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 2	99.95 1	98.13 2	99.37 1	99.57 1	99.82 1	99.86 1	99.85 1	99.52 1	99.73 2	97.58 2	99.94 1	99.85 2

mamv4		98.21 2	97.86 3	99.26 1	98.24 74	99.36 1	96.10 63	99.32 2	98.75 2	99.58 2	98.70 20	91.78 133	99.88 1	98.60 1	99.67 20	98.54 124

LTVRE_ROB	93.87 1	97.93 3	98.16 2	97.26 30	98.81 27	93.86 35	99.07 2	98.98 9	97.01 15	98.92 5	98.78 16	95.22 42	98.61 176	96.85 7	99.77 9	99.31 30
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
TDRefinement		97.68 4	97.60 5	97.93 3	99.02 12	95.95 9	98.61 3	98.81 11	97.41 11	97.28 59	98.46 33	94.62 66	98.84 134	94.64 41	99.53 37	98.99 58

reproduce_model		97.35 5	97.24 12	97.70 5	98.44 58	95.08 12	95.88 74	98.50 18	96.62 22	98.27 21	97.93 57	94.57 68	99.50 22	95.57 24	99.35 60	98.52 127

UA-Net		97.35 5	97.24 12	97.69 6	98.22 75	93.87 34	98.42 6	98.19 49	96.95 16	95.46 151	99.23 6	93.45 88	99.57 15	95.34 33	99.89 2	99.63 12

reproduce-ours		97.28 7	97.19 14	97.57 12	98.37 63	94.84 13	95.57 89	98.40 24	96.36 29	98.18 25	97.78 68	95.47 28	99.50 22	95.26 34	99.33 66	98.36 138

our_new_method		97.28 7	97.19 14	97.57 12	98.37 63	94.84 13	95.57 89	98.40 24	96.36 29	98.18 25	97.78 68	95.47 28	99.50 22	95.26 34	99.33 66	98.36 138

UniMVSNet_ETH3D		97.13 9	97.72 4	95.35 86	99.51 2	87.38 138	97.70 8	97.54 125	98.16 3	98.94 3	99.33 3	97.84 4	99.08 100	90.73 153	99.73 13	99.59 15

HPM-MVS_fast		97.01 10	96.89 18	97.39 25	99.12 8	93.92 32	97.16 14	98.17 55	93.11 80	96.48 96	97.36 104	96.92 6	99.34 65	94.31 48	99.38 58	98.92 74

SR-MVS-dyc-post		96.84 11	96.60 28	97.56 14	98.07 84	95.27 10	96.37 46	98.12 61	95.66 39	97.00 72	97.03 134	94.85 60	99.42 36	93.49 71	98.84 136	98.00 171

mvs_tets		96.83 12	96.71 22	97.17 31	98.83 24	92.51 52	96.58 33	97.61 119	87.57 220	98.80 8	98.90 11	96.50 9	99.59 14	96.15 16	99.47 42	99.40 24

v7n		96.82 13	97.31 11	95.33 88	98.54 46	86.81 153	96.83 22	98.07 71	96.59 23	98.46 18	98.43 35	92.91 109	99.52 20	96.25 15	99.76 10	99.65 11

APD-MVS_3200maxsize		96.82 13	96.65 24	97.32 29	97.95 96	93.82 37	96.31 52	98.25 39	95.51 41	96.99 74	97.05 133	95.63 23	99.39 52	93.31 83	98.88 131	98.75 94

HPM-MVS		96.81 15	96.62 26	97.36 27	98.89 20	93.53 42	97.51 10	98.44 20	92.35 93	95.95 123	96.41 174	96.71 8	99.42 36	93.99 56	99.36 59	99.13 43
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
pmmvs6		96.80 16	97.36 10	95.15 100	99.12 8	87.82 132	96.68 29	97.86 97	96.10 33	98.14 28	99.28 5	97.94 3	98.21 216	91.38 142	99.69 14	99.42 21

OurMVSNet-221017-0		96.80 16	96.75 21	96.96 39	99.03 11	91.85 61	97.98 7	98.01 83	94.15 58	98.93 4	99.07 7	88.07 199	99.57 15	95.86 19	99.69 14	99.46 20

testf1		96.77 18	96.49 30	97.60 10	99.01 14	96.70 4	96.31 52	98.33 30	94.96 45	97.30 57	97.93 57	96.05 16	97.90 246	89.32 194	99.23 87	98.19 154

APD_test2		96.77 18	96.49 30	97.60 10	99.01 14	96.70 4	96.31 52	98.33 30	94.96 45	97.30 57	97.93 57	96.05 16	97.90 246	89.32 194	99.23 87	98.19 154

COLMAP_ROB	91.06 5	96.75 20	96.62 26	97.13 32	98.38 61	94.31 21	96.79 25	98.32 32	96.69 19	96.86 79	97.56 85	95.48 27	98.77 151	90.11 177	99.44 49	98.31 145
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
anonymousdsp		96.74 21	96.42 33	97.68 8	98.00 92	94.03 29	96.97 19	97.61 119	87.68 218	98.45 19	98.77 17	94.20 77	99.50 22	96.70 9	99.40 56	99.53 17

DTE-MVSNet		96.74 21	97.43 6	94.67 118	99.13 6	84.68 199	96.51 36	97.94 94	98.14 4	98.67 13	98.32 37	95.04 50	99.69 4	93.27 86	99.82 7	99.62 13

SR-MVS		96.70 23	96.42 33	97.54 15	98.05 86	94.69 15	96.13 62	98.07 71	95.17 43	96.82 82	96.73 157	95.09 49	99.43 35	92.99 97	98.71 158	98.50 128

PS-CasMVS		96.69 24	97.43 6	94.49 131	99.13 6	84.09 210	96.61 32	97.97 88	97.91 6	98.64 14	98.13 43	95.24 40	99.65 5	93.39 81	99.84 3	99.72 4

PEN-MVS		96.69 24	97.39 9	94.61 121	99.16 4	84.50 200	96.54 34	98.05 75	98.06 5	98.64 14	98.25 40	95.01 53	99.65 5	92.95 98	99.83 5	99.68 7

MTAPA		96.65 26	96.38 37	97.47 19	98.95 18	94.05 27	95.88 74	97.62 117	94.46 54	96.29 105	96.94 140	93.56 85	99.37 60	94.29 49	99.42 51	98.99 58

test_djsdf		96.62 27	96.49 30	97.01 36	98.55 44	91.77 63	97.15 15	97.37 137	88.98 186	98.26 24	98.86 12	93.35 93	99.60 10	96.41 12	99.45 46	99.66 9

ACMMP		96.61 28	96.34 38	97.43 22	98.61 37	93.88 33	96.95 20	98.18 51	92.26 96	96.33 101	96.84 148	95.10 48	99.40 49	93.47 74	99.33 66	99.02 55
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
Anonymous20231211		96.60 29	97.13 16	95.00 103	97.46 132	86.35 169	97.11 18	98.24 42	97.58 9	98.72 9	98.97 9	93.15 100	99.15 91	93.18 89	99.74 12	99.50 19

WR-MVS_H		96.60 29	97.05 17	95.24 94	99.02 12	86.44 165	96.78 26	98.08 68	97.42 10	98.48 17	97.86 66	91.76 136	99.63 8	94.23 50	99.84 3	99.66 9

jajsoiax		96.59 31	96.42 33	97.12 33	98.76 30	92.49 53	96.44 43	97.42 135	86.96 232	98.71 11	98.72 19	95.36 34	99.56 18	95.92 17	99.45 46	99.32 29

ACMH	88.36 12	96.59 31	97.43 6	94.07 145	98.56 41	85.33 193	96.33 49	98.30 35	94.66 49	98.72 9	98.30 38	97.51 5	98.00 239	94.87 38	99.59 27	98.86 80
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
XVS		96.49 33	96.18 46	97.44 20	98.56 41	93.99 30	96.50 37	97.95 91	94.58 50	94.38 200	96.49 168	94.56 69	99.39 52	93.57 67	99.05 107	98.93 70

ACMH+	88.43 11	96.48 34	96.82 19	95.47 83	98.54 46	89.06 104	95.65 83	98.61 15	96.10 33	98.16 27	97.52 90	96.90 7	98.62 175	90.30 168	99.60 25	98.72 99

APDe-MVS		96.46 35	96.64 25	95.93 64	97.68 118	89.38 98	96.90 21	98.41 23	92.52 88	97.43 50	97.92 61	95.11 47	99.50 22	94.45 44	99.30 73	98.92 74
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
ACMMPR		96.46 35	96.14 49	97.41 24	98.60 38	93.82 37	96.30 56	97.96 89	92.35 93	95.57 144	96.61 164	94.93 58	99.41 42	93.78 61	99.15 99	99.00 56

mPP-MVS		96.46 35	96.05 55	97.69 6	98.62 35	94.65 17	96.45 41	97.74 110	92.59 87	95.47 149	96.68 160	94.50 71	99.42 36	93.10 92	99.26 83	98.99 58

CP-MVS		96.44 38	96.08 53	97.54 15	98.29 68	94.62 18	96.80 24	98.08 68	92.67 86	95.08 177	96.39 179	94.77 62	99.42 36	93.17 90	99.44 49	98.58 121

ZNCC-MVS		96.42 39	96.20 45	97.07 34	98.80 29	92.79 50	96.08 65	98.16 58	91.74 122	95.34 158	96.36 182	95.68 21	99.44 32	94.41 46	99.28 81	98.97 64

region2R		96.41 40	96.09 51	97.38 26	98.62 35	93.81 39	96.32 51	97.96 89	92.26 96	95.28 163	96.57 166	95.02 52	99.41 42	93.63 65	99.11 102	98.94 68

SteuartSystems-ACMMP		96.40 41	96.30 40	96.71 44	98.63 34	91.96 59	95.70 80	98.01 83	93.34 77	96.64 90	96.57 166	94.99 54	99.36 61	93.48 73	99.34 64	98.82 84
Skip Steuart: Steuart Systems R&D Blog.
HFP-MVS		96.39 42	96.17 48	97.04 35	98.51 49	93.37 43	96.30 56	97.98 86	92.35 93	95.63 141	96.47 169	95.37 32	99.27 80	93.78 61	99.14 100	98.48 131

LPG-MVS_test		96.38 43	96.23 43	96.84 42	98.36 66	92.13 56	95.33 98	98.25 39	91.78 118	97.07 67	97.22 118	96.38 12	99.28 78	92.07 119	99.59 27	99.11 46

nrg030		96.32 44	96.55 29	95.62 78	97.83 103	88.55 118	95.77 78	98.29 38	92.68 84	98.03 30	97.91 63	95.13 45	98.95 120	93.85 59	99.49 41	99.36 27

PGM-MVS		96.32 44	95.94 61	97.43 22	98.59 40	93.84 36	95.33 98	98.30 35	91.40 135	95.76 133	96.87 145	95.26 39	99.45 31	92.77 100	99.21 91	99.00 56

ACMM	88.83 9	96.30 46	96.07 54	96.97 38	98.39 60	92.95 48	94.74 121	98.03 80	90.82 148	97.15 64	96.85 146	96.25 14	99.00 112	93.10 92	99.33 66	98.95 67
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GST-MVS		96.24 47	95.99 59	97.00 37	98.65 33	92.71 51	95.69 82	98.01 83	92.08 103	95.74 136	96.28 188	95.22 42	99.42 36	93.17 90	99.06 104	98.88 79

ACMMP_NAP		96.21 48	96.12 50	96.49 52	98.90 19	91.42 67	94.57 129	98.03 80	90.42 160	96.37 99	97.35 107	95.68 21	99.25 81	94.44 45	99.34 64	98.80 88

CP-MVSNet		96.19 49	96.80 20	94.38 136	98.99 16	83.82 213	96.31 52	97.53 127	97.60 8	98.34 20	97.52 90	91.98 129	99.63 8	93.08 94	99.81 8	99.70 5

MP-MVS		96.14 50	95.68 77	97.51 17	98.81 27	94.06 25	96.10 63	97.78 108	92.73 83	93.48 226	96.72 158	94.23 76	99.42 36	91.99 121	99.29 76	99.05 53
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
LS3D		96.11 51	95.83 70	96.95 40	94.75 292	94.20 23	97.34 13	97.98 86	97.31 12	95.32 159	96.77 150	93.08 103	99.20 87	91.79 128	98.16 215	97.44 228

MP-MVS-pluss		96.08 52	95.92 64	96.57 48	99.06 10	91.21 69	93.25 175	98.32 32	87.89 211	96.86 79	97.38 100	95.55 26	99.39 52	95.47 27	99.47 42	99.11 46
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TranMVSNet+NR-MVSNet		96.07 53	96.26 42	95.50 82	98.26 71	87.69 134	93.75 159	97.86 97	95.96 38	97.48 48	97.14 125	95.33 36	99.44 32	90.79 151	99.76 10	99.38 25

PS-MVSNAJss		96.01 54	96.04 56	95.89 69	98.82 25	88.51 119	95.57 89	97.88 95	88.72 192	98.81 7	98.86 12	90.77 159	99.60 10	95.43 29	99.53 37	99.57 16

SED-MVS		96.00 55	96.41 36	94.76 112	98.51 49	86.97 149	95.21 104	98.10 65	91.95 105	97.63 38	97.25 114	96.48 10	99.35 62	93.29 84	99.29 76	97.95 179

