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 35	98.30 20	94.90 15	98.61 1	97.73 3	97.97 25	98.57 23	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 66	98.10 2	99.08 2	93.92 31	98.24 3	96.44 13	98.12 20	97.86 53	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 10	94.57 15	99.06 3	98.80 2	99.32 1	96.92 26

SixPastTwentyTwo		97.36 4	97.73 10	96.92 2	97.36 13	96.15 55	98.29 21	94.43 23	96.50 13	96.96 7	98.74 5	98.74 18	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 30	97.02 2	94.71 27	95.32 21	98.52 12	98.97 9	92.91 42	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 25	94.93 26	98.66 7	99.16 5	92.27 52	98.98 6	98.39 7	98.49 19	96.83 30

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 20	95.15 24	98.66 7	98.80 15	92.77 46	98.97 7	98.27 9	98.44 23	96.28 40

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 86	97.53 7	96.01 15	98.14 19	98.76 17	95.28 5	98.76 11	98.23 10	98.77 5	96.67 34
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 33	93.71 52	98.55 10	98.89 11	92.97 39	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 33	96.98 3	93.50 47	95.21 23	97.98 24	98.44 26	92.83 45	98.93 8	98.37 8	98.46 22	96.91 27

DVP-MVS++		96.63 11	97.92 5	95.12 40	97.77 6	97.52 15	98.29 21	93.83 34	96.72 9	92.52 75	98.10 21	99.07 8	90.87 78	97.83 31	97.44 28	97.44 59	98.76 1

ACMH	90.17 8	96.61 12	97.69 12	95.35 30	95.29 81	96.94 35	98.43 14	92.05 74	98.04 4	95.38 19	98.07 22	99.25 4	93.23 33	98.35 16	97.16 39	97.72 51	96.00 45
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
UA-Net		96.56 13	96.73 24	96.36 9	98.99 1	97.90 7	97.79 43	95.64 10	92.78 60	92.54 74	96.23 72	95.02 129	94.31 18	98.43 15	98.12 11	98.89 3	98.58 3

ACMMPR		96.54 14	96.71 25	96.35 10	97.55 9	97.63 11	98.62 9	94.54 19	94.45 30	94.19 39	95.04 96	97.35 67	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 76	96.04 57	97.93 38	92.49 58	96.40 14	97.13 6	98.99 2	99.41 3	93.79 25	97.84 30	96.15 65	97.00 81	95.60 53

DeepC-MVS	92.47 4	96.44 16	96.75 23	96.08 16	97.57 7	97.19 31	97.96 37	94.28 24	95.29 21	94.92 27	98.31 17	96.92 78	93.69 27	96.81 67	96.50 56	98.06 40	96.27 41
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 32	96.19 14	97.21 20	97.16 33	98.71 5	93.79 37	94.35 34	93.81 46	92.80 131	98.23 34	95.11 6	98.07 20	97.45 27	98.51 18	96.86 29
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 84	97.18 32	97.97 36	92.52 56	96.72 9	90.50 122	97.31 46	99.11 6	94.10 19	98.67 12	97.90 14	98.56 15	95.79 49

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 28	89.80 130	97.73 33	98.13 38	93.84 24	97.79 33	97.63 18	97.87 47	97.08 21
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 43	96.27 12	97.56 8	97.13 34	98.43 14	94.70 18	92.62 63	94.13 41	92.71 132	98.03 44	94.54 16	98.00 24	97.60 20	98.23 31	97.05 22

HFP-MVS		96.18 21	96.53 29	95.77 20	97.34 16	97.26 28	98.16 31	94.54 19	94.45 30	92.52 75	95.05 94	96.95 77	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 40	95.06 107	96.91 4	97.86 5	90.42 123	98.55 10	99.60 1	88.01 117	98.51 13	97.81 15	98.26 29	94.95 65

MP-MVS		96.13 23	95.93 47	96.37 8	98.19 3	97.31 27	98.49 13	94.53 22	91.39 95	94.38 35	94.32 109	96.43 91	94.59 14	97.75 35	97.44 28	98.04 41	96.88 28
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
ACMMP		96.12 24	96.27 39	95.93 18	97.20 21	97.60 12	98.64 7	93.74 38	92.47 67	93.13 66	93.23 124	98.06 41	94.51 17	97.99 25	97.57 22	98.39 27	96.99 23
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 16	97.90 39	93.60 43	95.47 18	89.57 136	97.32 45	97.72 56	93.89 22	97.74 36	97.53 23	97.51 56	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 36	95.87 19	96.72 38	97.35 26	98.43 14	93.83 34	90.81 109	92.67 73	95.05 94	98.86 13	95.01 7	98.11 18	97.37 35	98.52 17	96.50 36

