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	courty.	delive.	electro	facade	kicker	meadow	office	pipes	playgr.	relief	relief.	terrace	terrai.

LTVRE_ROB	97.71 1	99.33 1	99.47 1	99.16 7	99.16 43	99.11 14	99.39 12	99.16 11	99.26 2	99.22 5	99.51 18	99.75 4	98.54 15	99.71 1	99.47 3	99.52 12	99.46 1
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
SixPastTwentyTwo		99.25 2	99.20 3	99.32 1	99.53 14	99.32 8	99.64 2	99.19 10	98.05 11	99.19 6	99.74 4	98.96 71	99.03 2	99.69 2	99.58 1	99.32 25	99.06 6

WR-MVS		99.22 3	99.15 5	99.30 2	99.54 10	99.62 1	99.63 4	99.45 1	97.75 16	98.47 22	99.71 5	99.05 60	98.88 4	99.54 5	99.49 2	99.81 1	98.87 11

PS-CasMVS		99.08 4	98.90 11	99.28 3	99.65 3	99.56 4	99.59 6	99.39 3	96.36 52	98.83 14	99.46 21	99.09 52	98.62 10	99.51 7	99.36 8	99.63 3	98.97 7

PEN-MVS		99.08 4	98.95 8	99.23 5	99.65 3	99.59 2	99.64 2	99.34 6	96.68 44	98.65 17	99.43 25	99.33 26	98.47 17	99.50 8	99.32 9	99.60 5	98.79 13

v7n		99.03 6	99.03 7	99.02 9	99.09 55	99.11 14	99.57 9	98.82 19	98.21 9	99.25 3	99.84 2	99.59 6	98.76 6	99.23 19	98.83 32	98.63 75	98.40 35

DTE-MVSNet		99.03 6	98.88 12	99.21 6	99.66 2	99.59 2	99.62 5	99.34 6	96.92 35	98.52 19	99.36 34	98.98 67	98.57 13	99.49 9	99.23 12	99.56 9	98.55 27

TDRefinement		99.00 8	99.13 6	98.86 10	98.99 65	99.05 19	99.58 7	98.29 49	98.96 4	97.96 36	99.40 31	98.67 103	98.87 5	99.60 3	99.46 4	99.46 18	98.74 16

WR-MVS_H		98.97 9	98.82 14	99.14 8	99.56 8	99.56 4	99.54 11	99.42 2	96.07 59	98.37 24	99.34 37	99.09 52	98.43 18	99.45 10	99.41 5	99.53 10	98.86 12

UniMVSNet_ETH3D		98.93 10	99.20 3	98.63 22	99.54 10	99.33 7	98.73 67	99.37 4	98.87 5	97.86 38	99.27 43	99.78 2	96.59 87	99.52 6	99.40 6	99.67 2	98.21 44

CP-MVSNet		98.91 11	98.61 19	99.25 4	99.63 5	99.50 6	99.55 10	99.36 5	95.53 93	98.77 16	99.11 58	98.64 107	98.57 13	99.42 11	99.28 11	99.61 4	98.78 14

anonymousdsp		98.85 12	98.88 12	98.83 11	98.69 85	98.20 91	99.68 1	97.35 133	97.09 32	98.98 10	99.86 1	99.43 19	98.94 3	99.28 14	99.19 13	99.33 23	99.08 5

pmmvs6		98.77 13	99.35 2	98.09 43	98.32 107	98.92 25	98.57 75	99.03 12	99.36 1	96.86 85	99.77 3	99.86 1	96.20 104	99.56 4	99.39 7	99.59 6	98.61 24

ACMH	95.26 7	98.75 14	98.93 9	98.54 25	98.86 70	99.01 21	99.58 7	98.10 68	98.67 6	97.30 61	99.18 50	99.42 20	98.40 19	99.19 21	98.86 30	98.99 48	98.19 45
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
COLMAP_ROB	96.84 2	98.75 14	98.82 14	98.66 20	99.14 48	98.79 41	99.30 17	97.67 99	98.33 8	97.82 40	99.20 48	99.18 47	98.76 6	99.27 17	98.96 22	99.29 27	98.03 50
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
UA-Net		98.66 16	98.60 22	98.73 15	99.83 1	99.28 9	98.56 77	99.24 8	96.04 60	97.12 70	98.44 106	98.95 72	98.17 28	99.15 24	99.00 21	99.48 17	99.33 3

DeepC-MVS	96.08 5	98.58 17	98.49 24	98.68 18	99.37 26	98.52 69	99.01 36	98.17 63	97.17 31	98.25 27	99.56 15	99.62 5	98.29 22	98.40 64	98.09 72	98.97 50	98.08 48
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TranMVSNet+NR-MVSNet		98.45 18	98.22 33	98.72 17	99.32 31	99.06 17	98.99 37	98.89 14	95.52 94	97.53 49	99.42 30	98.83 88	98.01 34	98.55 55	98.34 58	99.57 8	97.80 63

CSCG		98.45 18	98.61 19	98.26 37	99.11 52	99.06 17	98.17 107	97.49 112	97.93 13	97.37 58	98.88 77	99.29 30	98.10 29	98.40 64	97.51 97	99.32 25	99.16 4

DVP-MVS++		98.44 20	98.92 10	97.88 63	99.17 41	99.00 22	98.89 49	98.26 51	97.54 19	96.05 124	99.35 35	99.76 3	96.34 99	98.79 37	98.65 41	98.56 81	99.35 2

Gipuma		98.43 21	98.15 36	98.76 14	99.00 64	98.29 84	97.91 127	98.06 70	99.02 3	99.50 1	96.33 164	98.67 103	99.22 1	99.02 27	98.02 78	98.88 63	97.66 71
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
ACMH+	94.90 8	98.40 22	98.71 17	98.04 53	98.93 67	98.84 33	99.30 17	97.86 90	97.78 15	94.19 202	98.77 89	99.39 22	98.61 11	99.33 13	99.07 14	99.33 23	97.81 62

ACMMPR		98.31 23	98.07 41	98.60 23	99.58 6	98.83 35	99.09 27	98.48 31	96.25 55	97.03 74	96.81 152	99.09 52	98.39 20	98.55 55	98.45 49	99.01 45	98.53 30

APDe-MVS		98.29 24	98.42 27	98.14 40	99.45 21	98.90 26	99.18 23	98.30 47	95.96 67	95.13 168	98.79 86	99.25 39	97.92 38	98.80 35	98.71 36	98.85 66	98.54 28
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
DVP-MVS		98.27 25	98.61 19	97.87 64	99.17 41	99.03 20	99.07 30	98.17 63	96.75 41	94.35 196	98.92 72	99.58 7	97.86 41	98.67 46	98.70 37	98.63 75	98.63 22
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
TransMVSNet (Re)		98.23 26	98.72 16	97.66 79	98.22 122	98.73 53	98.66 70	98.03 75	98.60 7	96.40 108	99.60 12	98.24 129	95.26 148	99.19 21	99.05 17	99.36 20	97.64 72

DU-MVS		98.23 26	97.74 62	98.81 12	99.23 34	98.77 44	98.76 61	98.88 15	94.10 146	98.50 20	98.87 79	98.32 126	97.99 35	98.40 64	98.08 75	99.49 16	97.64 72

UniMVSNet (Re)		98.23 26	97.85 51	98.67 19	99.15 44	98.87 28	98.74 64	98.84 17	94.27 144	97.94 37	99.01 64	98.39 122	97.82 42	98.35 69	98.29 63	99.51 15	97.78 64

MIMVSNet1		98.22 29	98.51 23	97.87 64	99.40 25	98.82 39	99.31 16	98.53 28	97.39 22	96.59 99	99.31 39	99.23 41	94.76 167	98.93 32	98.67 39	98.63 75	97.25 95

HFP-MVS		98.17 30	98.02 42	98.35 35	99.36 27	98.62 61	98.79 60	98.46 34	96.24 56	96.53 101	97.13 147	98.98 67	98.02 33	98.20 72	98.42 51	98.95 54	98.54 28

Baseline_NR-MVSNet		98.17 30	97.90 48	98.48 29	99.23 34	98.59 62	98.83 57	98.73 24	93.97 153	96.95 77	99.66 7	98.23 131	97.90 39	98.40 64	99.06 16	99.25 29	97.42 87

TSAR-MVS + MP.		98.15 32	98.23 31	98.06 51	98.47 97	98.16 96	99.23 20	96.87 160	95.58 88	96.72 91	98.41 107	99.06 57	98.05 32	98.99 29	98.90 26	99.00 46	98.51 31
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
MED-MVS		98.14 33	98.48 25	97.73 76	99.15 44	98.84 33	98.48 86	97.88 87	97.26 26	94.88 179	99.43 25	99.08 55	97.16 66	98.44 62	98.39 53	98.85 66	97.91 58

pm-mvs1		98.14 33	98.66 18	97.53 88	97.93 154	98.49 72	98.14 110	98.19 59	97.95 12	96.17 119	99.63 10	98.85 84	95.41 137	98.91 33	98.89 27	99.34 22	97.86 61

