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	95.06 1	97.73 1	98.39 1	96.95 1	96.33 51	96.94 38	98.30 20	94.90 15	98.61 1	97.73 3	97.97 28	98.57 37	95.74 4	99.24 1	98.70 4	98.72 7	98.70 2
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
TDRefinement		97.59 2	98.32 2	96.73 4	95.90 67	98.10 2	99.08 2	93.92 31	98.24 3	96.44 13	98.12 22	97.86 76	96.06 2	99.24 1	98.93 1	99.00 2	97.77 5

WR-MVS		97.53 3	98.20 3	96.76 3	96.93 29	98.17 1	98.60 10	96.67 7	96.39 15	94.46 32	99.14 1	98.92 16	94.57 15	99.06 3	98.80 2	99.32 1	96.92 28

SixPastTwentyTwo		97.36 4	97.73 10	96.92 2	97.36 13	96.15 59	98.29 21	94.43 23	96.50 13	96.96 7	98.74 5	98.74 28	96.04 3	99.03 5	97.74 16	98.44 23	97.22 14

PS-CasMVS		97.22 5	97.84 7	96.50 5	97.08 25	97.92 6	98.17 32	97.02 2	94.71 31	95.32 21	98.52 12	98.97 15	92.91 43	99.04 4	98.47 5	98.49 19	97.24 13

PEN-MVS		97.16 6	97.87 6	96.33 11	97.20 21	97.97 4	98.25 25	96.86 6	95.09 27	94.93 26	98.66 7	99.16 7	92.27 54	98.98 6	98.39 7	98.49 19	96.83 32

DTE-MVSNet		97.16 6	97.75 9	96.47 6	97.40 12	97.95 5	98.20 28	96.89 5	95.30 22	95.15 24	98.66 7	98.80 23	92.77 48	98.97 7	98.27 9	98.44 23	96.28 44

COLMAP_ROB	93.74 2	97.09 8	97.98 4	96.05 17	95.97 63	97.78 9	98.56 11	91.72 90	97.53 7	96.01 15	98.14 21	98.76 27	95.28 5	98.76 11	98.23 10	98.77 5	96.67 36
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
WR-MVS_H		97.06 9	97.78 8	96.23 13	96.74 37	98.04 3	98.25 25	97.32 1	94.40 41	93.71 51	98.55 10	98.89 18	92.97 40	98.91 9	98.45 6	98.38 28	97.19 15

CP-MVSNet		96.97 10	97.42 14	96.44 7	97.06 26	97.82 8	98.12 35	96.98 3	93.50 60	95.21 23	97.98 26	98.44 40	92.83 47	98.93 8	98.37 8	98.46 22	96.91 29

DVP-MVS++		96.63 11	97.92 5	95.12 40	97.77 6	97.52 16	98.29 21	93.83 34	96.72 9	92.52 75	98.10 23	99.07 13	90.87 80	97.83 31	97.44 28	97.44 62	98.76 1

ACMH	90.17 8	96.61 12	97.69 12	95.35 30	95.29 85	96.94 38	98.43 14	92.05 75	98.04 4	95.38 19	98.07 24	99.25 4	93.23 33	98.35 16	97.16 39	97.72 52	96.00 50
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
UA-Net		96.56 13	96.73 25	96.36 9	98.99 1	97.90 7	97.79 45	95.64 10	92.78 79	92.54 74	96.23 94	95.02 160	94.31 18	98.43 15	98.12 11	98.89 3	98.58 3

ACMMPR		96.54 14	96.71 27	96.35 10	97.55 9	97.63 11	98.62 9	94.54 19	94.45 38	94.19 38	95.04 125	97.35 96	94.92 10	97.85 28	97.50 25	98.26 29	97.17 16

v7n		96.49 15	97.20 18	95.65 22	95.57 77	96.04 61	97.93 40	92.49 59	96.40 14	97.13 6	98.99 2	99.41 3	93.79 25	97.84 30	96.15 67	97.00 84	95.60 58

DeepC-MVS	92.47 4	96.44 16	96.75 24	96.08 16	97.57 7	97.19 33	97.96 39	94.28 24	95.29 23	94.92 27	98.31 17	96.92 107	93.69 27	96.81 69	96.50 58	98.06 40	96.27 45
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ACMM	90.06 9	96.31 17	96.42 34	96.19 14	97.21 20	97.16 35	98.71 5	93.79 37	94.35 42	93.81 45	92.80 164	98.23 53	95.11 6	98.07 20	97.45 27	98.51 18	96.86 31
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMH+	89.90 10	96.27 18	97.52 13	94.81 47	95.19 88	97.18 34	97.97 38	92.52 57	96.72 9	90.50 128	97.31 58	99.11 10	94.10 19	98.67 12	97.90 14	98.56 15	95.79 54

APDe-MVS		96.23 19	97.22 17	95.08 41	96.66 41	97.56 14	98.63 8	93.69 41	94.62 34	89.80 140	97.73 39	98.13 57	93.84 24	97.79 33	97.63 18	97.87 47	97.08 23
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
CP-MVS		96.21 20	96.16 45	96.27 12	97.56 8	97.13 36	98.43 14	94.70 18	92.62 83	94.13 40	92.71 165	98.03 64	94.54 16	98.00 24	97.60 20	98.23 31	97.05 24

HFP-MVS		96.18 21	96.53 31	95.77 20	97.34 16	97.26 30	98.16 33	94.54 19	94.45 38	92.52 75	95.05 123	96.95 106	93.89 22	97.28 49	97.46 26	98.19 33	97.25 11

UniMVSNet_ETH3D		96.15 22	97.71 11	94.33 55	97.31 17	96.71 43	95.06 122	96.91 4	97.86 5	90.42 129	98.55 10	99.60 1	88.01 122	98.51 13	97.81 15	98.26 29	94.95 71

MP-MVS		96.13 23	95.93 49	96.37 8	98.19 3	97.31 29	98.49 13	94.53 22	91.39 119	94.38 34	94.32 140	96.43 121	94.59 14	97.75 35	97.44 28	98.04 41	96.88 30
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
ACMMP		96.12 24	96.27 41	95.93 18	97.20 21	97.60 12	98.64 7	93.74 38	92.47 87	93.13 65	93.23 156	98.06 61	94.51 17	97.99 25	97.57 22	98.39 27	96.99 25
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
DVP-MVS		96.10 25	97.23 16	94.79 49	96.28 54	97.49 17	97.90 41	93.60 43	95.47 19	89.57 146	97.32 57	97.72 81	93.89 22	97.74 36	97.53 23	97.51 58	97.34 9
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
LGP-MVS_train		96.10 25	96.29 38	95.87 19	96.72 38	97.35 28	98.43 14	93.83 34	90.81 134	92.67 73	95.05 123	98.86 21	95.01 7	98.11 18	97.37 35	98.52 17	96.50 38

CSCG		96.07 27	97.15 19	94.81 47	96.06 62	97.58 13	96.52 77	90.98 102	96.51 12	93.60 53	97.13 68	98.55 38	93.01 37	97.17 54	95.36 84	98.68 9	97.78 4

DPE-MVS		96.00 28	96.80 23	95.06 42	95.87 70	97.47 22	98.25 25	93.73 39	92.38 91	91.57 103	97.55 50	97.97 67	92.98 38	97.49 47	97.61 19	97.96 45	97.16 17
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
SMA-MVS		95.99 29	96.48 32	95.41 29	97.43 11	97.36 26	97.55 51	93.70 40	94.05 51	93.79 46	97.02 71	94.53 166	92.28 53	97.53 45	97.19 37	97.73 51	97.67 7
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
TSAR-MVS + MP.		95.99 29	96.57 30	95.31 32	96.87 30	96.50 50	98.71 5	91.58 91	93.25 68	92.71 70	96.86 76	96.57 119	93.92 20	98.09 19	97.91 13	98.08 38	96.81 33
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
OPM-MVS		95.96 31	96.59 29	95.23 35	96.67 40	96.52 49	97.86 43	93.28 47	95.27 25	93.46 55	96.26 91	98.85 22	92.89 44	97.09 55	96.37 62	97.22 76	95.78 55
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
SteuartSystems-ACMMP		95.96 31	96.13 46	95.76 21	97.06 26	97.36 26	98.40 18	94.24 26	91.49 112	91.91 93	94.50 136	96.89 108	94.99 8	98.01 23	97.44 28	97.97 44	97.25 11
Skip Steuart: Steuart Systems R&D Blog.
ACMP	89.62 11	95.96 31	96.28 39	95.59 23	96.58 43	97.23 32	98.26 24	93.22 48	92.33 95	92.31 83	94.29 141	98.73 29	94.68 12	98.04 21	97.14 40	98.47 21	96.17 47
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PGM-MVS		95.90 34	95.72 53	96.10 15	97.53 10	97.45 23	98.55 12	94.12 28	90.25 140	93.71 51	93.20 157	97.18 100	94.63 13	97.68 39	97.34 36	98.08 38	96.97 26

