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 2	99.85 1

test_part1		98.39 2	98.94 2	96.75 42	99.23 3	90.73 73	98.63 3	99.28 2	99.01 2	99.60 2	99.55 2	98.75 2	99.68 3	96.41 4	99.97 1	99.84 2

LTVRE_ROB	93.87 1	97.93 3	98.16 3	97.26 26	98.81 24	93.86 30	99.07 2	98.98 4	97.01 13	98.92 5	98.78 15	95.22 38	98.61 167	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
UniMVSNet_ETH3D		97.13 7	97.72 4	95.35 83	99.51 2	87.38 127	97.70 7	97.54 105	98.16 3	98.94 3	99.33 3	97.84 5	99.08 92	90.73 119	99.73 14	99.59 13

TDRefinement		97.68 4	97.60 5	97.93 2	99.02 12	95.95 5	98.61 4	98.81 5	97.41 10	97.28 46	98.46 26	94.62 57	98.84 129	94.64 18	99.53 34	98.99 53

PS-CasMVS		96.69 21	97.43 6	94.49 122	99.13 6	84.09 185	96.61 25	97.97 70	97.91 6	98.64 14	98.13 32	95.24 37	99.65 4	93.39 55	99.84 4	99.72 3

DTE-MVSNet		96.74 18	97.43 6	94.67 109	99.13 6	84.68 175	96.51 29	97.94 76	98.14 4	98.67 13	98.32 29	95.04 45	99.69 2	93.27 61	99.82 8	99.62 11

ACMH	88.36 12	96.59 28	97.43 6	94.07 134	98.56 37	85.33 170	96.33 40	98.30 21	94.66 39	98.72 9	98.30 30	97.51 6	98.00 220	94.87 15	99.59 26	98.86 73
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PEN-MVS		96.69 21	97.39 9	94.61 111	99.16 4	84.50 176	96.54 28	98.05 55	98.06 5	98.64 14	98.25 31	95.01 48	99.65 4	92.95 74	99.83 6	99.68 5

pmmvs6		96.80 14	97.36 10	95.15 94	99.12 8	87.82 123	96.68 23	97.86 78	96.10 26	98.14 23	99.28 4	97.94 4	98.21 203	91.38 113	99.69 15	99.42 19

v7n		96.82 11	97.31 11	95.33 85	98.54 42	86.81 140	96.83 19	98.07 51	96.59 20	98.46 18	98.43 28	92.91 90	99.52 18	96.25 7	99.76 11	99.65 9

UA-Net		97.35 5	97.24 12	97.69 5	98.22 68	93.87 29	98.42 5	98.19 32	96.95 14	95.46 124	99.23 5	93.45 73	99.57 14	95.34 13	99.89 3	99.63 10

Anonymous20231211		96.60 26	97.13 13	95.00 98	97.46 117	86.35 154	97.11 15	98.24 28	97.58 8	98.72 9	98.97 8	93.15 84	99.15 83	93.18 64	99.74 13	99.50 17

abl_6		97.31 6	97.12 14	97.86 3	98.54 42	95.32 7	96.61 25	98.35 17	95.81 31	97.55 35	97.44 63	96.51 10	99.40 40	94.06 31	99.23 74	98.85 76

WR-MVS_H		96.60 26	97.05 15	95.24 90	99.02 12	86.44 150	96.78 22	98.08 48	97.42 9	98.48 17	97.86 45	91.76 116	99.63 7	94.23 27	99.84 4	99.66 7

HPM-MVS_fast		97.01 8	96.89 16	97.39 22	99.12 8	93.92 27	97.16 11	98.17 36	93.11 66	96.48 76	97.36 70	96.92 7	99.34 59	94.31 24	99.38 54	98.92 67

ACMH+	88.43 11	96.48 31	96.82 17	95.47 81	98.54 42	89.06 94	95.65 66	98.61 7	96.10 26	98.16 22	97.52 58	96.90 8	98.62 166	90.30 131	99.60 24	98.72 89

CP-MVSNet		96.19 47	96.80 18	94.38 128	98.99 14	83.82 189	96.31 42	97.53 107	97.60 7	98.34 20	97.52 58	91.98 112	99.63 7	93.08 70	99.81 9	99.70 4

OurMVSNet-221017-0		96.80 14	96.75 19	96.96 36	99.03 11	91.85 57	97.98 6	98.01 64	94.15 48	98.93 4	99.07 6	88.07 178	99.57 14	95.86 10	99.69 15	99.46 18

mvs_tets		96.83 10	96.71 20	97.17 27	98.83 22	92.51 48	96.58 27	97.61 100	87.57 192	98.80 8	98.90 10	96.50 11	99.59 13	96.15 8	99.47 38	99.40 21

RE-MVS-def			96.66 21		98.07 77	95.27 8	96.37 37	98.12 42	95.66 33	97.00 57	97.03 89	95.40 28		93.49 45	98.84 116	98.00 143

APD-MVS_3200maxsize		96.82 11	96.65 22	97.32 25	97.95 89	93.82 32	96.31 42	98.25 25	95.51 35	96.99 59	97.05 88	95.63 22	99.39 45	93.31 58	98.88 111	98.75 85

APDe-MVS		96.46 33	96.64 23	95.93 61	97.68 104	89.38 91	96.90 18	98.41 14	92.52 73	97.43 41	97.92 41	95.11 42	99.50 20	94.45 20	99.30 62	98.92 67

HPM-MVS		96.81 13	96.62 24	97.36 24	98.89 19	93.53 37	97.51 8	98.44 10	92.35 78	95.95 104	96.41 129	96.71 9	99.42 28	93.99 32	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
COLMAP_ROB	91.06 5	96.75 17	96.62 24	97.13 28	98.38 58	94.31 15	96.79 21	98.32 18	96.69 17	96.86 62	97.56 55	95.48 26	98.77 146	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
SR-MVS-dyc-post		96.84 9	96.60 26	97.56 10	98.07 77	95.27 8	96.37 37	98.12 42	95.66 33	97.00 57	97.03 89	94.85 52	99.42 28	93.49 45	98.84 116	98.00 143

nrg030		96.32 42	96.55 27	95.62 76	97.83 93	88.55 107	95.77 62	98.29 24	92.68 69	98.03 25	97.91 43	95.13 41	98.95 114	93.85 34	99.49 37	99.36 24

test1172		96.79 16	96.52 28	97.60 9	98.03 82	94.87 10	96.07 51	98.06 54	95.76 32	96.89 61	96.85 100	94.85 52	99.42 28	93.35 57	98.81 124	98.53 106

test_djsdf		96.62 24	96.49 29	97.01 33	98.55 40	91.77 59	97.15 12	97.37 115	88.98 160	98.26 21	98.86 11	93.35 78	99.60 9	96.41 4	99.45 42	99.66 7

SR-MVS		96.70 20	96.42 30	97.54 11	98.05 79	94.69 11	96.13 48	98.07 51	95.17 36	96.82 64	96.73 111	95.09 44	99.43 27	92.99 73	98.71 133	98.50 108

anonymousdsp		96.74 18	96.42 30	97.68 7	98.00 85	94.03 24	96.97 16	97.61 100	87.68 189	98.45 19	98.77 16	94.20 66	99.50 20	96.70 3	99.40 52	99.53 15

jajsoiax		96.59 28	96.42 30	97.12 29	98.76 27	92.49 49	96.44 34	97.42 113	86.96 201	98.71 11	98.72 18	95.36 32	99.56 17	95.92 9	99.45 42	99.32 26

SED-MVS		96.00 53	96.41 33	94.76 106	98.51 46	86.97 136	95.21 79	98.10 45	91.95 88	97.63 31	97.25 76	96.48 12	99.35 55	93.29 59	99.29 63	97.95 151

MTAPA		96.65 23	96.38 34	97.47 15	98.95 16	94.05 21	95.88 59	97.62 97	94.46 44	96.29 86	96.94 93	93.56 71	99.37 52	94.29 25	99.42 46	98.99 53

ACMMP		96.61 25	96.34 35	97.43 19	98.61 33	93.88 28	96.95 17	98.18 33	92.26 81	96.33 82	96.84 103	95.10 43	99.40 40	93.47 49	99.33 58	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
SteuartSystems-ACMMP		96.40 39	96.30 36	96.71 43	98.63 30	91.96 55	95.70 63	98.01 64	93.34 64	96.64 71	96.57 121	94.99 49	99.36 54	93.48 48	99.34 56	98.82 78
Skip Steuart: Steuart Systems R&D Blog.
ANet_high		94.83 94	96.28 37	90.47 249	96.65 150	73.16 314	94.33 113	98.74 6	96.39 23	98.09 24	98.93 9	93.37 77	98.70 159	90.38 126	99.68 18	99.53 15

TranMVSNet+NR-MVSNet		96.07 51	96.26 38	95.50 80	98.26 66	87.69 124	93.75 128	97.86 78	95.96 30	97.48 39	97.14 83	95.33 33	99.44 24	90.79 118	99.76 11	99.38 22

LPG-MVS_test		96.38 41	96.23 39	96.84 40	98.36 61	92.13 52	95.33 75	98.25 25	91.78 101	97.07 51	97.22 79	96.38 14	99.28 70	92.07 91	99.59 26	99.11 41

test_0402		95.73 60	96.22 40	94.26 130	98.19 70	85.77 165	93.24 141	97.24 131	96.88 16	97.69 29	97.77 48	94.12 67	99.13 86	91.54 110	99.29 63	97.88 159

ZNCC-MVS		96.42 37	96.20 41	97.07 30	98.80 26	92.79 46	96.08 50	98.16 39	91.74 105	95.34 128	96.36 137	95.68 20	99.44 24	94.41 22	99.28 68	98.97 59

DVP-MVS		95.82 58	96.18 42	94.72 108	98.51 46	86.69 143	95.20 81	97.00 144	91.85 94	97.40 44	97.35 71	95.58 23	99.34 59	93.44 52	99.31 60	98.13 134

XVS		96.49 30	96.18 42	97.44 17	98.56 37	93.99 25	96.50 30	97.95 73	94.58 40	94.38 165	96.49 123	94.56 58	99.39 45	93.57 41	99.05 91	98.93 63

HFP-MVS		96.39 40	96.17 44	97.04 31	98.51 46	93.37 38	96.30 44	97.98 67	92.35 78	95.63 116	96.47 124	95.37 29	99.27 72	93.78 36	99.14 82	98.48 110

zzz-MVS		96.47 32	96.14 45	97.47 15	98.95 16	94.05 21	93.69 130	97.62 97	94.46 44	96.29 86	96.94 93	93.56 71	99.37 52	94.29 25	99.42 46	98.99 53

ACMMPR		96.46 33	96.14 45	97.41 21	98.60 34	93.82 32	96.30 44	97.96 71	92.35 78	95.57 119	96.61 119	94.93 51	99.41 35	93.78 36	99.15 81	99.00 51