CSCG		96.07 27	97.15 19	94.81 47	96.06 61	97.58 13	96.52 73	90.98 97	96.51 12	93.60 54	97.13 53	98.55 24	93.01 37	97.17 53	95.36 80	98.68 9	97.78 4

DPE-MVS		96.00 28	96.80 22	95.06 42	95.87 69	97.47 21	98.25 25	93.73 39	92.38 69	91.57 103	97.55 40	97.97 46	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 30	95.41 29	97.43 11	97.36 24	97.55 48	93.70 40	94.05 41	93.79 47	97.02 56	94.53 134	92.28 51	97.53 45	97.19 37	97.73 50	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 28	95.31 32	96.87 30	96.50 47	98.71 5	91.58 87	93.25 52	92.71 70	96.86 58	96.57 89	93.92 20	98.09 19	97.91 13	98.08 38	96.81 31
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 27	95.23 35	96.67 40	96.52 46	97.86 41	93.28 47	95.27 23	93.46 56	96.26 69	98.85 14	92.89 43	97.09 54	96.37 60	97.22 73	95.78 50
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
SteuartSystems-ACMMP		95.96 31	96.13 44	95.76 21	97.06 26	97.36 24	98.40 18	94.24 26	91.49 89	91.91 93	94.50 105	96.89 79	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 37	95.59 23	96.58 43	97.23 30	98.26 24	93.22 48	92.33 72	92.31 83	94.29 110	98.73 19	94.68 12	98.04 21	97.14 40	98.47 21	96.17 43
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PGM-MVS		95.90 34	95.72 51	96.10 15	97.53 10	97.45 22	98.55 12	94.12 28	90.25 112	93.71 52	93.20 125	97.18 71	94.63 13	97.68 39	97.34 36	98.08 38	96.97 24

PMVS	87.16 16	95.88 35	96.47 31	95.19 37	97.00 28	96.02 58	96.70 64	91.57 88	94.43 32	95.33 20	97.16 52	95.37 117	92.39 48	98.89 10	98.72 3	98.17 35	94.71 71
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
ACMMP_NAP		95.86 36	96.18 40	95.47 28	97.11 24	97.26 28	98.37 19	93.48 45	93.49 48	93.99 44	95.61 80	94.11 138	92.49 47	97.87 27	97.44 28	97.40 62	97.52 8

Gipuma		95.86 36	96.17 41	95.50 27	95.92 65	94.59 104	94.77 116	92.50 57	97.82 6	97.90 2	95.56 83	97.88 51	94.71 11	98.02 22	94.81 94	97.23 72	94.48 78
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
LS3D		95.83 38	96.35 34	95.22 36	96.47 47	97.49 16	97.99 34	92.35 61	94.92 26	94.58 30	94.88 100	95.11 127	91.52 63	98.48 14	98.05 12	98.42 25	95.49 54

SD-MVS		95.77 39	96.17 41	95.30 33	96.72 38	96.19 54	97.01 56	93.04 49	94.03 42	92.71 70	96.45 67	96.78 86	93.91 21	96.79 68	95.89 71	98.42 25	97.09 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
SED-MVS		95.73 40	96.98 20	94.28 56	96.08 59	97.39 23	98.18 29	93.80 36	94.20 36	89.61 135	97.29 47	97.49 64	90.69 82	97.74 36	97.41 32	97.32 67	97.34 9

TranMVSNet+NR-MVSNet		95.72 41	96.42 32	94.91 46	96.21 55	96.77 39	96.90 61	94.99 13	92.62 63	91.92 92	98.51 13	98.63 21	90.82 79	97.27 50	96.83 45	98.63 12	94.31 79

DU-MVS		95.51 42	95.68 52	95.33 31	96.45 48	96.44 49	96.61 70	95.32 11	89.97 117	93.78 48	97.46 42	98.07 40	91.19 70	97.03 57	96.53 53	98.61 13	94.22 80

UniMVSNet (Re)		95.46 43	95.86 49	95.00 45	96.09 57	96.60 41	96.68 68	94.99 13	90.36 111	92.13 86	97.64 37	98.13 38	91.38 64	96.90 62	96.74 47	98.73 6	94.63 74

RPSCF		95.46 43	96.95 21	93.73 79	95.72 73	95.94 62	95.58 98	88.08 144	95.31 19	91.34 106	96.26 69	98.04 43	93.63 28	98.28 17	97.67 17	98.01 42	97.13 18

anonymousdsp		95.45 45	96.70 26	93.99 67	88.43 202	92.05 151	99.18 1	85.42 179	94.29 35	96.10 14	98.63 9	99.08 7	96.11 1	97.77 34	97.41 32	98.70 8	97.69 6