PMVS	87.16 16	95.88 35	96.47 33	95.19 37	97.00 28	96.02 62	96.70 68	91.57 92	94.43 40	95.33 20	97.16 66	95.37 148	92.39 50	98.89 10	98.72 3	98.17 35	94.71 77
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
ACMMP_NAP		95.86 36	96.18 42	95.47 28	97.11 24	97.26 30	98.37 19	93.48 45	93.49 61	93.99 43	95.61 107	94.11 170	92.49 49	97.87 27	97.44 28	97.40 65	97.52 8

Gipuma		95.86 36	96.17 43	95.50 27	95.92 66	94.59 111	94.77 133	92.50 58	97.82 6	97.90 2	95.56 111	97.88 74	94.71 11	98.02 22	94.81 100	97.23 75	94.48 83
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
LS3D		95.83 38	96.35 36	95.22 36	96.47 47	97.49 17	97.99 36	92.35 62	94.92 30	94.58 30	94.88 130	95.11 158	91.52 65	98.48 14	98.05 12	98.42 25	95.49 59

SD-MVS		95.77 39	96.17 43	95.30 33	96.72 38	96.19 58	97.01 60	93.04 49	94.03 52	92.71 70	96.45 89	96.78 115	93.91 21	96.79 70	95.89 73	98.42 25	97.09 22
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
MED-MVS		95.73 40	96.95 21	94.30 56	95.47 80	97.43 24	97.68 46	92.90 51	95.33 20	89.07 155	98.30 18	97.12 102	92.87 45	97.20 53	96.94 44	97.83 48	96.33 41

SED-MVS		95.73 40	96.98 20	94.28 57	96.08 60	97.39 25	98.18 31	93.80 36	94.20 44	89.61 145	97.29 61	97.49 92	90.69 84	97.74 36	97.41 32	97.32 70	97.34 9

TranMVSNet+NR-MVSNet		95.72 42	96.42 34	94.91 46	96.21 55	96.77 42	96.90 65	94.99 13	92.62 83	91.92 92	98.51 13	98.63 34	90.82 81	97.27 50	96.83 46	98.63 12	94.31 84

DU-MVS		95.51 43	95.68 54	95.33 31	96.45 48	96.44 52	96.61 74	95.32 11	89.97 146	93.78 47	97.46 53	98.07 59	91.19 72	97.03 58	96.53 55	98.61 13	94.22 85

aaEdge-Enhanced		95.48 44	96.73 25	94.02 65	95.47 80	97.55 15	98.20 28	91.80 86	93.84 54	89.07 155	98.30 18	97.53 91	92.98 38	96.86 68	96.68 53	96.59 94	96.33 41

UniMVSNet (Re)		95.46 45	95.86 51	95.00 45	96.09 58	96.60 44	96.68 72	94.99 13	90.36 139	92.13 86	97.64 45	98.13 57	91.38 66	96.90 63	96.74 48	98.73 6	94.63 79

RPSCF		95.46 45	96.95 21	93.73 81	95.72 74	95.94 66	95.58 105	88.08 165	95.31 21	91.34 106	96.26 91	98.04 63	93.63 28	98.28 17	97.67 17	98.01 42	97.13 18

anonymousdsp		95.45 47	96.70 28	93.99 69	88.43 245	92.05 176	99.18 1	85.42 208	94.29 43	96.10 14	98.63 9	99.08 12	96.11 1	97.77 34	97.41 32	98.70 8	97.69 6

APD-MVS		95.38 48	95.68 54	95.03 43	97.30 18	96.90 40	97.83 44	93.92 31	89.40 154	90.35 130	95.41 115	97.69 83	92.97 40	97.24 52	97.17 38	97.83 48	95.96 51
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
UniMVSNet_NR-MVSNet		95.34 49	95.51 58	95.14 39	95.80 72	96.55 45	96.61 74	94.79 16	90.04 145	93.78 47	97.51 52	97.25 97	91.19 72	96.68 72	96.31 64	98.65 11	94.22 85

X-MVS		95.33 50	95.13 67	95.57 25	97.35 14	97.48 19	98.43 14	94.28 24	92.30 96	93.28 58	86.89 229	96.82 111	91.87 59	97.85 28	97.59 21	98.19 33	96.95 27

MSP-MVS		95.32 51	96.28 39	94.19 60	96.87 30	97.77 10	98.27 23	93.88 33	94.15 50	89.63 144	95.36 116	98.37 44	90.73 82	94.37 123	97.53 23	95.77 129	96.40 39
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
3Dnovator+	92.82 3	95.22 52	95.16 65	95.29 34	96.17 56	96.55 45	97.64 48	94.02 30	94.16 49	94.29 36	92.09 172	93.71 178	91.90 57	96.68 72	96.51 56	97.70 54	96.40 39

HPM-MVS++		95.21 53	94.89 70	95.59 23	97.79 5	95.39 85	97.68 46	94.05 29	91.91 104	94.35 35	93.38 152	95.07 159	92.94 42	96.01 85	95.88 74	96.73 88	96.61 37

TSAR-MVS + ACMM		95.17 54	95.95 47	94.26 58	96.07 61	96.46 51	95.67 102	94.21 27	93.84 54	90.99 116	97.18 64	95.24 156	93.55 29	96.60 75	95.61 81	95.06 151	96.69 35

CPTT-MVS		95.00 55	94.52 85	95.57 25	96.84 34	96.78 41	97.88 42	93.67 42	92.20 97	92.35 82	85.87 236	97.56 90	94.98 9	96.96 61	96.07 70	97.70 54	96.18 46

SF-MVS		94.88 56	95.87 50	93.73 81	95.30 83	95.93 67	94.80 132	91.76 88	93.11 72	91.93 91	95.83 102	97.07 103	91.11 75	96.62 74	96.44 60	97.46 59	96.13 48

Baseline_NR-MVSNet		94.85 57	95.35 63	94.26 58	96.45 48	93.86 131	96.70 68	94.54 19	90.07 144	90.17 136	98.77 4	97.89 71	90.64 87	97.03 58	96.16 66	97.04 83	93.67 98

EG-PatchMatch MVS		94.81 58	95.53 57	93.97 70	95.89 69	94.62 109	95.55 107	88.18 163	92.77 80	94.88 28	97.04 70	98.61 35	93.31 30	96.89 64	95.19 90	95.99 121	93.56 101

CS-MVS		94.76 59	94.41 90	95.18 38	94.95 94	95.99 63	97.28 53	91.99 77	85.51 200	94.55 31	93.07 159	97.69 83	93.77 26	97.08 56	96.79 47	98.53 16	94.72 75

OMC-MVS		94.74 60	95.46 61	93.91 73	94.62 106	96.26 56	96.64 73	89.36 146	94.20 44	94.15 39	94.02 145	97.73 80	91.34 68	96.15 82	95.04 94	97.37 67	94.80 73

DeepC-MVS_fast	91.38 6	94.73 61	94.98 68	94.44 51	96.83 36	96.12 60	96.69 70	92.17 68	92.98 77	93.72 49	94.14 142	95.45 146	90.49 93	95.73 92	95.30 86	96.71 90	95.13 68
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PHI-MVS		94.65 62	94.84 72	94.44 51	94.95 94	96.55 45	96.46 80	91.10 100	88.96 158	96.00 16	94.55 135	95.32 151	90.67 85	96.97 60	96.69 52	97.44 62	94.84 72

SPE-MVS-test		94.63 63	94.30 96	95.02 44	94.63 104	95.71 74	98.15 34	92.13 70	85.62 199	94.22 37	93.63 150	97.63 88	93.08 36	97.50 46	96.51 56	97.88 46	93.50 102

pmmvs6		94.58 64	97.30 15	91.40 137	94.84 98	94.61 110	93.40 178	92.43 61	98.51 2	85.61 192	98.73 6	99.53 2	84.40 176	97.88 26	97.03 41	97.72 52	94.79 74

DeepPCF-MVS	90.68 7	94.56 65	94.92 69	94.15 61	94.11 120	95.71 74	97.03 59	90.65 107	93.39 66	94.08 41	95.29 120	94.15 169	93.21 34	95.22 106	94.92 98	95.82 128	95.75 56

NR-MVSNet		94.55 66	95.66 56	93.25 93	94.26 115	96.44 52	96.69 70	95.32 11	89.97 146	91.79 98	97.46 53	98.39 43	82.85 192	96.87 66	96.48 59	98.57 14	93.98 91

MGCNet		94.43 67	94.78 76	94.02 65	96.14 57	97.09 37	97.52 52	92.66 55	90.12 142	93.12 66	95.31 118	93.19 183	87.75 124	96.14 83	95.60 82	96.96 85	96.01 49