ACMMP_NAP		96.21 46	96.12 47	96.49 50	98.90 18	91.42 62	94.57 105	98.03 60	90.42 138	96.37 79	97.35 71	95.68 20	99.25 74	94.44 21	99.34 56	98.80 80

region2R		96.41 38	96.09 48	97.38 23	98.62 31	93.81 34	96.32 41	97.96 71	92.26 81	95.28 132	96.57 121	95.02 47	99.41 35	93.63 40	99.11 86	98.94 62

CP-MVS		96.44 36	96.08 49	97.54 11	98.29 63	94.62 13	96.80 20	98.08 48	92.67 71	95.08 142	96.39 134	94.77 54	99.42 28	93.17 65	99.44 44	98.58 104

ACMM	88.83 9	96.30 44	96.07 50	96.97 35	98.39 57	92.95 44	94.74 97	98.03 60	90.82 127	97.15 49	96.85 100	96.25 16	99.00 106	93.10 68	99.33 58	98.95 61
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
mPP-MVS		96.46 33	96.05 51	97.69 5	98.62 31	94.65 12	96.45 32	97.74 91	92.59 72	95.47 122	96.68 114	94.50 60	99.42 28	93.10 68	99.26 70	98.99 53

PS-MVSNAJss		96.01 52	96.04 52	95.89 64	98.82 23	88.51 109	95.57 69	97.88 77	88.72 166	98.81 7	98.86 11	90.77 141	99.60 9	95.43 12	99.53 34	99.57 14

TransMVSNet (Re)		95.27 79	96.04 52	92.97 171	98.37 60	81.92 208	95.07 87	96.76 165	93.97 52	97.77 27	98.57 20	95.72 19	97.90 225	88.89 166	99.23 74	99.08 45

GST-MVS		96.24 45	95.99 54	97.00 34	98.65 29	92.71 47	95.69 65	98.01 64	92.08 86	95.74 112	96.28 142	95.22 38	99.42 28	93.17 65	99.06 88	98.88 71

pm-mvs1		95.43 69	95.94 55	93.93 140	98.38 58	85.08 172	95.46 72	97.12 139	91.84 97	97.28 46	98.46 26	95.30 35	97.71 245	90.17 137	99.42 46	98.99 53

PGM-MVS		96.32 42	95.94 55	97.43 19	98.59 36	93.84 31	95.33 75	98.30 21	91.40 114	95.76 110	96.87 99	95.26 36	99.45 23	92.77 76	99.21 76	99.00 51

MP-MVS-pluss		96.08 50	95.92 57	96.57 46	99.06 10	91.21 64	93.25 140	98.32 18	87.89 183	96.86 62	97.38 66	95.55 25	99.39 45	95.47 11	99.47 38	99.11 41
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
SF-MVS		95.88 56	95.88 58	95.87 65	98.12 73	89.65 85	95.58 68	98.56 8	91.84 97	96.36 80	96.68 114	94.37 63	99.32 65	92.41 85	99.05 91	98.64 95

DPE-MVS		95.89 54	95.88 58	95.92 63	97.93 90	89.83 82	93.46 136	98.30 21	92.37 76	97.75 28	96.95 92	95.14 40	99.51 19	91.74 101	99.28 68	98.41 116

FC-MVSNet-test		95.32 74	95.88 58	93.62 149	98.49 54	81.77 209	95.90 58	98.32 18	93.93 53	97.53 37	97.56 55	88.48 171	99.40 40	92.91 75	99.83 6	99.68 5

DP-MVS		95.62 63	95.84 61	94.97 99	97.16 129	88.62 104	94.54 109	97.64 96	96.94 15	96.58 74	97.32 74	93.07 87	98.72 152	90.45 123	98.84 116	97.57 183

Anonymous20240529		95.50 67	95.83 62	94.50 120	97.33 123	85.93 162	95.19 83	96.77 164	96.64 19	97.61 34	98.05 34	93.23 81	98.79 138	88.60 173	99.04 96	98.78 82

LS3D		96.11 49	95.83 62	96.95 37	94.75 243	94.20 17	97.34 10	97.98 67	97.31 11	95.32 129	96.77 105	93.08 86	99.20 79	91.79 99	98.16 188	97.44 190

Gipuma		95.31 76	95.80 64	93.81 146	97.99 88	90.91 69	96.42 35	97.95 73	96.69 17	91.78 237	98.85 13	91.77 115	95.49 308	91.72 102	99.08 87	95.02 274
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
3Dnovator+	92.74 2	95.86 57	95.77 65	96.13 53	96.81 145	90.79 72	96.30 44	97.82 84	96.13 25	94.74 156	97.23 78	91.33 126	99.16 82	93.25 62	98.30 172	98.46 112

SD-MVS		95.19 80	95.73 66	93.55 152	96.62 153	88.88 100	94.67 99	98.05 55	91.26 117	97.25 48	96.40 130	95.42 27	94.36 325	92.72 80	99.19 77	97.40 194
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
MP-MVS		96.14 48	95.68 67	97.51 13	98.81 24	94.06 19	96.10 49	97.78 90	92.73 68	93.48 187	96.72 112	94.23 65	99.42 28	91.99 93	99.29 63	99.05 48
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
VPA-MVSNet		95.14 81	95.67 68	93.58 151	97.76 95	83.15 197	94.58 104	97.58 102	93.39 63	97.05 55	98.04 35	93.25 80	98.51 180	89.75 149	99.59 26	99.08 45

SMA-MVS		95.77 59	95.54 69	96.47 51	98.27 65	91.19 65	95.09 85	97.79 89	86.48 205	97.42 43	97.51 60	94.47 62	99.29 68	93.55 43	99.29 63	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
#test#		95.89 54	95.51 70	97.04 31	98.51 46	93.37 38	95.14 84	97.98 67	89.34 155	95.63 116	96.47 124	95.37 29	99.27 72	91.99 93	99.14 82	98.48 110

Vis-MVSNet		95.50 67	95.48 71	95.56 79	98.11 74	89.40 90	95.35 73	98.22 30	92.36 77	94.11 169	98.07 33	92.02 108	99.44 24	93.38 56	97.67 220	97.85 163
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
OPM-MVS		95.61 64	95.45 72	96.08 54	98.49 54	91.00 67	92.65 157	97.33 123	90.05 143	96.77 67	96.85 100	95.04 45	98.56 175	92.77 76	99.06 88	98.70 90

MIMVSNet1		95.52 66	95.45 72	95.72 73	99.14 5	89.02 95	96.23 47	96.87 158	93.73 56	97.87 26	98.49 25	90.73 145	99.05 97	86.43 209	99.60 24	99.10 44

ACMP	88.15 13	95.71 61	95.43 74	96.54 47	98.17 71	91.73 60	94.24 115	98.08 48	89.46 153	96.61 73	96.47 124	95.85 18	99.12 88	90.45 123	99.56 32	98.77 84
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
FIs		94.90 88	95.35 75	93.55 152	98.28 64	81.76 210	95.33 75	98.14 40	93.05 67	97.07 51	97.18 81	87.65 185	99.29 68	91.72 102	99.69 15	99.61 12

XVG-ACMP-BASELINE		95.68 62	95.34 76	96.69 44	98.40 56	93.04 41	94.54 109	98.05 55	90.45 137	96.31 84	96.76 107	92.91 90	98.72 152	91.19 114	99.42 46	98.32 119

DeepC-MVS	91.39 4	95.43 69	95.33 77	95.71 74	97.67 105	90.17 76	93.86 126	98.02 62	87.35 194	96.22 92	97.99 38	94.48 61	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
PMVS	87.21 14	94.97 85	95.33 77	93.91 141	98.97 15	97.16 2	95.54 70	95.85 204	96.47 21	93.40 190	97.46 62	95.31 34	95.47 309	86.18 213	98.78 128	89.11 336
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
v8		94.65 101	95.29 79	92.74 181	96.65 150	79.77 242	94.59 102	97.17 135	91.86 93	97.47 40	97.93 40	88.16 176	99.08 92	94.32 23	99.47 38	99.38 22

NR-MVSNet		95.28 77	95.28 80	95.26 89	97.75 96	87.21 131	95.08 86	97.37 115	93.92 54	97.65 30	95.90 157	90.10 158	99.33 64	90.11 139	99.66 20	99.26 29

v10		94.68 100	95.27 81	92.90 176	96.57 156	80.15 228	94.65 101	97.57 103	90.68 131	97.43 41	98.00 37	88.18 175	99.15 83	94.84 16	99.55 33	99.41 20

UniMVSNet_NR-MVSNet		95.35 72	95.21 82	95.76 71	97.69 103	88.59 105	92.26 176	97.84 82	94.91 37	96.80 65	95.78 167	90.42 150	99.41 35	91.60 107	99.58 30	99.29 28

SixPastTwentyTwo		94.91 87	95.21 82	93.98 136	98.52 45	83.19 196	95.93 56	94.84 233	94.86 38	98.49 16	98.74 17	81.45 248	99.60 9	94.69 17	99.39 53	99.15 37

UniMVSNet (Re)		95.32 74	95.15 84	95.80 68	97.79 94	88.91 97	92.91 148	98.07 51	93.46 62	96.31 84	95.97 156	90.14 154	99.34 59	92.11 88	99.64 22	99.16 36

FMVSNet1		94.84 93	95.13 85	93.97 137	97.60 108	84.29 178	95.99 52	96.56 174	92.38 75	97.03 56	98.53 22	90.12 155	98.98 107	88.78 168	99.16 80	98.65 91

DU-MVS		95.28 77	95.12 86	95.75 72	97.75 96	88.59 105	92.58 158	97.81 85	93.99 50	96.80 65	95.90 157	90.10 158	99.41 35	91.60 107	99.58 30	99.26 29

Baseline_NR-MVSNet		94.47 107	95.09 87	92.60 188	98.50 53	80.82 224	92.08 182	96.68 168	93.82 55	96.29 86	98.56 21	90.10 158	97.75 243	90.10 141	99.66 20	99.24 31

XVG-OURS-SEG-HR		95.38 71	95.00 88	96.51 48	98.10 75	94.07 18	92.46 164	98.13 41	90.69 130	93.75 180	96.25 145	98.03 3	97.02 273	92.08 90	95.55 270	98.45 113

xxxxxxxxxxxxxcwj		95.03 82	94.93 89	95.33 85	97.46 117	88.05 117	92.04 184	98.42 13	87.63 190	96.36 80	96.68 114	94.37 63	99.32 65	92.41 85	99.05 91	98.64 95

3Dnovator	92.54 3	94.80 96	94.90 90	94.47 123	95.47 224	87.06 133	96.63 24	97.28 129	91.82 100	94.34 167	97.41 64	90.60 148	98.65 165	92.47 84	98.11 194	97.70 175