DVP-MVS++		95.93 56	96.34 38	94.70 115	96.54 186	86.66 159	98.45 4	98.22 46	93.26 78	97.54 43	97.36 104	93.12 101	99.38 58	93.88 57	98.68 162	98.04 166

APD_test1		95.91 57	95.42 88	97.36 27	98.82 25	96.62 7	95.64 84	97.64 115	93.38 76	95.89 128	97.23 116	93.35 93	97.66 275	88.20 220	98.66 166	97.79 201

test_fmvsmconf0.01_n		95.90 58	96.09 51	95.31 91	97.30 139	89.21 100	94.24 140	98.76 13	86.25 241	97.56 42	98.66 21	95.73 19	98.44 197	97.35 4	98.99 115	98.27 148

DPE-MVS		95.89 59	95.88 66	95.92 66	97.93 97	89.83 88	93.46 169	98.30 35	92.37 91	97.75 35	96.95 139	95.14 44	99.51 21	91.74 129	99.28 81	98.41 137
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
SF-MVS		95.88 60	95.88 66	95.87 70	98.12 80	89.65 90	95.58 88	98.56 17	91.84 114	96.36 100	96.68 160	94.37 75	99.32 71	92.41 112	99.05 107	98.64 114

3Dnovator+	92.74 2	95.86 61	95.77 74	96.13 56	96.81 167	90.79 77	96.30 56	97.82 102	96.13 32	94.74 191	97.23 116	91.33 143	99.16 90	93.25 87	98.30 201	98.46 132

mmtdpeth		95.82 62	96.02 58	95.23 95	96.91 158	88.62 113	96.49 39	99.26 4	95.07 44	93.41 228	99.29 4	90.25 172	97.27 297	94.49 43	99.01 114	99.80 3

DVP-MVS		95.82 62	96.18 46	94.72 114	98.51 49	86.69 157	95.20 106	97.00 170	91.85 111	97.40 54	97.35 107	95.58 24	99.34 65	93.44 77	99.31 71	98.13 160
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
CS-MVS		95.77 64	95.58 81	96.37 54	96.84 164	91.72 65	96.73 28	99.06 8	94.23 56	92.48 267	94.79 259	93.56 85	99.49 28	93.47 74	99.05 107	97.89 188

SMA-MVS		95.77 64	95.54 82	96.47 53	98.27 70	91.19 70	95.09 109	97.79 107	86.48 236	97.42 52	97.51 94	94.47 74	99.29 74	93.55 69	99.29 76	98.93 70
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
test_0402		95.73 66	96.22 44	94.26 139	98.19 77	85.77 183	93.24 176	97.24 154	96.88 18	97.69 36	97.77 72	94.12 79	99.13 95	91.54 138	99.29 76	97.88 189

ACMP	88.15 13	95.71 67	95.43 87	96.54 49	98.17 78	91.73 64	94.24 140	98.08 68	89.46 175	96.61 92	96.47 169	95.85 18	99.12 96	90.45 160	99.56 34	98.77 93
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
XVG-ACMP-BASELINE		95.68 68	95.34 92	96.69 45	98.40 59	93.04 45	94.54 133	98.05 75	90.45 159	96.31 103	96.76 152	92.91 109	98.72 157	91.19 143	99.42 51	98.32 143

DP-MVS		95.62 69	95.84 69	94.97 104	97.16 146	88.62 113	94.54 133	97.64 115	96.94 17	96.58 94	97.32 111	93.07 104	98.72 157	90.45 160	98.84 136	97.57 218

test_fmvsmconf0.1_n		95.61 70	95.72 76	95.26 92	96.85 163	89.20 101	93.51 167	98.60 16	85.68 255	97.42 52	98.30 38	95.34 35	98.39 198	96.85 7	98.98 116	98.19 154

OPM-MVS		95.61 70	95.45 85	96.08 57	98.49 56	91.00 72	92.65 198	97.33 145	90.05 165	96.77 85	96.85 146	95.04 50	98.56 183	92.77 100	99.06 104	98.70 103
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
RPSCF		95.58 72	94.89 109	97.62 9	97.58 124	96.30 8	95.97 70	97.53 127	92.42 89	93.41 228	97.78 68	91.21 148	97.77 265	91.06 145	97.06 276	98.80 88

MIMVSNet1		95.52 73	95.45 85	95.72 75	99.14 5	89.02 105	96.23 59	96.87 182	93.73 67	97.87 31	98.49 31	90.73 163	99.05 105	86.43 257	99.60 25	99.10 49

Anonymous20240529		95.50 74	95.83 70	94.50 129	97.33 138	85.93 179	95.19 108	96.77 190	96.64 21	97.61 41	98.05 47	93.23 97	98.79 145	88.60 217	99.04 112	98.78 90

Vis-MVSNet		95.50 74	95.48 84	95.56 81	98.11 81	89.40 97	95.35 96	98.22 46	92.36 92	94.11 205	98.07 46	92.02 127	99.44 32	93.38 82	97.67 252	97.85 194
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
EC-MVSNet		95.44 76	95.62 79	94.89 106	96.93 157	87.69 134	96.48 40	99.14 7	93.93 63	92.77 258	94.52 271	93.95 82	99.49 28	93.62 66	99.22 90	97.51 223

test_fmvsmconf_n		95.43 77	95.50 83	95.22 97	96.48 193	89.19 102	93.23 177	98.36 29	85.61 258	96.92 77	98.02 51	95.23 41	98.38 201	96.69 10	98.95 125	98.09 162

pm-mvs1		95.43 77	95.94 61	93.93 152	98.38 61	85.08 196	95.46 94	97.12 163	91.84 114	97.28 59	98.46 33	95.30 38	97.71 272	90.17 175	99.42 51	98.99 58

DeepC-MVS	91.39 4	95.43 77	95.33 93	95.71 76	97.67 119	90.17 84	93.86 156	98.02 82	87.35 222	96.22 111	97.99 54	94.48 73	99.05 105	92.73 103	99.68 17	97.93 182
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
tt0805		95.42 80	95.93 63	93.86 156	98.75 31	88.47 120	97.68 9	94.29 282	96.48 24	95.38 154	93.63 299	94.89 59	97.94 245	95.38 31	96.92 284	95.17 327

XVG-OURS-SEG-HR		95.38 81	95.00 107	96.51 50	98.10 82	94.07 24	92.46 206	98.13 60	90.69 151	93.75 219	96.25 192	98.03 2	97.02 313	92.08 118	95.55 320	98.45 133

UniMVSNet_NR-MVSNet		95.35 82	95.21 98	95.76 73	97.69 117	88.59 116	92.26 220	97.84 100	94.91 47	96.80 83	95.78 217	90.42 168	99.41 42	91.60 134	99.58 31	99.29 31

MSP-MVS		95.34 83	94.63 125	97.48 18	98.67 32	94.05 27	96.41 45	98.18 51	91.26 138	95.12 173	95.15 241	86.60 228	99.50 22	93.43 80	96.81 288	98.89 77
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
SPE-MVS-test		95.32 84	95.10 103	95.96 60	96.86 162	90.75 78	96.33 49	99.20 5	93.99 60	91.03 302	93.73 297	93.52 87	99.55 19	91.81 127	99.45 46	97.58 217

FC-MVSNet-test		95.32 84	95.88 66	93.62 167	98.49 56	81.77 244	95.90 73	98.32 32	93.93 63	97.53 45	97.56 85	88.48 190	99.40 49	92.91 99	99.83 5	99.68 7

UniMVSNet (Re)		95.32 84	95.15 100	95.80 72	97.79 107	88.91 107	92.91 188	98.07 71	93.46 74	96.31 103	95.97 206	90.14 174	99.34 65	92.11 116	99.64 23	99.16 40

Gipuma		95.31 87	95.80 73	93.81 159	97.99 95	90.91 74	96.42 44	97.95 91	96.69 19	91.78 289	98.85 14	91.77 134	95.49 359	91.72 130	99.08 103	95.02 336
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
mvs5depth		95.28 88	95.82 72	93.66 165	96.42 196	83.08 227	97.35 12	99.28 3	96.44 26	96.20 113	99.65 2	84.10 253	98.01 237	94.06 53	98.93 126	99.87 1

DU-MVS		95.28 88	95.12 102	95.75 74	97.75 109	88.59 116	92.58 200	97.81 103	93.99 60	96.80 83	95.90 207	90.10 177	99.41 42	91.60 134	99.58 31	99.26 32

NR-MVSNet		95.28 88	95.28 96	95.26 92	97.75 109	87.21 142	95.08 110	97.37 137	93.92 65	97.65 37	95.90 207	90.10 177	99.33 70	90.11 177	99.66 21	99.26 32

TransMVSNet (Re)		95.27 91	96.04 56	92.97 191	98.37 63	81.92 243	95.07 111	96.76 191	93.97 62	97.77 34	98.57 26	95.72 20	97.90 246	88.89 211	99.23 87	99.08 50

fmvsm_s_conf0.5_n_3		95.20 92	95.95 60	92.94 194	96.60 181	82.18 240	93.13 180	98.39 26	91.44 133	97.16 63	97.68 75	93.03 106	97.82 257	97.54 3	98.63 167	98.81 86

fmvsm_l_conf0.5_n_3		95.19 93	95.36 90	94.68 117	96.79 169	87.49 136	93.05 183	98.38 27	87.21 226	96.59 93	97.76 73	94.20 77	98.11 227	95.90 18	98.40 187	98.42 136

SD-MVS		95.19 93	95.73 75	93.55 170	96.62 180	88.88 109	94.67 123	98.05 75	91.26 138	97.25 61	96.40 175	95.42 30	94.36 380	92.72 104	99.19 93	97.40 232
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
VPA-MVSNet		95.14 95	95.67 78	93.58 169	97.76 108	83.15 225	94.58 128	97.58 122	93.39 75	97.05 70	98.04 49	93.25 96	98.51 189	89.75 187	99.59 27	99.08 50

casdiffmvs_mvg		95.10 96	95.62 79	93.53 173	96.25 216	83.23 222	92.66 197	98.19 49	93.06 81	97.49 47	97.15 124	94.78 61	98.71 163	92.27 114	98.72 156	98.65 109
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
test_fmvsmvis_n_1920		95.08 97	95.40 89	94.13 143	96.66 174	87.75 133	93.44 171	98.49 19	85.57 259	98.27 21	97.11 128	94.11 80	97.75 268	96.26 14	98.72 156	96.89 257

HPM-MVS++		95.02 98	94.39 130	96.91 41	97.88 100	93.58 41	94.09 149	96.99 172	91.05 143	92.40 272	95.22 240	91.03 155	99.25 81	92.11 116	98.69 161	97.90 186

APD-MVS		95.00 99	94.69 119	95.93 64	97.38 134	90.88 75	94.59 126	97.81 103	89.22 182	95.46 151	96.17 197	93.42 91	99.34 65	89.30 196	98.87 134	97.56 220
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PMVS	87.21 14	94.97 100	95.33 93	93.91 153	98.97 17	97.16 3	95.54 92	95.85 234	96.47 25	93.40 231	97.46 97	95.31 37	95.47 360	86.18 261	98.78 149	89.11 407
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
TSAR-MVS + MP.		94.96 101	94.75 115	95.57 80	98.86 22	88.69 110	96.37 46	96.81 186	85.23 264	94.75 190	97.12 127	91.85 131	99.40 49	93.45 76	98.33 198	98.62 118
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
SixPastTwentyTwo		94.91 102	95.21 98	93.98 147	98.52 48	83.19 224	95.93 71	94.84 268	94.86 48	98.49 16	98.74 18	81.45 280	99.60 10	94.69 40	99.39 57	99.15 41

FIs		94.90 103	95.35 91	93.55 170	98.28 69	81.76 245	95.33 98	98.14 59	93.05 82	97.07 67	97.18 122	87.65 206	99.29 74	91.72 130	99.69 14	99.61 14

AllTest		94.88 104	94.51 128	96.00 58	98.02 90	92.17 54	95.26 102	98.43 21	90.48 157	95.04 178	96.74 155	92.54 118	97.86 254	85.11 274	98.98 116	97.98 175

FMVSNet1		94.84 105	95.13 101	93.97 148	97.60 122	84.29 203	95.99 67	96.56 203	92.38 90	97.03 71	98.53 28	90.12 175	98.98 113	88.78 213	99.16 98	98.65 109

ANet_high		94.83 106	96.28 41	90.47 285	96.65 175	73.16 364	94.33 137	98.74 14	96.39 28	98.09 29	98.93 10	93.37 92	98.70 164	90.38 163	99.68 17	99.53 17

MVSMamba_PlusPlus		94.82 107	95.89 65	91.62 244	97.82 104	78.88 299	96.52 35	97.60 121	97.14 14	94.23 203	98.48 32	87.01 218	99.71 3	95.43 29	98.80 146	96.28 284

3Dnovator	92.54 3	94.80 108	94.90 108	94.47 132	95.47 270	87.06 146	96.63 31	97.28 151	91.82 117	94.34 202	97.41 98	90.60 166	98.65 173	92.47 111	98.11 220	97.70 209