Vis-MVSNet		94.39 68	95.85 52	92.68 101	90.91 218	95.88 69	97.62 50	91.41 93	91.95 103	89.20 152	97.29 61	96.26 124	90.60 92	96.95 62	95.91 71	96.32 107	96.71 34
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
TSAR-MVS + GP.		94.25 69	94.81 74	93.60 83	96.52 46	95.80 72	94.37 145	92.47 60	90.89 130	88.92 159	95.34 117	94.38 167	92.85 46	96.36 80	95.62 80	96.47 100	95.28 65

CNVR-MVS		94.24 70	94.47 86	93.96 71	96.56 44	95.67 76	96.43 81	91.95 79	92.08 100	91.28 108	90.51 188	95.35 149	91.20 71	96.34 81	95.50 83	96.34 105	95.88 53

EC-MVSNet		94.23 71	93.81 119	94.71 50	94.85 97	96.23 57	97.14 55	93.40 46	81.79 224	91.58 102	93.29 155	95.21 157	93.13 35	97.73 38	96.95 42	98.20 32	95.45 60

v1192		93.98 72	93.94 111	94.01 67	93.91 129	94.63 108	97.00 61	89.75 130	91.01 128	96.50 10	97.93 29	98.26 50	91.74 61	92.06 169	92.05 150	95.18 146	91.66 148

Casviewmamba		93.97 73	95.50 59	92.18 111	94.23 116	95.44 81	95.94 90	91.14 98	93.80 57	86.49 186	97.98 26	98.66 30	88.55 115	95.26 104	94.08 116	96.73 88	93.30 106

v10		93.96 74	94.12 104	93.77 80	93.37 151	95.45 80	96.83 67	91.13 99	89.70 151	95.02 25	97.88 35	98.23 53	91.27 69	92.39 164	92.18 145	94.99 156	93.00 112

CDPH-MVS		93.96 74	93.86 113	94.08 63	96.31 52	95.84 70	96.92 63	91.85 82	87.21 178	91.25 110	92.83 161	96.06 132	91.05 77	95.57 95	94.81 100	97.12 78	94.72 75

MSLP-MVS++		93.91 76	94.30 96	93.45 85	95.51 78	95.83 71	93.12 188	91.93 81	91.45 115	91.40 105	87.42 224	96.12 131	93.27 31	96.57 76	96.40 61	95.49 134	96.29 43

v1921920		93.90 77	93.82 117	94.00 68	93.74 136	94.31 117	97.12 56	89.33 147	91.13 125	96.77 9	97.90 32	98.06 61	91.95 56	91.93 176	91.54 162	95.10 149	91.85 140

train_agg		93.89 78	93.46 132	94.40 53	97.35 14	93.78 134	97.63 49	92.19 67	88.12 167	90.52 127	93.57 151	95.78 138	92.31 52	94.78 115	93.46 126	96.36 103	94.70 78

v144192		93.89 78	93.85 114	93.94 72	93.50 145	94.33 115	97.12 56	89.49 138	90.89 130	96.49 11	97.78 37	98.27 49	91.89 58	92.17 168	91.70 159	95.19 145	91.78 143

v1240		93.89 78	93.72 122	94.09 62	93.98 125	94.31 117	97.12 56	89.37 143	90.74 136	96.92 8	98.05 25	97.89 71	92.15 55	91.53 187	91.60 160	94.99 156	91.93 136

NCCC		93.87 81	93.42 133	94.40 53	96.84 34	95.42 82	96.47 79	92.62 56	92.36 93	92.05 88	83.83 244	95.55 142	91.84 60	95.89 87	95.23 88	96.56 97	95.63 57

v1144		93.83 82	93.87 112	93.78 79	93.72 137	94.57 112	96.85 66	89.98 122	91.31 121	95.90 17	97.89 33	98.40 42	91.13 74	92.01 172	92.01 152	95.10 149	90.94 166

MVS_111021_HR		93.82 83	94.26 100	93.31 88	95.01 92	93.97 127	95.73 99	89.75 130	92.06 101	92.49 77	94.01 146	96.05 133	90.61 91	95.95 86	94.78 103	96.28 108	93.04 111

thisisatest0515		93.79 84	94.41 90	93.06 98	94.14 117	92.50 167	95.56 106	88.55 159	91.61 108	92.45 78	96.84 77	95.71 139	90.62 89	94.58 118	95.07 92	97.05 81	94.58 80

TAPA-MVS	88.94 13	93.78 85	94.31 95	93.18 95	94.14 117	95.99 63	95.74 98	86.98 188	93.43 65	93.88 44	90.16 195	96.88 109	91.05 77	94.33 124	93.95 117	97.28 73	95.40 61
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
GeoE		93.72 86	93.62 127	93.84 74	94.75 101	94.90 102	97.24 54	91.81 85	86.97 185	92.74 69	93.83 148	97.24 99	90.46 94	95.10 110	94.09 115	96.08 118	93.18 109

casdiffseed414692147		93.69 87	94.80 75	92.40 104	93.85 131	94.47 113	95.64 103	90.17 115	92.40 90	89.43 148	97.16 66	99.09 11	89.22 106	94.45 121	93.37 129	96.09 117	92.66 124

EPP-MVSNet		93.63 88	93.95 110	93.26 91	95.15 89	96.54 48	96.18 88	91.97 78	91.74 105	85.76 190	94.95 128	84.27 231	91.60 64	97.61 43	97.38 34	98.87 4	95.18 67

v8		93.60 89	93.82 117	93.34 86	93.13 163	95.06 95	96.39 82	90.75 105	89.90 149	94.03 42	97.70 41	98.21 55	91.08 76	92.36 165	91.47 163	94.63 173	92.07 132

MCST-MVS		93.60 89	93.40 135	93.83 75	95.30 83	95.40 84	96.49 78	90.87 103	90.08 143	91.72 99	90.28 193	95.99 134	91.69 62	93.94 136	92.99 135	96.93 86	95.13 68

PVSNet_Blended_VisFu		93.60 89	93.41 134	93.83 75	96.31 52	95.65 77	95.71 100	90.58 109	88.08 169	93.17 63	95.29 120	92.20 188	90.72 83	94.69 117	93.41 128	96.51 99	94.54 81

TransMVSNet (Re)		93.55 92	96.32 37	90.32 159	94.38 111	94.05 122	93.30 185	89.53 137	97.15 8	85.12 197	98.83 3	97.89 71	82.21 199	96.75 71	96.14 68	97.35 68	93.46 103

E6new		93.49 93	94.68 81	92.10 114	93.52 142	93.87 129	95.80 95	89.59 135	95.07 28	91.10 112	97.93 29	99.22 5	87.59 127	93.32 145	91.86 154	95.00 154	91.49 152

E6		93.49 93	94.68 81	92.10 114	93.52 142	93.87 129	95.80 95	89.59 135	95.07 28	91.10 112	97.93 29	99.22 5	87.59 127	93.32 145	91.86 154	95.00 154	91.49 152

DCV-MVSNet		93.49 93	95.15 66	91.55 129	94.05 121	95.92 68	95.15 119	91.21 95	92.76 81	87.01 182	89.71 199	97.16 101	83.90 185	97.65 40	96.87 45	97.99 43	95.95 52

v2v482		93.42 96	93.49 131	93.32 87	93.44 150	94.05 122	96.36 85	89.76 129	91.41 117	95.24 22	97.63 46	98.34 46	90.44 95	91.65 185	91.76 158	94.69 170	89.62 185

sasdasda		93.38 97	94.36 92	92.24 108	93.94 127	96.41 54	94.18 156	90.47 110	93.07 75	88.47 170	88.66 210	93.78 175	88.80 110	95.74 90	95.75 77	97.57 56	97.13 18

canonicalmvs		93.38 97	94.36 92	92.24 108	93.94 127	96.41 54	94.18 156	90.47 110	93.07 75	88.47 170	88.66 210	93.78 175	88.80 110	95.74 90	95.75 77	97.57 56	97.13 18

3Dnovator	91.81 5	93.36 99	94.27 99	92.29 107	92.99 170	95.03 96	95.76 97	87.79 169	93.82 56	92.38 81	92.19 171	93.37 182	88.14 121	95.26 104	94.85 99	96.69 91	95.40 61

pm-mvs1		93.27 100	95.94 48	90.16 160	94.13 119	93.66 137	92.61 205	89.91 125	95.73 18	84.28 212	98.51 13	98.29 48	82.80 193	96.44 78	95.76 76	97.25 74	93.21 108

casdiffmvs_mvg		93.27 100	94.83 73	91.45 135	93.59 141	94.47 113	94.91 128	89.83 128	92.04 102	87.14 180	97.57 49	98.47 39	86.03 154	94.07 134	94.44 110	97.21 77	92.76 118
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
test1111		93.25 102	94.43 88	91.88 118	95.09 91	94.97 100	94.58 140	92.81 52	93.60 59	83.79 217	97.17 65	89.25 216	87.59 127	97.54 44	96.57 54	97.42 64	91.89 137

Anonymous20231211		93.19 103	95.50 59	90.49 156	93.77 134	95.29 87	94.36 149	90.04 121	91.44 116	84.59 207	96.72 80	97.65 86	82.45 198	97.25 51	96.32 63	97.74 50	93.79 94