RPSCF		95.58 65	94.89 91	97.62 8	97.58 109	96.30 4	95.97 55	97.53 107	92.42 74	93.41 188	97.78 46	91.21 132	97.77 240	91.06 115	97.06 237	98.80 80

tfpnnormal		94.27 114	94.87 92	92.48 192	97.71 100	80.88 223	94.55 108	95.41 220	93.70 57	96.67 70	97.72 49	91.40 124	98.18 207	87.45 192	99.18 79	98.36 117

9.14			94.81 93		97.49 114		94.11 118	98.37 15	87.56 193	95.38 126	96.03 153	94.66 56	99.08 92	90.70 120	98.97 104

casdiffmvs		94.32 112	94.80 94	92.85 178	96.05 191	81.44 215	92.35 171	98.05 55	91.53 112	95.75 111	96.80 104	93.35 78	98.49 181	91.01 116	98.32 169	98.64 95

baseline		94.26 115	94.80 94	92.64 184	96.08 189	80.99 221	93.69 130	98.04 59	90.80 128	94.89 150	96.32 139	93.19 82	98.48 185	91.68 105	98.51 150	98.43 114

TSAR-MVS + MP.		94.96 86	94.75 96	95.57 78	98.86 21	88.69 101	96.37 37	96.81 160	85.23 225	94.75 155	97.12 84	91.85 114	99.40 40	93.45 50	98.33 167	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
CSCG		94.69 99	94.75 96	94.52 119	97.55 111	87.87 121	95.01 90	97.57 103	92.68 69	96.20 94	93.44 253	91.92 113	98.78 142	89.11 163	99.24 73	96.92 210

canonicalmvs		94.59 102	94.69 98	94.30 129	95.60 221	87.03 135	95.59 67	98.24 28	91.56 111	95.21 137	92.04 283	94.95 50	98.66 163	91.45 111	97.57 224	97.20 203

APD-MVS		95.00 84	94.69 98	95.93 61	97.38 120	90.88 70	94.59 102	97.81 85	89.22 158	95.46 124	96.17 149	93.42 76	99.34 59	89.30 155	98.87 114	97.56 185
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
Regformer-4		94.90 88	94.67 100	95.59 77	92.78 287	89.02 95	92.39 168	95.91 201	94.50 42	96.41 77	95.56 178	92.10 107	99.01 105	94.23 27	98.14 190	98.74 86

EG-PatchMatch MVS		94.54 105	94.67 100	94.14 132	97.87 92	86.50 146	92.00 187	96.74 166	88.16 178	96.93 60	97.61 53	93.04 88	97.90 225	91.60 107	98.12 193	98.03 141

MSP-MVS		95.34 73	94.63 102	97.48 14	98.67 28	94.05 21	96.41 36	98.18 33	91.26 117	95.12 138	95.15 193	86.60 207	99.50 20	93.43 54	96.81 247	98.89 69

LCM-MVSNet-Re		94.20 118	94.58 103	93.04 167	95.91 202	83.13 198	93.79 127	99.19 3	92.00 87	98.84 6	98.04 35	93.64 70	99.02 103	81.28 261	98.54 146	96.96 209

ETH3D-3000-0.1		94.86 91	94.55 104	95.81 66	97.61 107	89.72 83	94.05 120	98.37 15	88.09 179	95.06 143	95.85 159	92.58 98	99.10 91	90.33 130	98.99 99	98.62 99

Regformer-2		94.86 91	94.55 104	95.77 70	92.83 285	89.98 78	91.87 196	96.40 182	94.38 46	96.19 96	95.04 200	92.47 103	99.04 100	93.49 45	98.31 170	98.28 123

AllTest		94.88 90	94.51 106	96.00 56	98.02 83	92.17 50	95.26 78	98.43 11	90.48 135	95.04 144	96.74 109	92.54 100	97.86 231	85.11 224	98.98 100	97.98 147

testtj		94.81 95	94.42 107	96.01 55	97.23 125	90.51 74	94.77 96	97.85 81	91.29 116	94.92 149	95.66 171	91.71 117	99.40 40	88.07 181	98.25 178	98.11 136

HPM-MVS++		95.02 83	94.39 108	96.91 38	97.88 91	93.58 36	94.09 119	96.99 146	91.05 122	92.40 221	95.22 192	91.03 139	99.25 74	92.11 88	98.69 136	97.90 157

testing_2		94.03 121	94.38 109	93.00 169	96.79 147	81.41 216	92.87 150	96.96 147	85.88 217	97.06 54	97.92 41	91.18 136	98.71 158	91.72 102	99.04 96	98.87 72

VDD-MVS		94.37 108	94.37 110	94.40 127	97.49 114	86.07 160	93.97 124	93.28 263	94.49 43	96.24 90	97.78 46	87.99 181	98.79 138	88.92 165	99.14 82	98.34 118

IS-MVSNet		94.49 106	94.35 111	94.92 100	98.25 67	86.46 149	97.13 14	94.31 247	96.24 24	96.28 89	96.36 137	82.88 232	99.35 55	88.19 177	99.52 36	98.96 60

Regformer-1		94.55 104	94.33 112	95.19 92	92.83 285	88.54 108	91.87 196	95.84 205	93.99 50	95.95 104	95.04 200	92.00 109	98.79 138	93.14 67	98.31 170	98.23 126

CNVR-MVS		94.58 103	94.29 113	95.46 82	96.94 137	89.35 92	91.81 202	96.80 161	89.66 150	93.90 178	95.44 184	92.80 94	98.72 152	92.74 78	98.52 148	98.32 119

EI-MVSNet-Vis-set		94.36 109	94.28 114	94.61 111	92.55 289	85.98 161	92.44 165	94.69 240	93.70 57	96.12 99	95.81 163	91.24 130	98.86 126	93.76 39	98.22 183	98.98 58

IterMVS-LS		93.78 126	94.28 114	92.27 195	96.27 176	79.21 253	91.87 196	96.78 162	91.77 103	96.57 75	97.07 86	87.15 194	98.74 150	91.99 93	99.03 98	98.86 73
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet-UG-set		94.35 110	94.27 116	94.59 116	92.46 290	85.87 163	92.42 167	94.69 240	93.67 61	96.13 98	95.84 162	91.20 133	98.86 126	93.78 36	98.23 181	99.03 49

VDDNet		94.03 121	94.27 116	93.31 161	98.87 20	82.36 204	95.51 71	91.78 290	97.19 12	96.32 83	98.60 19	84.24 224	98.75 147	87.09 197	98.83 121	98.81 79

Regformer-3		94.28 113	94.23 118	94.46 124	92.78 287	86.28 156	92.39 168	94.70 239	93.69 60	95.97 102	95.56 178	91.34 125	98.48 185	93.45 50	98.14 190	98.62 99

XVG-OURS		94.72 98	94.12 119	96.50 49	98.00 85	94.23 16	91.48 209	98.17 36	90.72 129	95.30 130	96.47 124	87.94 182	96.98 274	91.41 112	97.61 223	98.30 122

CPTT-MVS		94.74 97	94.12 119	96.60 45	98.15 72	93.01 42	95.84 60	97.66 95	89.21 159	93.28 194	95.46 182	88.89 168	98.98 107	89.80 146	98.82 122	97.80 168

HQP_MVS		94.26 115	93.93 121	95.23 91	97.71 100	88.12 115	94.56 106	97.81 85	91.74 105	93.31 191	95.59 173	86.93 199	98.95 114	89.26 159	98.51 150	98.60 102

MSLP-MVS++		93.25 141	93.88 122	91.37 221	96.34 170	82.81 201	93.11 142	97.74 91	89.37 154	94.08 171	95.29 191	90.40 153	96.35 296	90.35 128	98.25 178	94.96 275

v1144		93.50 130	93.81 123	92.57 189	96.28 175	79.61 245	91.86 200	96.96 147	86.95 202	95.91 107	96.32 139	87.65 185	98.96 112	93.51 44	98.88 111	99.13 39

PHI-MVS		94.34 111	93.80 124	95.95 58	95.65 217	91.67 61	94.82 94	97.86 78	87.86 184	93.04 204	94.16 232	91.58 120	98.78 142	90.27 133	98.96 106	97.41 191

v1192		93.49 131	93.78 125	92.62 187	96.16 185	79.62 244	91.83 201	97.22 133	86.07 213	96.10 100	96.38 135	87.22 192	99.02 103	94.14 30	98.88 111	99.22 32

VPNet		93.08 145	93.76 126	91.03 233	98.60 34	75.83 297	91.51 208	95.62 209	91.84 97	95.74 112	97.10 85	89.31 165	98.32 194	85.07 226	99.06 88	98.93 63

WR-MVS		93.49 131	93.72 127	92.80 180	97.57 110	80.03 234	90.14 245	95.68 208	93.70 57	96.62 72	95.39 188	87.21 193	99.04 100	87.50 191	99.64 22	99.33 25

v1240		93.29 136	93.71 128	92.06 204	96.01 196	77.89 270	91.81 202	97.37 115	85.12 229	96.69 69	96.40 130	86.67 205	99.07 96	94.51 19	98.76 130	99.22 32

OMC-MVS		94.22 117	93.69 129	95.81 66	97.25 124	91.27 63	92.27 175	97.40 114	87.10 200	94.56 160	95.42 185	93.74 69	98.11 212	86.62 204	98.85 115	98.06 137

EPP-MVSNet		93.91 124	93.68 130	94.59 116	98.08 76	85.55 168	97.44 9	94.03 252	94.22 47	94.94 147	96.19 147	82.07 243	99.57 14	87.28 196	98.89 109	98.65 91

v2v482		93.29 136	93.63 131	92.29 194	96.35 169	78.82 258	91.77 204	96.28 186	88.45 172	95.70 115	96.26 144	86.02 213	98.90 118	93.02 71	98.81 124	99.14 38

v1921920		93.26 139	93.61 132	92.19 198	96.04 195	78.31 264	91.88 195	97.24 131	85.17 227	96.19 96	96.19 147	86.76 204	99.05 97	94.18 29	98.84 116	99.22 32

V42		93.43 133	93.58 133	92.97 171	95.34 230	81.22 218	92.67 156	96.49 179	87.25 196	96.20 94	96.37 136	87.32 191	98.85 128	92.39 87	98.21 184	98.85 76

DeepPCF-MVS	90.46 6	94.20 118	93.56 134	96.14 52	95.96 198	92.96 43	89.48 263	97.46 111	85.14 228	96.23 91	95.42 185	93.19 82	98.08 213	90.37 127	98.76 130	97.38 197

v144192		93.20 144	93.54 135	92.16 201	96.05 191	78.26 265	91.95 188	97.14 136	84.98 233	95.96 103	96.11 150	87.08 196	99.04 100	93.79 35	98.84 116	99.17 35

NCCC		94.08 120	93.54 135	95.70 75	96.49 160	89.90 81	92.39 168	96.91 154	90.64 132	92.33 227	94.60 218	90.58 149	98.96 112	90.21 136	97.70 218	98.23 126