CPTT-MVS		94.74 109	94.12 143	96.60 47	98.15 79	93.01 46	95.84 76	97.66 114	89.21 183	93.28 236	95.46 230	88.89 188	98.98 113	89.80 184	98.82 142	97.80 200

test_fmvsm_n_1920		94.72 110	94.74 117	94.67 118	96.30 210	88.62 113	93.19 178	98.07 71	85.63 257	97.08 66	97.35 107	90.86 156	97.66 275	95.70 20	98.48 184	97.74 207

XVG-OURS		94.72 110	94.12 143	96.50 51	98.00 92	94.23 22	91.48 250	98.17 55	90.72 150	95.30 160	96.47 169	87.94 203	96.98 314	91.41 141	97.61 256	98.30 146

CSCG		94.69 112	94.75 115	94.52 128	97.55 126	87.87 130	95.01 114	97.57 123	92.68 84	96.20 113	93.44 305	91.92 130	98.78 148	89.11 205	99.24 86	96.92 255

v10		94.68 113	95.27 97	92.90 197	96.57 183	80.15 265	94.65 125	97.57 123	90.68 152	97.43 50	98.00 52	88.18 196	99.15 91	94.84 39	99.55 35	99.41 23

v8		94.65 114	95.29 95	92.74 202	96.65 175	79.77 280	94.59 126	97.17 158	91.86 110	97.47 49	97.93 57	88.16 197	99.08 100	94.32 47	99.47 42	99.38 25

sasdasda		94.59 115	94.69 119	94.30 137	95.60 264	87.03 147	95.59 85	98.24 42	91.56 128	95.21 169	92.04 339	94.95 55	98.66 170	91.45 139	97.57 257	97.20 243

canonicalmvs		94.59 115	94.69 119	94.30 137	95.60 264	87.03 147	95.59 85	98.24 42	91.56 128	95.21 169	92.04 339	94.95 55	98.66 170	91.45 139	97.57 257	97.20 243

CNVR-MVS		94.58 117	94.29 135	95.46 84	96.94 155	89.35 99	91.81 242	96.80 187	89.66 172	93.90 217	95.44 232	92.80 113	98.72 157	92.74 102	98.52 179	98.32 143

GeoE		94.55 118	94.68 122	94.15 141	97.23 141	85.11 195	94.14 146	97.34 144	88.71 193	95.26 164	95.50 229	94.65 65	99.12 96	90.94 149	98.40 187	98.23 150

EG-PatchMatch MVS		94.54 119	94.67 123	94.14 142	97.87 102	86.50 161	92.00 228	96.74 192	88.16 207	96.93 76	97.61 82	93.04 105	97.90 246	91.60 134	98.12 219	98.03 169

IS-MVSNet		94.49 120	94.35 134	94.92 105	98.25 73	86.46 164	97.13 17	94.31 281	96.24 31	96.28 107	96.36 182	82.88 263	99.35 62	88.19 221	99.52 39	98.96 66

Baseline_NR-MVSNet		94.47 121	95.09 104	92.60 212	98.50 55	80.82 261	92.08 224	96.68 195	93.82 66	96.29 105	98.56 27	90.10 177	97.75 268	90.10 179	99.66 21	99.24 34

MGCFI-Net		94.44 122	94.67 123	93.75 161	95.56 266	85.47 190	95.25 103	98.24 42	91.53 130	95.04 178	92.21 334	94.94 57	98.54 186	91.56 137	97.66 253	97.24 241

SDMVSNet		94.43 123	95.02 105	92.69 204	97.93 97	82.88 231	91.92 234	95.99 231	93.65 72	95.51 146	98.63 23	94.60 67	96.48 333	87.57 235	99.35 60	98.70 103

MM		94.41 124	94.14 142	95.22 97	95.84 246	87.21 142	94.31 139	90.92 342	94.48 53	92.80 256	97.52 90	85.27 243	99.49 28	96.58 11	99.57 33	98.97 64

fmvsm_s_conf0.1_n_2		94.38 125	94.78 114	93.19 185	97.07 150	81.72 247	91.97 229	97.51 130	87.05 231	97.31 56	97.92 61	88.29 194	98.15 223	97.10 5	98.81 144	99.70 5

VDD-MVS		94.37 126	94.37 132	94.40 135	97.49 129	86.07 176	93.97 153	93.28 302	94.49 52	96.24 109	97.78 68	87.99 202	98.79 145	88.92 209	99.14 100	98.34 142

EI-MVSNet-Vis-set		94.36 127	94.28 136	94.61 121	92.55 343	85.98 178	92.44 208	94.69 275	93.70 68	96.12 118	95.81 213	91.24 146	98.86 131	93.76 64	98.22 210	98.98 62

EI-MVSNet-UG-set		94.35 128	94.27 138	94.59 125	92.46 346	85.87 181	92.42 210	94.69 275	93.67 71	96.13 117	95.84 211	91.20 149	98.86 131	93.78 61	98.23 208	99.03 54

PHI-MVS		94.34 129	93.80 150	95.95 61	95.65 260	91.67 66	94.82 119	97.86 97	87.86 212	93.04 248	94.16 282	91.58 138	98.78 148	90.27 170	98.96 123	97.41 229

casdiffmvs		94.32 130	94.80 112	92.85 199	96.05 232	81.44 252	92.35 213	98.05 75	91.53 130	95.75 135	96.80 149	93.35 93	98.49 190	91.01 148	98.32 200	98.64 114
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
tfpnnormal		94.27 131	94.87 110	92.48 216	97.71 114	80.88 260	94.55 132	95.41 253	93.70 68	96.67 89	97.72 74	91.40 142	98.18 220	87.45 237	99.18 95	98.36 138

fmvsm_s_conf0.1_n_a		94.26 132	94.37 132	93.95 151	97.36 136	85.72 185	94.15 144	95.44 250	83.25 291	95.51 146	98.05 47	92.54 118	97.19 303	95.55 25	97.46 263	98.94 68

HQP_MVS		94.26 132	93.93 146	95.23 95	97.71 114	88.12 125	94.56 130	97.81 103	91.74 122	93.31 233	95.59 224	86.93 221	98.95 120	89.26 200	98.51 181	98.60 119

baseline		94.26 132	94.80 112	92.64 206	96.08 230	80.99 258	93.69 162	98.04 79	90.80 149	94.89 185	96.32 184	93.19 98	98.48 194	91.68 132	98.51 181	98.43 135

fmvsm_s_conf0.5_n_2		94.25 135	94.63 125	93.10 187	96.65 175	81.75 246	91.72 245	97.25 152	86.93 235	97.20 62	97.67 77	88.44 192	98.14 226	97.06 6	98.77 150	99.42 21

OMC-MVS		94.22 136	93.69 155	95.81 71	97.25 140	91.27 68	92.27 219	97.40 136	87.10 230	94.56 195	95.42 233	93.74 83	98.11 227	86.62 251	98.85 135	98.06 163

LCM-MVSNet-Re		94.20 137	94.58 127	93.04 188	95.91 242	83.13 226	93.79 158	99.19 6	92.00 104	98.84 6	98.04 49	93.64 84	99.02 110	81.28 316	98.54 177	96.96 254

DeepPCF-MVS	90.46 6	94.20 137	93.56 162	96.14 55	95.96 239	92.96 47	89.48 310	97.46 133	85.14 267	96.23 110	95.42 233	93.19 98	98.08 230	90.37 164	98.76 152	97.38 235

fmvsm_s_conf0.1_n		94.19 139	94.41 129	93.52 175	97.22 143	84.37 201	93.73 160	95.26 257	84.45 279	95.76 133	98.00 52	91.85 131	97.21 300	95.62 21	97.82 244	98.98 62

KD-MVS_self_test		94.10 140	94.73 118	92.19 223	97.66 120	79.49 286	94.86 118	97.12 163	89.59 174	96.87 78	97.65 79	90.40 170	98.34 206	89.08 206	99.35 60	98.75 94

NCCC		94.08 141	93.54 163	95.70 77	96.49 191	89.90 87	92.39 212	96.91 179	90.64 153	92.33 279	94.60 267	90.58 167	98.96 118	90.21 174	97.70 250	98.23 150

VDDNet		94.03 142	94.27 138	93.31 181	98.87 21	82.36 237	95.51 93	91.78 334	97.19 13	96.32 102	98.60 25	84.24 251	98.75 152	87.09 244	98.83 141	98.81 86

fmvsm_s_conf0.5_n_a		94.02 143	94.08 145	93.84 157	96.72 171	85.73 184	93.65 165	95.23 258	83.30 289	95.13 172	97.56 85	92.22 123	97.17 304	95.51 26	97.41 265	98.64 114

fmvsm_s_conf0.5_n		94.00 144	94.20 140	93.42 179	96.69 172	84.37 201	93.38 173	95.13 260	84.50 278	95.40 153	97.55 89	91.77 134	97.20 301	95.59 22	97.79 245	98.69 106

dcpmvs_2		93.96 145	95.01 106	90.82 277	97.60 122	74.04 359	93.68 163	98.85 10	89.80 170	97.82 32	97.01 137	91.14 153	99.21 84	90.56 157	98.59 172	99.19 38

sd_testset		93.94 146	94.39 130	92.61 211	97.93 97	83.24 221	93.17 179	95.04 262	93.65 72	95.51 146	98.63 23	94.49 72	95.89 352	81.72 311	99.35 60	98.70 103

EPP-MVSNet		93.91 147	93.68 156	94.59 125	98.08 83	85.55 189	97.44 11	94.03 287	94.22 57	94.94 182	96.19 194	82.07 275	99.57 15	87.28 241	98.89 129	98.65 109

Effi-MVS+-dtu		93.90 148	92.60 187	97.77 4	94.74 293	96.67 6	94.00 151	95.41 253	89.94 166	91.93 288	92.13 337	90.12 175	98.97 117	87.68 234	97.48 261	97.67 212

fmvsm_l_conf0.5_n		93.79 149	93.81 148	93.73 163	96.16 222	86.26 171	92.46 206	96.72 193	81.69 313	95.77 132	97.11 128	90.83 158	97.82 257	95.58 23	97.99 233	97.11 246

IterMVS-LS		93.78 150	94.28 136	92.27 220	96.27 213	79.21 293	91.87 238	96.78 188	91.77 120	96.57 95	97.07 131	87.15 215	98.74 155	91.99 121	99.03 113	98.86 80
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DeepC-MVS_fast	89.96 7	93.73 151	93.44 165	94.60 124	96.14 225	87.90 129	93.36 174	97.14 160	85.53 260	93.90 217	95.45 231	91.30 145	98.59 180	89.51 190	98.62 168	97.31 238
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MVS_111021_LR		93.66 152	93.28 169	94.80 110	96.25 216	90.95 73	90.21 287	95.43 252	87.91 209	93.74 221	94.40 273	92.88 111	96.38 338	90.39 162	98.28 202	97.07 247

MVS_111021_HR		93.63 153	93.42 166	94.26 139	96.65 175	86.96 151	89.30 317	96.23 219	88.36 203	93.57 224	94.60 267	93.45 88	97.77 265	90.23 173	98.38 192	98.03 169

fmvsm_l_conf0.5_n_a		93.59 154	93.63 157	93.49 177	96.10 228	85.66 187	92.32 215	96.57 202	81.32 316	95.63 141	97.14 125	90.19 173	97.73 271	95.37 32	98.03 228	97.07 247

v1144		93.50 155	93.81 148	92.57 213	96.28 211	79.61 283	91.86 240	96.96 173	86.95 233	95.91 126	96.32 184	87.65 206	98.96 118	93.51 70	98.88 131	99.13 43

v1192		93.49 156	93.78 151	92.62 210	96.16 222	79.62 282	91.83 241	97.22 156	86.07 246	96.10 119	96.38 180	87.22 213	99.02 110	94.14 52	98.88 131	99.22 35

WR-MVS		93.49 156	93.72 153	92.80 201	97.57 125	80.03 271	90.14 290	95.68 238	93.70 68	96.62 91	95.39 237	87.21 214	99.04 108	87.50 236	99.64 23	99.33 28

balanced_conf03		93.45 158	94.17 141	91.28 258	95.81 250	78.40 306	96.20 60	97.48 132	88.56 198	95.29 162	97.20 121	85.56 242	99.21 84	92.52 110	98.91 128	96.24 287

V42		93.43 159	93.58 160	92.97 191	95.34 276	81.22 255	92.67 196	96.49 208	87.25 225	96.20 113	96.37 181	87.32 212	98.85 133	92.39 113	98.21 211	98.85 83

K. test v3		93.37 160	93.27 170	93.66 165	98.05 86	82.62 233	94.35 136	86.62 374	96.05 35	97.51 46	98.85 14	76.59 325	99.65 5	93.21 88	98.20 213	98.73 98

PM-MVS		93.33 161	92.67 185	95.33 88	96.58 182	94.06 25	92.26 220	92.18 324	85.92 249	96.22 111	96.61 164	85.64 240	95.99 350	90.35 165	98.23 208	95.93 301

v1240		93.29 162	93.71 154	92.06 230	96.01 237	77.89 314	91.81 242	97.37 137	85.12 268	96.69 88	96.40 175	86.67 226	99.07 104	94.51 42	98.76 152	99.22 35

v2v482		93.29 162	93.63 157	92.29 219	96.35 203	78.82 301	91.77 244	96.28 215	88.45 199	95.70 140	96.26 191	86.02 235	98.90 124	93.02 95	98.81 144	99.14 42

alignmvs		93.26 164	92.85 178	94.50 129	95.70 256	87.45 137	93.45 170	95.76 235	91.58 127	95.25 166	92.42 332	81.96 277	98.72 157	91.61 133	97.87 242	97.33 237

v1921920		93.26 164	93.61 159	92.19 223	96.04 236	78.31 308	91.88 237	97.24 154	85.17 266	96.19 116	96.19 194	86.76 225	99.05 105	94.18 51	98.84 136	99.22 35