TinyColmap		93.17 104	93.33 136	93.00 99	93.84 132	92.76 159	94.75 136	88.90 154	93.97 53	97.48 4	95.28 122	95.29 152	88.37 117	95.31 103	91.58 161	94.65 172	89.10 189

E4		93.16 105	94.30 96	91.84 119	93.48 147	93.69 136	95.42 109	89.49 138	94.67 33	90.67 123	97.52 51	99.01 14	86.97 134	92.46 163	91.21 167	94.98 158	91.54 151

viewmacassd2359aftdt		93.16 105	94.69 80	91.39 138	93.30 155	93.71 135	95.03 124	87.70 170	94.69 32	89.53 147	97.63 46	98.92 16	87.73 125	93.63 141	92.14 147	95.05 152	92.08 131

MVS_111021_LR		93.15 107	93.65 124	92.56 102	93.89 130	92.28 170	95.09 120	86.92 190	91.26 124	92.99 68	94.46 138	96.22 127	90.64 87	95.11 109	93.45 127	95.85 126	92.74 119

FE-MVSNET2		93.14 108	94.47 86	91.60 128	91.62 205	93.79 133	95.37 112	89.92 124	94.18 46	90.83 117	96.68 83	98.24 52	85.30 164	93.77 137	94.37 113	96.58 96	90.24 179

CNLPA		93.14 108	93.67 123	92.53 103	94.62 106	94.73 105	95.00 126	86.57 196	92.85 78	92.43 79	90.94 182	94.67 163	90.35 96	95.41 98	93.70 123	96.23 111	93.37 105

PLC	87.27 15	93.08 110	92.92 145	93.26 91	94.67 102	95.03 96	94.38 144	90.10 116	91.69 106	92.14 85	87.24 225	93.91 173	91.61 63	95.05 111	94.73 106	96.67 92	92.80 115
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CANet		93.07 111	93.05 143	93.10 96	95.90 67	95.41 83	95.88 92	91.94 80	84.77 206	93.36 56	94.05 144	95.25 155	86.25 150	94.33 124	93.94 118	95.30 139	93.58 100

TSAR-MVS + COLMAP		93.06 112	93.65 124	92.36 105	94.62 106	94.28 119	95.36 114	89.46 141	92.18 98	91.64 100	95.55 112	95.27 154	88.60 114	93.24 147	92.50 141	94.46 177	92.55 126

viewdifsd2359ckpt09		93.05 113	93.85 114	92.11 113	93.66 140	95.22 90	95.50 108	89.84 127	90.44 138	88.67 168	94.97 127	97.67 85	89.07 108	93.11 153	93.35 130	95.94 123	92.23 129

ECVR-MVS		93.05 113	94.25 101	91.65 125	94.76 99	95.23 88	94.26 153	92.80 53	92.49 85	83.90 215	96.75 79	89.99 207	86.84 139	97.62 41	96.72 49	97.32 70	90.92 167

hybridcas		93.00 115	94.72 78	91.00 146	93.68 139	94.33 115	95.09 120	89.23 148	93.77 58	84.96 201	97.89 33	98.43 41	87.27 131	94.08 133	92.63 139	95.77 129	91.88 138

E5new		92.97 116	94.09 105	91.68 123	93.48 147	93.65 139	95.26 115	89.37 143	94.47 35	90.54 125	97.30 59	98.79 25	86.56 145	92.00 173	90.74 178	94.86 163	91.65 149

E5		92.97 116	94.09 105	91.68 123	93.48 147	93.65 139	95.26 115	89.37 143	94.47 35	90.54 125	97.30 59	98.79 25	86.56 145	92.00 173	90.74 178	94.86 163	91.65 149

Effi-MVS+		92.93 118	92.16 160	93.83 75	94.29 113	93.53 149	95.04 123	92.98 50	85.27 203	94.46 32	90.24 194	95.34 150	89.99 99	93.72 138	94.23 114	96.22 112	92.79 116

Fast-Effi-MVS+		92.93 118	92.64 152	93.27 90	93.81 133	93.88 128	95.90 91	90.61 108	83.98 212	92.71 70	92.81 163	96.22 127	90.67 85	94.90 114	93.92 119	95.92 124	92.77 117

HQP-MVS		92.87 120	92.49 153	93.31 88	95.75 73	95.01 99	95.64 103	91.06 101	88.54 162	91.62 101	88.16 216	96.25 125	89.47 103	92.26 167	91.81 156	96.34 105	95.40 61

FMVSNet1		92.86 121	95.26 64	90.06 162	92.40 187	95.16 91	94.37 145	92.22 64	93.18 71	82.16 227	96.76 78	97.48 94	81.85 203	95.32 100	94.98 95	97.34 69	93.93 92

CLD-MVS		92.81 122	94.32 94	91.05 145	95.39 82	95.31 86	95.82 94	81.44 245	89.40 154	91.94 90	95.86 100	97.36 95	85.83 157	95.35 99	94.59 108	95.85 126	92.34 127
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
IS_MVSNet		92.76 123	93.25 138	92.19 110	94.91 96	95.56 78	95.86 93	92.12 71	88.10 168	82.71 222	93.15 158	88.30 219	88.86 109	97.29 48	96.95 42	98.66 10	93.38 104

E3new		92.75 124	93.78 120	91.55 129	93.35 152	93.54 147	95.17 117	89.17 149	93.49 61	90.29 135	97.00 73	98.65 31	86.58 143	91.86 179	90.64 180	94.75 166	91.27 156

E3		92.75 124	93.78 120	91.55 129	93.35 152	93.54 147	95.16 118	89.17 149	93.48 64	90.32 132	97.01 72	98.65 31	86.58 143	91.86 179	90.64 180	94.75 166	91.27 156

FC-MVSNet-train		92.75 124	95.40 62	89.66 171	95.21 87	94.82 103	97.00 61	89.40 142	91.13 125	81.71 229	97.72 40	96.43 121	77.57 234	96.89 64	96.72 49	97.05 81	94.09 88

V42		92.67 127	93.50 130	91.71 122	91.41 207	92.96 157	95.71 100	85.00 211	89.67 152	93.22 61	97.67 44	98.01 65	91.02 79	92.65 158	92.12 148	93.86 190	91.42 154

PM-MVS		92.65 128	93.20 141	92.00 116	92.11 195	90.16 215	95.99 89	84.81 216	91.31 121	92.41 80	95.87 99	96.64 117	92.35 51	93.65 140	92.91 136	94.34 181	91.85 140

MVSMamba_PlusPlus		92.57 129	93.24 139	91.79 121	95.49 79	95.10 94	93.82 163	89.60 134	86.44 190	89.06 157	90.82 184	94.93 162	87.09 132	95.00 112	95.23 88	95.68 131	95.13 68

QAPM		92.57 129	93.51 129	91.47 134	92.91 172	94.82 103	93.01 190	87.51 175	91.49 112	91.21 111	92.24 169	91.70 193	88.74 112	94.54 120	94.39 112	95.41 136	95.37 64

MIMVSNet1		92.52 131	94.88 71	89.77 167	96.09 58	91.99 177	96.92 63	89.68 132	95.92 17	84.55 208	96.64 84	98.21 55	78.44 226	96.08 84	95.10 91	92.91 216	90.22 180

viewmanbaseed2359cas		92.46 132	93.85 114	90.83 149	93.07 165	93.47 151	94.55 142	87.10 186	92.76 81	88.70 167	96.72 80	98.35 45	86.85 138	92.70 156	91.22 166	94.71 169	91.76 145

tfpnnormal		92.45 133	94.77 77	89.74 168	93.95 126	93.44 153	93.25 186	88.49 161	95.27 25	83.20 220	96.51 87	96.23 126	83.17 190	95.47 97	94.52 109	96.38 102	91.97 135

PCF-MVS	87.46 14	92.44 134	91.80 163	93.19 94	94.66 103	95.80 72	96.37 83	90.19 114	87.57 174	92.23 84	89.26 204	93.97 172	89.24 104	91.32 191	90.82 177	96.46 101	93.86 93
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
casdiffmvs		92.42 135	93.99 109	90.60 154	93.25 157	93.82 132	94.28 151	88.73 157	91.53 110	84.53 210	97.74 38	98.64 33	86.60 142	93.21 149	91.20 168	96.21 113	91.76 145
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
viewcassd2359sk11		92.41 136	93.33 136	91.34 140	93.24 158	93.43 154	94.96 127	88.94 153	92.44 89	90.07 137	96.53 86	98.31 47	86.27 149	91.34 190	90.17 188	94.57 175	91.11 162