DeepC-MVS_fast	89.96 7	93.73 127	93.44 137	94.60 115	96.14 186	87.90 120	93.36 139	97.14 136	85.53 222	93.90 178	95.45 183	91.30 128	98.59 171	89.51 152	98.62 139	97.31 200
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MVS_111021_HR		93.63 129	93.42 138	94.26 130	96.65 150	86.96 138	89.30 269	96.23 190	88.36 175	93.57 186	94.60 218	93.45 73	97.77 240	90.23 135	98.38 160	98.03 141

ETH3D cwj APD-0.16		93.99 123	93.38 139	95.80 68	96.82 143	89.92 79	92.72 153	98.02 62	84.73 237	93.65 184	95.54 180	91.68 118	99.22 77	88.78 168	98.49 153	98.26 125

v148		92.87 153	93.29 140	91.62 215	96.25 179	77.72 272	91.28 214	95.05 226	89.69 149	95.93 106	96.04 152	87.34 190	98.38 190	90.05 142	97.99 204	98.78 82

MVS_Test		92.57 164	93.29 140	90.40 251	93.53 272	75.85 295	92.52 160	96.96 147	88.73 165	92.35 224	96.70 113	90.77 141	98.37 193	92.53 83	95.49 272	96.99 208

MVS_111021_LR		93.66 128	93.28 142	94.80 104	96.25 179	90.95 68	90.21 241	95.43 219	87.91 181	93.74 182	94.40 223	92.88 92	96.38 294	90.39 125	98.28 173	97.07 204

K. test v3		93.37 134	93.27 143	93.66 148	98.05 79	82.62 202	94.35 112	86.62 317	96.05 28	97.51 38	98.85 13	76.59 284	99.65 4	93.21 63	98.20 186	98.73 88

EI-MVSNet		92.99 149	93.26 144	92.19 198	92.12 297	79.21 253	92.32 173	94.67 242	91.77 103	95.24 135	95.85 159	87.14 195	98.49 181	91.99 93	98.26 175	98.86 73

XXY-MVS		92.58 162	93.16 145	90.84 242	97.75 96	79.84 238	91.87 196	96.22 192	85.94 215	95.53 121	97.68 50	92.69 96	94.48 321	83.21 242	97.51 225	98.21 128

VNet		92.67 159	92.96 146	91.79 209	96.27 176	80.15 228	91.95 188	94.98 228	92.19 84	94.52 162	96.07 151	87.43 189	97.39 262	84.83 228	98.38 160	97.83 164

GBi-Net		93.21 142	92.96 146	93.97 137	95.40 226	84.29 178	95.99 52	96.56 174	88.63 167	95.10 139	98.53 22	81.31 250	98.98 107	86.74 200	98.38 160	98.65 91

test1		93.21 142	92.96 146	93.97 137	95.40 226	84.29 178	95.99 52	96.56 174	88.63 167	95.10 139	98.53 22	81.31 250	98.98 107	86.74 200	98.38 160	98.65 91

alignmvs		93.26 139	92.85 149	94.50 120	95.70 213	87.45 125	93.45 137	95.76 206	91.58 110	95.25 134	92.42 277	81.96 245	98.72 152	91.61 106	97.87 210	97.33 199

test_prior3		93.29 136	92.85 149	94.61 111	95.95 199	87.23 129	90.21 241	97.36 120	89.33 156	90.77 250	94.81 210	90.41 151	98.68 161	88.21 175	98.55 143	97.93 153

QAPM		92.88 152	92.77 151	93.22 165	95.82 206	83.31 193	96.45 32	97.35 122	83.91 242	93.75 180	96.77 105	89.25 166	98.88 121	84.56 232	97.02 239	97.49 187

TinyColmap		92.00 176	92.76 152	89.71 265	95.62 220	77.02 280	90.72 226	96.17 195	87.70 188	95.26 133	96.29 141	92.54 100	96.45 291	81.77 256	98.77 129	95.66 260

ETV-MVS		92.99 149	92.74 153	93.72 147	95.86 205	86.30 155	92.33 172	97.84 82	91.70 108	92.81 209	86.17 338	92.22 104	99.19 80	88.03 182	97.73 214	95.66 260

Effi-MVS+		92.79 154	92.74 153	92.94 174	95.10 233	83.30 194	94.00 122	97.53 107	91.36 115	89.35 277	90.65 305	94.01 68	98.66 163	87.40 194	95.30 278	96.88 213

FMVSNet2		92.78 155	92.73 155	92.95 173	95.40 226	81.98 207	94.18 117	95.53 217	88.63 167	96.05 101	97.37 67	81.31 250	98.81 136	87.38 195	98.67 137	98.06 137

PM-MVS		93.33 135	92.67 156	95.33 85	96.58 155	94.06 19	92.26 176	92.18 283	85.92 216	96.22 92	96.61 119	85.64 218	95.99 301	90.35 128	98.23 181	95.93 247

ab-mvs		92.40 167	92.62 157	91.74 211	97.02 134	81.65 211	95.84 60	95.50 218	86.95 202	92.95 207	97.56 55	90.70 146	97.50 253	79.63 278	97.43 228	96.06 242

Effi-MVS+-dtu		93.90 125	92.60 158	97.77 4	94.74 244	96.67 3	94.00 122	95.41 220	89.94 144	91.93 235	92.13 281	90.12 155	98.97 111	87.68 188	97.48 226	97.67 178

MCST-MVS		92.91 151	92.51 159	94.10 133	97.52 112	85.72 166	91.36 213	97.13 138	80.33 267	92.91 208	94.24 228	91.23 131	98.72 152	89.99 143	97.93 207	97.86 161

Anonymous202405211		92.58 162	92.50 160	92.83 179	96.55 157	83.22 195	92.43 166	91.64 291	94.10 49	95.59 118	96.64 117	81.88 247	97.50 253	85.12 223	98.52 148	97.77 170

UGNet		93.08 145	92.50 160	94.79 105	93.87 268	87.99 119	95.07 87	94.26 249	90.64 132	87.33 305	97.67 51	86.89 202	98.49 181	88.10 180	98.71 133	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
TSAR-MVS + GP.		93.07 147	92.41 162	95.06 97	95.82 206	90.87 71	90.97 220	92.61 277	88.04 180	94.61 159	93.79 245	88.08 177	97.81 235	89.41 154	98.39 158	96.50 224

CS-MVS		92.54 165	92.31 163	93.23 164	95.89 204	84.07 186	93.58 133	98.48 9	88.60 170	90.41 258	86.23 337	92.00 109	99.35 55	87.54 190	98.06 198	96.26 234

MVSFormer		92.18 173	92.23 164	92.04 205	94.74 244	80.06 232	97.15 12	97.37 115	88.98 160	88.83 281	92.79 266	77.02 280	99.60 9	96.41 4	96.75 250	96.46 226

Fast-Effi-MVS+-dtu		92.77 156	92.16 165	94.58 118	94.66 250	88.25 112	92.05 183	96.65 170	89.62 151	90.08 262	91.23 293	92.56 99	98.60 169	86.30 211	96.27 259	96.90 211

DELS-MVS		92.05 175	92.16 165	91.72 212	94.44 254	80.13 230	87.62 290	97.25 130	87.34 195	92.22 229	93.18 260	89.54 164	98.73 151	89.67 150	98.20 186	96.30 232
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
OpenMVS	89.45 8	92.27 172	92.13 167	92.68 183	94.53 253	84.10 184	95.70 63	97.03 142	82.44 256	91.14 247	96.42 128	88.47 172	98.38 190	85.95 214	97.47 227	95.55 264

EIA-MVS		92.35 169	92.03 168	93.30 162	95.81 208	83.97 187	92.80 152	98.17 36	87.71 187	89.79 271	87.56 327	91.17 137	99.18 81	87.97 183	97.27 232	96.77 216

LF4IMVS		92.72 157	92.02 169	94.84 103	95.65 217	91.99 54	92.92 147	96.60 172	85.08 231	92.44 219	93.62 248	86.80 203	96.35 296	86.81 199	98.25 178	96.18 238

CANet		92.38 168	91.99 170	93.52 156	93.82 270	83.46 192	91.14 216	97.00 144	89.81 148	86.47 309	94.04 235	87.90 183	99.21 78	89.50 153	98.27 174	97.90 157

diffmvs		91.74 179	91.93 171	91.15 231	93.06 280	78.17 266	88.77 280	97.51 110	86.28 209	92.42 220	93.96 240	88.04 179	97.46 256	90.69 121	96.67 252	97.82 166

DP-MVS Recon		92.31 170	91.88 172	93.60 150	97.18 128	86.87 139	91.10 218	97.37 115	84.92 234	92.08 232	94.08 234	88.59 170	98.20 204	83.50 239	98.14 190	95.73 256

train_agg		92.71 158	91.83 173	95.35 83	96.45 162	89.46 86	90.60 229	96.92 152	79.37 276	90.49 255	94.39 224	91.20 133	98.88 121	88.66 172	98.43 155	97.72 174

CDPH-MVS		92.67 159	91.83 173	95.18 93	96.94 137	88.46 110	90.70 227	97.07 141	77.38 292	92.34 226	95.08 198	92.67 97	98.88 121	85.74 215	98.57 142	98.20 129

mvs-test1		93.07 147	91.80 175	96.89 39	94.74 244	95.83 6	92.17 179	95.41 220	89.94 144	89.85 268	90.59 306	90.12 155	98.88 121	87.68 188	95.66 268	95.97 245

agg_prior1		92.60 161	91.76 176	95.10 96	96.20 181	88.89 98	90.37 236	96.88 156	79.67 273	90.21 259	94.41 222	91.30 128	98.78 142	88.46 174	98.37 165	97.64 180

TAPA-MVS	88.58 10	92.49 166	91.75 177	94.73 107	96.50 159	89.69 84	92.91 148	97.68 94	78.02 290	92.79 210	94.10 233	90.85 140	97.96 224	84.76 230	98.16 188	96.54 219
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
API-MVS		91.52 185	91.61 178	91.26 225	94.16 259	86.26 157	94.66 100	94.82 234	91.17 120	92.13 231	91.08 296	90.03 161	97.06 272	79.09 285	97.35 231	90.45 334

IterMVS-SCA-FT		91.65 181	91.55 179	91.94 206	93.89 267	79.22 252	87.56 293	93.51 260	91.53 112	95.37 127	96.62 118	78.65 265	98.90 118	91.89 98	94.95 284	97.70 175

xiu_mvs_v1_base_debu		91.47 186	91.52 180	91.33 222	95.69 214	81.56 212	89.92 252	96.05 198	83.22 246	91.26 243	90.74 300	91.55 121	98.82 131	89.29 156	95.91 263	93.62 307

xiu_mvs_v1_base		91.47 186	91.52 180	91.33 222	95.69 214	81.56 212	89.92 252	96.05 198	83.22 246	91.26 243	90.74 300	91.55 121	98.82 131	89.29 156	95.91 263	93.62 307

xiu_mvs_v1_base_debi		91.47 186	91.52 180	91.33 222	95.69 214	81.56 212	89.92 252	96.05 198	83.22 246	91.26 243	90.74 300	91.55 121	98.82 131	89.29 156	95.91 263	93.62 307