MSLP-MVS++		93.25 166	93.88 147	91.37 252	96.34 204	82.81 232	93.11 181	97.74 110	89.37 178	94.08 207	95.29 239	90.40 170	96.35 340	90.35 165	98.25 206	94.96 337

GBi-Net		93.21 167	92.96 174	93.97 148	95.40 272	84.29 203	95.99 67	96.56 203	88.63 194	95.10 174	98.53 28	81.31 282	98.98 113	86.74 247	98.38 192	98.65 109

test1		93.21 167	92.96 174	93.97 148	95.40 272	84.29 203	95.99 67	96.56 203	88.63 194	95.10 174	98.53 28	81.31 282	98.98 113	86.74 247	98.38 192	98.65 109

v144192		93.20 169	93.54 163	92.16 227	96.05 232	78.26 309	91.95 230	97.14 160	84.98 272	95.96 122	96.11 199	87.08 217	99.04 108	93.79 60	98.84 136	99.17 39

VPNet		93.08 170	93.76 152	91.03 267	98.60 38	75.83 343	91.51 248	95.62 239	91.84 114	95.74 136	97.10 130	89.31 185	98.32 207	85.07 276	99.06 104	98.93 70

UGNet		93.08 170	92.50 189	94.79 111	93.87 317	87.99 128	95.07 111	94.26 284	90.64 153	87.33 368	97.67 77	86.89 223	98.49 190	88.10 224	98.71 158	97.91 185
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
TSAR-MVS + GP.		93.07 172	92.41 191	95.06 102	95.82 248	90.87 76	90.97 263	92.61 317	88.04 208	94.61 194	93.79 296	88.08 198	97.81 259	89.41 193	98.39 191	96.50 273

ETV-MVS		92.99 173	92.74 181	93.72 164	95.86 245	86.30 170	92.33 214	97.84 100	91.70 125	92.81 255	86.17 401	92.22 123	99.19 88	88.03 228	97.73 247	95.66 315

EI-MVSNet		92.99 173	93.26 171	92.19 223	92.12 356	79.21 293	92.32 215	94.67 277	91.77 120	95.24 167	95.85 209	87.14 216	98.49 190	91.99 121	98.26 204	98.86 80

MCST-MVS		92.91 175	92.51 188	94.10 144	97.52 127	85.72 185	91.36 254	97.13 162	80.33 324	92.91 254	94.24 278	91.23 147	98.72 157	89.99 181	97.93 238	97.86 192

h-mvs33		92.89 176	91.99 201	95.58 79	96.97 153	90.55 80	93.94 154	94.01 290	89.23 180	93.95 214	96.19 194	76.88 321	99.14 93	91.02 146	95.71 316	97.04 251

MVS_0304		92.88 177	92.27 193	94.69 116	92.35 347	86.03 177	92.88 190	89.68 349	90.53 156	91.52 292	96.43 172	82.52 271	99.32 71	95.01 36	99.54 36	98.71 102

QAPM		92.88 177	92.77 179	93.22 184	95.82 248	83.31 219	96.45 41	97.35 143	83.91 284	93.75 219	96.77 150	89.25 186	98.88 127	84.56 282	97.02 278	97.49 224

v148		92.87 179	93.29 167	91.62 244	96.25 216	77.72 317	91.28 255	95.05 261	89.69 171	95.93 125	96.04 202	87.34 211	98.38 201	90.05 180	97.99 233	98.78 90

Anonymous20240521		92.86 180	93.57 161	90.74 279	96.57 183	75.50 345	94.15 144	95.60 240	89.38 177	95.90 127	97.90 65	80.39 289	97.96 243	92.60 108	99.68 17	98.75 94

Effi-MVS+		92.79 181	92.74 181	92.94 194	95.10 280	83.30 220	94.00 151	97.53 127	91.36 136	89.35 334	90.65 363	94.01 81	98.66 170	87.40 239	95.30 329	96.88 259

FMVSNet2		92.78 182	92.73 183	92.95 193	95.40 272	81.98 242	94.18 143	95.53 248	88.63 194	96.05 120	97.37 101	81.31 282	98.81 141	87.38 240	98.67 164	98.06 163

Fast-Effi-MVS+-dtu		92.77 183	92.16 195	94.58 127	94.66 298	88.25 123	92.05 225	96.65 197	89.62 173	90.08 319	91.23 350	92.56 117	98.60 178	86.30 259	96.27 304	96.90 256

LF4IMVS		92.72 184	92.02 200	94.84 109	95.65 260	91.99 58	92.92 187	96.60 199	85.08 270	92.44 270	93.62 300	86.80 224	96.35 340	86.81 246	98.25 206	96.18 290

train_agg		92.71 185	91.83 206	95.35 86	96.45 194	89.46 93	90.60 274	96.92 177	79.37 335	90.49 310	94.39 274	91.20 149	98.88 127	88.66 216	98.43 186	97.72 208

VNet		92.67 186	92.96 174	91.79 236	96.27 213	80.15 265	91.95 230	94.98 264	92.19 100	94.52 197	96.07 201	87.43 210	97.39 292	84.83 278	98.38 192	97.83 196

CDPH-MVS		92.67 186	91.83 206	95.18 99	96.94 155	88.46 121	90.70 271	97.07 166	77.38 351	92.34 278	95.08 246	92.67 116	98.88 127	85.74 264	98.57 174	98.20 153

Anonymous202405211		92.58 188	92.50 189	92.83 200	96.55 185	83.22 223	92.43 209	91.64 336	94.10 59	95.59 143	96.64 162	81.88 279	97.50 282	85.12 273	98.52 179	97.77 203

XXY-MVS		92.58 188	93.16 172	90.84 276	97.75 109	79.84 276	91.87 238	96.22 221	85.94 248	95.53 145	97.68 75	92.69 115	94.48 376	83.21 293	97.51 259	98.21 152

MVS_Test		92.57 190	93.29 167	90.40 288	93.53 323	75.85 341	92.52 202	96.96 173	88.73 191	92.35 276	96.70 159	90.77 159	98.37 205	92.53 109	95.49 322	96.99 253

TAPA-MVS	88.58 10	92.49 191	91.75 208	94.73 113	96.50 190	89.69 89	92.91 188	97.68 113	78.02 348	92.79 257	94.10 283	90.85 157	97.96 243	84.76 280	98.16 215	96.54 268
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
patch_mono-2		92.46 192	92.72 184	91.71 240	96.65 175	78.91 298	88.85 327	97.17 158	83.89 285	92.45 269	96.76 152	89.86 181	97.09 309	90.24 172	98.59 172	99.12 45

test_fmvs3		92.42 193	92.40 192	92.46 218	93.80 320	87.28 140	93.86 156	97.05 167	76.86 357	96.25 108	98.66 21	82.87 264	91.26 400	95.44 28	96.83 287	98.82 84

ab-mvs		92.40 194	92.62 186	91.74 238	97.02 151	81.65 248	95.84 76	95.50 249	86.95 233	92.95 253	97.56 85	90.70 164	97.50 282	79.63 335	97.43 264	96.06 295

CANet		92.38 195	91.99 201	93.52 175	93.82 319	83.46 217	91.14 258	97.00 170	89.81 169	86.47 372	94.04 285	87.90 204	99.21 84	89.50 191	98.27 203	97.90 186

EIA-MVS		92.35 196	92.03 199	93.30 182	95.81 250	83.97 211	92.80 192	98.17 55	87.71 216	89.79 327	87.56 391	91.17 152	99.18 89	87.97 229	97.27 269	96.77 263

DP-MVS Recon		92.31 197	91.88 204	93.60 168	97.18 145	86.87 152	91.10 260	97.37 137	84.92 273	92.08 285	94.08 284	88.59 189	98.20 217	83.50 290	98.14 217	95.73 310

RRT-MVS		92.28 198	93.01 173	90.07 297	94.06 312	73.01 366	95.36 95	97.88 95	92.24 98	95.16 171	97.52 90	78.51 303	99.29 74	90.55 158	95.83 314	97.92 184

F-COLMAP		92.28 198	91.06 225	95.95 61	97.52 127	91.90 60	93.53 166	97.18 157	83.98 283	88.70 347	94.04 285	88.41 193	98.55 185	80.17 328	95.99 309	97.39 233

OpenMVS	89.45 8	92.27 200	92.13 198	92.68 205	94.53 301	84.10 209	95.70 80	97.03 168	82.44 305	91.14 301	96.42 173	88.47 191	98.38 201	85.95 262	97.47 262	95.55 320

hse-mvs2		92.24 201	91.20 220	95.38 85	96.16 222	90.65 79	92.52 202	92.01 331	89.23 180	93.95 214	92.99 316	76.88 321	98.69 166	91.02 146	96.03 307	96.81 261

MVSFormer		92.18 202	92.23 194	92.04 231	94.74 293	80.06 269	97.15 15	97.37 137	88.98 186	88.83 339	92.79 321	77.02 318	99.60 10	96.41 12	96.75 291	96.46 276

HQP-MVS		92.09 203	91.49 214	93.88 154	96.36 200	84.89 197	91.37 251	97.31 146	87.16 227	88.81 341	93.40 306	84.76 248	98.60 178	86.55 254	97.73 247	98.14 159

DELS-MVS		92.05 204	92.16 195	91.72 239	94.44 302	80.13 267	87.62 344	97.25 152	87.34 223	92.22 281	93.18 313	89.54 184	98.73 156	89.67 188	98.20 213	96.30 282
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
TinyColmap		92.00 205	92.76 180	89.71 306	95.62 263	77.02 325	90.72 270	96.17 224	87.70 217	95.26 164	96.29 186	92.54 118	96.45 335	81.77 309	98.77 150	95.66 315

CLD-MVS		91.82 206	91.41 216	93.04 188	96.37 198	83.65 215	86.82 363	97.29 149	84.65 277	92.27 280	89.67 372	92.20 125	97.85 256	83.95 288	99.47 42	97.62 214
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
FA-MVS(test-final)		91.81 207	91.85 205	91.68 242	94.95 283	79.99 273	96.00 66	93.44 300	87.80 213	94.02 212	97.29 112	77.60 309	98.45 196	88.04 227	97.49 260	96.61 267

BP-MVS1		91.77 208	91.10 224	93.75 161	96.42 196	83.40 218	94.10 148	91.89 332	91.27 137	93.36 232	94.85 254	64.43 379	99.29 74	94.88 37	98.74 155	98.56 123

diffmvs		91.74 209	91.93 203	91.15 265	93.06 331	78.17 310	88.77 330	97.51 130	86.28 240	92.42 271	93.96 290	88.04 200	97.46 285	90.69 155	96.67 294	97.82 198
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
CNLPA		91.72 210	91.20 220	93.26 183	96.17 221	91.02 71	91.14 258	95.55 247	90.16 164	90.87 303	93.56 303	86.31 231	94.40 379	79.92 334	97.12 274	94.37 355

IterMVS-SCA-FT		91.65 211	91.55 210	91.94 232	93.89 316	79.22 292	87.56 347	93.51 298	91.53 130	95.37 156	96.62 163	78.65 299	98.90 124	91.89 125	94.95 338	97.70 209

PVSNet_Blended_VisFu		91.63 212	91.20 220	92.94 194	97.73 112	83.95 212	92.14 223	97.46 133	78.85 344	92.35 276	94.98 249	84.16 252	99.08 100	86.36 258	96.77 290	95.79 308

AdaColmap		91.63 212	91.36 217	92.47 217	95.56 266	86.36 168	92.24 222	96.27 216	88.88 190	89.90 324	92.69 324	91.65 137	98.32 207	77.38 354	97.64 254	92.72 389

GDP-MVS		91.56 214	90.83 231	93.77 160	96.34 204	83.65 215	93.66 164	98.12 61	87.32 224	92.98 251	94.71 262	63.58 385	99.30 73	92.61 107	98.14 217	98.35 141

pmmvs-eth3d		91.54 215	90.73 235	93.99 146	95.76 254	87.86 131	90.83 266	93.98 291	78.23 347	94.02 212	96.22 193	82.62 270	96.83 323	86.57 252	98.33 198	97.29 239

API-MVS		91.52 216	91.61 209	91.26 259	94.16 307	86.26 171	94.66 124	94.82 269	91.17 141	92.13 284	91.08 353	90.03 180	97.06 312	79.09 342	97.35 268	90.45 405

xiu_mvs_v1_base_debu		91.47 217	91.52 211	91.33 254	95.69 257	81.56 249	89.92 297	96.05 228	83.22 292	91.26 297	90.74 358	91.55 139	98.82 136	89.29 197	95.91 310	93.62 374

xiu_mvs_v1_base		91.47 217	91.52 211	91.33 254	95.69 257	81.56 249	89.92 297	96.05 228	83.22 292	91.26 297	90.74 358	91.55 139	98.82 136	89.29 197	95.91 310	93.62 374

xiu_mvs_v1_base_debi		91.47 217	91.52 211	91.33 254	95.69 257	81.56 249	89.92 297	96.05 228	83.22 292	91.26 297	90.74 358	91.55 139	98.82 136	89.29 197	95.91 310	93.62 374