SMA-MVS		98.13 35	98.22 33	98.02 56	99.44 23	98.73 53	98.24 102	97.87 89	95.22 102	96.76 90	98.66 97	99.35 24	97.03 72	98.53 58	98.39 53	98.80 69	98.69 18
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
ACMMP_NAP		98.12 36	98.08 40	98.18 39	99.34 28	98.74 52	98.97 39	98.00 77	95.13 106	96.90 79	97.54 135	99.27 34	97.18 65	98.72 42	98.45 49	98.68 74	98.69 18

UniMVSNet_NR-MVSNet		98.12 36	97.56 72	98.78 13	99.13 50	98.89 27	98.76 61	98.78 20	93.81 156	98.50 20	98.81 84	97.64 154	97.99 35	98.18 75	97.92 81	99.53 10	97.64 72

ACMM	94.29 11	98.12 36	97.71 63	98.59 24	99.51 16	98.58 64	99.24 19	98.25 52	96.22 57	96.90 79	95.01 193	98.89 78	98.52 16	98.66 48	98.32 61	99.13 36	98.28 41
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
SteuartSystems-ACMMP		98.06 39	97.78 57	98.39 33	99.54 10	98.79 41	98.94 43	98.42 36	93.98 151	95.85 134	96.66 158	99.25 39	98.61 11	98.71 44	98.38 55	98.97 50	98.67 21
Skip Steuart: Steuart Systems R&D Blog.
SED-MVS		98.05 40	98.46 26	97.57 84	99.01 61	98.99 23	98.82 59	98.24 53	95.76 77	94.70 186	98.96 67	99.49 15	96.19 105	98.74 38	98.65 41	98.46 90	98.63 22

OPM-MVS		98.01 41	98.01 43	98.00 58	99.11 52	98.12 101	98.68 68	97.72 97	96.65 46	96.68 95	98.40 109	99.28 33	97.44 55	98.20 72	97.82 89	98.40 97	97.58 77
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
Vis-MVSNet		98.01 41	98.42 27	97.54 87	96.89 216	98.82 39	99.14 24	97.59 102	96.30 54	97.04 73	99.26 46	98.83 88	96.01 114	98.73 40	98.21 65	98.58 80	98.75 15
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CS-MVS		98.00 43	97.38 80	98.73 15	98.72 80	99.15 11	99.12 26	98.76 21	91.58 195	98.15 31	96.70 156	98.72 101	98.20 24	98.64 51	98.92 24	99.43 19	97.97 53

NR-MVSNet		98.00 43	97.88 49	98.13 41	98.33 105	98.77 44	98.83 57	98.88 15	94.10 146	97.46 55	98.87 79	98.58 112	95.78 119	99.13 25	98.16 69	99.52 12	97.53 80

CP-MVS		98.00 43	97.57 71	98.50 26	99.47 20	98.56 66	98.91 47	98.38 42	94.71 123	97.01 75	95.20 189	99.06 57	98.20 24	98.61 52	98.46 46	99.02 43	98.40 35

DPE-MVS		97.99 46	98.12 37	97.84 67	98.65 90	98.86 29	98.86 53	98.05 73	94.18 145	95.49 157	98.90 75	99.33 26	97.11 68	98.53 58	98.65 41	98.86 65	98.39 37
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
ACMMP		97.99 46	97.60 70	98.45 31	99.53 14	98.83 35	99.13 25	98.30 47	94.57 130	96.39 112	95.32 187	98.95 72	98.37 21	98.61 52	98.47 45	99.00 46	98.45 32
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
MP-MVS		97.98 48	97.53 73	98.50 26	99.56 8	98.58 64	98.97 39	98.39 41	93.49 160	97.14 67	96.08 171	99.23 41	98.06 31	98.50 60	98.38 55	98.90 58	98.44 33
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
EG-PatchMatch MVS		97.98 48	97.92 46	98.04 53	98.84 73	98.04 111	97.90 128	96.83 164	95.07 108	98.79 15	99.07 60	99.37 23	97.88 40	98.74 38	98.16 69	98.01 123	96.96 103

ACMP	94.03 12	97.97 50	97.61 69	98.39 33	99.43 24	98.51 71	98.97 39	98.06 70	94.63 128	96.10 121	96.12 170	99.20 45	98.63 9	98.68 45	98.20 68	99.14 33	97.93 56
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
SPE-MVS-test		97.96 51	97.38 80	98.64 21	98.57 92	99.13 12	99.36 13	98.66 25	91.67 194	98.17 30	96.91 151	98.84 86	97.99 35	98.80 35	98.88 28	99.08 41	97.43 86

LGP-MVS_train		97.96 51	97.53 73	98.45 31	99.45 21	98.64 59	99.09 27	98.27 50	92.99 174	96.04 125	96.57 159	99.29 30	98.66 8	98.73 40	98.42 51	99.19 31	98.09 47

ME-MVS		97.94 53	98.23 31	97.60 82	99.15 44	98.85 30	98.92 44	97.17 143	96.03 64	94.88 179	99.43 25	99.18 47	97.31 62	98.07 77	98.14 71	98.14 114	97.91 58

LS3D		97.93 54	97.80 53	98.08 47	99.20 38	98.77 44	98.89 49	97.92 83	96.59 47	96.99 76	96.71 155	97.14 168	96.39 98	99.04 26	98.96 22	99.10 40	97.39 88

SD-MVS		97.84 55	97.78 57	97.90 61	98.33 105	98.06 106	97.95 122	97.80 94	96.03 64	96.72 91	97.57 133	99.18 47	97.50 53	97.88 80	97.08 111	99.11 38	98.68 20
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
RPSCF		97.83 56	98.27 29	97.31 104	98.23 120	98.06 106	97.44 165	95.79 199	96.90 36	95.81 138	98.76 90	98.61 111	97.70 47	98.90 34	98.36 57	98.90 58	98.29 38

thisisatest0515		97.82 57	97.67 64	97.99 59	98.49 96	98.07 105	98.48 86	98.06 70	95.35 99	97.74 42	98.83 82	97.61 155	96.74 79	97.53 102	98.30 62	98.43 96	98.01 52

PGM-MVS		97.82 57	97.25 90	98.48 29	99.54 10	98.75 51	99.02 32	98.35 45	92.41 179	96.84 86	95.39 186	98.99 66	98.24 23	98.43 63	98.34 58	98.90 58	98.41 34

PMVS	90.51 17	97.77 59	97.98 44	97.53 88	98.68 86	98.14 100	97.67 147	97.03 154	96.43 48	98.38 23	98.72 93	97.03 170	94.44 173	99.37 12	99.30 10	98.98 49	96.86 111
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
FE-MVSNET2		97.75 60	97.79 54	97.70 78	97.41 195	98.37 79	99.09 27	97.73 96	96.88 37	97.47 52	99.43 25	99.35 24	96.00 115	96.66 142	97.74 91	98.48 88	96.10 144

MSP-MVS		97.67 61	97.88 49	97.43 95	99.34 28	98.99 23	98.87 52	98.12 66	95.63 83	94.16 205	97.45 136	99.50 14	96.44 97	96.35 148	98.70 37	97.65 155	98.57 26
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
tfpnnormal		97.66 62	97.79 54	97.52 90	98.32 107	98.53 68	98.45 90	97.69 98	97.59 18	96.12 120	97.79 126	96.70 175	95.69 124	98.35 69	98.34 58	98.85 66	97.22 98

FC-MVSNet-train		97.65 63	98.16 35	97.05 122	98.85 71	98.85 30	99.34 14	98.08 69	94.50 135	94.41 193	99.21 47	98.80 93	92.66 206	98.98 30	98.85 31	98.96 52	97.94 55

v10		97.64 64	97.26 88	98.08 47	98.07 141	98.56 66	98.86 53	98.18 61	94.48 136	98.24 28	99.56 15	98.98 67	97.72 46	96.05 162	96.26 143	97.42 167	96.93 104

EC-MVSNet		97.63 65	96.88 119	98.50 26	98.74 79	99.16 10	99.33 15	98.83 18	88.77 226	96.62 98	96.48 161	97.75 147	98.19 26	99.00 28	98.76 34	99.29 27	98.27 42

X-MVS		97.60 66	97.00 111	98.29 36	99.50 17	98.76 47	98.90 48	98.37 43	94.67 127	96.40 108	91.47 240	98.78 95	97.60 52	98.55 55	98.50 44	98.96 52	98.29 38

Casviewmamba		97.58 67	97.98 44	97.11 117	98.13 136	98.52 69	98.51 79	97.40 124	96.76 40	93.79 215	99.18 50	99.19 46	96.13 109	97.73 90	97.56 95	98.69 73	96.92 105

E6new		97.58 67	97.78 57	97.34 99	98.30 112	98.16 96	98.50 80	97.36 129	97.45 20	95.96 128	99.46 21	99.57 8	96.03 111	96.88 132	96.67 128	97.88 135	96.30 133

E6		97.58 67	97.78 57	97.34 99	98.30 112	98.16 96	98.50 80	97.36 129	97.45 20	95.96 128	99.46 21	99.57 8	96.03 111	96.88 132	96.67 128	97.88 135	96.30 133