HQP-MVS		92.09 174	91.49 183	93.88 143	96.36 166	84.89 173	91.37 210	97.31 124	87.16 197	88.81 283	93.40 254	84.76 221	98.60 169	86.55 206	97.73 214	98.14 132

cl_fuxian		91.32 191	91.42 184	91.00 236	92.29 292	76.79 286	87.52 296	96.42 181	85.76 220	94.72 158	93.89 242	82.73 235	98.16 209	90.93 117	98.55 143	98.04 140

CLD-MVS		91.82 178	91.41 185	93.04 167	96.37 164	83.65 191	86.82 309	97.29 127	84.65 238	92.27 228	89.67 315	92.20 105	97.85 233	83.95 236	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
AdaColmap		91.63 182	91.36 186	92.47 193	95.56 222	86.36 153	92.24 178	96.27 187	88.88 164	89.90 267	92.69 269	91.65 119	98.32 194	77.38 297	97.64 221	92.72 319

testgi		90.38 209	91.34 187	87.50 298	97.49 114	71.54 323	89.43 264	95.16 225	88.38 174	94.54 161	94.68 217	92.88 92	93.09 335	71.60 327	97.85 211	97.88 159

mvs_anonymous		90.37 210	91.30 188	87.58 297	92.17 296	68.00 334	89.84 256	94.73 238	83.82 243	93.22 198	97.40 65	87.54 187	97.40 261	87.94 184	95.05 283	97.34 198

ETH3 D test6400		91.91 177	91.25 189	93.89 142	96.59 154	84.41 177	92.10 181	97.72 93	78.52 286	91.82 236	93.78 246	88.70 169	99.13 86	83.61 238	98.39 158	98.14 132

PVSNet_Blended_VisFu		91.63 182	91.20 190	92.94 174	97.73 99	83.95 188	92.14 180	97.46 111	78.85 285	92.35 224	94.98 203	84.16 225	99.08 92	86.36 210	96.77 249	95.79 254

CNLPA		91.72 180	91.20 190	93.26 163	96.17 184	91.02 66	91.14 216	95.55 216	90.16 142	90.87 249	93.56 251	86.31 209	94.40 324	79.92 277	97.12 236	94.37 288

LFMVS		91.33 190	91.16 192	91.82 208	96.27 176	79.36 248	95.01 90	85.61 328	96.04 29	94.82 152	97.06 87	72.03 298	98.46 187	84.96 227	98.70 135	97.65 179

F-COLMAP		92.28 171	91.06 193	95.95 58	97.52 112	91.90 56	93.53 134	97.18 134	83.98 241	88.70 289	94.04 235	88.41 173	98.55 177	80.17 272	95.99 262	97.39 195

BH-untuned		90.68 201	90.90 194	90.05 262	95.98 197	79.57 246	90.04 248	94.94 230	87.91 181	94.07 172	93.00 262	87.76 184	97.78 239	79.19 284	95.17 281	92.80 317

MDA-MVSNet-bldmvs		91.04 193	90.88 195	91.55 217	94.68 249	80.16 227	85.49 320	92.14 286	90.41 139	94.93 148	95.79 164	85.10 219	96.93 277	85.15 221	94.19 301	97.57 183

Fast-Effi-MVS+		91.28 192	90.86 196	92.53 191	95.45 225	82.53 203	89.25 272	96.52 178	85.00 232	89.91 266	88.55 323	92.94 89	98.84 129	84.72 231	95.44 274	96.22 236

test20.03		90.80 197	90.85 197	90.63 246	95.63 219	79.24 251	89.81 257	92.87 269	89.90 146	94.39 164	96.40 130	85.77 214	95.27 316	73.86 314	99.05 91	97.39 195

PAPM_NR		91.03 194	90.81 198	91.68 214	96.73 148	81.10 220	93.72 129	96.35 185	88.19 177	88.77 287	92.12 282	85.09 220	97.25 266	82.40 251	93.90 302	96.68 218

new-patchmatchnet		88.97 238	90.79 199	83.50 323	94.28 258	55.83 355	85.34 321	93.56 259	86.18 211	95.47 122	95.73 168	83.10 230	96.51 289	85.40 218	98.06 198	98.16 130

wuyk23d		87.83 257	90.79 199	78.96 332	90.46 321	88.63 103	92.72 153	90.67 296	91.65 109	98.68 12	97.64 52	96.06 17	77.53 352	59.84 347	99.41 51	70.73 349

pmmvs-eth3d		91.54 184	90.73 201	93.99 135	95.76 211	87.86 122	90.83 223	93.98 255	78.23 289	94.02 176	96.22 146	82.62 238	96.83 280	86.57 205	98.33 167	97.29 201

MSDG		90.82 196	90.67 202	91.26 225	94.16 259	83.08 199	86.63 314	96.19 193	90.60 134	91.94 234	91.89 284	89.16 167	95.75 303	80.96 267	94.51 294	94.95 276

eth_miper_zixun_eth		90.72 199	90.61 203	91.05 232	92.04 299	76.84 285	86.91 305	96.67 169	85.21 226	94.41 163	93.92 241	79.53 260	98.26 200	89.76 148	97.02 239	98.06 137

cl-mvsnet_		90.65 202	90.56 204	90.91 240	91.85 301	76.98 283	86.75 310	95.36 223	85.53 222	94.06 173	94.89 207	77.36 278	97.98 223	90.27 133	98.98 100	97.76 171

cl-mvsnet1		90.65 202	90.56 204	90.91 240	91.85 301	76.99 282	86.75 310	95.36 223	85.52 224	94.06 173	94.89 207	77.37 277	97.99 222	90.28 132	98.97 104	97.76 171

BH-RMVSNet		90.47 206	90.44 206	90.56 248	95.21 232	78.65 262	89.15 273	93.94 256	88.21 176	92.74 211	94.22 229	86.38 208	97.88 227	78.67 287	95.39 276	95.14 271

miper_ehance_all_eth		90.48 205	90.42 207	90.69 244	91.62 306	76.57 288	86.83 308	96.18 194	83.38 244	94.06 173	92.66 271	82.20 241	98.04 215	89.79 147	97.02 239	97.45 189

UnsupCasMVSNet_eth		90.33 212	90.34 208	90.28 253	94.64 251	80.24 226	89.69 259	95.88 202	85.77 219	93.94 177	95.69 169	81.99 244	92.98 336	84.21 235	91.30 327	97.62 181

FMVSNet3		90.78 198	90.32 209	92.16 201	93.03 282	79.92 237	92.54 159	94.95 229	86.17 212	95.10 139	96.01 154	69.97 302	98.75 147	86.74 200	98.38 160	97.82 166

IterMVS		90.18 216	90.16 210	90.21 257	93.15 278	75.98 294	87.56 293	92.97 268	86.43 207	94.09 170	96.40 130	78.32 269	97.43 258	87.87 185	94.69 291	97.23 202
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Vis-MVSNet (Re-imp)		90.42 207	90.16 210	91.20 229	97.66 106	77.32 277	94.33 113	87.66 311	91.20 119	92.99 205	95.13 195	75.40 287	98.28 196	77.86 290	99.19 77	97.99 146

MVS_0304		90.96 195	90.15 212	93.37 158	93.17 277	87.06 133	93.62 132	92.43 281	89.60 152	82.25 334	95.50 181	82.56 239	97.83 234	84.41 234	97.83 212	95.22 268

RPMNet		90.31 214	90.14 213	90.81 243	91.01 313	78.93 255	92.52 160	98.12 42	91.91 91	89.10 278	96.89 98	68.84 303	99.41 35	90.17 137	92.70 318	94.08 292

RRT_MVS		91.36 189	90.05 214	95.29 88	89.21 334	88.15 114	92.51 163	94.89 231	86.73 204	95.54 120	95.68 170	61.82 336	99.30 67	94.91 14	99.13 85	98.43 114

PVSNet_BlendedMVS		90.35 211	89.96 215	91.54 218	94.81 240	78.80 260	90.14 245	96.93 150	79.43 275	88.68 290	95.06 199	86.27 210	98.15 210	80.27 269	98.04 201	97.68 177

Patchmtry		90.11 218	89.92 216	90.66 245	90.35 322	77.00 281	92.96 146	92.81 270	90.25 141	94.74 156	96.93 95	67.11 308	97.52 252	85.17 219	98.98 100	97.46 188

miper_lstm_enhance		89.90 223	89.80 217	90.19 259	91.37 310	77.50 274	83.82 335	95.00 227	84.84 235	93.05 203	94.96 204	76.53 285	95.20 317	89.96 144	98.67 137	97.86 161

114514_t		90.51 204	89.80 217	92.63 186	98.00 85	82.24 205	93.40 138	97.29 127	65.84 341	89.40 276	94.80 213	86.99 197	98.75 147	83.88 237	98.61 140	96.89 212

MG-MVS		89.54 228	89.80 217	88.76 280	94.88 236	72.47 320	89.60 260	92.44 280	85.82 218	89.48 275	95.98 155	82.85 233	97.74 244	81.87 255	95.27 279	96.08 241

test_yl		90.11 218	89.73 220	91.26 225	94.09 262	79.82 239	90.44 233	92.65 275	90.90 123	93.19 199	93.30 256	73.90 290	98.03 216	82.23 252	96.87 245	95.93 247

DCV-MVSNet		90.11 218	89.73 220	91.26 225	94.09 262	79.82 239	90.44 233	92.65 275	90.90 123	93.19 199	93.30 256	73.90 290	98.03 216	82.23 252	96.87 245	95.93 247

D2MVS		89.93 222	89.60 222	90.92 238	94.03 264	78.40 263	88.69 282	94.85 232	78.96 283	93.08 201	95.09 197	74.57 288	96.94 275	88.19 177	98.96 106	97.41 191

1121		90.26 215	89.23 223	93.34 159	97.15 131	87.40 126	91.94 190	94.39 245	67.88 336	91.02 248	94.91 206	86.91 201	98.59 171	81.17 264	97.71 217	94.02 297

xiu_mvs_v2_base		89.00 237	89.19 224	88.46 287	94.86 238	74.63 302	86.97 303	95.60 210	80.88 263	87.83 299	88.62 322	91.04 138	98.81 136	82.51 250	94.38 295	91.93 325

CANet_DTU		89.85 224	89.17 225	91.87 207	92.20 295	80.02 235	90.79 224	95.87 203	86.02 214	82.53 333	91.77 286	80.01 257	98.57 174	85.66 216	97.70 218	97.01 207