LFMVS		91.33 220	91.16 223	91.82 235	96.27 213	79.36 288	95.01 114	85.61 387	96.04 36	94.82 187	97.06 132	72.03 344	98.46 195	84.96 277	98.70 160	97.65 213

c3_l		91.32 221	91.42 215	91.00 270	92.29 349	76.79 331	87.52 350	96.42 211	85.76 253	94.72 193	93.89 293	82.73 267	98.16 222	90.93 150	98.55 175	98.04 166

Fast-Effi-MVS+		91.28 222	90.86 229	92.53 215	95.45 271	82.53 234	89.25 320	96.52 207	85.00 271	89.91 323	88.55 384	92.94 107	98.84 134	84.72 281	95.44 324	96.22 288

MDA-MVSNet-bldmvs		91.04 223	90.88 228	91.55 247	94.68 297	80.16 264	85.49 384	92.14 327	90.41 161	94.93 183	95.79 214	85.10 245	96.93 318	85.15 271	94.19 359	97.57 218

PAPM_NR		91.03 224	90.81 232	91.68 242	96.73 170	81.10 257	93.72 161	96.35 214	88.19 205	88.77 345	92.12 338	85.09 246	97.25 298	82.40 304	93.90 364	96.68 266

MSDG		90.82 225	90.67 236	91.26 259	94.16 307	83.08 227	86.63 368	96.19 222	90.60 155	91.94 287	91.89 341	89.16 187	95.75 354	80.96 321	94.51 349	94.95 338

test20.03		90.80 226	90.85 230	90.63 282	95.63 262	79.24 291	89.81 301	92.87 308	89.90 167	94.39 199	96.40 175	85.77 236	95.27 367	73.86 379	99.05 107	97.39 233

FMVSNet3		90.78 227	90.32 245	92.16 227	93.03 333	79.92 275	92.54 201	94.95 265	86.17 245	95.10 174	96.01 204	69.97 352	98.75 152	86.74 247	98.38 192	97.82 198

eth_miper_zixun_eth		90.72 228	90.61 237	91.05 266	92.04 359	76.84 330	86.91 359	96.67 196	85.21 265	94.41 198	93.92 291	79.53 293	98.26 213	89.76 186	97.02 278	98.06 163

X-MVStestdata		90.70 229	88.45 278	97.44 20	98.56 41	93.99 30	96.50 37	97.95 91	94.58 50	94.38 200	26.89 429	94.56 69	99.39 52	93.57 67	99.05 107	98.93 70

BH-untuned		90.68 230	90.90 227	90.05 300	95.98 238	79.57 284	90.04 293	94.94 266	87.91 209	94.07 208	93.00 315	87.76 205	97.78 264	79.19 341	95.17 333	92.80 388

cl____		90.65 231	90.56 239	90.91 274	91.85 364	76.98 328	86.75 364	95.36 255	85.53 260	94.06 209	94.89 252	77.36 315	97.98 242	90.27 170	98.98 116	97.76 204

DIV-MVS_self_test		90.65 231	90.56 239	90.91 274	91.85 364	76.99 327	86.75 364	95.36 255	85.52 262	94.06 209	94.89 252	77.37 314	97.99 241	90.28 169	98.97 121	97.76 204

test_fmvs2		90.62 233	90.40 243	91.29 257	91.93 363	85.46 191	92.70 195	96.48 209	74.44 372	94.91 184	97.59 83	75.52 329	90.57 403	93.44 77	96.56 296	97.84 195

114514_t		90.51 234	89.80 255	92.63 209	98.00 92	82.24 239	93.40 172	97.29 149	65.84 415	89.40 333	94.80 258	86.99 219	98.75 152	83.88 289	98.61 169	96.89 257

miper_ehance_all_eth		90.48 235	90.42 242	90.69 280	91.62 371	76.57 334	86.83 362	96.18 223	83.38 288	94.06 209	92.66 326	82.20 273	98.04 232	89.79 185	97.02 278	97.45 226

BH-RMVSNet		90.47 236	90.44 241	90.56 284	95.21 279	78.65 305	89.15 321	93.94 292	88.21 204	92.74 259	94.22 279	86.38 229	97.88 250	78.67 344	95.39 326	95.14 330

Vis-MVSNet (Re-imp)		90.42 237	90.16 246	91.20 263	97.66 120	77.32 322	94.33 137	87.66 366	91.20 140	92.99 249	95.13 243	75.40 330	98.28 209	77.86 347	99.19 93	97.99 174

test_vis3_rt		90.40 238	90.03 250	91.52 249	92.58 341	88.95 106	90.38 282	97.72 112	73.30 380	97.79 33	97.51 94	77.05 317	87.10 418	89.03 207	94.89 339	98.50 128

PLC	85.34 15	90.40 238	88.92 270	94.85 108	96.53 189	90.02 85	91.58 247	96.48 209	80.16 325	86.14 374	92.18 335	85.73 237	98.25 214	76.87 357	94.61 348	96.30 282
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
test1111		90.39 240	90.61 237	89.74 305	98.04 89	71.50 376	95.59 85	79.72 418	89.41 176	95.94 124	98.14 42	70.79 348	98.81 141	88.52 218	99.32 70	98.90 76

testgi		90.38 241	91.34 218	87.50 346	97.49 129	71.54 375	89.43 312	95.16 259	88.38 201	94.54 196	94.68 264	92.88 111	93.09 392	71.60 392	97.85 243	97.88 189

mvs_anonymous		90.37 242	91.30 219	87.58 345	92.17 355	68.00 392	89.84 300	94.73 274	83.82 286	93.22 242	97.40 99	87.54 208	97.40 291	87.94 230	95.05 336	97.34 236

PVSNet_BlendedMVS		90.35 243	89.96 251	91.54 248	94.81 288	78.80 303	90.14 290	96.93 175	79.43 334	88.68 348	95.06 247	86.27 232	98.15 223	80.27 324	98.04 227	97.68 211

UnsupCasMVSNet_eth		90.33 244	90.34 244	90.28 290	94.64 299	80.24 263	89.69 305	95.88 232	85.77 252	93.94 216	95.69 221	81.99 276	92.98 393	84.21 286	91.30 397	97.62 214

MAR-MVS		90.32 245	88.87 273	94.66 120	94.82 287	91.85 61	94.22 142	94.75 273	80.91 319	87.52 366	88.07 389	86.63 227	97.87 253	76.67 358	96.21 305	94.25 358
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
RPMNet		90.31 246	90.14 249	90.81 278	91.01 379	78.93 295	92.52 202	98.12 61	91.91 108	89.10 335	96.89 144	68.84 354	99.41 42	90.17 175	92.70 386	94.08 359

mvsmamba		90.24 247	89.43 261	92.64 206	95.52 268	82.36 237	96.64 30	92.29 322	81.77 311	92.14 283	96.28 188	70.59 349	99.10 99	84.44 284	95.22 332	96.47 275

IterMVS		90.18 248	90.16 246	90.21 294	93.15 329	75.98 340	87.56 347	92.97 307	86.43 238	94.09 206	96.40 175	78.32 304	97.43 288	87.87 231	94.69 346	97.23 242
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
SSC-MVS		90.16 249	92.96 174	81.78 397	97.88 100	48.48 430	90.75 268	87.69 365	96.02 37	96.70 87	97.63 81	85.60 241	97.80 260	85.73 265	98.60 171	99.06 52

TAMVS		90.16 249	89.05 266	93.49 177	96.49 191	86.37 167	90.34 284	92.55 318	80.84 322	92.99 249	94.57 270	81.94 278	98.20 217	73.51 380	98.21 211	95.90 304

ECVR-MVS		90.12 251	90.16 246	90.00 301	97.81 105	72.68 370	95.76 79	78.54 421	89.04 184	95.36 157	98.10 44	70.51 350	98.64 174	87.10 243	99.18 95	98.67 107

test_yl		90.11 252	89.73 258	91.26 259	94.09 310	79.82 277	90.44 278	92.65 314	90.90 144	93.19 243	93.30 308	73.90 334	98.03 233	82.23 305	96.87 285	95.93 301

DCV-MVSNet		90.11 252	89.73 258	91.26 259	94.09 310	79.82 277	90.44 278	92.65 314	90.90 144	93.19 243	93.30 308	73.90 334	98.03 233	82.23 305	96.87 285	95.93 301

Patchmtry		90.11 252	89.92 252	90.66 281	90.35 390	77.00 326	92.96 186	92.81 309	90.25 163	94.74 191	96.93 141	67.11 361	97.52 281	85.17 269	98.98 116	97.46 225

MVP-Stereo		90.07 255	88.92 270	93.54 172	96.31 208	86.49 162	90.93 264	95.59 244	79.80 327	91.48 293	95.59 224	80.79 286	97.39 292	78.57 345	91.19 398	96.76 264
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
AUN-MVS		90.05 256	88.30 282	95.32 90	96.09 229	90.52 81	92.42 210	92.05 330	82.08 309	88.45 351	92.86 318	65.76 371	98.69 166	88.91 210	96.07 306	96.75 265

CL-MVSNet_self_test		90.04 257	89.90 253	90.47 285	95.24 278	77.81 315	86.60 370	92.62 316	85.64 256	93.25 240	93.92 291	83.84 254	96.06 347	79.93 332	98.03 228	97.53 222

D2MVS		89.93 258	89.60 260	90.92 272	94.03 313	78.40 306	88.69 332	94.85 267	78.96 342	93.08 245	95.09 245	74.57 332	96.94 316	88.19 221	98.96 123	97.41 229

miper_lstm_enhance		89.90 259	89.80 255	90.19 296	91.37 375	77.50 319	83.82 402	95.00 263	84.84 275	93.05 247	94.96 250	76.53 326	95.20 368	89.96 182	98.67 164	97.86 192

SSC-MVS3.2		89.88 260	91.06 225	86.31 365	95.90 243	63.76 413	82.68 407	92.43 321	91.42 134	92.37 275	94.58 269	86.34 230	96.60 329	84.35 285	99.50 40	98.57 122

CANet_DTU		89.85 261	89.17 264	91.87 233	92.20 353	80.02 272	90.79 267	95.87 233	86.02 247	82.53 405	91.77 343	80.01 290	98.57 182	85.66 266	97.70 250	97.01 252

tttt0517		89.81 262	88.90 272	92.55 214	97.00 152	79.73 281	95.03 113	83.65 400	89.88 168	95.30 160	94.79 259	53.64 408	99.39 52	91.99 121	98.79 148	98.54 124

EPNet		89.80 263	88.25 286	94.45 133	83.91 427	86.18 173	93.87 155	87.07 372	91.16 142	80.64 415	94.72 261	78.83 297	98.89 126	85.17 269	98.89 129	98.28 147
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CDS-MVSNet		89.55 264	88.22 289	93.53 173	95.37 275	86.49 162	89.26 318	93.59 295	79.76 329	91.15 300	92.31 333	77.12 316	98.38 201	77.51 352	97.92 239	95.71 311
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MG-MVS		89.54 265	89.80 255	88.76 322	94.88 284	72.47 372	89.60 306	92.44 320	85.82 251	89.48 331	95.98 205	82.85 265	97.74 270	81.87 308	95.27 330	96.08 294

OpenMVS_ROB	85.12 16	89.52 266	89.05 266	90.92 272	94.58 300	81.21 256	91.10 260	93.41 301	77.03 356	93.41 228	93.99 289	83.23 259	97.80 260	79.93 332	94.80 343	93.74 370

test_vis1_n_1920		89.45 267	89.85 254	88.28 333	93.59 322	76.71 332	90.67 272	97.78 108	79.67 331	90.30 316	96.11 199	76.62 324	92.17 396	90.31 167	93.57 369	95.96 299

WB-MVS		89.44 268	92.15 197	81.32 398	97.73 112	48.22 431	89.73 303	87.98 363	95.24 42	96.05 120	96.99 138	85.18 244	96.95 315	82.45 303	97.97 235	98.78 90

DPM-MVS		89.35 269	88.40 279	92.18 226	96.13 227	84.20 207	86.96 358	96.15 225	75.40 366	87.36 367	91.55 348	83.30 258	98.01 237	82.17 307	96.62 295	94.32 357

MVSTER		89.32 270	88.75 274	91.03 267	90.10 393	76.62 333	90.85 265	94.67 277	82.27 306	95.24 167	95.79 214	61.09 395	98.49 190	90.49 159	98.26 204	97.97 178

PatchMatch-RL		89.18 271	88.02 294	92.64 206	95.90 243	92.87 49	88.67 334	91.06 339	80.34 323	90.03 321	91.67 345	83.34 257	94.42 378	76.35 362	94.84 342	90.64 404

jason		89.17 272	88.32 281	91.70 241	95.73 255	80.07 268	88.10 339	93.22 303	71.98 388	90.09 318	92.79 321	78.53 302	98.56 183	87.43 238	97.06 276	96.46 276
jason: jason.
PCF-MVS	84.52 17	89.12 273	87.71 297	93.34 180	96.06 231	85.84 182	86.58 371	97.31 146	68.46 408	93.61 223	93.89 293	87.51 209	98.52 188	67.85 405	98.11 220	95.66 315
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
mvsany_test3		89.11 274	88.21 290	91.83 234	91.30 376	90.25 83	88.09 340	78.76 419	76.37 360	96.43 97	98.39 36	83.79 255	90.43 406	86.57 252	94.20 357	94.80 344