3Dnovator+	96.20 4	97.58 67	97.14 102	98.10 42	98.98 66	97.85 129	98.60 74	98.33 46	96.41 50	97.23 65	94.66 202	97.26 164	96.91 76	97.91 79	97.87 84	98.53 84	98.03 50

DCV-MVSNet		97.56 71	97.63 68	97.47 93	98.41 101	99.12 13	98.63 71	98.57 26	95.71 80	95.60 153	93.79 219	98.01 142	94.25 175	99.16 23	98.88 28	99.35 21	98.74 16

HPM-MVS++		97.56 71	97.11 106	98.09 43	99.18 40	97.95 120	98.57 75	98.20 57	94.08 149	97.25 64	95.96 177	98.81 92	97.13 67	97.51 103	97.30 107	98.21 109	98.15 46

FC-MVSNet-test		97.54 73	98.26 30	96.70 148	98.87 69	97.79 138	98.49 84	98.56 27	96.04 60	90.39 252	99.65 8	98.67 103	95.15 153	99.23 19	99.07 14	98.73 72	97.39 88

TSAR-MVS + ACMM		97.54 73	97.79 54	97.26 105	98.23 120	98.10 104	97.71 141	97.88 87	95.97 66	95.57 155	98.71 94	98.57 113	97.36 58	97.74 89	96.81 121	96.83 198	98.59 25

casdiffseed414692147		97.53 75	97.64 67	97.41 96	98.18 131	98.22 89	98.63 71	97.45 117	95.90 69	95.35 160	99.20 48	99.51 12	96.45 96	97.32 113	96.81 121	98.39 98	96.53 126

DeepC-MVS_fast	95.38 6	97.53 75	97.30 87	97.79 72	98.83 74	97.64 142	98.18 105	97.14 147	95.57 89	97.83 39	97.10 148	98.80 93	96.53 93	97.41 106	97.32 105	98.24 108	97.26 94
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
v1192		97.52 77	97.03 110	98.09 43	98.31 110	98.01 115	98.96 42	97.25 138	95.22 102	98.89 12	99.64 9	98.83 88	97.68 48	95.63 176	95.91 159	97.47 163	95.97 153

v1144		97.51 78	97.05 109	98.04 53	98.26 118	97.98 117	98.88 51	97.42 123	95.38 98	98.56 18	99.59 14	99.01 64	97.65 49	95.77 172	96.06 153	97.47 163	95.56 173

v8		97.51 78	97.16 100	97.91 60	97.99 150	98.48 73	98.76 61	98.17 63	94.54 134	97.69 44	99.48 20	98.76 98	97.63 51	96.10 159	96.14 147	97.20 180	96.64 119

v1921920		97.50 80	97.00 111	98.07 49	98.20 126	97.94 125	99.03 31	97.06 152	95.29 101	99.01 9	99.62 11	98.73 100	97.74 45	95.52 182	95.78 168	97.39 169	96.12 142

Anonymous20231211		97.49 81	97.91 47	97.00 128	98.31 110	98.72 55	98.27 99	97.84 92	94.76 122	94.77 185	98.14 118	98.38 124	93.60 190	98.96 31	98.66 40	99.22 30	97.77 66

v144192		97.49 81	96.99 113	98.07 49	98.11 138	97.95 120	99.02 32	97.21 141	94.90 117	98.88 13	99.53 17	98.89 78	97.75 44	95.59 179	95.90 160	97.43 166	96.16 140

test1111		97.48 83	97.20 95	97.81 71	98.78 77	98.85 30	98.68 68	98.40 37	96.68 44	94.84 181	99.13 54	90.32 232	97.01 73	99.27 17	99.05 17	99.19 31	97.10 100

GeoE		97.48 83	96.84 124	98.22 38	99.01 61	98.39 76	98.85 56	98.76 21	92.37 180	97.53 49	97.58 132	98.23 131	97.11 68	97.57 101	96.98 115	98.10 118	96.78 114

APD-MVS		97.47 85	97.16 100	97.84 67	99.32 31	98.39 76	98.47 89	98.21 56	92.08 186	95.23 164	96.68 157	98.90 76	96.99 74	98.20 72	98.21 65	98.80 69	97.67 70
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PVSNet_Blended_VisFu		97.44 86	97.14 102	97.79 72	99.15 44	98.44 74	98.32 96	97.66 100	93.74 159	97.73 43	98.79 86	96.93 173	95.64 129	97.69 92	96.91 118	98.25 107	97.50 82

PHI-MVS		97.44 86	97.17 99	97.74 75	98.14 133	98.41 75	98.03 118	97.50 110	92.07 187	98.01 35	97.33 140	98.62 110	96.02 113	98.34 71	98.21 65	98.76 71	97.24 97

v1240		97.43 88	96.87 123	98.09 43	98.25 119	97.92 126	99.02 32	97.06 152	94.77 121	99.09 8	99.68 6	98.51 117	97.78 43	95.25 189	95.81 166	97.32 175	96.13 141

viewmacassd2359aftdt		97.42 89	97.67 64	97.13 114	98.20 126	98.06 106	98.16 108	97.16 146	97.27 25	95.23 164	99.29 40	99.48 16	96.05 110	96.73 137	96.66 130	98.00 124	96.17 139

ECVR-MVS		97.40 90	97.11 106	97.73 76	98.66 87	98.83 35	98.50 80	98.40 37	96.04 60	95.00 176	98.95 69	91.07 229	96.70 81	99.28 14	99.04 19	99.14 33	96.58 121

FMVSNet1		97.40 90	98.09 38	96.60 154	97.80 169	98.76 47	98.26 101	98.50 30	96.79 39	93.13 227	99.28 42	98.64 107	92.90 203	97.67 94	97.86 86	99.02 43	97.64 72

MGCNet		97.38 92	97.26 88	97.51 91	99.28 33	98.79 41	98.86 53	97.79 95	94.68 125	96.79 87	97.69 128	95.75 192	93.91 183	98.10 76	97.76 90	98.45 91	98.08 48

E4		97.37 93	97.52 75	97.20 110	98.29 115	98.05 110	98.27 99	97.33 134	97.28 24	95.81 138	99.29 40	99.51 12	95.64 129	96.20 155	96.24 145	97.89 134	96.07 145

casdiffmvs_mvg		97.34 94	97.65 66	96.97 129	97.74 172	98.33 81	98.45 90	97.18 142	95.81 73	93.92 211	99.04 62	99.05 60	95.48 135	97.00 128	97.71 94	99.07 42	96.63 120
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
v2v482		97.33 95	96.84 124	97.90 61	98.19 128	97.83 130	98.74 64	97.44 120	95.42 97	98.23 29	99.46 21	98.84 86	97.46 54	95.51 183	96.10 150	97.36 172	94.72 190

EPP-MVSNet		97.29 96	96.88 119	97.76 74	98.70 82	99.10 16	98.92 44	98.36 44	95.12 107	93.36 225	97.39 137	91.00 230	97.65 49	98.72 42	98.91 25	99.58 7	97.92 57

hybridcas		97.27 97	97.75 61	96.71 146	97.88 159	98.23 88	98.29 97	97.11 150	96.86 38	92.95 235	99.18 50	99.12 51	95.74 120	97.22 116	97.27 109	98.30 101	96.36 130

MVS_111021_HR		97.27 97	97.11 106	97.46 94	98.46 98	97.82 134	97.50 161	96.86 161	94.97 112	97.13 69	96.99 149	98.39 122	96.82 78	97.65 98	97.38 100	98.02 122	96.56 124

E5new		97.26 99	97.38 80	97.13 114	98.29 115	98.02 112	98.19 103	97.24 139	97.21 28	95.82 136	99.13 54	99.44 17	95.39 141	95.81 169	95.99 154	97.83 139	96.05 146

E5		97.26 99	97.38 80	97.13 114	98.29 115	98.02 112	98.19 103	97.24 139	97.21 28	95.82 136	99.13 54	99.44 17	95.39 141	95.81 169	95.99 154	97.83 139	96.05 146

SF-MVS		97.26 99	97.43 78	97.05 122	98.80 76	97.83 130	96.02 225	97.44 120	94.98 111	95.74 143	97.16 145	98.45 121	95.72 122	97.85 81	97.97 80	98.60 78	97.78 64

TSAR-MVS + GP.		97.26 99	97.33 86	97.18 111	98.21 123	98.06 106	96.38 216	97.66 100	93.92 155	95.23 164	98.48 103	98.33 125	97.41 56	97.63 99	97.35 101	98.18 111	97.57 78

OMC-MVS		97.23 103	97.21 94	97.25 108	97.85 160	97.52 152	97.92 126	95.77 200	95.83 72	97.09 72	97.86 124	98.52 116	96.62 85	97.51 103	96.65 131	98.26 105	96.57 122