USDC		89.02 235	89.08 226	88.84 279	95.07 234	74.50 305	88.97 275	96.39 183	73.21 313	93.27 195	96.28 142	82.16 242	96.39 293	77.55 294	98.80 126	95.62 263

TAMVS		90.16 217	89.05 227	93.49 157	96.49 160	86.37 152	90.34 238	92.55 278	80.84 265	92.99 205	94.57 220	81.94 246	98.20 204	73.51 315	98.21 184	95.90 250

OpenMVS_ROB	85.12 16	89.52 229	89.05 227	90.92 238	94.58 252	81.21 219	91.10 218	93.41 262	77.03 296	93.41 188	93.99 239	83.23 229	97.80 236	79.93 276	94.80 288	93.74 304

PS-MVSNAJ		88.86 241	88.99 229	88.48 286	94.88 236	74.71 300	86.69 312	95.60 210	80.88 263	87.83 299	87.37 330	90.77 141	98.82 131	82.52 249	94.37 296	91.93 325

MVP-Stereo		90.07 221	88.92 230	93.54 154	96.31 173	86.49 147	90.93 221	95.59 213	79.80 269	91.48 239	95.59 173	80.79 254	97.39 262	78.57 288	91.19 328	96.76 217
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PLC	85.34 15	90.40 208	88.92 230	94.85 102	96.53 158	90.02 77	91.58 207	96.48 180	80.16 268	86.14 311	92.18 279	85.73 215	98.25 201	76.87 300	94.61 293	96.30 232
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
tttt0517		89.81 225	88.90 232	92.55 190	97.00 135	79.73 243	95.03 89	83.65 340	89.88 147	95.30 130	94.79 214	53.64 350	99.39 45	91.99 93	98.79 127	98.54 105

MAR-MVS		90.32 213	88.87 233	94.66 110	94.82 239	91.85 57	94.22 116	94.75 237	80.91 262	87.52 303	88.07 326	86.63 206	97.87 230	76.67 301	96.21 260	94.25 291
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
MVSTER		89.32 231	88.75 234	91.03 233	90.10 324	76.62 287	90.85 222	94.67 242	82.27 257	95.24 135	95.79 164	61.09 339	98.49 181	90.49 122	98.26 175	97.97 150

ppachtmachnet_test		88.61 246	88.64 235	88.50 285	91.76 303	70.99 326	84.59 328	92.98 267	79.30 280	92.38 222	93.53 252	79.57 259	97.45 257	86.50 208	97.17 235	97.07 204

Patchmatch-RL test		88.81 242	88.52 236	89.69 266	95.33 231	79.94 236	86.22 317	92.71 274	78.46 287	95.80 109	94.18 231	66.25 316	95.33 314	89.22 161	98.53 147	93.78 302

cl-mvsnet2		89.02 235	88.50 237	90.59 247	89.76 326	76.45 289	86.62 315	94.03 252	82.98 252	92.65 213	92.49 272	72.05 297	97.53 251	88.93 164	97.02 239	97.78 169

X-MVStestdata		90.70 200	88.45 238	97.44 17	98.56 37	93.99 25	96.50 30	97.95 73	94.58 40	94.38 165	26.89 353	94.56 58	99.39 45	93.57 41	99.05 91	98.93 63

DPM-MVS		89.35 230	88.40 239	92.18 200	96.13 188	84.20 182	86.96 304	96.15 196	75.40 303	87.36 304	91.55 291	83.30 228	98.01 219	82.17 254	96.62 253	94.32 290

jason		89.17 233	88.32 240	91.70 213	95.73 212	80.07 231	88.10 287	93.22 264	71.98 319	90.09 261	92.79 266	78.53 268	98.56 175	87.43 193	97.06 237	96.46 226
jason: jason.
Anonymous20231206		88.77 243	88.29 241	90.20 258	96.31 173	78.81 259	89.56 262	93.49 261	74.26 307	92.38 222	95.58 176	82.21 240	95.43 311	72.07 323	98.75 132	96.34 230

EPNet		89.80 226	88.25 242	94.45 125	83.91 355	86.18 158	93.87 125	87.07 315	91.16 121	80.64 343	94.72 215	78.83 263	98.89 120	85.17 219	98.89 109	98.28 123
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
YYNet1		88.17 252	88.24 243	87.93 293	92.21 294	73.62 311	80.75 341	88.77 301	82.51 255	94.99 146	95.11 196	82.70 236	93.70 330	83.33 240	93.83 303	96.48 225

MDA-MVSNet_test_wron		88.16 253	88.23 244	87.93 293	92.22 293	73.71 310	80.71 342	88.84 300	82.52 254	94.88 151	95.14 194	82.70 236	93.61 331	83.28 241	93.80 304	96.46 226

CDS-MVSNet		89.55 227	88.22 245	93.53 155	95.37 229	86.49 147	89.26 270	93.59 258	79.76 271	91.15 246	92.31 278	77.12 279	98.38 190	77.51 295	97.92 208	95.71 257
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
RRT_test8_iter05		88.21 251	88.17 246	88.33 289	91.62 306	66.82 340	91.73 205	96.60 172	86.34 208	94.14 168	95.38 190	47.72 356	99.11 89	91.78 100	98.26 175	99.06 47

PatchT		87.51 265	88.17 246	85.55 309	90.64 316	66.91 336	92.02 186	86.09 321	92.20 83	89.05 280	97.16 82	64.15 325	96.37 295	89.21 162	92.98 316	93.37 311

PVSNet_Blended		88.74 244	88.16 248	90.46 250	94.81 240	78.80 260	86.64 313	96.93 150	74.67 304	88.68 290	89.18 319	86.27 210	98.15 210	80.27 269	96.00 261	94.44 287

UnsupCasMVSNet_bld		88.50 247	88.03 249	89.90 263	95.52 223	78.88 257	87.39 297	94.02 254	79.32 279	93.06 202	94.02 237	80.72 255	94.27 326	75.16 310	93.08 314	96.54 219

PatchMatch-RL		89.18 232	88.02 250	92.64 184	95.90 203	92.87 45	88.67 284	91.06 294	80.34 266	90.03 264	91.67 288	83.34 227	94.42 323	76.35 304	94.84 287	90.64 333

miper_enhance_ethall		88.42 248	87.87 251	90.07 260	88.67 339	75.52 298	85.10 322	95.59 213	75.68 299	92.49 217	89.45 318	78.96 262	97.88 227	87.86 186	97.02 239	96.81 215

MS-PatchMatch		88.05 254	87.75 252	88.95 276	93.28 274	77.93 268	87.88 289	92.49 279	75.42 302	92.57 216	93.59 250	80.44 256	94.24 328	81.28 261	92.75 317	94.69 283

PCF-MVS	84.52 17	89.12 234	87.71 253	93.34 159	96.06 190	85.84 164	86.58 316	97.31 124	68.46 334	93.61 185	93.89 242	87.51 188	98.52 179	67.85 338	98.11 194	95.66 260
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
pmmvs4		88.95 239	87.70 254	92.70 182	94.30 257	85.60 167	87.22 299	92.16 285	74.62 305	89.75 273	94.19 230	77.97 272	96.41 292	82.71 246	96.36 258	96.09 240

our_test_3		87.55 264	87.59 255	87.44 299	91.76 303	70.48 327	83.83 334	90.55 297	79.79 270	92.06 233	92.17 280	78.63 267	95.63 304	84.77 229	94.73 289	96.22 236

thisisatest0530		88.69 245	87.52 256	92.20 197	96.33 171	79.36 248	92.81 151	84.01 339	86.44 206	93.67 183	92.68 270	53.62 351	99.25 74	89.65 151	98.45 154	98.00 143

1112_ss		88.42 248	87.41 257	91.45 219	96.69 149	80.99 221	89.72 258	96.72 167	73.37 312	87.00 307	90.69 303	77.38 276	98.20 204	81.38 260	93.72 305	95.15 270

baseline1		87.62 263	87.31 258	88.54 284	94.71 248	74.27 308	93.10 143	88.20 307	86.20 210	92.18 230	93.04 261	73.21 293	95.52 306	79.32 282	85.82 340	95.83 252

lupinMVS		88.34 250	87.31 258	91.45 219	94.74 244	80.06 232	87.23 298	92.27 282	71.10 323	88.83 281	91.15 294	77.02 280	98.53 178	86.67 203	96.75 250	95.76 255

N_pmnet		88.90 240	87.25 260	93.83 145	94.40 256	93.81 34	84.73 325	87.09 314	79.36 278	93.26 196	92.43 276	79.29 261	91.68 340	77.50 296	97.22 234	96.00 244

SCA		87.43 267	87.21 261	88.10 292	92.01 300	71.98 322	89.43 264	88.11 309	82.26 258	88.71 288	92.83 264	78.65 265	97.59 249	79.61 279	93.30 309	94.75 280

TR-MVS		87.70 259	87.17 262	89.27 273	94.11 261	79.26 250	88.69 282	91.86 289	81.94 259	90.69 253	89.79 312	82.82 234	97.42 259	72.65 321	91.98 324	91.14 330

pmmvs5		87.87 256	87.14 263	90.07 260	93.26 276	76.97 284	88.89 277	92.18 283	73.71 311	88.36 292	93.89 242	76.86 283	96.73 283	80.32 268	96.81 247	96.51 221

CR-MVSNet		87.89 255	87.12 264	90.22 256	91.01 313	78.93 255	92.52 160	92.81 270	73.08 314	89.10 278	96.93 95	67.11 308	97.64 248	88.80 167	92.70 318	94.08 292

thres600view7		87.66 261	87.10 265	89.36 271	96.05 191	73.17 313	92.72 153	85.31 331	91.89 92	93.29 193	90.97 297	63.42 329	98.39 188	73.23 317	96.99 244	96.51 221

BH-w/o		87.21 272	87.02 266	87.79 296	94.77 242	77.27 278	87.90 288	93.21 266	81.74 260	89.99 265	88.39 325	83.47 226	96.93 277	71.29 328	92.43 320	89.15 335

thres100view900		87.35 269	86.89 267	88.72 281	96.14 186	73.09 315	93.00 145	85.31 331	92.13 85	93.26 196	90.96 298	63.42 329	98.28 196	71.27 329	96.54 254	94.79 278

GA-MVS		87.70 259	86.82 268	90.31 252	93.27 275	77.22 279	84.72 327	92.79 272	85.11 230	89.82 269	90.07 307	66.80 311	97.76 242	84.56 232	94.27 299	95.96 246

sss		87.23 271	86.82 268	88.46 287	93.96 265	77.94 267	86.84 307	92.78 273	77.59 291	87.61 302	91.83 285	78.75 264	91.92 339	77.84 291	94.20 300	95.52 265