FE-MVS		89.06 275	88.29 283	91.36 253	94.78 290	79.57 284	96.77 27	90.99 340	84.87 274	92.96 252	96.29 186	60.69 397	98.80 144	80.18 327	97.11 275	95.71 311

cl22		89.02 276	88.50 277	90.59 283	89.76 395	76.45 335	86.62 369	94.03 287	82.98 298	92.65 261	92.49 327	72.05 343	97.53 280	88.93 208	97.02 278	97.78 202

USDC		89.02 276	89.08 265	88.84 321	95.07 281	74.50 353	88.97 323	96.39 212	73.21 381	93.27 237	96.28 188	82.16 274	96.39 337	77.55 351	98.80 146	95.62 318

test_vis1_n		89.01 278	89.01 268	89.03 317	92.57 342	82.46 236	92.62 199	96.06 226	73.02 383	90.40 313	95.77 218	74.86 331	89.68 409	90.78 152	94.98 337	94.95 338

xiu_mvs_v2_base		89.00 279	89.19 263	88.46 331	94.86 286	74.63 350	86.97 357	95.60 240	80.88 320	87.83 360	88.62 383	91.04 154	98.81 141	82.51 302	94.38 351	91.93 395

new-patchmatchnet		88.97 280	90.79 233	83.50 390	94.28 306	55.83 426	85.34 386	93.56 297	86.18 244	95.47 149	95.73 220	83.10 260	96.51 332	85.40 268	98.06 225	98.16 157

pmmvs4		88.95 281	87.70 298	92.70 203	94.30 305	85.60 188	87.22 353	92.16 326	74.62 371	89.75 329	94.19 280	77.97 307	96.41 336	82.71 297	96.36 301	96.09 293

N_pmnet		88.90 282	87.25 305	93.83 158	94.40 304	93.81 39	84.73 390	87.09 370	79.36 337	93.26 238	92.43 331	79.29 295	91.68 398	77.50 353	97.22 271	96.00 297

PS-MVSNAJ		88.86 283	88.99 269	88.48 330	94.88 284	74.71 348	86.69 366	95.60 240	80.88 320	87.83 360	87.37 394	90.77 159	98.82 136	82.52 301	94.37 352	91.93 395

Patchmatch-RL test		88.81 284	88.52 276	89.69 307	95.33 277	79.94 274	86.22 376	92.71 313	78.46 345	95.80 131	94.18 281	66.25 369	95.33 365	89.22 202	98.53 178	93.78 368

Anonymous20231206		88.77 285	88.29 283	90.20 295	96.31 208	78.81 302	89.56 308	93.49 299	74.26 375	92.38 273	95.58 227	82.21 272	95.43 362	72.07 388	98.75 154	96.34 280

PVSNet_Blended		88.74 286	88.16 292	90.46 287	94.81 288	78.80 303	86.64 367	96.93 175	74.67 370	88.68 348	89.18 379	86.27 232	98.15 223	80.27 324	96.00 308	94.44 354

test_fmvs1_n		88.73 287	88.38 280	89.76 304	92.06 358	82.53 234	92.30 218	96.59 201	71.14 393	92.58 264	95.41 236	68.55 355	89.57 411	91.12 144	95.66 317	97.18 245

thisisatest0530		88.69 288	87.52 300	92.20 222	96.33 206	79.36 288	92.81 191	84.01 399	86.44 237	93.67 222	92.68 325	53.62 409	99.25 81	89.65 189	98.45 185	98.00 171

ppachtmachnet_test		88.61 289	88.64 275	88.50 329	91.76 366	70.99 379	84.59 394	92.98 306	79.30 339	92.38 273	93.53 304	79.57 292	97.45 286	86.50 256	97.17 273	97.07 247

UnsupCasMVSNet_bld		88.50 290	88.03 293	89.90 302	95.52 268	78.88 299	87.39 351	94.02 289	79.32 338	93.06 246	94.02 287	80.72 287	94.27 381	75.16 370	93.08 382	96.54 268

MonoMVSNet		88.46 291	89.28 262	85.98 367	90.52 386	70.07 385	95.31 101	94.81 271	88.38 201	93.47 227	96.13 198	73.21 337	95.07 369	82.61 299	89.12 406	92.81 387

miper_enhance_ethall		88.42 292	87.87 295	90.07 297	88.67 408	75.52 344	85.10 387	95.59 244	75.68 362	92.49 266	89.45 375	78.96 296	97.88 250	87.86 232	97.02 278	96.81 261

1112_ss		88.42 292	87.41 301	91.45 250	96.69 172	80.99 258	89.72 304	96.72 193	73.37 379	87.00 370	90.69 361	77.38 313	98.20 217	81.38 315	93.72 367	95.15 329

lupinMVS		88.34 294	87.31 302	91.45 250	94.74 293	80.06 269	87.23 352	92.27 323	71.10 394	88.83 339	91.15 351	77.02 318	98.53 187	86.67 250	96.75 291	95.76 309

test_cas_vis1_n_1920		88.25 295	88.27 285	88.20 335	92.19 354	78.92 297	89.45 311	95.44 250	75.29 369	93.23 241	95.65 223	71.58 345	90.23 407	88.05 226	93.55 371	95.44 323

YYNet1		88.17 296	88.24 287	87.93 339	92.21 352	73.62 361	80.75 413	88.77 353	82.51 304	94.99 181	95.11 244	82.70 268	93.70 386	83.33 291	93.83 365	96.48 274

MDA-MVSNet_test_wron		88.16 297	88.23 288	87.93 339	92.22 351	73.71 360	80.71 414	88.84 352	82.52 303	94.88 186	95.14 242	82.70 268	93.61 387	83.28 292	93.80 366	96.46 276

MS-PatchMatch		88.05 298	87.75 296	88.95 318	93.28 326	77.93 312	87.88 342	92.49 319	75.42 365	92.57 265	93.59 302	80.44 288	94.24 383	81.28 316	92.75 385	94.69 350

CR-MVSNet		87.89 299	87.12 310	90.22 293	91.01 379	78.93 295	92.52 202	92.81 309	73.08 382	89.10 335	96.93 141	67.11 361	97.64 277	88.80 212	92.70 386	94.08 359

pmmvs5		87.87 300	87.14 308	90.07 297	93.26 328	76.97 329	88.89 325	92.18 324	73.71 378	88.36 352	93.89 293	76.86 323	96.73 326	80.32 323	96.81 288	96.51 270

wuyk23d		87.83 301	90.79 233	78.96 404	90.46 389	88.63 112	92.72 193	90.67 345	91.65 126	98.68 12	97.64 80	96.06 15	77.53 426	59.84 419	99.41 55	70.73 424

FMVSNet5		87.82 302	86.56 321	91.62 244	92.31 348	79.81 279	93.49 168	94.81 271	83.26 290	91.36 295	96.93 141	52.77 410	97.49 284	76.07 364	98.03 228	97.55 221

GA-MVS		87.70 303	86.82 315	90.31 289	93.27 327	77.22 324	84.72 392	92.79 311	85.11 269	89.82 325	90.07 364	66.80 364	97.76 267	84.56 282	94.27 355	95.96 299

TR-MVS		87.70 303	87.17 307	89.27 314	94.11 309	79.26 290	88.69 332	91.86 333	81.94 310	90.69 308	89.79 369	82.82 266	97.42 289	72.65 386	91.98 394	91.14 401

thres600view7		87.66 305	87.10 311	89.36 312	96.05 232	73.17 363	92.72 193	85.31 390	91.89 109	93.29 235	90.97 355	63.42 386	98.39 198	73.23 382	96.99 283	96.51 270

PAPR		87.65 306	86.77 317	90.27 291	92.85 338	77.38 321	88.56 335	96.23 219	76.82 359	84.98 383	89.75 371	86.08 234	97.16 306	72.33 387	93.35 374	96.26 286

baseline1		87.62 307	87.31 302	88.54 327	94.71 296	74.27 356	93.10 182	88.20 359	86.20 243	92.18 282	93.04 314	73.21 337	95.52 357	79.32 339	85.82 414	95.83 306

test_fmvs1		87.59 308	87.27 304	88.54 327	88.32 409	81.26 254	90.43 281	95.72 237	70.55 399	91.70 290	94.63 265	68.13 356	89.42 413	90.59 156	95.34 328	94.94 340

our_test_3		87.55 309	87.59 299	87.44 347	91.76 366	70.48 380	83.83 401	90.55 346	79.79 328	92.06 286	92.17 336	78.63 301	95.63 355	84.77 279	94.73 344	96.22 288

PatchT		87.51 310	88.17 291	85.55 371	90.64 383	66.91 396	92.02 227	86.09 378	92.20 99	89.05 338	97.16 123	64.15 381	96.37 339	89.21 203	92.98 384	93.37 378

Test_1112_low_res		87.50 311	86.58 319	90.25 292	96.80 168	77.75 316	87.53 349	96.25 217	69.73 404	86.47 372	93.61 301	75.67 328	97.88 250	79.95 330	93.20 377	95.11 333

SCA		87.43 312	87.21 306	88.10 337	92.01 360	71.98 374	89.43 312	88.11 361	82.26 307	88.71 346	92.83 319	78.65 299	97.59 278	79.61 336	93.30 375	94.75 347

EU-MVSNet		87.39 313	86.71 318	89.44 309	93.40 324	76.11 338	94.93 117	90.00 348	57.17 424	95.71 139	97.37 101	64.77 378	97.68 274	92.67 105	94.37 352	94.52 352

thres100view900		87.35 314	86.89 314	88.72 323	96.14 225	73.09 365	93.00 185	85.31 390	92.13 102	93.26 238	90.96 356	63.42 386	98.28 209	71.27 394	96.54 297	94.79 345

CMPMVS	68.83 22	87.28 315	85.67 331	92.09 229	88.77 407	85.42 192	90.31 285	94.38 280	70.02 402	88.00 357	93.30 308	73.78 336	94.03 385	75.96 366	96.54 297	96.83 260
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
sss		87.23 316	86.82 315	88.46 331	93.96 314	77.94 311	86.84 361	92.78 312	77.59 350	87.61 365	91.83 342	78.75 298	91.92 397	77.84 348	94.20 357	95.52 322

BH-w/o		87.21 317	87.02 312	87.79 344	94.77 291	77.27 323	87.90 341	93.21 305	81.74 312	89.99 322	88.39 386	83.47 256	96.93 318	71.29 393	92.43 390	89.15 406

thres400		87.20 318	86.52 323	89.24 316	95.77 252	72.94 367	91.89 235	86.00 379	90.84 146	92.61 262	89.80 367	63.93 382	98.28 209	71.27 394	96.54 297	96.51 270

CHOSEN 1792x2688		87.19 319	85.92 330	91.00 270	97.13 148	79.41 287	84.51 395	95.60 240	64.14 418	90.07 320	94.81 256	78.26 305	97.14 307	73.34 381	95.38 327	96.46 276

HyFIR lowres test		87.19 319	85.51 332	92.24 221	97.12 149	80.51 262	85.03 388	96.06 226	66.11 414	91.66 291	92.98 317	70.12 351	99.14 93	75.29 369	95.23 331	97.07 247

reproduce_monomvs		87.13 321	86.90 313	87.84 343	90.92 381	68.15 391	91.19 257	93.75 293	85.84 250	94.21 204	95.83 212	42.99 426	97.10 308	89.46 192	97.88 241	98.26 149

MIMVSNet		87.13 321	86.54 322	88.89 320	96.05 232	76.11 338	94.39 135	88.51 355	81.37 315	88.27 354	96.75 154	72.38 341	95.52 357	65.71 410	95.47 323	95.03 335

tfpn200view9		87.05 323	86.52 323	88.67 324	95.77 252	72.94 367	91.89 235	86.00 379	90.84 146	92.61 262	89.80 367	63.93 382	98.28 209	71.27 394	96.54 297	94.79 345

cascas		87.02 324	86.28 327	89.25 315	91.56 373	76.45 335	84.33 397	96.78 188	71.01 395	86.89 371	85.91 402	81.35 281	96.94 316	83.09 294	95.60 319	94.35 356

WTY-MVS		86.93 325	86.50 325	88.24 334	94.96 282	74.64 349	87.19 354	92.07 329	78.29 346	88.32 353	91.59 347	78.06 306	94.27 381	74.88 371	93.15 379	95.80 307

ttmdpeth		86.91 326	86.57 320	87.91 341	89.68 397	74.24 357	91.49 249	87.09 370	79.84 326	89.46 332	97.86 66	65.42 373	91.04 401	81.57 313	96.74 293	98.44 134

HY-MVS	82.50 18	86.81 327	85.93 329	89.47 308	93.63 321	77.93 312	94.02 150	91.58 337	75.68 362	83.64 395	93.64 298	77.40 312	97.42 289	71.70 391	92.07 393	93.05 383

test_f		86.65 328	87.13 309	85.19 375	90.28 391	86.11 175	86.52 372	91.66 335	69.76 403	95.73 138	97.21 120	69.51 353	81.28 425	89.15 204	94.40 350	88.17 411