3Dnovator	96.31 3	97.22 104	97.19 97	97.25 108	98.14 133	97.95 120	98.03 118	96.77 169	96.42 49	97.14 67	95.11 190	97.59 156	95.14 155	97.79 86	97.72 92	98.26 105	97.76 68

usedtu_dtu_shiyan2		97.20 105	97.35 85	97.03 124	99.23 34	98.25 85	98.34 94	97.49 112	97.86 14	95.90 131	98.27 114	99.30 29	93.22 196	97.41 106	96.26 143	97.99 127	94.14 201

E3new		97.13 106	97.22 92	97.03 124	98.21 123	97.95 120	98.09 111	97.13 148	96.71 42	95.63 150	99.01 64	99.27 34	95.38 143	95.82 168	95.86 164	97.73 147	95.90 155

E3		97.13 106	97.22 92	97.03 124	98.21 123	97.95 120	98.09 111	97.13 148	96.70 43	95.64 149	99.02 63	99.27 34	95.38 143	95.81 169	95.86 164	97.73 147	95.90 155

sasdasda		97.11 108	96.88 119	97.38 97	98.34 103	98.72 55	97.52 159	97.94 80	95.60 85	95.01 174	94.58 204	94.50 202	96.59 87	97.84 82	98.03 76	98.90 58	98.91 9

canonicalmvs		97.11 108	96.88 119	97.38 97	98.34 103	98.72 55	97.52 159	97.94 80	95.60 85	95.01 174	94.58 204	94.50 202	96.59 87	97.84 82	98.03 76	98.90 58	98.91 9

V42		97.10 110	96.97 114	97.26 105	97.64 176	97.60 144	98.45 90	95.99 188	94.44 137	97.35 59	99.40 31	98.63 109	97.34 60	96.33 151	96.38 140	96.82 200	96.00 150

CPTT-MVS		97.08 111	96.25 144	98.05 52	99.21 37	98.30 83	98.54 78	97.98 78	94.28 142	95.89 133	89.57 249	98.54 114	98.18 27	97.82 85	97.32 105	98.54 82	97.91 58

DeepPCF-MVS	94.55 10	97.05 112	97.13 105	96.95 131	96.06 234	97.12 171	98.01 120	95.44 211	95.18 104	97.50 51	97.86 124	98.08 137	97.31 62	97.23 115	97.00 114	97.36 172	97.45 84

QAPM		97.04 113	97.14 102	96.93 133	97.78 171	98.02 112	97.36 175	96.72 171	94.68 125	96.23 114	97.21 142	97.68 152	95.70 123	97.37 108	97.24 110	97.78 145	97.77 66

CNVR-MVS		97.03 114	96.77 130	97.34 99	98.89 68	97.67 141	97.64 150	97.17 143	94.40 140	95.70 147	94.02 214	98.76 98	96.49 95	97.78 87	97.29 108	98.12 117	97.47 83

viewmanbaseed2359cas		97.01 115	97.20 95	96.79 143	98.06 142	97.90 127	97.80 134	96.78 168	96.34 53	94.82 182	98.80 85	99.15 50	95.50 134	96.14 156	96.07 152	97.79 143	96.00 150

casdiffmvs		97.00 116	97.36 84	96.59 155	97.65 175	97.98 117	98.06 114	96.81 165	95.78 75	92.77 238	99.40 31	99.26 38	95.65 128	96.70 139	96.39 139	98.59 79	95.99 152
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
v148		96.99 117	96.70 134	97.34 99	97.89 157	97.23 163	98.33 95	96.96 156	95.57 89	97.12 70	98.99 66	99.40 21	97.23 64	96.22 154	95.45 175	96.50 207	94.02 204

viewdifsd2359ckpt09		96.95 118	96.77 130	97.15 113	98.55 94	98.24 87	97.80 134	97.30 136	94.93 115	95.25 163	98.13 119	98.53 115	95.97 117	95.57 180	95.96 157	98.03 121	96.05 146

viewcassd2359sk11		96.93 119	96.96 115	96.90 135	98.14 133	97.88 128	97.95 122	96.98 155	96.18 58	95.53 156	98.75 91	99.06 57	95.17 151	95.49 184	95.54 171	97.62 157	95.81 159

viewdifsd2359ckpt11		96.92 120	97.45 76	96.31 167	97.53 182	97.42 157	97.70 143	95.37 213	96.93 33	94.17 204	99.27 43	99.52 10	95.11 156	97.33 110	95.90 160	97.98 128	95.79 162

viewmsd2359difaftdt		96.92 120	97.45 76	96.31 167	97.53 182	97.42 157	97.70 143	95.37 213	96.93 33	94.18 203	99.27 43	99.52 10	95.11 156	97.33 110	95.90 160	97.98 128	95.79 162

DELS-MVS		96.90 122	97.24 91	96.50 160	97.85 160	98.18 92	97.88 131	95.92 192	93.48 161	95.34 161	98.86 81	98.94 74	94.03 178	97.33 110	97.04 113	98.00 124	96.85 112
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
MVS_111021_LR		96.86 123	96.72 133	97.03 124	97.80 169	97.06 176	97.04 192	95.51 210	94.55 131	97.47 52	97.35 139	97.68 152	96.66 83	97.11 121	96.73 124	97.69 152	96.57 122

PM-MVS		96.85 124	96.62 137	97.11 117	97.13 208	96.51 195	98.29 97	94.65 232	94.84 118	98.12 32	98.59 99	97.20 166	97.41 56	96.24 153	96.41 138	97.09 185	96.56 124

FE-MVSNET		96.84 125	96.84 124	96.84 140	96.93 214	97.58 145	98.49 84	97.43 122	95.70 82	95.08 171	98.40 109	98.08 137	95.17 151	95.92 165	97.05 112	97.96 131	95.14 183

pmmvs-eth3d		96.84 125	96.22 147	97.56 85	97.63 178	96.38 203	98.74 64	96.91 159	94.63 128	98.26 26	99.43 25	98.28 127	96.58 90	94.52 202	95.54 171	97.24 178	94.75 189

MVSMamba_PlusPlus		96.81 127	96.92 116	96.69 150	98.66 87	98.33 81	96.65 211	96.73 170	92.78 177	94.79 184	96.01 172	97.55 157	95.44 136	97.67 94	97.87 84	97.85 138	98.24 43

CANet		96.81 127	96.50 139	97.17 112	99.10 54	97.96 119	97.86 132	97.51 108	91.30 199	97.75 41	97.64 129	97.89 145	93.39 194	96.98 129	96.73 124	97.40 168	96.99 102

Fast-Effi-MVS+		96.80 129	95.92 160	97.84 67	98.57 92	97.46 155	98.06 114	98.24 53	89.64 221	97.57 48	96.45 162	97.35 162	96.73 80	97.22 116	96.64 132	97.86 137	96.65 118

viewdifsd2359ckpt13		96.79 130	96.77 130	96.81 141	98.08 140	97.83 130	97.74 139	96.79 166	95.30 100	94.89 178	98.41 107	98.88 80	95.57 132	95.61 177	95.49 174	97.81 141	95.87 157

MCST-MVS		96.79 130	96.08 152	97.62 81	98.78 77	97.52 152	98.01 120	97.32 135	93.20 166	95.84 135	93.97 216	98.12 135	97.34 60	96.34 149	95.88 163	98.45 91	97.51 81

UGNet		96.79 130	97.82 52	95.58 196	97.57 181	98.39 76	98.48 86	97.84 92	95.85 71	94.68 187	97.91 123	99.07 56	87.12 252	97.71 91	97.51 97	97.80 142	98.29 38
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
TAPA-MVS	93.96 13	96.79 130	96.70 134	96.90 135	97.64 176	97.58 145	97.54 158	94.50 234	95.14 105	96.64 97	96.76 154	97.90 144	96.63 84	95.98 163	96.14 147	98.45 91	97.39 88
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
E2		96.74 134	96.70 134	96.78 144	98.09 139	97.82 134	97.80 134	96.86 161	95.62 84	95.42 158	98.47 104	98.83 88	94.96 161	95.19 191	95.24 181	97.53 158	95.75 167

CLD-MVS		96.73 135	96.92 116	96.51 159	98.70 82	97.57 148	97.64 150	92.07 249	93.10 172	96.31 113	98.29 112	99.02 63	95.99 116	97.20 118	96.47 136	98.37 100	96.81 113
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
viewdifsd2359ckpt07		96.69 136	97.19 97	96.10 172	98.01 145	97.73 139	97.69 145	96.10 184	97.21 28	94.10 207	99.10 59	99.21 43	95.06 158	96.10 159	94.90 185	95.62 227	96.11 143

MGCFI-Net		96.69 136	96.89 118	96.44 163	98.30 112	98.63 60	97.39 172	97.90 84	95.72 79	91.16 250	94.65 203	94.55 200	95.04 160	97.78 87	98.00 79	98.87 64	98.93 8

train_agg		96.68 138	95.93 159	97.56 85	99.08 56	97.16 167	98.44 93	97.37 128	91.12 203	95.18 167	95.43 185	98.48 119	97.36 58	96.48 145	95.52 173	97.95 132	97.34 92