PAPR		87.65 262	86.77 270	90.27 254	92.85 284	77.38 276	88.56 285	96.23 190	76.82 298	84.98 317	89.75 314	86.08 212	97.16 269	72.33 322	93.35 308	96.26 234

EU-MVSNet		87.39 268	86.71 271	89.44 268	93.40 273	76.11 292	94.93 93	90.00 298	57.17 350	95.71 114	97.37 67	64.77 323	97.68 247	92.67 81	94.37 296	94.52 285

Test_1112_low_res		87.50 266	86.58 272	90.25 255	96.80 146	77.75 271	87.53 295	96.25 188	69.73 330	86.47 309	93.61 249	75.67 286	97.88 227	79.95 274	93.20 310	95.11 272

FMVSNet5		87.82 258	86.56 273	91.62 215	92.31 291	79.81 241	93.49 135	94.81 236	83.26 245	91.36 241	96.93 95	52.77 352	97.49 255	76.07 305	98.03 202	97.55 186

MIMVSNet		87.13 276	86.54 274	88.89 278	96.05 191	76.11 292	94.39 111	88.51 303	81.37 261	88.27 294	96.75 108	72.38 295	95.52 306	65.71 343	95.47 273	95.03 273

tfpn200view9		87.05 277	86.52 275	88.67 282	95.77 209	72.94 316	91.89 193	86.00 323	90.84 125	92.61 214	89.80 310	63.93 326	98.28 196	71.27 329	96.54 254	94.79 278

thres400		87.20 273	86.52 275	89.24 275	95.77 209	72.94 316	91.89 193	86.00 323	90.84 125	92.61 214	89.80 310	63.93 326	98.28 196	71.27 329	96.54 254	96.51 221

WTY-MVS		86.93 279	86.50 277	88.24 290	94.96 235	74.64 301	87.19 300	92.07 288	78.29 288	88.32 293	91.59 290	78.06 271	94.27 326	74.88 311	93.15 312	95.80 253

1314		86.46 281	86.33 278	86.87 303	91.65 305	74.54 303	91.94 190	94.10 251	74.28 306	84.78 319	87.33 331	83.03 231	95.00 318	78.72 286	91.16 329	91.06 331

cascas		87.02 278	86.28 279	89.25 274	91.56 308	76.45 289	84.33 331	96.78 162	71.01 324	86.89 308	85.91 339	81.35 249	96.94 275	83.09 243	95.60 269	94.35 289

Patchmatch-test		86.10 283	86.01 280	86.38 306	90.63 317	74.22 309	89.57 261	86.69 316	85.73 221	89.81 270	92.83 264	65.24 321	91.04 342	77.82 293	95.78 267	93.88 301

HY-MVS	82.50 18	86.81 280	85.93 281	89.47 267	93.63 271	77.93 268	94.02 121	91.58 292	75.68 299	83.64 326	93.64 247	77.40 275	97.42 259	71.70 326	92.07 323	93.05 314

CHOSEN 1792x2688		87.19 274	85.92 282	91.00 236	97.13 132	79.41 247	84.51 329	95.60 210	64.14 344	90.07 263	94.81 210	78.26 270	97.14 270	73.34 316	95.38 277	96.46 226

CMPMVS	68.83 22	87.28 270	85.67 283	92.09 203	88.77 338	85.42 169	90.31 239	94.38 246	70.02 329	88.00 297	93.30 256	73.78 292	94.03 329	75.96 307	96.54 254	96.83 214
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
HyFIR lowres test		87.19 274	85.51 284	92.24 196	97.12 133	80.51 225	85.03 323	96.06 197	66.11 340	91.66 238	92.98 263	70.12 301	99.14 85	75.29 309	95.23 280	97.07 204

thres200		85.85 284	85.18 285	87.88 295	94.44 254	72.52 319	89.08 274	86.21 319	88.57 171	91.44 240	88.40 324	64.22 324	98.00 220	68.35 337	95.88 266	93.12 313

CVMVSNet		85.16 287	84.72 286	86.48 304	92.12 297	70.19 328	92.32 173	88.17 308	56.15 351	90.64 254	95.85 159	67.97 306	96.69 284	88.78 168	90.52 331	92.56 320

PatchmatchNet		85.22 286	84.64 287	86.98 302	89.51 331	69.83 331	90.52 231	87.34 313	78.87 284	87.22 306	92.74 268	66.91 310	96.53 287	81.77 256	86.88 339	94.58 284
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
EPNet_dtu		85.63 285	84.37 288	89.40 270	86.30 349	74.33 307	91.64 206	88.26 305	84.84 235	72.96 352	89.85 308	71.27 300	97.69 246	76.60 302	97.62 222	96.18 238
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MVS		84.98 289	84.30 289	87.01 301	91.03 312	77.69 273	91.94 190	94.16 250	59.36 349	84.23 323	87.50 329	85.66 216	96.80 281	71.79 324	93.05 315	86.54 341

ET-MVSNet_ETH3D		86.15 282	84.27 290	91.79 209	93.04 281	81.28 217	87.17 301	86.14 320	79.57 274	83.65 325	88.66 321	57.10 344	98.18 207	87.74 187	95.40 275	95.90 250

tpm		84.38 292	84.08 291	85.30 313	90.47 320	63.43 350	89.34 267	85.63 327	77.24 295	87.62 301	95.03 202	61.00 340	97.30 265	79.26 283	91.09 330	95.16 269

tpmvs		84.22 293	83.97 292	84.94 314	87.09 346	65.18 343	91.21 215	88.35 304	82.87 253	85.21 314	90.96 298	65.24 321	96.75 282	79.60 281	85.25 341	92.90 316

MDTV_nov1_ep13			83.88 293		89.42 332	61.52 351	88.74 281	87.41 312	73.99 309	84.96 318	94.01 238	65.25 320	95.53 305	78.02 289	93.16 311

PMMVS2		81.31 308	83.44 294	74.92 334	90.52 319	46.49 357	69.19 349	85.23 334	84.30 240	87.95 298	94.71 216	76.95 282	84.36 351	64.07 344	98.09 196	93.89 300

FPMVS		84.50 291	83.28 295	88.16 291	96.32 172	94.49 14	85.76 318	85.47 329	83.09 249	85.20 315	94.26 227	63.79 328	86.58 349	63.72 345	91.88 326	83.40 344

test-LLR		83.58 295	83.17 296	84.79 316	89.68 328	66.86 338	83.08 336	84.52 336	83.07 250	82.85 331	84.78 342	62.86 332	93.49 332	82.85 244	94.86 285	94.03 295

JIA-IIPM		85.08 288	83.04 297	91.19 230	87.56 341	86.14 159	89.40 266	84.44 338	88.98 160	82.20 335	97.95 39	56.82 346	96.15 298	76.55 303	83.45 344	91.30 329

thisisatest0515		84.72 290	82.99 298	89.90 263	92.96 283	75.33 299	84.36 330	83.42 341	77.37 293	88.27 294	86.65 332	53.94 349	98.72 152	82.56 248	97.40 229	95.67 259

tpmrst		82.85 301	82.93 299	82.64 325	87.65 340	58.99 353	90.14 245	87.90 310	75.54 301	83.93 324	91.63 289	66.79 313	95.36 312	81.21 263	81.54 348	93.57 310

PVSNet	76.22 20	82.89 300	82.37 300	84.48 318	93.96 265	64.38 348	78.60 344	88.61 302	71.50 321	84.43 322	86.36 336	74.27 289	94.60 320	69.87 335	93.69 306	94.46 286

CostFormer		83.09 298	82.21 301	85.73 308	89.27 333	67.01 335	90.35 237	86.47 318	70.42 327	83.52 328	93.23 259	61.18 338	96.85 279	77.21 298	88.26 337	93.34 312

ADS-MVSNet2		84.01 294	82.20 302	89.41 269	89.04 335	76.37 291	87.57 291	90.98 295	72.71 317	84.46 320	92.45 273	68.08 304	96.48 290	70.58 333	83.97 342	95.38 266

DSMNet-mixed		82.21 304	81.56 303	84.16 320	89.57 330	70.00 330	90.65 228	77.66 352	54.99 352	83.30 329	97.57 54	77.89 273	90.50 344	66.86 341	95.54 271	91.97 324

ADS-MVSNet		82.25 303	81.55 304	84.34 319	89.04 335	65.30 342	87.57 291	85.13 335	72.71 317	84.46 320	92.45 273	68.08 304	92.33 338	70.58 333	83.97 342	95.38 266

baseline2		83.38 296	81.54 305	88.90 277	91.38 309	72.84 318	88.78 279	81.22 346	78.97 282	79.82 345	87.56 327	61.73 337	97.80 236	74.30 312	90.05 333	96.05 243

test0.0.03 1		82.48 302	81.47 306	85.48 310	89.70 327	73.57 312	84.73 325	81.64 345	83.07 250	88.13 296	86.61 333	62.86 332	89.10 348	66.24 342	90.29 332	93.77 303

PMMVS		83.00 299	81.11 307	88.66 283	83.81 356	86.44 150	82.24 340	85.65 326	61.75 348	82.07 336	85.64 340	79.75 258	91.59 341	75.99 306	93.09 313	87.94 340

IB-MVS	77.21 19	83.11 297	81.05 308	89.29 272	91.15 311	75.85 295	85.66 319	86.00 323	79.70 272	82.02 338	86.61 333	48.26 355	98.39 188	77.84 291	92.22 321	93.63 306
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
gg-mvs-nofinetune		82.10 305	81.02 309	85.34 312	87.46 344	71.04 324	94.74 97	67.56 354	96.44 22	79.43 346	98.99 7	45.24 357	96.15 298	67.18 340	92.17 322	88.85 337

new_pmnet		81.22 309	81.01 310	81.86 327	90.92 315	70.15 329	84.03 332	80.25 350	70.83 325	85.97 312	89.78 313	67.93 307	84.65 350	67.44 339	91.90 325	90.78 332

E-PMN		80.72 314	80.86 311	80.29 330	85.11 352	68.77 333	72.96 346	81.97 344	87.76 186	83.25 330	83.01 346	62.22 335	89.17 347	77.15 299	94.31 298	82.93 345

MVS-HIRNet		78.83 320	80.60 312	73.51 335	93.07 279	47.37 356	87.10 302	78.00 351	68.94 332	77.53 348	97.26 75	71.45 299	94.62 319	63.28 346	88.74 335	78.55 348

EPMVS		81.17 311	80.37 313	83.58 322	85.58 351	65.08 345	90.31 239	71.34 353	77.31 294	85.80 313	91.30 292	59.38 341	92.70 337	79.99 273	82.34 347	92.96 315

tpm2		81.46 307	80.35 314	84.80 315	89.90 325	65.14 344	90.44 233	85.36 330	65.82 342	82.05 337	92.44 275	57.94 343	96.69 284	70.71 332	88.49 336	92.56 320

