80 313	63.93 329	98.28 198	71.27 334	96.54 256	96.51 224

FPMVS		84.50 294	83.28 298	88.16 294	96.32 171	94.49 14	85.76 320	85.47 334	83.09 251	85.20 320	94.26 227	63.79 331	86.58 354	63.72 350	91.88 331	83.40 349

thres100view900		87.35 272	86.89 270	88.72 284	96.14 185	73.09 318	93.00 146	85.31 336	92.13 85	93.26 197	90.96 301	63.42 332	98.28 198	71.27 334	96.54 256	94.79 281

thres600view7		87.66 264	87.10 268	89.36 274	96.05 192	73.17 316	92.72 153	85.31 336	91.89 92	93.29 194	90.97 300	63.42 332	98.39 189	73.23 322	96.99 246	96.51 224

EMVS		80.35 321	80.28 320	80.54 334	84.73 357	69.07 337	72.54 353	80.73 352	87.80 186	81.66 345	81.73 352	62.89 334	89.84 350	75.79 311	94.65 295	82.71 351

test-LLR		83.58 298	83.17 299	84.79 321	89.68 331	66.86 343	83.08 339	84.52 341	83.07 252	82.85 336	84.78 347	62.86 335	93.49 337	82.85 246	94.86 288	94.03 298

test0.0.03 1		82.48 305	81.47 309	85.48 315	89.70 330	73.57 315	84.73 327	81.64 350	83.07 252	88.13 301	86.61 338	62.86 335	89.10 353	66.24 347	90.29 337	93.77 306

tpm cat1		80.61 320	79.46 323	84.07 326	88.78 340	65.06 351	89.26 271	88.23 310	62.27 352	81.90 344	89.66 319	62.70 337	95.29 320	71.72 330	80.60 354	91.86 332

E-PMN		80.72 319	80.86 314	80.29 335	85.11 355	68.77 338	72.96 351	81.97 349	87.76 187	83.25 335	83.01 351	62.22 338	89.17 352	77.15 302	94.31 301	82.93 350

RRT_MVS		91.36 189	90.05 214	95.29 88	89.21 337	88.15 114	92.51 163	94.89 231	86.73 205	95.54 120	95.68 170	61.82 339	99.30 67	94.91 13	99.13 87	98.43 114

baseline2		83.38 299	81.54 308	88.90 280	91.38 312	72.84 321	88.78 280	81.22 351	78.97 287	79.82 350	87.56 332	61.73 340	97.80 238	74.30 317	90.05 338	96.05 246

CostFormer		83.09 301	82.21 304	85.73 313	89.27 336	67.01 340	90.35 238	86.47 323	70.42 332	83.52 333	93.23 260	61.18 341	96.85 281	77.21 301	88.26 342	93.34 315

MVSTER		89.32 234	88.75 236	91.03 235	90.10 327	76.62 290	90.85 223	94.67 242	82.27 261	95.24 135	95.79 164	61.09 342	98.49 182	90.49 122	98.26 176	97.97 150

tpm		84.38 295	84.08 294	85.30 318	90.47 323	63.43 355	89.34 268	85.63 332	77.24 300	87.62 306	95.03 202	61.00 343	97.30 267	79.26 286	91.09 335	95.16 272

EPMVS		81.17 316	80.37 318	83.58 327	85.58 354	65.08 350	90.31 240	71.34 358	77.31 299	85.80 318	91.30 295	59.38 344	92.70 342	79.99 275	82.34 352	92.96 320

tmp_tt		37.97 328	44.33 331	18.88 342	11.80 363	21.54 364	63.51 355	45.66 364	4.23 359	51.34 360	50.48 357	59.08 345	22.11 360	44.50 357	68.35 357	13.00 356

tpm2		81.46 312	80.35 319	84.80 320	89.90 328	65.14 349	90.44 234	85.36 335	65.82 347	82.05 342	92.44 278	57.94 346	96.69 286	70.71 337	88.49 341	92.56 325