CDPH-MVS		96.68 138	95.99 156	97.48 92	99.13 50	97.64 142	98.08 113	97.46 115	90.56 210	95.13 168	94.87 198	98.27 128	96.56 91	97.09 122	96.45 137	98.54 82	97.08 101

MSLP-MVS++		96.66 140	96.46 142	96.89 137	98.02 144	97.71 140	95.57 232	96.96 156	94.36 141	96.19 118	91.37 241	98.24 129	97.07 70	97.69 92	97.89 82	97.52 160	97.95 54

TinyColmap		96.64 141	96.07 153	97.32 103	97.84 165	96.40 200	97.63 152	96.25 182	95.86 70	98.98 10	97.94 122	96.34 182	96.17 106	97.30 114	95.38 178	97.04 188	93.24 217

IS_MVSNet		96.62 142	96.48 141	96.78 144	98.46 98	98.68 58	98.61 73	98.24 53	92.23 183	89.63 257	95.90 179	94.40 204	96.23 101	98.65 49	98.77 33	99.52 12	96.76 115

NCCC		96.56 143	95.68 163	97.59 83	99.04 60	97.54 151	97.67 147	97.56 106	94.84 118	96.10 121	87.91 252	98.09 136	96.98 75	97.20 118	96.80 123	98.21 109	97.38 91

WB-MVS		96.54 144	98.09 38	94.73 218	96.68 223	98.34 80	94.77 252	97.39 125	98.12 10	89.72 256	98.95 69	99.32 28	93.33 195	98.67 46	97.88 83	96.47 209	95.38 177

ETV-MVS		96.54 144	95.27 172	98.02 56	99.07 58	97.48 154	98.16 108	98.19 59	87.33 241	97.58 47	92.67 228	95.93 188	96.22 102	98.49 61	98.46 46	98.91 57	96.50 128

Effi-MVS+		96.46 146	95.28 171	97.85 66	98.64 91	97.16 167	97.15 189	98.75 23	90.27 214	98.03 34	93.93 217	96.21 183	96.55 92	96.34 149	96.69 127	97.97 130	96.33 132

IterMVS-LS		96.35 147	95.85 162	96.93 133	97.53 182	98.00 116	97.37 173	97.97 79	95.49 96	96.71 94	98.94 71	93.23 212	94.82 166	93.15 223	95.05 183	97.17 182	97.12 99
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
diffmvs_AUTHOR		96.30 148	96.79 128	95.73 190	97.43 193	97.06 176	97.24 183	95.65 205	95.76 77	92.97 234	99.35 35	99.21 43	93.99 181	95.61 177	94.85 187	97.09 185	95.65 170

USDC		96.30 148	95.64 165	97.07 120	97.62 179	96.35 205	97.17 188	95.71 203	95.52 94	99.17 7	98.11 120	97.46 159	95.67 125	95.44 186	93.60 210	97.09 185	92.99 221

Vis-MVSNet (Re-imp)		96.29 150	96.50 139	96.05 173	97.96 153	97.83 130	97.30 178	97.86 90	93.14 168	88.90 260	96.80 153	95.28 194	95.15 153	98.37 68	98.25 64	99.12 37	95.84 158

viewmamba		96.27 151	96.61 138	95.87 181	97.38 196	97.07 175	97.40 171	95.73 202	95.71 80	93.65 217	99.12 57	99.00 65	93.81 185	95.94 164	94.27 196	97.27 176	95.13 184

MSDG		96.27 151	96.17 150	96.38 166	97.85 160	96.27 207	96.55 213	94.41 235	94.55 131	95.62 152	97.56 134	97.80 146	96.22 102	97.17 120	96.27 142	97.67 154	93.60 212

CNLPA		96.24 153	95.97 157	96.57 157	97.48 191	97.10 174	96.75 205	94.95 226	94.92 116	96.20 117	94.81 199	96.61 177	96.25 100	96.94 130	95.64 169	97.79 143	95.74 168

EIA-MVS		96.23 154	94.85 184	97.84 67	99.08 56	98.21 90	97.69 145	98.03 75	85.68 251	98.09 33	91.75 239	97.07 169	95.66 127	97.58 100	97.72 92	98.47 89	95.91 154

onestephybrid01		96.10 155	96.33 143	95.84 184	97.29 203	97.01 178	97.61 153	95.69 204	93.41 162	93.45 222	98.92 72	98.92 75	93.62 189	96.06 161	94.68 192	97.35 174	95.34 178

PLC	92.55 15	96.10 155	95.36 168	96.96 130	98.13 136	96.88 182	96.49 214	96.67 175	94.07 150	95.71 146	91.14 242	96.09 185	96.84 77	96.70 139	96.58 134	97.92 133	96.03 149
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
test20.03		96.08 157	96.80 127	95.25 208	99.19 39	97.58 145	97.24 183	97.56 106	94.95 114	91.91 246	98.58 100	98.03 140	87.88 248	97.43 105	96.94 117	97.69 152	94.05 203

FA-MVS(training)		96.07 158	95.59 166	96.63 152	98.00 149	97.44 156	97.36 175	98.53 28	92.21 184	95.97 127	96.18 168	94.22 207	92.98 200	96.79 135	96.70 126	96.95 194	95.56 173

TSAR-MVS + COLMAP		96.05 159	95.94 158	96.18 171	97.46 192	96.41 199	97.26 182	95.83 196	94.69 124	95.30 162	98.31 111	96.52 178	94.71 168	95.48 185	94.87 186	96.54 206	95.33 180

EU-MVSNet		96.03 160	96.23 146	95.80 187	95.48 253	94.18 234	98.99 37	91.51 251	97.22 27	97.66 45	99.15 53	98.51 117	98.08 30	95.92 165	92.88 218	93.09 242	95.72 169

dtuplus		95.99 161	96.16 151	95.80 187	97.37 197	96.47 196	97.23 185	95.76 201	94.81 120	93.04 232	98.67 96	98.88 80	93.93 182	95.14 192	93.69 208	97.49 162	95.44 176

PCF-MVS	92.69 14	95.98 162	95.05 179	97.06 121	98.43 100	97.56 149	97.76 137	96.65 176	89.95 219	95.70 147	96.18 168	98.48 119	95.74 120	93.64 213	93.35 215	98.09 120	96.18 138
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
HQP-MVS		95.97 163	95.01 181	97.08 119	98.72 80	97.19 165	97.07 191	96.69 174	91.49 197	95.77 142	92.19 234	97.93 143	96.15 107	94.66 199	94.16 199	98.10 118	97.45 84

Effi-MVS+-dtu		95.94 164	95.08 178	96.94 132	98.54 95	97.38 159	96.66 209	97.89 86	88.68 227	95.92 130	92.90 227	97.28 163	94.18 177	96.68 141	96.13 149	98.45 91	96.51 127

usedtu_dtu_shiyan1		95.91 165	95.40 167	96.50 160	96.40 228	97.12 171	97.95 122	96.35 181	93.25 165	96.07 123	97.21 142	97.22 165	94.48 171	93.47 215	95.28 179	97.74 146	94.28 198

diffmvs		95.86 166	96.21 148	95.44 201	97.25 205	96.85 185	96.99 195	95.23 219	94.96 113	92.82 237	98.89 76	98.85 84	93.52 192	94.21 208	94.25 197	96.84 197	95.49 175
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
AdaColmap		95.85 167	94.65 188	97.26 105	98.70 82	97.20 164	97.33 177	97.30 136	91.28 201	95.90 131	88.16 251	96.17 184	96.60 86	97.34 109	96.82 120	97.71 149	95.60 172

viewmambaseed2359dif		95.80 168	95.87 161	95.73 190	97.17 207	96.55 193	97.15 189	95.60 207	93.77 157	93.06 230	98.63 98	98.66 106	94.03 178	94.76 197	93.36 214	97.37 171	95.34 178

hybridnocas07		95.78 169	96.24 145	95.26 207	97.02 212	96.76 189	96.93 198	95.26 217	93.98 151	92.77 238	99.05 61	98.86 83	92.56 208	95.70 174	94.18 198	97.00 191	95.13 184

FMVSNet2		95.77 170	96.20 149	95.27 205	96.77 219	98.18 92	97.28 179	97.90 84	93.12 169	91.37 248	98.25 115	96.05 186	90.04 229	94.96 196	95.94 158	98.28 102	96.90 106

OpenMVS	94.63 9	95.75 171	95.04 180	96.58 156	97.85 160	97.55 150	96.71 207	96.07 185	90.15 217	96.47 103	90.77 247	95.95 187	94.41 174	97.01 127	96.95 116	98.00 124	96.90 106

pmmvs5		95.70 172	95.22 173	96.26 169	96.55 227	97.24 162	97.50 161	94.99 225	90.95 206	96.87 82	98.47 104	97.40 160	94.45 172	92.86 224	94.98 184	97.23 179	94.64 192

Anonymous20231206		95.69 173	95.68 163	95.70 192	98.32 107	96.95 180	97.37 173	96.65 176	93.33 163	93.61 218	98.70 95	98.03 140	91.04 217	95.07 194	94.59 195	97.20 180	93.09 220

hybrid		95.66 174	96.07 153	95.19 210	97.03 210	96.68 191	96.90 202	95.12 220	94.10 146	92.85 236	98.82 83	98.71 102	92.52 211	95.42 187	93.82 207	96.72 202	94.93 187