AdaColmap		92.41 136	91.49 169	93.48 84	95.96 64	95.02 98	95.37 112	91.73 89	87.97 171	91.28 108	82.82 248	91.04 200	90.62 89	95.82 89	95.07 92	95.95 122	92.67 120

v148		92.38 138	92.78 150	91.91 117	92.86 173	92.13 173	94.84 130	87.03 187	91.47 114	93.07 67	96.92 75	98.89 18	90.10 98	92.05 170	89.69 193	93.56 196	88.27 200

pmmvs-eth3d		92.34 139	92.33 155	92.34 106	92.67 178	90.67 203	96.37 83	89.06 151	90.98 129	93.60 53	97.13 68	97.02 105	88.29 118	90.20 202	91.42 164	94.07 184	88.89 194

DELS-MVS		92.33 140	93.61 128	90.83 149	92.84 175	95.13 93	94.76 134	87.22 183	87.78 173	88.42 173	95.78 103	95.28 153	85.71 160	94.44 122	93.91 120	96.01 120	92.97 113
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
Effi-MVS+-dtu		92.32 141	91.66 167	93.09 97	95.13 90	94.73 105	94.57 141	92.14 69	81.74 225	90.33 131	88.13 217	95.91 135	89.24 104	94.23 129	93.65 125	97.12 78	93.23 107

MGCFI-Net		92.31 142	94.25 101	90.04 165	93.75 135	95.96 65	93.32 183	90.28 113	93.28 67	80.57 240	88.79 208	93.78 175	84.89 171	95.55 96	95.31 85	97.45 61	97.10 21

UGNet		92.31 142	94.70 79	89.53 173	90.99 215	95.53 79	96.19 87	92.10 73	91.35 120	85.76 190	95.31 118	95.48 145	76.84 239	95.22 106	94.79 102	95.32 138	95.19 66
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
viewdifsd2359ckpt13		92.24 144	93.22 140	91.10 144	93.01 169	93.63 141	94.65 139	87.69 171	90.81 134	88.80 165	95.59 110	97.98 66	87.51 130	91.98 175	90.83 176	94.94 159	91.74 147

USDC		92.17 145	92.17 159	92.18 111	92.93 171	92.22 171	93.66 169	87.41 178	93.49 61	97.99 1	94.10 143	96.68 116	86.46 147	92.04 171	89.18 200	94.61 174	87.47 207

ETV-MVS		92.12 146	90.44 180	94.08 63	96.36 50	93.63 141	96.27 86	92.00 76	78.90 244	92.13 86	85.29 238	89.85 210	90.26 97	97.07 57	96.29 65	97.46 59	92.04 133

IterMVS-LS		92.10 147	92.33 155	91.82 120	93.18 159	93.66 137	92.80 201	92.27 63	90.82 132	90.59 124	97.19 63	90.97 201	87.76 123	89.60 209	90.94 173	94.34 181	93.16 110
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
E2		92.09 148	92.90 146	91.16 143	93.16 162	93.35 155	94.76 134	88.75 156	91.40 118	89.85 138	95.98 97	97.95 69	85.98 156	90.86 196	89.74 191	94.43 178	90.99 165

MSDG		92.09 148	92.84 149	91.22 142	92.55 180	92.97 156	93.42 177	85.43 207	90.24 141	91.83 95	94.70 132	94.59 164	88.48 116	94.91 113	93.31 132	95.59 133	89.15 188

EIA-MVS		91.95 150	90.36 182	93.81 78	96.54 45	94.65 107	95.38 111	90.40 112	78.01 249	93.72 49	86.70 232	91.95 190	89.93 100	95.67 94	94.72 107	96.89 87	90.79 170

MAR-MVS		91.86 151	91.14 174	92.71 100	94.29 113	94.24 120	94.91 128	91.82 84	81.66 226	93.32 57	84.51 241	93.42 181	86.86 137	95.16 108	94.44 110	95.05 152	94.53 82
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
viewdifsd2359ckpt11		91.80 152	94.01 107	89.22 181	92.52 181	91.95 179	93.78 164	84.14 223	93.11 72	83.97 213	97.68 42	99.12 8	86.05 152	94.17 130	90.89 174	94.88 161	91.18 160

viewmsd2359difaftdt		91.80 152	94.01 107	89.22 181	92.52 181	91.95 179	93.78 164	84.14 223	93.11 72	83.97 213	97.68 42	99.12 8	86.05 152	94.16 131	90.89 174	94.88 161	91.18 160

EU-MVSNet		91.63 154	92.73 151	90.35 158	88.36 246	87.89 227	96.53 76	81.51 244	92.45 88	91.82 96	96.44 90	97.05 104	93.26 32	94.10 132	88.94 205	90.61 225	92.24 128

viewdifsd2359ckpt07		91.59 155	93.64 126	89.19 183	92.86 173	92.58 165	94.25 155	84.97 212	94.17 48	85.53 193	97.60 48	98.59 36	85.99 155	91.85 181	88.85 207	91.52 222	91.87 139

FC-MVSNet-test		91.49 156	94.43 88	88.07 205	94.97 93	90.53 208	95.42 109	91.18 97	93.24 69	72.94 260	98.37 15	93.86 174	78.78 219	97.82 32	96.13 69	95.13 147	91.05 163

FA-MVS(training)		91.38 157	91.18 173	91.62 127	93.49 146	92.38 168	95.03 124	90.81 104	87.20 179	91.46 104	93.00 160	89.47 213	84.19 179	93.20 151	92.08 149	94.74 168	90.90 168

viewmamba		91.25 158	92.87 148	89.36 178	91.65 203	91.96 178	93.62 171	86.76 192	90.57 137	86.42 187	97.00 73	98.07 59	83.99 182	92.49 162	89.54 196	93.75 193	90.44 175

FE-MVSNET		91.21 159	92.90 146	89.24 180	90.93 217	91.69 183	93.46 175	87.85 168	92.35 94	85.06 200	94.84 131	96.63 118	82.80 193	92.98 154	93.22 133	95.36 137	88.58 196

usedtu_dtu_shiyan2		91.17 160	93.05 143	88.98 186	95.95 65	92.70 163	93.66 169	91.85 82	96.05 16	82.16 227	93.34 154	98.87 20	76.62 241	93.56 142	92.03 151	93.66 195	84.77 223

OpenMVS	89.22 12	91.09 161	91.42 170	90.71 152	92.79 177	93.61 144	92.74 203	85.47 206	86.10 196	90.73 118	85.71 237	93.07 186	86.69 141	94.07 134	93.34 131	95.86 125	94.02 90

onestephybrid01		91.06 162	92.45 154	89.44 175	91.76 199	92.07 175	93.67 167	87.22 183	87.19 180	85.83 189	96.07 96	97.93 70	84.20 178	92.82 155	90.21 187	93.99 185	90.87 169

diffmvs_AUTHOR		91.06 162	93.06 142	88.71 195	91.67 202	91.66 184	92.77 202	85.36 209	91.29 123	85.38 195	97.45 55	98.26 50	83.74 186	91.81 182	89.70 192	93.37 207	91.27 156

FPMVS		90.81 164	91.60 168	89.88 166	92.52 181	88.18 223	93.31 184	83.62 227	91.59 109	88.45 172	88.96 207	89.73 212	86.96 135	96.42 79	95.69 79	94.43 178	90.65 171

DI_MVS_pp		90.68 165	90.40 181	91.00 146	92.43 186	92.61 164	94.17 158	88.98 152	88.32 166	88.76 166	93.67 149	87.58 221	86.44 148	89.74 207	90.33 184	95.24 142	90.56 174

Vis-MVSNet (Re-imp)		90.68 165	92.18 158	88.92 189	94.63 104	92.75 160	92.91 194	91.20 96	89.21 157	75.01 256	93.96 147	89.07 217	82.72 196	95.88 88	95.30 86	97.08 80	89.08 190

DPM-MVS		90.67 167	89.86 186	91.63 126	95.29 85	94.16 121	94.52 143	89.63 133	89.59 153	89.67 143	81.95 250	88.64 218	85.75 159	90.46 199	90.43 183	94.91 160	93.77 95

dtuplus		90.47 168	91.79 164	88.92 189	91.92 197	90.59 207	92.93 193	85.60 204	89.34 156	85.12 197	95.71 105	97.78 78	84.05 180	90.93 195	87.82 212	93.88 188	90.39 176

diffmvs		90.44 169	92.23 157	88.35 201	91.36 209	91.38 190	92.45 209	84.84 215	89.88 150	85.09 199	96.69 82	97.71 82	83.33 189	90.01 206	88.96 204	93.03 213	91.00 164
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
FMVSNet2		90.28 170	92.04 162	88.23 203	91.22 211	94.05 122	92.88 195	90.69 106	86.53 188	79.89 244	94.38 139	92.73 187	78.54 222	91.64 186	92.26 144	96.17 114	92.67 120

IterMVS-SCA-FT		90.24 171	89.37 192	91.26 141	92.50 184	92.11 174	91.69 222	87.48 176	87.05 184	91.82 96	95.76 104	87.25 222	91.36 67	89.02 215	85.53 226	92.68 217	88.90 193

MVS_Test		90.19 172	90.58 176	89.74 168	92.12 194	91.74 182	92.51 206	88.54 160	82.80 218	87.50 178	94.62 133	95.02 160	83.97 183	88.69 218	89.32 198	93.79 191	91.85 140