ET-MVSNet_ETH3D		86.15 285	84.27 293	91.79 211	93.04 284	81.28 218	87.17 302	86.14 325	79.57 279	83.65 330	88.66 326	57.10 347	98.18 209	87.74 189	95.40 278	95.90 253

CHOSEN 280x420		80.04 322	77.97 328	86.23 312	90.13 326	74.53 307	72.87 352	89.59 303	66.38 344	76.29 354	85.32 346	56.96 348	95.36 317	69.49 341	94.72 293	88.79 343

JIA-IIPM		85.08 291	83.04 300	91.19 232	87.56 344	86.14 159	89.40 267	84.44 343	88.98 161	82.20 340	97.95 39	56.82 349	96.15 300	76.55 306	83.45 349	91.30 334

DeepMVS_CX				53.83 341	70.38 362	64.56 352		48.52 363	33.01 358	65.50 359	74.21 356	56.19 350	46.64 359	38.45 358	70.07 356	50.30 355

dp		79.28 323	78.62 326	81.24 333	85.97 353	56.45 359	86.91 306	85.26 338	72.97 320	81.45 346	89.17 325	56.01 351	95.45 315	73.19 323	76.68 355	91.82 333

thisisatest0515		84.72 293	82.99 301	89.90 266	92.96 286	75.33 302	84.36 332	83.42 346	77.37 298	88.27 299	86.65 337	53.94 352	98.72 153	82.56 250	97.40 231	95.67 262

tttt0517		89.81 228	88.90 234	92.55 190	97.00 135	79.73 244	95.03 89	83.65 345	89.88 147	95.30 130	94.79 214	53.64 353	99.39 45	91.99 94	98.79 128	98.54 105

thisisatest0530		88.69 248	87.52 259	92.20 198	96.33 170	79.36 250	92.81 151	84.01 344	86.44 207	93.67 184	92.68 272	53.62 354	99.25 74	89.65 151	98.45 155	98.00 143

FMVSNet5		87.82 261	86.56 276	91.62 217	92.31 294	79.81 242	93.49 136	94.81 236	83.26 247	91.36 243	96.93 95	52.77 355	97.49 257	76.07 308	98.03 203	97.55 186

pmmvs3		80.83 317	78.96 325	86.45 308	87.23 348	77.48 278	84.87 326	82.31 348	63.83 350	85.03 321	89.50 320	49.66 356	93.10 339	73.12 324	95.10 285	88.78 344

DWT-MVSNet_test		80.74 318	79.18 324	85.43 316	87.51 346	66.87 342	89.87 256	86.01 327	74.20 313	80.86 347	80.62 353	48.84 357	96.68 288	81.54 260	83.14 351	92.75 323

IB-MVS	77.21 19	83.11 300	81.05 311	89.29 275	91.15 314	75.85 298	85.66 321	86.00 328	79.70 277	82.02 343	86.61 338	48.26 358	98.39 189	77.84 294	92.22 326	93.63 309
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
RRT_test8_iter05		88.21 254	88.17 249	88.33 292	91.62 309	66.82 345	91.73 206	96.60 172	86.34 209	94.14 168	95.38 190	47.72 359	99.11 89	91.78 101	98.26 176	99.06 47

gg-mvs-nofinetune		82.10 310	81.02 312	85.34 317	87.46 347	71.04 327	94.74 98	67.56 359	96.44 21	79.43 351	98.99 6	45.24 360	96.15 300	67.18 345	92.17 327	88.85 342

GG-mvs-BLEND				83.24 329	85.06 356	71.03 328	94.99 92	65.55 360		74.09 356	75.51 355	44.57 361	94.46 327	59.57 353	87.54 343	84.24 348