MAR-MVS		95.51 175	94.49 192	96.71 146	97.92 155	96.40 200	96.72 206	98.04 74	86.74 245	96.72 91	92.52 231	95.14 196	94.02 180	96.81 134	96.54 135	96.85 195	97.25 95
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
DI_MVS_pp		95.48 176	94.51 190	96.61 153	97.13 208	97.30 161	98.05 116	96.79 166	93.75 158	95.08 171	96.38 163	89.76 234	94.95 162	93.97 212	94.82 191	97.64 156	95.63 171

MDA-MVSNet-bldmvs		95.45 177	95.20 174	95.74 189	94.24 258	96.38 203	97.93 125	94.80 227	95.56 92	96.87 82	98.29 112	95.24 195	96.50 94	98.65 49	90.38 231	94.09 234	91.93 228

PVSNet_BlendedMVS		95.44 178	95.09 176	95.86 182	97.31 201	97.13 169	96.31 219	95.01 223	88.55 230	96.23 114	94.55 208	97.75 147	92.56 208	96.42 146	95.44 176	97.71 149	95.81 159

PVSNet_Blended		95.44 178	95.09 176	95.86 182	97.31 201	97.13 169	96.31 219	95.01 223	88.55 230	96.23 114	94.55 208	97.75 147	92.56 208	96.42 146	95.44 176	97.71 149	95.81 159

pmmvs4		95.37 180	94.25 193	96.67 151	97.01 213	95.28 227	97.60 154	96.07 185	93.11 170	97.29 62	98.09 121	94.23 206	95.21 149	91.56 235	93.91 205	96.82 200	93.59 213

MVS_Test		95.34 181	94.88 183	95.89 180	96.93 214	96.84 186	96.66 209	97.08 151	90.06 218	94.02 208	97.61 130	96.64 176	93.59 191	92.73 227	94.02 203	97.03 189	96.24 135

GBi-Net		95.21 182	95.35 169	95.04 212	96.77 219	98.18 92	97.28 179	97.58 103	88.43 232	90.28 253	96.01 172	92.43 220	90.04 229	97.67 94	97.86 86	98.28 102	96.90 106

test1		95.21 182	95.35 169	95.04 212	96.77 219	98.18 92	97.28 179	97.58 103	88.43 232	90.28 253	96.01 172	92.43 220	90.04 229	97.67 94	97.86 86	98.28 102	96.90 106

IterMVS-SCA-FT		95.16 184	93.95 197	96.56 158	97.89 157	96.69 190	96.94 197	96.05 187	93.06 173	97.35 59	98.79 86	91.45 225	95.93 118	92.78 225	91.00 228	95.22 230	93.91 206

HyFIR lowres test		95.05 185	93.54 202	96.81 141	97.81 168	96.88 182	98.18 105	97.46 115	94.28 142	94.98 177	96.57 159	92.89 218	96.15 107	90.90 240	91.87 224	96.28 213	91.35 229

CHOSEN 1792x2688		94.98 186	94.69 187	95.31 203	97.27 204	95.58 219	97.90 128	95.56 209	95.03 109	93.77 216	95.65 183	99.29 30	95.30 145	91.51 236	91.28 227	92.05 252	94.50 194

CANet_DTU		94.96 187	94.62 189	95.35 202	98.03 143	96.11 209	96.92 200	95.60 207	88.59 229	97.27 63	95.27 188	96.50 179	88.77 244	95.53 181	95.59 170	95.54 228	94.78 188

CDS-MVSNet		94.91 188	95.17 175	94.60 222	97.85 160	96.21 208	96.90 202	96.39 179	90.81 207	93.40 223	97.24 141	94.54 201	85.78 258	96.25 152	96.15 146	97.26 177	95.01 186
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
DPM-MVS		94.86 189	93.90 199	95.99 175	98.19 128	96.52 194	96.29 221	95.95 189	93.11 170	94.61 189	88.17 250	96.44 180	93.77 188	93.33 218	93.54 212	97.11 184	96.22 136

MS-PatchMatch		94.84 190	94.76 186	94.94 215	96.38 229	94.69 233	95.90 227	94.03 237	92.49 178	93.81 213	95.79 180	96.38 181	94.54 169	94.70 198	94.85 187	94.97 232	94.43 196

thisisatest0530		94.81 191	93.06 208	96.85 139	98.01 145	97.18 166	96.93 198	97.36 129	89.73 220	95.80 140	94.98 194	77.88 255	94.89 163	96.73 137	97.35 101	98.13 116	97.54 79

tttt0517		94.81 191	93.04 209	96.88 138	98.15 132	97.37 160	96.99 195	97.36 129	89.51 222	95.74 143	94.89 196	77.53 257	94.89 163	96.94 130	97.35 101	98.17 112	97.70 69

testgi		94.81 191	96.05 155	93.35 234	99.06 59	96.87 184	97.57 156	96.70 173	95.77 76	88.60 262	93.19 225	98.87 82	81.21 266	97.03 126	96.64 132	96.97 193	93.99 205

PatchMatch-RL		94.79 194	93.75 201	96.00 174	96.80 218	95.00 230	95.47 237	95.25 218	90.68 209	95.80 140	92.97 226	93.64 209	95.67 125	96.13 158	95.81 166	96.99 192	92.01 227

FPMVS		94.70 195	94.99 182	94.37 224	95.84 242	93.20 239	96.00 226	91.93 250	95.03 109	94.64 188	94.68 200	93.29 211	90.95 218	98.07 77	97.34 104	96.85 195	93.29 216

new-patchmatchnet		94.48 196	94.02 195	95.02 214	97.51 189	95.00 230	95.68 231	94.26 236	97.32 23	95.73 145	99.60 12	98.22 133	91.30 213	94.13 209	84.41 250	95.65 226	89.45 241

IterMVS		94.48 196	93.46 204	95.66 193	97.52 185	96.43 197	97.20 186	94.73 230	92.91 176	96.44 104	98.75 91	91.10 227	94.53 170	92.10 231	90.10 233	93.51 238	92.84 225
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MDTV_nov1_ep13_2view		94.39 198	93.34 205	95.63 194	97.23 206	95.33 226	97.76 137	96.84 163	94.55 131	97.47 52	98.96 67	97.70 150	93.88 184	92.27 229	86.81 240	90.56 254	87.73 251

Fast-Effi-MVS+-dtu		94.34 199	93.26 207	95.62 195	97.82 166	95.97 212	95.86 228	99.01 13	86.88 243	93.39 224	90.83 245	95.46 193	90.61 222	94.46 204	94.68 192	97.01 190	94.51 193

thres600view7		94.34 199	92.31 218	96.70 148	98.19 128	98.12 101	97.85 133	97.45 117	91.49 197	93.98 210	84.27 255	82.02 246	94.24 176	97.04 123	98.76 34	98.49 86	94.47 195

EPNet		94.33 201	93.52 203	95.27 205	98.81 75	94.71 232	96.77 204	98.20 57	88.12 235	96.53 101	92.53 230	91.19 226	85.25 262	95.22 190	95.26 180	96.09 218	97.63 76
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test2506		94.29 202	91.43 232	97.64 80	98.66 87	98.83 35	98.50 80	98.40 37	96.04 60	94.45 192	94.88 197	55.05 277	96.70 81	99.28 14	99.04 19	99.14 33	96.87 110

GA-MVS		94.18 203	92.98 210	95.58 196	97.36 198	96.42 198	96.21 222	95.86 193	90.29 213	95.08 171	96.19 167	85.37 238	92.82 204	94.01 211	94.14 200	96.16 217	94.41 197

gg-mvs-nofinetune		94.13 204	93.93 198	94.37 224	97.99 150	95.86 213	95.45 240	99.22 9	97.61 17	95.10 170	99.50 19	84.50 239	81.73 265	95.31 188	94.12 201	96.71 204	90.59 233

baseline		94.07 205	94.50 191	93.57 232	96.34 230	93.40 238	95.56 235	92.39 244	92.07 187	94.00 209	98.24 116	97.51 158	89.19 237	91.75 233	92.72 219	93.96 236	95.79 162

FMVSNet3		94.06 206	93.85 200	94.31 227	95.46 254	97.80 137	96.34 217	97.58 103	88.43 232	90.28 253	96.01 172	92.43 220	88.67 245	91.82 232	93.96 204	97.53 158	96.50 128

thres400		94.04 207	91.94 224	96.50 160	97.98 152	97.82 134	97.66 149	96.96 156	90.96 205	94.20 200	83.24 257	82.82 244	93.80 186	96.50 144	98.09 72	98.38 99	94.15 200

dmvs_re		94.02 208	92.39 216	95.91 179	97.71 173	95.43 221	97.00 194	95.94 190	82.49 260	94.61 189	83.69 256	93.01 217	92.71 205	97.83 84	97.56 95	97.50 161	96.73 116