1314		86.46 329	86.33 326	86.87 355	91.65 370	74.54 351	91.94 232	94.10 286	74.28 374	84.78 385	87.33 395	83.03 262	95.00 370	78.72 343	91.16 399	91.06 402

ET-MVSNet_ETH3D		86.15 330	84.27 341	91.79 236	93.04 332	81.28 253	87.17 355	86.14 377	79.57 332	83.65 394	88.66 381	57.10 401	98.18 220	87.74 233	95.40 325	95.90 304

Patchmatch-test		86.10 331	86.01 328	86.38 363	90.63 384	74.22 358	89.57 307	86.69 373	85.73 254	89.81 326	92.83 319	65.24 376	91.04 401	77.82 350	95.78 315	93.88 367

thres200		85.85 332	85.18 333	87.88 342	94.44 302	72.52 371	89.08 322	86.21 376	88.57 197	91.44 294	88.40 385	64.22 380	98.00 239	68.35 403	95.88 313	93.12 380

EPNet_dtu		85.63 333	84.37 339	89.40 311	86.30 419	74.33 355	91.64 246	88.26 357	84.84 275	72.96 425	89.85 365	71.27 347	97.69 273	76.60 359	97.62 255	96.18 290
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test_vis1_rt		85.58 334	84.58 337	88.60 326	87.97 410	86.76 154	85.45 385	93.59 295	66.43 412	87.64 363	89.20 378	79.33 294	85.38 422	81.59 312	89.98 405	93.66 372

test2506		85.42 335	84.57 338	87.96 338	97.81 105	66.53 399	96.14 61	56.35 432	89.04 184	93.55 225	98.10 44	42.88 429	98.68 168	88.09 225	99.18 95	98.67 107

PatchmatchNet		85.22 336	84.64 336	86.98 351	89.51 401	69.83 387	90.52 276	87.34 369	78.87 343	87.22 369	92.74 323	66.91 363	96.53 330	81.77 309	86.88 412	94.58 351
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CVMVSNet		85.16 337	84.72 335	86.48 359	92.12 356	70.19 381	92.32 215	88.17 360	56.15 425	90.64 309	95.85 209	67.97 359	96.69 327	88.78 213	90.52 402	92.56 390

JIA-IIPM		85.08 338	83.04 353	91.19 264	87.56 412	86.14 174	89.40 314	84.44 398	88.98 186	82.20 406	97.95 56	56.82 403	96.15 343	76.55 361	83.45 418	91.30 400

MVS		84.98 339	84.30 340	87.01 350	91.03 378	77.69 318	91.94 232	94.16 285	59.36 423	84.23 390	87.50 393	85.66 238	96.80 324	71.79 389	93.05 383	86.54 415

Syy-MVS		84.81 340	84.93 334	84.42 382	91.71 368	63.36 415	85.89 379	81.49 409	81.03 317	85.13 380	81.64 419	77.44 311	95.00 370	85.94 263	94.12 360	94.91 341

MVStest1		84.79 341	84.06 344	86.98 351	77.73 432	74.76 347	91.08 262	85.63 384	77.70 349	96.86 79	97.97 55	41.05 431	88.24 416	92.22 115	96.28 303	97.94 181

thisisatest0515		84.72 342	82.99 354	89.90 302	92.96 335	75.33 346	84.36 396	83.42 401	77.37 352	88.27 354	86.65 396	53.94 407	98.72 157	82.56 300	97.40 266	95.67 314

dmvs_re		84.69 343	83.94 346	86.95 353	92.24 350	82.93 230	89.51 309	87.37 368	84.38 281	85.37 377	85.08 409	72.44 340	86.59 419	68.05 404	91.03 401	91.33 399

FPMVS		84.50 344	83.28 351	88.16 336	96.32 207	94.49 20	85.76 382	85.47 388	83.09 295	85.20 379	94.26 277	63.79 384	86.58 420	63.72 414	91.88 396	83.40 418

tpm		84.38 345	84.08 343	85.30 374	90.47 388	63.43 414	89.34 315	85.63 384	77.24 355	87.62 364	95.03 248	61.00 396	97.30 295	79.26 340	91.09 400	95.16 328

tpmvs		84.22 346	83.97 345	84.94 377	87.09 416	65.18 406	91.21 256	88.35 356	82.87 299	85.21 378	90.96 356	65.24 376	96.75 325	79.60 338	85.25 415	92.90 386

WB-MVSnew		84.20 347	83.89 347	85.16 376	91.62 371	66.15 403	88.44 338	81.00 412	76.23 361	87.98 358	87.77 390	84.98 247	93.35 390	62.85 417	94.10 362	95.98 298

ADS-MVSNet2		84.01 348	82.20 361	89.41 310	89.04 404	76.37 337	87.57 345	90.98 341	72.71 386	84.46 386	92.45 328	68.08 357	96.48 333	70.58 399	83.97 416	95.38 324

WBMVS		84.00 349	83.48 349	85.56 370	92.71 339	61.52 417	83.82 402	89.38 351	79.56 333	90.74 306	93.20 312	48.21 413	97.28 296	75.63 368	98.10 222	97.88 189

testing3-2		83.95 350	84.22 342	83.13 392	96.28 211	54.34 429	88.51 336	83.01 404	92.19 100	89.09 337	90.98 354	45.51 419	97.44 287	74.38 375	98.01 231	97.60 216

mvsany_test1		83.91 351	82.93 355	86.84 356	86.18 420	85.93 179	81.11 412	75.03 426	70.80 398	88.57 350	94.63 265	83.08 261	87.38 417	80.39 322	86.57 413	87.21 413

testing3		83.66 352	82.52 357	87.08 349	95.84 246	65.84 404	89.80 302	77.17 425	88.17 206	90.84 304	88.63 382	30.95 434	98.11 227	84.05 287	97.19 272	97.28 240

test-LLR		83.58 353	83.17 352	84.79 379	89.68 397	66.86 397	83.08 404	84.52 396	83.07 296	82.85 401	84.78 410	62.86 389	93.49 388	82.85 295	94.86 340	94.03 362

testing91		83.56 354	82.45 358	86.91 354	92.92 336	67.29 393	86.33 374	88.07 362	86.22 242	84.26 389	85.76 403	48.15 414	97.17 304	76.27 363	94.08 363	96.27 285

baseline2		83.38 355	81.54 365	88.90 319	91.38 374	72.84 369	88.78 329	81.22 411	78.97 341	79.82 417	87.56 391	61.73 393	97.80 260	74.30 376	90.05 404	96.05 296

IB-MVS	77.21 19	83.11 356	81.05 368	89.29 313	91.15 377	75.85 341	85.66 383	86.00 379	79.70 330	82.02 409	86.61 397	48.26 412	98.39 198	77.84 348	92.22 391	93.63 373
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
CostFormer		83.09 357	82.21 360	85.73 368	89.27 403	67.01 395	90.35 283	86.47 375	70.42 400	83.52 397	93.23 311	61.18 394	96.85 322	77.21 355	88.26 410	93.34 379

PMMVS		83.00 358	81.11 367	88.66 325	83.81 428	86.44 165	82.24 409	85.65 383	61.75 422	82.07 407	85.64 405	79.75 291	91.59 399	75.99 365	93.09 381	87.94 412

testing99		82.94 359	81.72 362	86.59 357	92.55 343	66.53 399	86.08 378	85.70 382	85.47 263	83.95 392	85.70 404	45.87 418	97.07 311	76.58 360	93.56 370	96.17 292

PVSNet	76.22 20	82.89 360	82.37 359	84.48 381	93.96 314	64.38 411	78.60 416	88.61 354	71.50 391	84.43 388	86.36 400	74.27 333	94.60 375	69.87 401	93.69 368	94.46 353

tpmrst		82.85 361	82.93 355	82.64 393	87.65 411	58.99 423	90.14 290	87.90 364	75.54 364	83.93 393	91.63 346	66.79 366	95.36 363	81.21 318	81.54 422	93.57 377

test0.0.03 1		82.48 362	81.47 366	85.48 372	89.70 396	73.57 362	84.73 390	81.64 408	83.07 296	88.13 356	86.61 397	62.86 389	89.10 415	66.24 409	90.29 403	93.77 369

ADS-MVSNet		82.25 363	81.55 364	84.34 383	89.04 404	65.30 405	87.57 345	85.13 394	72.71 386	84.46 386	92.45 328	68.08 357	92.33 395	70.58 399	83.97 416	95.38 324

DSMNet-mixed		82.21 364	81.56 363	84.16 385	89.57 400	70.00 386	90.65 273	77.66 423	54.99 426	83.30 399	97.57 84	77.89 308	90.50 405	66.86 408	95.54 321	91.97 394

KD-MVS_2432*1600		82.17 365	80.75 372	86.42 361	82.04 429	70.09 383	81.75 410	90.80 343	82.56 301	90.37 314	89.30 376	42.90 427	96.11 345	74.47 373	92.55 388	93.06 381

miper_refine_blended		82.17 365	80.75 372	86.42 361	82.04 429	70.09 383	81.75 410	90.80 343	82.56 301	90.37 314	89.30 376	42.90 427	96.11 345	74.47 373	92.55 388	93.06 381

gg-mvs-nofinetune		82.10 367	81.02 369	85.34 373	87.46 414	71.04 377	94.74 121	67.56 428	96.44 26	79.43 418	98.99 8	45.24 420	96.15 343	67.18 407	92.17 392	88.85 408

testing11		81.98 368	80.52 375	86.38 363	92.69 340	67.13 394	85.79 381	84.80 395	82.16 308	81.19 414	85.41 406	45.24 420	96.88 321	74.14 377	93.24 376	95.14 330

PAPM		81.91 369	80.11 380	87.31 348	93.87 317	72.32 373	84.02 399	93.22 303	69.47 405	76.13 423	89.84 366	72.15 342	97.23 299	53.27 424	89.02 407	92.37 392

tpm2		81.46 370	80.35 378	84.80 378	89.90 394	65.14 407	90.44 278	85.36 389	65.82 416	82.05 408	92.44 330	57.94 400	96.69 327	70.71 398	88.49 409	92.56 390

PMMVS2		81.31 371	83.44 350	74.92 407	90.52 386	46.49 433	69.19 423	85.23 393	84.30 282	87.95 359	94.71 262	76.95 320	84.36 424	64.07 413	98.09 223	93.89 366

new_pmnet		81.22 372	81.01 370	81.86 396	90.92 381	70.15 382	84.03 398	80.25 417	70.83 396	85.97 375	89.78 370	67.93 360	84.65 423	67.44 406	91.90 395	90.78 403

test-mter		81.21 373	80.01 381	84.79 379	89.68 397	66.86 397	83.08 404	84.52 396	73.85 377	82.85 401	84.78 410	43.66 425	93.49 388	82.85 295	94.86 340	94.03 362

EPMVS		81.17 374	80.37 377	83.58 389	85.58 422	65.08 408	90.31 285	71.34 427	77.31 354	85.80 376	91.30 349	59.38 398	92.70 394	79.99 329	82.34 421	92.96 385

myMVS_eth3d28		80.97 375	80.42 376	82.62 394	93.35 325	58.25 424	84.70 393	85.62 386	86.31 239	84.04 391	85.20 408	46.00 417	94.07 384	62.93 416	95.65 318	95.53 321

EGC-MVSNET		80.97 375	75.73 393	96.67 46	98.85 23	94.55 19	96.83 22	96.60 199	2.44 431	5.32 432	98.25 40	92.24 122	98.02 236	91.85 126	99.21 91	97.45 226

pmmvs3		80.83 377	78.96 385	86.45 360	87.23 415	77.48 320	84.87 389	82.31 406	63.83 419	85.03 382	89.50 374	49.66 411	93.10 391	73.12 384	95.10 334	88.78 410

E-PMN		80.72 378	80.86 371	80.29 401	85.11 424	68.77 389	72.96 420	81.97 407	87.76 215	83.25 400	83.01 417	62.22 392	89.17 414	77.15 356	94.31 354	82.93 419

tpm cat1		80.61 379	79.46 382	84.07 386	88.78 406	65.06 409	89.26 318	88.23 358	62.27 421	81.90 410	89.66 373	62.70 391	95.29 366	71.72 390	80.60 423	91.86 397

testing222		80.54 380	78.53 388	86.58 358	92.54 345	68.60 390	86.24 375	82.72 405	83.78 287	82.68 404	84.24 412	39.25 432	95.94 351	60.25 418	95.09 335	95.20 326

EMVS		80.35 381	80.28 379	80.54 400	84.73 426	69.07 388	72.54 422	80.73 414	87.80 213	81.66 411	81.73 418	62.89 388	89.84 408	75.79 367	94.65 347	82.71 420

UWE-MVS		80.29 382	79.10 383	83.87 387	91.97 362	59.56 421	86.50 373	77.43 424	75.40 366	87.79 362	88.10 388	44.08 424	96.90 320	64.23 412	96.36 301	95.14 330

UBG		80.28 383	78.94 386	84.31 384	92.86 337	61.77 416	83.87 400	83.31 403	77.33 353	82.78 403	83.72 414	47.60 416	96.06 347	65.47 411	93.48 372	95.11 333

CHOSEN 280x420		80.04 384	77.97 391	86.23 366	90.13 392	74.53 352	72.87 421	89.59 350	66.38 413	76.29 422	85.32 407	56.96 402	95.36 363	69.49 402	94.72 345	88.79 409