EPNet		90.17 173	89.07 194	91.45 135	97.25 19	90.62 206	94.84 130	93.54 44	80.96 228	91.85 94	86.98 228	85.88 227	77.79 231	92.30 166	92.58 140	93.41 202	94.20 87
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
viewmambaseed2359dif		90.16 174	91.38 171	88.72 193	91.64 204	90.75 199	92.73 204	85.32 210	87.92 172	84.90 202	95.63 106	97.49 92	84.05 180	90.27 201	87.28 214	93.71 194	90.35 178

hybridnocas07		90.14 175	92.12 161	87.83 208	90.95 216	90.85 197	92.22 212	84.61 219	88.53 163	84.79 205	96.64 84	97.86 76	81.44 208	91.88 178	88.90 206	92.97 214	90.17 182

PVSNet_BlendedMVS		90.09 176	90.12 184	90.05 163	92.40 187	92.74 161	91.74 218	85.89 201	80.54 231	90.30 133	88.54 212	95.51 143	84.69 174	92.64 159	90.25 185	95.28 140	90.61 172

PVSNet_Blended		90.09 176	90.12 184	90.05 163	92.40 187	92.74 161	91.74 218	85.89 201	80.54 231	90.30 133	88.54 212	95.51 143	84.69 174	92.64 159	90.25 185	95.28 140	90.61 172

usedtu_dtu_shiyan1		90.01 178	90.53 179	89.39 177	90.47 223	91.62 187	93.36 179	87.13 185	87.52 175	87.00 183	92.63 166	94.03 171	82.94 191	89.33 213	91.00 172	95.46 135	87.61 203

pmmvs4		89.95 179	89.32 193	90.69 153	91.60 206	89.17 220	94.37 145	87.63 172	88.07 170	91.02 115	94.50 136	90.50 205	86.13 151	86.33 232	89.40 197	93.39 204	87.29 211

hybrid		89.90 180	91.77 165	87.72 210	90.87 219	90.63 205	92.16 214	84.26 221	88.34 165	84.87 203	95.91 98	97.63 88	81.53 207	91.51 188	88.47 209	92.61 218	89.87 183

MDA-MVSNet-bldmvs		89.75 181	91.67 166	87.50 212	74.25 270	90.88 196	94.68 137	85.89 201	91.64 107	91.03 114	95.86 100	94.35 168	89.10 107	96.87 66	86.37 221	90.04 227	85.72 220

WB-MVS		89.70 182	94.13 103	84.54 234	88.16 248	92.57 166	88.90 243	88.32 162	96.67 11	73.61 259	98.29 20	98.80 23	80.60 211	95.73 92	92.18 145	87.66 242	84.64 224

tttt0517		89.64 183	88.05 206	91.49 133	93.52 142	91.65 185	93.67 167	87.53 173	82.77 219	89.39 150	90.37 192	70.05 256	88.21 119	93.71 139	93.79 121	96.63 93	94.04 89

PatchMatch-RL		89.59 184	88.80 198	90.51 155	92.20 193	88.00 226	91.72 220	86.64 193	84.75 207	88.25 174	87.10 227	90.66 204	89.85 102	93.23 148	92.28 143	94.41 180	85.60 221

Fast-Effi-MVS+-dtu		89.57 185	88.42 202	90.92 148	93.35 152	91.57 188	93.01 190	95.71 9	78.94 243	87.65 177	84.68 240	93.14 185	82.00 201	90.84 197	91.01 171	93.78 192	88.77 195

thisisatest0530		89.54 186	87.99 210	91.35 139	93.17 160	91.31 191	93.45 176	87.53 173	82.96 217	89.17 154	90.45 189	70.32 255	88.21 119	93.37 144	93.79 121	96.54 98	93.71 97

test2506		89.51 187	87.77 213	91.55 129	94.76 99	95.23 88	94.26 153	92.80 53	92.49 85	83.31 219	89.97 197	50.93 276	86.84 139	97.62 41	96.72 49	97.32 70	91.42 154

GBi-Net		89.35 188	90.58 176	87.91 206	91.22 211	94.05 122	92.88 195	90.05 118	79.40 235	78.60 247	90.58 185	87.05 223	78.54 222	95.32 100	94.98 95	96.17 114	92.67 120

test1		89.35 188	90.58 176	87.91 206	91.22 211	94.05 122	92.88 195	90.05 118	79.40 235	78.60 247	90.58 185	87.05 223	78.54 222	95.32 100	94.98 95	96.17 114	92.67 120

thres600view7		89.14 190	88.83 196	89.51 174	93.71 138	93.55 145	93.93 162	88.02 166	87.30 177	82.40 223	81.18 251	80.63 242	82.69 197	94.27 126	95.90 72	96.27 109	88.94 192

CVMVSNet		88.97 191	89.73 188	88.10 204	87.33 253	85.22 239	94.68 137	78.68 247	88.94 159	86.98 184	95.55 112	85.71 228	89.87 101	91.19 192	89.69 193	91.05 223	91.78 143

CANet_DTU		88.95 192	89.51 191	88.29 202	93.12 164	91.22 194	93.61 172	83.47 230	80.07 234	90.71 122	89.19 205	93.68 179	76.27 244	91.44 189	91.17 170	92.59 219	89.83 184

gbinet_0.2-2-1-0.02		88.79 193	88.26 203	89.40 176	89.67 232	91.24 192	94.03 160	84.65 218	85.76 198	89.02 158	92.83 161	90.75 203	85.62 161	85.86 234	82.42 233	93.41 202	88.98 191

GA-MVS		88.76 194	88.04 208	89.59 172	92.32 190	91.46 189	92.28 211	86.62 194	83.82 214	89.84 139	92.51 168	81.94 236	83.53 188	89.41 211	89.27 199	92.95 215	87.90 201

pmmvs5		88.63 195	89.70 189	87.39 213	89.24 235	90.64 204	91.87 217	82.13 240	83.34 215	87.86 176	94.58 134	96.15 130	79.87 215	87.33 227	89.07 203	93.39 204	86.76 214

thres400		88.54 196	88.15 205	88.98 186	93.17 160	92.84 158	93.56 173	86.93 189	86.45 189	82.37 224	79.96 253	81.46 239	81.83 204	93.21 149	94.76 104	96.04 119	88.39 198

blended_shiyan8		88.52 197	88.03 209	89.08 184	89.78 230	90.69 200	93.34 181	82.82 233	87.12 182	89.21 151	91.51 175	91.71 192	85.38 162	85.01 238	82.73 232	93.96 186	87.47 207

blended_shiyan6		88.52 197	88.05 206	89.07 185	89.79 228	90.69 200	93.34 181	82.81 234	87.12 182	89.19 153	91.48 176	91.81 191	85.32 163	84.98 239	82.74 231	93.95 187	87.52 205

CDS-MVSNet		88.41 199	89.79 187	86.79 219	94.55 109	90.82 198	92.50 207	89.85 126	83.26 216	80.52 241	91.05 178	89.93 209	69.11 256	93.17 152	92.71 138	94.21 183	87.63 202
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
gg-mvs-nofinetune		88.32 200	88.81 197	87.75 209	93.07 165	89.37 219	89.06 242	95.94 8	95.29 23	87.15 179	97.38 56	76.38 245	68.05 259	91.04 193	89.10 202	93.24 209	83.10 231

IterMVS		88.32 200	88.25 204	88.41 200	90.83 220	91.24 192	93.07 189	81.69 242	86.77 186	88.55 169	95.61 107	86.91 226	87.01 133	87.38 226	83.77 228	89.29 231	86.06 219
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
thres200		88.29 202	87.88 211	88.76 192	92.50 184	93.55 145	92.47 208	88.02 166	84.80 205	81.44 231	79.28 255	82.20 235	81.83 204	94.27 126	93.67 124	96.27 109	87.40 209

IB-MVS	86.01 17	88.24 203	87.63 214	88.94 188	92.03 196	91.77 181	92.40 210	85.58 205	78.24 246	84.85 204	71.99 262	93.45 180	83.96 184	93.48 143	92.33 142	94.84 165	92.15 130
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
MDTV_nov1_ep13_2view		88.22 204	87.85 212	88.65 196	91.40 208	86.75 231	94.07 159	84.97 212	88.86 161	93.20 62	96.11 95	96.21 129	83.70 187	87.29 228	80.29 243	84.56 251	79.46 247

test20.03		88.20 205	91.26 172	84.63 232	96.64 42	89.39 218	90.73 229	89.97 123	91.07 127	72.02 262	94.98 126	95.45 146	69.35 255	92.70 156	91.19 169	89.06 233	84.02 225

HyFIR lowres test		88.19 206	86.56 223	90.09 161	91.24 210	92.17 172	94.30 150	88.79 155	84.06 209	85.45 194	89.52 202	85.64 229	88.64 113	85.40 236	87.28 214	92.14 221	81.87 235