TESTMET0.1,1		79.09 324	78.04 327	82.25 331	87.52 345	64.03 354	83.08 339	80.62 353	70.28 333	80.16 349	83.22 350	44.13 362	90.56 348	79.95 276	93.36 310	92.15 328

test-mter		81.21 315	80.01 322	84.79 321	89.68 331	66.86 343	83.08 339	84.52 341	73.85 315	82.85 336	84.78 347	43.66 363	93.49 337	82.85 246	94.86 288	94.03 298

KD-MVS_2432*1600		82.17 308	80.75 315	86.42 309	82.04 360	70.09 333	81.75 344	90.80 298	82.56 256	90.37 261	89.30 322	42.90 364	96.11 302	74.47 315	92.55 323	93.06 317

miper_refine_blended		82.17 308	80.75 315	86.42 309	82.04 360	70.09 333	81.75 344	90.80 298	82.56 256	90.37 261	89.30 322	42.90 364	96.11 302	74.47 315	92.55 323	93.06 317

test123		9.49 330	12.01 333	1.91 343	2.87 364	1.30 365	82.38 342	1.34 366	1.36 360	2.84 361	6.56 360	2.45 366	0.97 361	2.73 359	5.56 359	3.47 357

testmvs		9.02 331	11.42 334	1.81 344	2.77 365	1.13 366	79.44 348	1.90 365	1.18 361	2.65 362	6.80 359	1.95 367	0.87 362	2.62 360	3.45 360	3.44 358

uanet_test		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

sosnet-low-res		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

sosnet		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

uncertanet		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

Regformer		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

ab-mvs-re		7.56 332	10.08 336	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	90.69 306	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

uanet		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

IU-MVS					98.51 45	86.66 145		96.83 159	72.74 321	95.83 108				93.00 72	99.29 64	98.64 95

save fliter					97.46 117	88.05 117	92.04 185	97.08 141	87.63 191

test_0728_SECOND				94.88 101	98.55 39	86.72 142	95.20 81	98.22 29					99.38 51	93.44 51	99.31 61	98.53 106

GSMVS																94.75 283

test_part2					98.21 68	89.41 89				96.72 68

MTGPA								97.62 97

MTMP							94.82 95	54.62 362

gm-plane-assit					87.08 350	59.33 357			71.22 327		83.58 349		97.20 270	73.95 318

test9_res														88.16 181	98.40 157	97.83 164

agg_prior2														87.06 200	98.36 167	97.98 147

agg_prior					96.20 180	88.89 98		96.88 156		90.21 263			98.78 143

test_prior4						89.91 80	90.74 226

test_prior				94.61 111	95.95 200	87.23 129		97.36 120					98.68 162			97.93 153

旧先验2							90.00 251		68.65 338	92.71 214			96.52 290	85.15 223

新几何2							90.02 250

无先验							89.94 252	95.75 207	70.81 331				98.59 172	81.17 266		94.81 280

原ACMM2							89.34 268

testdata2													98.03 218	80.24 273

testdata1							88.96 277		88.44 174

plane_prior7					97.71 99	88.68 102

plane_prior5								97.81 85					98.95 115	89.26 159	98.51 151	98.60 102

plane_prior4											95.59 173

plane_prior3						88.43 111			90.35 140	93.31 192

plane_prior2							94.56 107		91.74 105

plane_prior1					97.38 120

plane_prior						88.12 115	93.01 145		88.98 161						98.06 199

n2								0.00 367

nn								0.00 367

door-mid								92.13 288

test11								96.65 170

door								91.26 295

HQP5-MVS						84.89 173

HQP-NCC					96.36 165		91.37 211		87.16 198	88.81 287

ACMP_Plane					96.36 165		91.37 211		87.16 198	88.81 287

BP-MVS														86.55 208

HQP4-MVS										88.81 287			98.61 168			98.15 131

HQP3-MVS								97.31 124							97.73 216

NP-MVS					96.82 143	87.10 132					93.40 255

ACMMP++_ref															98.82 123

ACMMP++															99.25 72

High-resolution multi-view benchmark