EMVS		80.35 316	80.28 315	80.54 329	84.73 354	69.07 332	72.54 348	80.73 347	87.80 185	81.66 340	81.73 347	62.89 331	89.84 345	75.79 308	94.65 292	82.71 346

PAPM		81.91 306	80.11 316	87.31 300	93.87 268	72.32 321	84.02 333	93.22 264	69.47 331	76.13 350	89.84 309	72.15 296	97.23 267	53.27 351	89.02 334	92.37 322

test-mter		81.21 310	80.01 317	84.79 316	89.68 328	66.86 338	83.08 336	84.52 336	73.85 310	82.85 331	84.78 342	43.66 360	93.49 332	82.85 244	94.86 285	94.03 295

tpm cat1		80.61 315	79.46 318	84.07 321	88.78 337	65.06 346	89.26 270	88.23 306	62.27 347	81.90 339	89.66 316	62.70 334	95.29 315	71.72 325	80.60 349	91.86 327

DWT-MVSNet_test		80.74 313	79.18 319	85.43 311	87.51 343	66.87 337	89.87 255	86.01 322	74.20 308	80.86 342	80.62 348	48.84 354	96.68 286	81.54 258	83.14 346	92.75 318

pmmvs3		80.83 312	78.96 320	86.45 305	87.23 345	77.48 275	84.87 324	82.31 343	63.83 345	85.03 316	89.50 317	49.66 353	93.10 334	73.12 319	95.10 282	88.78 339

dp		79.28 318	78.62 321	81.24 328	85.97 350	56.45 354	86.91 305	85.26 333	72.97 315	81.45 341	89.17 320	56.01 348	95.45 310	73.19 318	76.68 350	91.82 328

TESTMET0.1,1		79.09 319	78.04 322	82.25 326	87.52 342	64.03 349	83.08 336	80.62 348	70.28 328	80.16 344	83.22 345	44.13 359	90.56 343	79.95 274	93.36 307	92.15 323

CHOSEN 280x420		80.04 317	77.97 323	86.23 307	90.13 323	74.53 304	72.87 347	89.59 299	66.38 339	76.29 349	85.32 341	56.96 345	95.36 312	69.49 336	94.72 290	88.79 338

PVSNet_0	70.34 21	74.58 321	72.96 324	79.47 331	90.63 317	66.24 341	73.26 345	83.40 342	63.67 346	78.02 347	78.35 349	72.53 294	89.59 346	56.68 349	60.05 353	82.57 347

MVE	59.87 23	73.86 322	72.65 325	77.47 333	87.00 348	74.35 306	61.37 351	60.93 356	67.27 337	69.69 353	86.49 335	81.24 253	72.33 353	56.45 350	83.45 344	85.74 342
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt		37.97 323	44.33 326	18.88 337	11.80 358	21.54 359	63.51 350	45.66 359	4.23 354	51.34 355	50.48 352	59.08 342	22.11 355	44.50 352	68.35 352	13.00 351

cdsmvs_eth3d_5k		23.35 324	31.13 327	0.00 340	0.00 361	0.00 362	0.00 352	95.58 215	0.00 357	0.00 358	91.15 294	93.43 75	0.00 358	0.00 356	0.00 356	0.00 354

test123		9.49 325	12.01 328	1.91 338	2.87 359	1.30 360	82.38 339	1.34 361	1.36 355	2.84 356	6.56 355	2.45 361	0.97 356	2.73 354	5.56 354	3.47 352

testmvs		9.02 326	11.42 329	1.81 339	2.77 360	1.13 361	79.44 343	1.90 360	1.18 356	2.65 357	6.80 354	1.95 362	0.87 357	2.62 355	3.45 355	3.44 353

pcd_1.5k_mvsjas		7.56 327	10.09 330	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	90.77 141	0.00 358	0.00 356	0.00 356	0.00 354

ab-mvs-re		7.56 327	10.08 331	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	90.69 303	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

uanet_test		0.00 329	0.00 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

sosnet-low-res		0.00 329	0.00 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

sosnet		0.00 329	0.00 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

uncertanet		0.00 329	0.00 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

Regformer		0.00 329	0.00 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

uanet		0.00 329	0.00 332	0.00 340	0.00 361	0.00 362	0.00 352	0.00 362	0.00 357	0.00 358	0.00 358	0.00 363	0.00 358	0.00 356	0.00 356	0.00 354

ZD-MVS					97.23 125	90.32 75		97.54 105	84.40 239	94.78 154	95.79 164	92.76 95	99.39 45	88.72 171	98.40 156

IU-MVS					98.51 46	86.66 145		96.83 159	72.74 316	95.83 108				93.00 72	99.29 63	98.64 95

OPU-MVS				95.15 94	96.84 142	89.43 88	95.21 79				95.66 171	93.12 85	98.06 214	86.28 212	98.61 140	97.95 151

test_241102_TWO								98.10 45	91.95 88	97.54 36	97.25 76	95.37 29	99.35 55	93.29 59	99.25 71	98.49 109

test_241102_ONE					98.51 46	86.97 136		98.10 45	91.85 94	97.63 31	97.03 89	96.48 12	98.95 114

save fliter					97.46 117	88.05 117	92.04 184	97.08 140	87.63 190

test_0728_THIRD									93.26 65	97.40 44	97.35 71	94.69 55	99.34 59	93.88 33	99.42 46	98.89 69

test_0728_SECOND				94.88 101	98.55 40	86.72 142	95.20 81	98.22 30					99.38 51	93.44 52	99.31 60	98.53 106

test0726					98.51 46	86.69 143	95.34 74	98.18 33	91.85 94	97.63 31	97.37 67	95.58 23

GSMVS																94.75 280

test_part2					98.21 69	89.41 89				96.72 68

sam_mvs1												66.64 314				94.75 280

sam_mvs												66.41 315

ambc				92.98 170	96.88 140	83.01 200	95.92 57	96.38 184		96.41 77	97.48 61	88.26 174	97.80 236	89.96 144	98.93 108	98.12 135

MTGPA								97.62 97

test_post1							90.21 241				5.85 357	65.36 319	96.00 300	79.61 279

test_post											6.07 356	65.74 318	95.84 302

patchmatchnet-post											91.71 287	66.22 317	97.59 249

GG-mvs-BLEND				83.24 324	85.06 353	71.03 325	94.99 92	65.55 355		74.09 351	75.51 350	44.57 358	94.46 322	59.57 348	87.54 338	84.24 343

MTMP							94.82 94	54.62 357

gm-plane-assit					87.08 347	59.33 352			71.22 322		83.58 344		97.20 268	73.95 313

test9_res														88.16 179	98.40 156	97.83 164

TEST9					96.45 162	89.46 86	90.60 229	96.92 152	79.09 281	90.49 255	94.39 224	91.31 127	98.88 121

test_8					96.37 164	89.14 93	90.51 232	96.89 155	79.37 276	90.42 257	94.36 226	91.20 133	98.82 131

agg_prior2														87.06 198	98.36 166	97.98 147

agg_prior					96.20 181	88.89 98		96.88 156		90.21 259			98.78 142

TestCases				96.00 56	98.02 83	92.17 50		98.43 11	90.48 135	95.04 144	96.74 109	92.54 100	97.86 231	85.11 224	98.98 100	97.98 147

test_prior4						89.91 80	90.74 225

test_prior2							90.21 241		89.33 156	90.77 250	94.81 210	90.41 151		88.21 175	98.55 143

test_prior				94.61 111	95.95 199	87.23 129		97.36 120					98.68 161			97.93 153

旧先验2							90.00 250		68.65 333	92.71 212			96.52 288	85.15 221

新几何2							90.02 249

新几何1				93.17 166	97.16 129	87.29 128		94.43 244	67.95 335	91.29 242	94.94 205	86.97 198	98.23 202	81.06 266	97.75 213	93.98 298

旧先验1					96.20 181	84.17 183		94.82 234			95.57 177	89.57 163			97.89 209	96.32 231

无先验							89.94 251	95.75 207	70.81 326				98.59 171	81.17 264		94.81 277

原ACMM2							89.34 267

原ACMM1				92.87 177	96.91 139	84.22 181		97.01 143	76.84 297	89.64 274	94.46 221	88.00 180	98.70 159	81.53 259	98.01 203	95.70 258

test222					96.95 136	85.27 171	88.83 278	93.61 257	65.09 343	90.74 252	94.85 209	84.62 223			97.36 230	93.91 299

testdata2													98.03 216	80.24 271

segment_acmp												92.14 106

testdata				91.03 233	96.87 141	82.01 206		94.28 248	71.55 320	92.46 218	95.42 185	85.65 217	97.38 264	82.64 247	97.27 232	93.70 305

testdata1							88.96 276		88.44 173

test12				94.43 126	95.95 199	86.75 141		96.24 189		89.76 272		89.79 162	98.79 138		97.95 206	97.75 173

plane_prior7					97.71 100	88.68 102

plane_prior6					97.21 127	88.23 113						86.93 199

plane_prior5								97.81 85					98.95 114	89.26 159	98.51 150	98.60 102

plane_prior4											95.59 173

plane_prior3						88.43 111			90.35 140	93.31 191

plane_prior2							94.56 106		91.74 105

plane_prior1					97.38 120

plane_prior						88.12 115	93.01 144		88.98 160						98.06 198

n2								0.00 362

nn								0.00 362

door-mid								92.13 287

lessismore_v0				93.87 144	98.05 79	83.77 190		80.32 349		97.13 50	97.91 43	77.49 274	99.11 89	92.62 82	98.08 197	98.74 86

LGP-MVS_train				96.84 40	98.36 61	92.13 52		98.25 25	91.78 101	97.07 51	97.22 79	96.38 14	99.28 70	92.07 91	99.59 26	99.11 41

test11								96.65 170

door								91.26 293

HQP5-MVS						84.89 173

HQP-NCC					96.36 166		91.37 210		87.16 197	88.81 283

ACMP_Plane					96.36 166		91.37 210		87.16 197	88.81 283

BP-MVS														86.55 206

HQP4-MVS										88.81 283			98.61 167			98.15 131

HQP3-MVS								97.31 124							97.73 214

HQP2-MVS												84.76 221

NP-MVS					96.82 143	87.10 132					93.40 254

MDTV_nov1_ep13_2view						42.48 358	88.45 286		67.22 338	83.56 327		66.80 311		72.86 320		94.06 294

ACMMP++_ref															98.82 122

ACMMP++															99.25 71

Test By Simon												90.61 147

ITE_SJBPF				95.95 58	97.34 122	93.36 40		96.55 177	91.93 90	94.82 152	95.39 188	91.99 111	97.08 271	85.53 217	97.96 205	97.41 191

DeepMVS_CX				53.83 336	70.38 357	64.56 347		48.52 358	33.01 353	65.50 354	74.21 351	56.19 347	46.64 354	38.45 353	70.07 351	50.30 350

High-resolution multi-view benchmark