ET-MVSNet_ETH3D		88.06 207	85.75 228	90.74 151	92.82 176	90.68 202	93.77 166	88.59 158	81.22 227	89.78 141	89.15 206	66.79 269	84.29 177	91.72 184	91.34 165	95.22 143	89.36 187

wanda-best-256-512		87.94 208	87.36 218	88.61 197	89.23 236	90.35 210	92.84 198	82.30 235	86.26 192	88.91 160	90.96 180	91.43 195	84.94 168	84.27 240	81.61 236	93.45 197	86.67 216

FE-blended-shiyan7		87.94 208	87.36 218	88.61 197	89.23 236	90.35 210	92.84 198	82.30 235	86.26 192	88.91 160	90.96 180	91.43 195	84.94 168	84.27 240	81.61 236	93.45 197	86.67 216

tfpn200view9		87.94 208	87.51 216	88.44 199	92.28 191	93.63 141	93.35 180	88.11 164	80.90 229	80.89 237	78.25 256	82.25 233	79.65 217	94.27 126	94.76 104	96.36 103	88.48 197

FMVSNet3		87.90 211	88.63 200	87.04 215	89.78 230	93.46 152	91.62 223	90.05 118	79.40 235	78.60 247	90.58 185	87.05 223	77.07 238	88.03 223	89.86 190	95.12 148	92.04 133

MS-PatchMatch		87.72 212	88.62 201	86.66 220	90.81 221	88.18 223	90.92 226	82.25 239	85.86 197	80.40 242	90.14 196	89.29 215	84.93 170	89.39 212	89.12 201	90.67 224	88.34 199

Anonymous20231206		87.45 213	89.66 190	84.87 229	94.00 122	87.73 229	91.36 224	86.41 198	88.89 160	75.03 255	92.59 167	96.82 111	72.48 253	89.72 208	88.06 210	89.93 228	83.81 227

EPNet_dtu		87.40 214	86.27 224	88.72 193	95.68 75	83.37 246	92.09 215	90.08 117	78.11 248	91.29 107	86.33 233	89.74 211	75.39 248	89.07 214	87.89 211	87.81 238	89.38 186
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
baseline1		86.96 215	87.58 215	86.24 223	93.07 165	90.44 209	89.24 241	86.85 191	85.14 204	77.26 253	90.45 189	76.09 247	75.79 245	91.80 183	91.81 156	95.20 144	87.35 210

dtuonlycased		86.78 216	90.70 175	82.21 241	89.31 233	91.65 185	94.27 152	75.13 254	89.94 148	59.16 269	93.38 152	95.67 140	87.63 126	90.99 194	85.76 223	87.74 241	87.53 204

baseline		86.71 217	88.89 195	84.16 235	87.85 249	85.23 238	89.82 235	77.69 250	84.03 211	84.75 206	94.91 129	94.59 164	77.19 237	86.57 231	86.51 220	87.66 242	90.36 177

CHOSEN 1792x2688		86.64 218	86.62 221	86.65 221	90.33 225	87.86 228	93.19 187	83.30 231	83.95 213	82.32 225	87.93 219	89.34 214	86.92 136	85.64 235	84.95 227	83.85 255	86.68 215

dmvs_re		86.51 219	86.14 226	86.95 217	93.07 165	86.11 234	92.01 216	86.04 200	72.70 259	79.10 245	75.37 259	89.99 207	78.10 230	94.56 119	93.01 134	93.35 208	91.26 159

testgi		86.49 220	90.31 183	82.03 242	95.63 76	88.18 223	93.47 174	84.89 214	93.23 70	69.54 266	87.16 226	97.96 68	60.66 263	91.90 177	89.90 189	87.99 236	83.84 226

thres100view900		86.46 221	86.00 227	86.99 216	92.28 191	91.03 195	91.09 225	84.49 220	80.90 229	80.89 237	78.25 256	82.25 233	77.57 234	90.17 203	92.84 137	95.63 132	86.57 218

gm-plane-assit		86.15 222	82.51 237	90.40 157	95.81 71	92.29 169	97.99 36	84.66 217	92.15 99	93.15 64	97.84 36	44.65 277	78.60 221	88.02 224	85.95 222	92.20 220	76.69 256

CMPMVS	66.55 18	85.55 223	87.46 217	83.32 237	84.99 256	81.97 251	79.19 268	75.93 252	79.32 238	88.82 163	85.09 239	91.07 199	82.12 200	92.56 161	89.63 195	88.84 234	92.56 125
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
CR-MVSNet		85.32 224	81.58 239	89.69 170	90.36 224	84.79 242	86.72 258	92.22 64	75.38 254	90.73 118	90.41 191	67.88 260	84.86 172	83.76 246	85.74 224	93.24 209	83.14 229

baseline2		84.95 225	82.68 236	87.59 211	92.64 179	88.41 222	90.09 231	84.25 222	75.88 252	85.23 196	82.49 249	71.15 253	80.14 214	88.21 222	87.21 218	93.21 212	85.39 222

pmnet_mix02		84.85 226	86.58 222	82.83 238	90.19 226	81.10 254	88.52 246	78.58 248	91.50 111	80.32 243	96.48 88	95.86 136	75.42 247	85.17 237	76.44 254	83.91 254	79.51 246

MVSTER		84.79 227	83.79 231	85.96 225	89.14 240	89.80 216	89.39 239	82.99 232	74.16 258	82.78 221	85.97 235	66.81 268	76.84 239	90.77 198	88.83 208	94.66 171	90.19 181

MIMVSNet		84.76 228	86.75 220	82.44 240	91.71 201	85.95 235	89.74 237	89.49 138	85.28 202	69.69 265	87.93 219	90.88 202	64.85 261	88.26 221	87.74 213	89.18 232	81.24 236

SCA		84.69 229	81.10 240	88.87 191	89.02 241	90.31 214	92.21 213	92.09 74	82.72 220	89.68 142	86.83 230	73.08 249	85.80 158	80.50 255	77.51 250	84.45 253	76.80 255

new-patchmatchnet		84.45 230	88.75 199	79.43 251	93.28 156	81.87 252	81.68 265	83.48 229	94.47 35	71.53 263	98.33 16	97.88 74	58.61 266	90.35 200	77.33 251	87.99 236	81.05 238

FE-MVSNET3		83.78 231	80.73 243	87.34 214	89.23 236	90.35 210	92.84 198	82.30 235	86.26 192	81.00 233	68.18 265	66.96 263	85.24 165	84.27 240	81.61 236	93.45 197	87.52 205

PatchT		83.44 232	81.10 240	86.18 224	77.92 268	82.58 250	89.87 234	87.39 179	75.88 252	90.73 118	89.86 198	66.71 270	84.86 172	83.76 246	85.74 224	86.33 248	83.14 229

RPMNet		83.42 233	78.40 251	89.28 179	89.79 228	84.79 242	90.64 230	92.11 72	75.38 254	87.10 181	79.80 254	61.99 275	82.79 195	81.88 253	82.07 235	93.23 211	82.87 232

usedtu_blend_shiyan5		83.28 234	80.64 244	86.37 222	89.23 236	90.35 210	87.00 256	82.30 235	86.26 192	81.00 233	68.18 265	66.96 263	85.24 165	84.27 240	81.61 236	93.45 197	86.85 212

TAMVS		82.96 235	86.15 225	79.24 254	90.57 222	83.12 249	87.29 252	75.12 255	84.06 209	65.81 267	92.22 170	88.27 220	69.11 256	88.72 216	87.26 217	87.56 244	79.38 248

PatchmatchNet		82.44 236	78.69 250	86.83 218	89.81 227	81.55 253	90.78 228	87.27 182	82.39 223	88.85 162	88.31 215	70.96 254	81.90 202	78.58 259	74.33 262	82.35 259	74.69 259
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MDTV_nov1_ep13		82.33 237	79.66 245	85.45 227	88.83 243	83.88 244	90.09 231	81.98 241	79.07 242	88.82 163	88.70 209	73.77 248	78.41 227	80.29 257	76.08 255	84.56 251	75.83 257

CostFormer		82.15 238	79.54 246	85.20 228	88.92 242	85.70 236	90.87 227	86.26 199	79.19 241	83.87 216	87.89 221	69.20 258	76.62 241	77.50 262	75.28 258	84.69 250	82.02 234

dtuonly		82.04 239	83.24 235	80.64 247	86.49 255	76.95 259	90.09 231	69.99 262	82.43 222	81.66 230	91.23 177	91.26 197	75.79 245	83.81 245	79.65 244	79.82 262	77.38 253