ETVMVS		79.85 385	77.94 392	85.59 369	92.97 334	66.20 402	86.13 377	80.99 413	81.41 314	83.52 397	83.89 413	41.81 430	94.98 373	56.47 422	94.25 356	95.61 319

myMVS_eth3d		79.62 386	78.26 389	83.72 388	91.71 368	61.25 419	85.89 379	81.49 409	81.03 317	85.13 380	81.64 419	32.12 433	95.00 370	71.17 397	94.12 360	94.91 341

dp		79.28 387	78.62 387	81.24 399	85.97 421	56.45 425	86.91 359	85.26 392	72.97 384	81.45 413	89.17 380	56.01 405	95.45 361	73.19 383	76.68 424	91.82 398

TESTMET0.1,1		79.09 388	78.04 390	82.25 395	87.52 413	64.03 412	83.08 404	80.62 415	70.28 401	80.16 416	83.22 416	44.13 423	90.56 404	79.95 330	93.36 373	92.15 393

MVS-HIRNet		78.83 389	80.60 374	73.51 408	93.07 330	47.37 432	87.10 356	78.00 422	68.94 406	77.53 420	97.26 113	71.45 346	94.62 374	63.28 415	88.74 408	78.55 423

dmvs_testset		78.23 390	78.99 384	75.94 406	91.99 361	55.34 428	88.86 326	78.70 420	82.69 300	81.64 412	79.46 421	75.93 327	85.74 421	48.78 426	82.85 420	86.76 414

UWE-MVS-28		74.73 391	73.18 394	79.35 403	85.42 423	55.55 427	87.63 343	65.92 429	74.39 373	77.33 421	88.19 387	47.63 415	89.48 412	39.01 428	93.14 380	93.03 384

PVSNet_0	70.34 21	74.58 392	72.96 395	79.47 402	90.63 384	66.24 401	73.26 419	83.40 402	63.67 420	78.02 419	78.35 423	72.53 339	89.59 410	56.68 421	60.05 427	82.57 421

MVE	59.87 23	73.86 393	72.65 396	77.47 405	87.00 418	74.35 354	61.37 425	60.93 431	67.27 410	69.69 426	86.49 399	81.24 285	72.33 428	56.45 423	83.45 418	85.74 416
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dongtai		53.72 394	53.79 397	53.51 411	79.69 431	36.70 435	77.18 417	32.53 437	71.69 389	68.63 427	60.79 426	26.65 435	73.11 427	30.67 430	36.29 429	50.73 425

test_method		50.44 395	48.94 398	54.93 409	39.68 435	12.38 438	28.59 426	90.09 347	6.82 429	41.10 431	78.41 422	54.41 406	70.69 429	50.12 425	51.26 428	81.72 422

kuosan		43.63 396	44.25 400	41.78 412	66.04 434	34.37 436	75.56 418	32.62 436	53.25 427	50.46 430	51.18 427	25.28 436	49.13 430	13.44 431	30.41 430	41.84 427

tmp_tt		37.97 397	44.33 399	18.88 413	11.80 436	21.54 437	63.51 424	45.66 435	4.23 430	51.34 429	50.48 428	59.08 399	22.11 432	44.50 427	68.35 426	13.00 428

cdsmvs_eth3d_5k		23.35 398	31.13 401	0.00 416	0.00 439	0.00 441	0.00 427	95.58 246	0.00 434	0.00 435	91.15 351	93.43 90	0.00 435	0.00 434	0.00 433	0.00 431

test123		9.49 399	12.01 402	1.91 414	2.87 437	1.30 439	82.38 408	1.34 439	1.36 432	2.84 433	6.56 431	2.45 437	0.97 433	2.73 432	5.56 431	3.47 429

testmvs		9.02 400	11.42 403	1.81 415	2.77 438	1.13 440	79.44 415	1.90 438	1.18 433	2.65 434	6.80 430	1.95 438	0.87 434	2.62 433	3.45 432	3.44 430

pcd_1.5k_mvsjas		7.56 401	10.09 404	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.00 434	90.77 159	0.00 435	0.00 434	0.00 433	0.00 431

ab-mvs-re		7.56 401	10.08 405	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	90.69 361	0.00 439	0.00 435	0.00 434	0.00 433	0.00 431

mmdepth		0.00 403	0.00 406	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.00 434	0.00 439	0.00 435	0.00 434	0.00 433	0.00 431

monomultidepth		0.00 403	0.00 406	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.00 434	0.00 439	0.00 435	0.00 434	0.00 433	0.00 431

test_blank		0.00 403	0.00 406	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.00 434	0.00 439	0.00 435	0.00 434	0.00 433	0.00 431

uanet_test		0.00 403	0.00 406	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.00 434	0.00 439	0.00 435	0.00 434	0.00 433	0.00 431

DCPMVS		0.00 403	0.00 406	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.00 434	0.00 439	0.00 435	0.00 434	0.00 433	0.00 431

sosnet-low-res		0.00 403	0.00 406	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.00 434	0.00 439	0.00 435	0.00 434	0.00 433	0.00 431

sosnet		0.00 403	0.00 406	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.00 434	0.00 439	0.00 435	0.00 434	0.00 433	0.00 431

uncertanet		0.00 403	0.00 406	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.00 434	0.00 439	0.00 435	0.00 434	0.00 433	0.00 431

Regformer		0.00 403	0.00 406	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.00 434	0.00 439	0.00 435	0.00 434	0.00 433	0.00 431

uanet		0.00 403	0.00 406	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.00 434	0.00 439	0.00 435	0.00 434	0.00 433	0.00 431

WAC-MVS						61.25 419								74.55 372

FOURS1					99.21 3	94.68 16	98.45 4	98.81 11	97.73 7	98.27 21

MSC_two_6792asdad				95.90 67	96.54 186	89.57 91		96.87 182					99.41 42	94.06 53	99.30 73	98.72 99

PC_three_1452									75.31 368	95.87 129	95.75 219	92.93 108	96.34 342	87.18 242	98.68 162	98.04 166

No_MVS				95.90 67	96.54 186	89.57 91		96.87 182					99.41 42	94.06 53	99.30 73	98.72 99

test_one_0601					98.26 71	87.14 144		98.18 51	94.25 55	96.99 74	97.36 104	95.13 45

eth-test2					0.00 439

eth-test					0.00 439

ZD-MVS					97.23 141	90.32 82		97.54 125	84.40 280	94.78 189	95.79 214	92.76 114	99.39 52	88.72 215	98.40 187

RE-MVS-def			96.66 23		98.07 84	95.27 10	96.37 46	98.12 61	95.66 39	97.00 72	97.03 134	95.40 31		93.49 71	98.84 136	98.00 171

IU-MVS					98.51 49	86.66 159		96.83 185	72.74 385	95.83 130				93.00 96	99.29 76	98.64 114

OPU-MVS				95.15 100	96.84 164	89.43 95	95.21 104				95.66 222	93.12 101	98.06 231	86.28 260	98.61 169	97.95 179

test_241102_TWO								98.10 65	91.95 105	97.54 43	97.25 114	95.37 32	99.35 62	93.29 84	99.25 84	98.49 130

test_241102_ONE					98.51 49	86.97 149		98.10 65	91.85 111	97.63 38	97.03 134	96.48 10	98.95 120

9.14			94.81 111		97.49 129		94.11 147	98.37 28	87.56 221	95.38 154	96.03 203	94.66 64	99.08 100	90.70 154	98.97 121

save fliter					97.46 132	88.05 127	92.04 226	97.08 165	87.63 219

test_0728_THIRD									93.26 78	97.40 54	97.35 107	94.69 63	99.34 65	93.88 57	99.42 51	98.89 77

test_0728_SECOND				94.88 107	98.55 44	86.72 156	95.20 106	98.22 46					99.38 58	93.44 77	99.31 71	98.53 126

test0726					98.51 49	86.69 157	95.34 97	98.18 51	91.85 111	97.63 38	97.37 101	95.58 24

GSMVS																94.75 347

test_part2					98.21 76	89.41 96				96.72 86

sam_mvs1												66.64 367				94.75 347

sam_mvs												66.41 368

ambc				92.98 190	96.88 160	83.01 229	95.92 72	96.38 213		96.41 98	97.48 96	88.26 195	97.80 260	89.96 182	98.93 126	98.12 161

MTGPA								97.62 117

test_post1							90.21 287				5.85 433	65.36 374	96.00 349	79.61 336

test_post											6.07 432	65.74 372	95.84 353

patchmatchnet-post											91.71 344	66.22 370	97.59 278

GG-mvs-BLEND				83.24 391	85.06 425	71.03 378	94.99 116	65.55 430		74.09 424	75.51 424	44.57 422	94.46 377	59.57 420	87.54 411	84.24 417

MTMP							94.82 119	54.62 433

gm-plane-assit					87.08 417	59.33 422			71.22 392		83.58 415		97.20 301	73.95 378

test9_res														88.16 223	98.40 187	97.83 196

TEST9					96.45 194	89.46 93	90.60 274	96.92 177	79.09 340	90.49 310	94.39 274	91.31 144	98.88 127

test_8					96.37 198	89.14 103	90.51 277	96.89 180	79.37 335	90.42 312	94.36 276	91.20 149	98.82 136

agg_prior2														87.06 245	98.36 197	97.98 175

agg_prior					96.20 219	88.89 108		96.88 181		90.21 317			98.78 148

TestCases				96.00 58	98.02 90	92.17 54		98.43 21	90.48 157	95.04 178	96.74 155	92.54 118	97.86 254	85.11 274	98.98 116	97.98 175

test_prior4						89.91 86	90.74 269

test_prior2							90.21 287		89.33 179	90.77 305	94.81 256	90.41 169		88.21 219	98.55 175

test_prior				94.61 121	95.95 240	87.23 141		97.36 142					98.68 168			97.93 182

旧先验2							90.00 295		68.65 407	92.71 260			96.52 331	85.15 271

新几何2							90.02 294

新几何1				93.17 186	97.16 146	87.29 139		94.43 279	67.95 409	91.29 296	94.94 251	86.97 220	98.23 215	81.06 320	97.75 246	93.98 364

旧先验1					96.20 219	84.17 208		94.82 269			95.57 228	89.57 183			97.89 240	96.32 281

无先验							89.94 296	95.75 236	70.81 397				98.59 180	81.17 319		94.81 343

原ACMM2							89.34 315

原ACMM1				92.87 198	96.91 158	84.22 206		97.01 169	76.84 358	89.64 330	94.46 272	88.00 201	98.70 164	81.53 314	98.01 231	95.70 313

test222					96.95 154	85.27 194	88.83 328	93.61 294	65.09 417	90.74 306	94.85 254	84.62 250			97.36 267	93.91 365

testdata2													98.03 233	80.24 326

segment_acmp												92.14 126

testdata				91.03 267	96.87 161	82.01 241		94.28 283	71.55 390	92.46 268	95.42 233	85.65 239	97.38 294	82.64 298	97.27 269	93.70 371

testdata1							88.96 324		88.44 200

test12				94.43 134	95.95 240	86.75 155		96.24 218		89.76 328		89.79 182	98.79 145		97.95 237	97.75 206

plane_prior7					97.71 114	88.68 111

plane_prior6					97.21 144	88.23 124						86.93 221

plane_prior5								97.81 103					98.95 120	89.26 200	98.51 181	98.60 119

plane_prior4											95.59 224

plane_prior3						88.43 122			90.35 162	93.31 233

plane_prior2							94.56 130		91.74 122

plane_prior1					97.38 134

plane_prior						88.12 125	93.01 184		88.98 186						98.06 225

n2								0.00 440

nn								0.00 440

door-mid								92.13 328

lessismore_v0				93.87 155	98.05 86	83.77 214		80.32 416		97.13 65	97.91 63	77.49 310	99.11 98	92.62 106	98.08 224	98.74 97

LGP-MVS_train				96.84 42	98.36 66	92.13 56		98.25 39	91.78 118	97.07 67	97.22 118	96.38 12	99.28 78	92.07 119	99.59 27	99.11 46

test11								96.65 197

door								91.26 338

HQP5-MVS						84.89 197

HQP-NCC					96.36 200		91.37 251		87.16 227	88.81 341

ACMP_Plane					96.36 200		91.37 251		87.16 227	88.81 341

BP-MVS														86.55 254

HQP4-MVS										88.81 341			98.61 176			98.15 158

HQP3-MVS								97.31 146							97.73 247

HQP2-MVS												84.76 248

NP-MVS					96.82 166	87.10 145					93.40 306

MDTV_nov1_ep13_2view						42.48 434	88.45 337		67.22 411	83.56 396		66.80 364		72.86 385		94.06 361

MDTV_nov1_ep13			83.88 348		89.42 402	61.52 417	88.74 331	87.41 367	73.99 376	84.96 384	94.01 288	65.25 375	95.53 356	78.02 346	93.16 378

ACMMP++_ref															98.82 142

ACMMP++															99.25 84

Test By Simon												90.61 165

ITE_SJBPF				95.95 61	97.34 137	93.36 44		96.55 206	91.93 107	94.82 187	95.39 237	91.99 128	97.08 310	85.53 267	97.96 236	97.41 229

DeepMVS_CX				53.83 410	70.38 433	64.56 410		48.52 434	33.01 428	65.50 428	74.21 425	56.19 404	46.64 431	38.45 429	70.07 425	50.30 426

High-resolution multi-view benchmark