CVMVSNet		94.01 209	94.25 193	93.73 231	94.36 257	92.44 242	97.45 164	88.56 256	95.59 87	93.06 230	98.88 77	90.03 233	94.84 165	94.08 210	93.45 213	94.09 234	95.31 181

thres200		93.98 210	91.90 225	96.40 165	97.66 174	98.12 101	97.20 186	97.45 117	90.16 216	93.82 212	83.08 258	83.74 242	93.80 186	97.04 123	97.48 99	98.49 86	93.70 209

gbinet_0.2-2-1-0.02		93.92 211	92.20 222	95.93 178	96.24 231	95.75 214	98.05 116	93.85 239	91.55 196	96.68 95	96.95 150	92.86 219	95.06 158	88.67 246	85.96 244	95.71 225	93.65 211

blended_shiyan8		93.92 211	92.28 220	95.83 185	95.93 240	95.67 217	97.71 141	92.63 242	92.35 181	96.92 78	95.99 176	93.23 212	95.60 131	88.33 247	86.73 241	96.18 215	93.70 209

blended_shiyan6		93.92 211	92.29 219	95.82 186	95.95 238	95.66 218	97.72 140	92.62 243	92.31 182	96.89 81	95.96 177	93.33 210	95.55 133	88.31 248	86.73 241	96.17 216	93.73 207

baseline1		93.89 214	92.82 212	95.14 211	97.62 179	96.97 179	96.12 223	96.36 180	91.30 199	91.53 247	94.68 200	80.72 248	90.80 220	95.71 173	96.29 141	98.44 95	94.09 202

tfpn200view9		93.80 215	91.75 228	96.20 170	97.52 185	98.15 99	97.48 163	97.47 114	87.65 237	93.56 220	83.03 259	84.12 240	92.62 207	97.04 123	98.09 72	98.52 85	94.17 199

dtuonlycased		93.74 216	94.84 185	92.45 242	97.52 185	96.82 187	97.55 157	92.73 241	94.41 138	78.03 271	97.60 131	98.13 134	95.20 150	93.57 214	90.51 230	93.69 237	92.83 226

MIMVSNet		93.68 217	93.96 196	93.35 234	97.82 166	96.08 210	96.34 217	98.46 34	91.28 201	86.67 267	94.95 195	94.87 198	84.39 263	94.53 200	94.65 194	96.45 210	91.34 230

pmnet_mix02		93.59 218	92.65 213	94.69 220	96.76 222	94.16 235	97.03 193	93.00 240	95.79 74	96.03 126	98.91 74	97.69 151	92.99 199	90.03 244	84.10 252	92.35 250	87.89 250

wanda-best-256-512		93.50 219	91.78 226	95.51 198	95.64 246	95.41 222	97.43 166	92.21 245	91.80 189	96.77 88	95.73 181	93.11 214	95.28 146	87.72 250	85.73 245	95.75 221	92.99 221

FE-blended-shiyan7		93.50 219	91.78 226	95.51 198	95.64 246	95.41 222	97.43 166	92.21 245	91.80 189	96.77 88	95.73 181	93.11 214	95.28 146	87.72 250	85.73 245	95.75 221	92.99 221

EPNet_dtu		93.45 221	92.51 215	94.55 223	98.39 102	91.67 252	95.46 238	97.50 110	86.56 246	97.38 57	93.52 220	94.20 208	85.82 257	93.31 220	92.53 220	92.72 245	95.76 166
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
IB-MVS	92.44 16	93.33 222	92.15 223	94.70 219	97.42 194	96.39 202	95.57 232	94.67 231	86.40 249	93.59 219	78.28 266	95.76 191	89.59 235	95.88 167	95.98 156	97.39 169	96.34 131
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
ET-MVSNet_ETH3D		93.18 223	90.80 235	95.95 176	96.05 235	96.07 211	96.92 200	96.51 178	89.34 223	95.63 150	94.08 213	72.31 272	93.13 197	94.33 206	94.83 189	97.44 165	94.65 191

thres100view900		92.93 224	90.89 234	95.31 203	97.52 185	96.82 187	96.41 215	95.08 221	87.65 237	93.56 220	83.03 259	84.12 240	91.12 216	94.53 200	96.91 118	98.17 112	93.21 218

N_pmnet		92.46 225	92.38 217	92.55 240	97.91 156	93.47 237	97.42 169	94.01 238	96.40 51	88.48 263	98.50 102	98.07 139	88.14 247	91.04 239	84.30 251	89.35 259	84.85 258

TAMVS		92.46 225	93.34 205	91.44 251	97.03 210	93.84 236	94.68 253	90.60 253	90.44 212	85.31 268	97.14 146	93.03 216	85.78 258	94.34 205	93.67 209	95.22 230	90.93 232

CMPMVS	71.81 19	92.34 227	92.85 211	91.75 248	92.70 262	90.43 260	88.84 270	88.56 256	85.87 250	94.35 196	90.98 243	95.89 190	91.14 215	96.14 156	94.83 189	94.93 233	95.78 165
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
baseline2		92.06 228	89.82 238	94.68 221	97.32 199	95.72 215	94.97 249	95.08 221	84.75 254	94.34 198	90.68 248	77.75 256	90.13 228	93.38 216	93.58 211	96.25 214	92.90 224

dtuonly		91.97 229	91.47 230	92.55 240	96.68 223	91.24 254	95.21 245	91.44 252	91.11 204	94.12 206	97.19 144	95.93 188	89.16 238	90.89 241	87.43 239	88.71 261	86.52 254

MVSTER		91.97 229	90.31 236	93.91 229	96.81 217	96.91 181	94.22 254	95.64 206	84.98 252	92.98 233	93.42 221	72.56 270	86.64 256	95.11 193	93.89 206	97.16 183	95.31 181

CR-MVSNet		91.94 231	88.50 241	95.94 177	96.14 233	92.08 247	95.23 243	98.47 32	84.30 256	96.44 104	94.58 204	75.57 258	92.92 201	90.22 242	92.22 221	96.43 211	90.56 234

gm-plane-assit		91.85 232	87.91 243	96.44 163	99.14 48	98.25 85	99.02 32	97.38 127	95.57 89	98.31 25	99.34 37	51.00 278	88.93 241	93.16 222	91.57 225	95.85 219	86.50 255

PMMVS		91.67 233	91.47 230	91.91 247	89.43 267	88.61 266	94.99 248	85.67 261	87.50 239	93.80 214	94.42 211	94.88 197	90.71 221	92.26 230	92.96 217	96.83 198	89.65 239

CHOSEN 280x420		91.55 234	90.27 237	93.05 237	94.61 256	88.01 267	96.56 212	94.62 233	88.04 236	94.20 200	92.66 229	86.60 236	90.82 219	95.06 195	91.89 223	87.49 265	89.61 240

PatchT		91.40 235	88.54 240	94.74 217	91.48 266	92.18 245	97.42 169	97.51 108	84.96 253	96.44 104	94.16 212	75.47 259	92.92 201	90.22 242	92.22 221	92.66 248	90.56 234

pmmvs3		91.20 236	91.40 233	90.96 253	91.71 265	91.08 255	95.41 241	81.34 268	87.36 240	94.57 191	95.02 192	94.30 205	90.42 223	94.28 207	89.26 235	92.30 251	88.49 247

test0.0.03 1		91.17 237	91.50 229	90.80 254	98.01 145	95.46 220	94.22 254	95.80 197	86.55 247	81.75 270	90.83 245	87.93 235	78.48 267	94.51 203	94.11 202	96.50 207	91.08 231

SCA		91.15 238	87.65 245	95.23 209	96.15 232	95.68 216	96.68 208	98.18 61	90.46 211	97.21 66	92.44 232	80.17 250	93.51 193	86.04 258	83.58 255	89.68 258	85.21 257

new_pmnet		90.85 239	92.26 221	89.21 260	93.68 261	89.05 265	93.20 263	84.16 265	92.99 174	84.25 269	97.72 127	94.60 199	86.80 255	93.20 221	91.30 226	93.21 240	86.94 253

RPMNet		90.52 240	86.27 256	95.48 200	95.95 238	92.08 247	95.55 236	98.12 66	84.30 256	95.60 153	87.49 253	72.78 269	91.24 214	87.93 249	89.34 234	96.41 212	89.98 237

MDTV_nov1_ep13		90.30 241	87.32 249	93.78 230	96.00 237	92.97 240	95.46 238	95.39 212	88.61 228	95.41 159	94.45 210	80.39 249	89.87 232	86.58 256	83.54 256	90.56 254	84.71 259

FE-MVSNET3		90.29 242	86.44 253	94.78 216	95.64 246	95.41 222	97.43 166	92.21 245	91.80 189	92.27 241	77.48 268	73.25 265	95.41 137	87.72 250	85.73 245	95.75 221	93.73 207

PatchmatchNet		89.98 243	86.23 257	94.36 226	96.56 226	91.90 251	96.07 224	96.72 171	90.18 215	96.87 82	93.36 224	78.06 254	91.46 212	84.71 263	81.40 260	88.45 262	83.97 263
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
usedtu_blend_shiyan5		89.91 244	86.39 254	94.01 228	95.64 246	95.41 222	92.79 266	92.21 245	91.80 189	92.27 241	77.47 269	73.25 265	95.41 137	87.72 250	85.73 245	95.75 221	93.36 214