PMMVS		81.93 240	83.48 233	80.12 250	72.35 271	75.05 264	88.54 245	64.01 264	77.02 251	82.22 226	87.51 223	91.12 198	79.70 216	86.59 229	86.64 219	93.88 188	80.41 241

pmmvs3		81.69 241	83.83 230	79.19 255	78.33 267	78.57 257	89.53 238	58.71 267	78.88 245	84.34 211	88.36 214	91.96 189	77.69 233	87.48 225	82.42 233	86.54 247	79.18 249

tpm		81.58 242	78.84 248	84.79 231	91.11 214	79.50 255	89.79 236	83.75 225	79.30 239	92.05 88	90.98 179	64.78 272	74.54 249	80.50 255	76.67 252	77.49 265	80.15 244

test0.0.03 1		81.51 243	83.30 234	79.42 252	93.99 123	86.50 232	85.93 262	87.32 180	78.16 247	61.62 268	80.78 252	81.78 237	59.87 264	88.40 220	87.27 216	87.78 240	80.19 243

dps		81.42 244	77.88 256	85.56 226	87.67 251	85.17 240	88.37 248	87.46 177	74.37 257	84.55 208	86.80 231	62.18 274	80.20 213	81.13 254	77.52 249	85.10 249	77.98 252

test-LLR		80.62 245	77.20 259	84.62 233	93.99 123	75.11 262	87.04 254	87.32 180	70.11 262	78.59 250	83.17 246	71.60 251	73.88 251	82.32 250	79.20 246	86.91 245	78.87 250

blend_shiyan4		80.12 246	77.11 261	83.63 236	78.60 266	89.75 217	83.59 264	79.95 246	64.53 268	81.00 233	68.18 265	66.96 263	85.24 165	82.23 252	81.29 241	93.38 206	86.85 212

tpm cat1		80.03 247	75.93 263	84.81 230	89.31 233	83.26 248	88.86 244	86.55 197	79.24 240	86.10 188	84.22 242	63.62 273	77.37 236	73.43 266	70.88 265	80.67 260	76.87 254

N_pmnet		79.33 248	84.22 229	73.62 261	91.72 200	73.72 265	86.11 260	76.36 251	92.38 91	53.38 270	95.54 114	95.62 141	59.14 265	84.23 244	74.84 261	75.03 268	73.25 263

EPMVS		79.26 249	78.20 254	80.49 248	87.04 254	78.86 256	86.08 261	83.51 228	82.63 221	73.94 258	89.59 200	68.67 259	72.03 254	78.17 260	75.08 260	80.37 261	74.37 260

CHOSEN 280x420		79.24 250	78.26 253	80.38 249	79.60 265	68.80 270	89.32 240	75.38 253	77.25 250	78.02 252	75.57 258	76.17 246	81.19 209	88.61 219	81.39 240	78.79 263	80.03 245

ADS-MVSNet		79.11 251	79.38 247	78.80 257	81.90 261	75.59 261	84.36 263	83.69 226	87.31 176	76.76 254	87.58 222	76.90 244	68.55 258	78.70 258	75.56 257	77.53 264	74.07 261

FMVSNet5		79.08 252	78.83 249	79.38 253	87.52 252	86.78 230	87.64 250	78.15 249	69.54 264	70.64 264	65.97 269	65.44 271	63.87 262	90.17 203	90.46 182	88.48 235	83.45 228

0.4-1-1-0.1		78.93 253	75.69 264	82.71 239	82.54 259	86.31 233	88.34 249	74.63 256	67.88 265	81.41 232	73.65 260	67.37 261	79.03 218	75.97 263	76.53 253	90.33 226	82.09 233

tpmrst		78.81 254	76.18 262	81.87 244	88.56 244	77.45 258	86.74 257	81.52 243	80.08 233	83.48 218	90.84 183	66.88 267	74.54 249	73.04 267	71.02 264	76.38 266	73.95 262

test-mter		78.71 255	78.35 252	79.12 256	84.03 257	76.58 260	88.51 247	59.06 266	71.06 260	78.87 246	83.73 245	71.83 250	76.44 243	83.41 249	80.61 242	87.79 239	81.24 236

MVS-HIRNet		78.28 256	75.28 265	81.79 245	80.33 264	69.38 269	76.83 269	86.59 195	70.76 261	86.66 185	89.57 201	81.04 240	77.74 232	77.81 261	71.65 263	82.62 257	66.73 267

0.3-1-1-0.015		77.85 257	74.34 267	81.96 243	81.59 262	85.29 237	87.54 251	73.36 257	66.50 266	81.00 233	70.68 263	66.96 263	78.53 225	74.61 265	75.58 256	89.73 229	80.73 239

E-PMN		77.81 258	77.88 256	77.73 260	88.26 247	70.48 268	80.19 267	71.20 260	86.66 187	72.89 261	88.09 218	81.74 238	78.75 220	90.02 205	68.30 266	75.10 267	59.85 268

0.4-1-1-0.2		77.70 259	74.35 266	81.60 246	81.26 263	84.89 241	87.05 253	72.99 258	65.96 267	80.75 239	72.00 261	67.32 262	78.19 229	74.64 264	75.15 259	89.36 230	80.50 240

EMVS		77.65 260	77.49 258	77.83 258	87.75 250	71.02 267	81.13 266	70.54 261	86.38 191	74.52 257	89.38 203	80.19 243	78.22 228	89.48 210	67.13 267	74.83 269	58.84 269

TESTMET0.1,1		77.47 261	77.20 259	77.78 259	81.94 260	75.11 262	87.04 254	58.33 268	70.11 262	78.59 250	83.17 246	71.60 251	73.88 251	82.32 250	79.20 246	86.91 245	78.87 250

new_pmnet		76.65 262	83.52 232	68.63 262	82.60 258	72.08 266	76.76 270	64.17 263	84.41 208	49.73 272	91.77 173	91.53 194	56.16 267	86.59 229	83.26 230	82.37 258	75.02 258

MVE	60.41 19	73.21 263	80.84 242	64.30 263	56.34 272	57.24 272	75.28 272	72.76 259	87.14 181	41.39 274	86.31 234	85.30 230	80.66 210	86.17 233	83.36 229	59.35 271	80.38 242
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMMVS2		69.86 264	82.14 238	55.52 264	75.19 269	63.08 271	75.52 271	60.97 265	88.50 164	25.11 276	91.77 173	96.44 120	25.43 269	88.70 217	79.34 245	70.93 270	67.17 266

GG-mvs-BLEND		54.28 265	77.89 255	26.72 267	0.37 277	83.31 247	70.04 273	0.39 274	74.71 256	5.36 277	68.78 264	83.06 232	0.62 273	83.73 248	78.99 248	83.55 256	72.68 265

test_method		43.16 266	51.13 268	33.85 265	7.35 274	12.38 275	51.70 275	11.91 270	62.51 270	47.64 273	62.49 270	80.78 241	28.84 268	59.55 270	34.48 269	55.68 272	45.72 270

testmvs		2.38 267	3.35 269	1.26 269	0.83 275	0.96 277	1.53 278	0.83 272	3.59 272	1.63 279	6.03 272	2.93 279	1.55 272	3.49 271	2.51 271	1.21 276	3.92 272

test123		2.16 268	2.82 270	1.41 268	0.62 276	1.18 276	1.53 278	0.82 273	2.78 273	2.27 278	4.18 273	1.98 280	1.64 271	2.58 272	3.01 270	1.56 275	4.00 271

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							98.20 28	91.80 86		87.87 175						96.59 94

TPM-MVS					94.35 112	93.52 150	92.94 192			89.43 148	84.20 243	90.07 206	80.21 212			94.56 176	93.77 95
Ray Leroy Khuboni and Hongjun Xu: Textureless Resilient Propagation Matching in Multiple View Stereosis (TPM-MVS). SATNAC 2025
RE-MVS-def										97.21 5

9.14												93.19 183

SR-MVS					97.13 23			94.77 17				97.77 79

Anonymous202405211			94.63 83		94.51 110	94.96 101	93.94 161	91.35 94	90.82 132		95.60 109	95.85 137	81.74 206	96.47 77	95.84 75	97.39 66	92.85 114

our_test_3					91.78 198	88.87 221	94.37 145

ambc			94.61 84		98.09 4	95.14 92	91.71 221		94.18 46	96.46 12	96.26 91	96.30 123	91.26 70	94.70 116	92.00 153	93.45 197	93.67 98

MTAPA										94.88 28		96.88 109

MTMP										95.43 18		97.25 97

Patchmatch-RL test							8.96 277

tmp_tt				28.44 266	36.05 273	15.86 274	21.29 276	6.40 271	54.52 271	51.96 271	50.37 271	38.68 278	9.55 270	61.75 269	59.66 268	45.36 274

XVS					96.86 32	97.48 19	98.73 3			93.28 58		96.82 111				98.17 35

X-MVStestdata					96.86 32	97.48 19	98.73 3			93.28 58		96.82 111				98.17 35

mPP-MVS					98.24 2							97.65 86

NP-MVS									85.48 201

Patchmtry						83.74 245	86.72 258	92.22 64		90.73 118

DeepMVS_CX						47.68 273	53.20 274	19.21 269	63.24 269	26.96 275	66.50 268	69.82 257	66.91 260	64.27 268		54.91 273	72.72 264

High-resolution multi-view benchmark