ADS-MVSNet		89.89 245	87.70 244	92.43 244	95.52 251	90.91 258	95.57 232	95.33 215	93.19 167	91.21 249	93.41 222	82.12 245	89.05 239	86.21 257	83.77 254	87.92 263	84.31 260

tpm		89.84 246	86.81 251	93.36 233	96.60 225	91.92 250	95.02 247	97.39 125	86.79 244	96.54 100	95.03 191	69.70 273	87.66 249	88.79 245	86.19 243	86.95 267	89.27 242

test-LLR		89.77 247	87.47 247	92.45 242	98.01 145	89.77 262	93.25 261	95.80 197	81.56 262	89.19 258	92.08 235	79.59 251	85.77 260	91.47 237	89.04 237	92.69 246	88.75 243

FMVSNet5		89.65 248	87.60 246	92.04 246	95.63 250	96.61 192	94.82 251	94.75 228	80.11 266	87.72 265	77.73 267	73.81 262	83.81 264	95.64 175	96.08 151	95.49 229	93.21 218

EPMVS		89.28 249	86.28 255	92.79 239	96.01 236	92.00 249	95.83 229	95.85 195	90.78 208	91.00 251	94.58 204	74.65 260	88.93 241	85.00 261	82.88 258	89.09 260	84.09 262

test-mter		89.16 250	88.14 242	90.37 256	94.79 255	91.05 256	93.60 260	85.26 262	81.65 261	88.32 264	92.22 233	79.35 253	87.03 253	92.28 228	90.12 232	93.19 241	90.29 236

CostFormer		89.06 251	85.65 258	93.03 238	95.88 241	92.40 243	95.30 242	95.86 193	86.49 248	93.12 229	93.40 223	74.18 261	88.25 246	82.99 264	81.46 259	89.77 257	88.66 245

MVS-HIRNet		88.72 252	86.49 252	91.33 252	91.81 264	85.66 268	87.02 272	96.25 182	81.48 264	94.82 182	96.31 166	92.14 223	90.32 225	87.60 254	83.82 253	87.74 264	78.42 267

TESTMET0.1,1		88.60 253	87.47 247	89.93 258	94.23 259	89.77 262	93.25 261	84.47 264	81.56 262	89.19 258	92.08 235	79.59 251	85.77 260	91.47 237	89.04 237	92.69 246	88.75 243

dps		88.36 254	84.32 261	93.07 236	93.86 260	92.29 244	94.89 250	95.93 191	83.50 258	93.13 227	91.87 237	67.79 275	90.32 225	85.99 259	83.22 257	90.28 256	85.56 256

tpmrst		87.60 255	84.13 262	91.66 250	95.65 245	89.73 264	93.77 257	94.74 229	88.85 225	93.35 226	95.60 184	72.37 271	87.40 250	81.24 265	78.19 265	85.02 270	82.90 266

blend_shiyan4		87.32 256	83.58 263	91.68 249	85.86 271	95.01 229	90.28 267	90.47 254	74.69 271	92.27 241	77.47 269	73.25 265	95.41 137	85.88 260	85.38 249	95.81 220	93.36 214

tpm cat1		87.19 257	82.78 264	92.33 245	95.66 244	90.61 259	94.19 256	95.27 216	86.97 242	94.38 194	90.91 244	69.40 274	87.21 251	79.57 268	77.82 266	87.25 266	84.18 261

E-PMN		86.94 258	85.10 259	89.09 262	95.77 243	83.54 271	89.89 269	86.55 258	92.18 185	87.34 266	94.02 214	83.42 243	89.63 234	93.32 219	77.11 267	85.33 268	72.09 268

EMVS		86.63 259	84.48 260	89.15 261	95.51 252	83.66 270	90.19 268	86.14 260	91.78 193	88.68 261	93.83 218	81.97 247	89.05 239	92.76 226	76.09 268	85.31 269	71.28 269

PMMVS2		86.47 260	92.62 214	79.29 264	92.01 263	85.63 269	93.74 258	86.37 259	93.95 154	54.18 276	98.19 117	97.39 161	58.46 268	96.57 143	93.07 216	90.99 253	83.55 265

0.4-1-1-0.1		86.09 261	82.27 265	90.55 255	88.91 268	92.09 246	93.74 258	84.65 263	77.28 268	92.48 240	81.76 262	73.62 263	90.27 227	80.00 267	81.27 261	93.27 239	89.84 238

MVE	72.99 18	85.37 262	89.43 239	80.63 263	74.43 272	71.94 273	88.25 271	89.81 255	93.27 164	67.32 274	96.32 165	91.83 224	90.40 224	93.36 217	90.79 229	73.55 273	88.49 247
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
0.3-1-1-0.015		85.22 263	81.12 267	90.00 257	88.32 269	91.29 253	93.16 264	83.68 266	76.11 269	92.27 241	79.38 264	73.25 265	89.78 233	78.77 270	80.48 262	92.78 244	88.53 246

0.4-1-1-0.2		85.13 264	81.17 266	89.76 259	88.18 270	90.98 257	92.83 265	83.39 267	75.70 270	92.15 245	80.54 263	73.62 263	89.49 236	78.89 269	80.15 263	92.48 249	88.30 249

test_method		61.30 265	70.45 268	50.62 265	22.69 274	30.92 275	68.31 275	25.76 270	80.56 265	68.71 272	82.80 261	91.08 228	44.64 269	80.50 266	56.70 269	73.64 272	70.58 270

GG-mvs-BLEND		61.03 266	87.02 250	30.71 267	0.74 277	90.01 261	78.90 274	0.74 274	84.56 255	9.46 277	79.17 265	90.69 231	1.37 273	91.74 234	89.13 236	93.04 243	83.83 264

testmvs		4.99 267	6.88 269	2.78 269	1.73 275	2.04 277	3.10 278	1.71 272	7.27 272	3.92 279	12.18 272	6.71 279	3.31 272	6.94 271	5.51 271	2.94 275	7.51 271

test123		4.41 268	5.71 270	2.88 268	1.28 276	2.21 276	3.09 279	1.65 273	6.35 273	4.98 278	8.53 273	3.88 280	3.46 271	5.79 272	5.71 270	2.85 276	7.50 272

uanet_test		0.00 269	0.00 271	0.00 270	0.00 278	0.00 278	0.00 280	0.00 275	0.00 274	0.00 280	0.00 274	0.00 281	0.00 274	0.00 273	0.00 272	0.00 277	0.00 273

sosnet-low-res		0.00 269	0.00 271	0.00 270	0.00 278	0.00 278	0.00 280	0.00 275	0.00 274	0.00 280	0.00 274	0.00 281	0.00 274	0.00 273	0.00 272	0.00 277	0.00 273

sosnet		0.00 269	0.00 271	0.00 270	0.00 278	0.00 278	0.00 280	0.00 275	0.00 274	0.00 280	0.00 274	0.00 281	0.00 274	0.00 273	0.00 272	0.00 277	0.00 273

TestfortrainingZip							98.92 44	97.17 143		94.34 198						98.14 114

TPM-MVS					97.49 190	96.32 206	95.05 246			94.36 195	91.82 238	96.92 174	88.89 243			96.67 205	96.22 136
Ray Leroy Khuboni and Hongjun Xu: Textureless Resilient Propagation Matching in Multiple View Stereosis (TPM-MVS). SATNAC 2025
RE-MVS-def										99.38 2

9.14												96.98 172

SR-MVS					99.33 30			98.40 37				98.90 76

Anonymous202405211			97.39 79		98.85 71	98.59 62	97.89 130	97.93 82	94.41 138		97.37 138	96.99 171	93.09 198	98.61 52	98.46 46	99.11 38	97.27 93

our_test_3					97.32 199	95.13 228	97.59 155

ambc			96.78 129		99.01 61	97.11 173	95.73 230		95.91 68	99.25 3	98.56 101	97.17 167	97.04 71	96.76 136	95.22 182	96.72 202	96.73 116

MTAPA										97.43 56		99.27 34

MTMP										97.63 46		99.03 62

Patchmatch-RL test							17.42 277

tmp_tt				45.72 266	60.00 273	38.74 274	45.50 276	12.18 271	79.58 267	68.42 273	67.62 271	65.04 276	22.12 270	84.83 262	78.72 264	66.08 274

XVS					99.48 18	98.76 47	99.22 21			96.40 108		98.78 95				98.94 55

X-MVStestdata					99.48 18	98.76 47	99.22 21			96.40 108		98.78 95				98.94 55

mPP-MVS					99.58 6							98.98 67

NP-MVS									89.27 224

Patchmtry						92.70 241	95.23 243	98.47 32		96.44 104

DeepMVS_CX						72.99 272	80.14 273	37.34 269	83.46 259	60.13 275	84.40 254	85.48 237	86.93 254	87.22 255		79.61 271	87.32 252

High-resolution multi-view benchmark