APD-MVS		95.38 46	95.68 52	95.03 43	97.30 18	96.90 37	97.83 42	93.92 31	89.40 124	90.35 124	95.41 87	97.69 58	92.97 39	97.24 52	97.17 38	97.83 48	95.96 46
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
UniMVSNet_NR-MVSNet		95.34 47	95.51 56	95.14 39	95.80 71	96.55 42	96.61 70	94.79 16	90.04 116	93.78 48	97.51 41	97.25 68	91.19 70	96.68 70	96.31 62	98.65 11	94.22 80

X-MVS		95.33 48	95.13 64	95.57 25	97.35 14	97.48 18	98.43 14	94.28 24	92.30 73	93.28 59	86.89 187	96.82 82	91.87 57	97.85 28	97.59 21	98.19 33	96.95 25

MSP-MVS		95.32 49	96.28 37	94.19 59	96.87 30	97.77 10	98.27 23	93.88 33	94.15 40	89.63 134	95.36 88	98.37 29	90.73 80	94.37 116	97.53 23	95.77 120	96.40 37
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 50	95.16 62	95.29 34	96.17 56	96.55 42	97.64 45	94.02 30	94.16 39	94.29 37	92.09 138	93.71 143	91.90 55	96.68 70	96.51 54	97.70 53	96.40 37

HPM-MVS++		95.21 51	94.89 67	95.59 23	97.79 5	95.39 80	97.68 44	94.05 29	91.91 81	94.35 36	93.38 122	95.07 128	92.94 41	96.01 82	95.88 72	96.73 84	96.61 35

TSAR-MVS + ACMM		95.17 52	95.95 45	94.26 57	96.07 60	96.46 48	95.67 96	94.21 27	93.84 44	90.99 114	97.18 50	95.24 125	93.55 29	96.60 73	95.61 78	95.06 138	96.69 33

CPTT-MVS		95.00 53	94.52 76	95.57 25	96.84 34	96.78 38	97.88 40	93.67 42	92.20 74	92.35 82	85.87 194	97.56 63	94.98 9	96.96 60	96.07 68	97.70 53	96.18 42

SF-MVS		94.88 54	95.87 48	93.73 79	95.30 79	95.93 63	94.80 115	91.76 84	93.11 56	91.93 91	95.83 77	97.07 74	91.11 73	96.62 72	96.44 58	97.46 57	96.13 44

Baseline_NR-MVSNet		94.85 55	95.35 60	94.26 57	96.45 48	93.86 120	96.70 64	94.54 19	90.07 115	90.17 128	98.77 4	97.89 48	90.64 85	97.03 57	96.16 64	97.04 80	93.67 93

EG-PatchMatch MVS		94.81 56	95.53 55	93.97 68	95.89 68	94.62 102	95.55 100	88.18 142	92.77 61	94.88 28	97.04 55	98.61 22	93.31 30	96.89 63	95.19 84	95.99 113	93.56 96

CS-MVS		94.76 57	94.41 80	95.18 38	94.95 90	95.99 60	97.28 49	91.99 76	85.51 157	94.55 31	93.07 127	97.69 58	93.77 26	97.08 55	96.79 46	98.53 16	94.72 69

OMC-MVS		94.74 58	95.46 58	93.91 71	94.62 102	96.26 52	96.64 69	89.36 130	94.20 36	94.15 40	94.02 115	97.73 55	91.34 66	96.15 80	95.04 88	97.37 64	94.80 67

DeepC-MVS_fast	91.38 6	94.73 59	94.98 65	94.44 51	96.83 36	96.12 56	96.69 66	92.17 67	92.98 58	93.72 50	94.14 111	95.45 115	90.49 91	95.73 88	95.30 81	96.71 85	95.13 63
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 60	94.84 69	94.44 51	94.95 90	96.55 42	96.46 76	91.10 95	88.96 127	96.00 16	94.55 104	95.32 120	90.67 83	96.97 59	96.69 51	97.44 59	94.84 66

CS-MVS-test		94.63 61	94.30 85	95.02 44	94.63 100	95.71 70	98.15 32	92.13 69	85.62 156	94.22 38	93.63 120	97.63 62	93.08 36	97.50 46	96.51 54	97.88 46	93.50 97

pmmvs6		94.58 62	97.30 15	91.40 122	94.84 94	94.61 103	93.40 147	92.43 60	98.51 2	85.61 163	98.73 6	99.53 2	84.40 142	97.88 26	97.03 41	97.72 51	94.79 68

DeepPCF-MVS	90.68 7	94.56 63	94.92 66	94.15 60	94.11 115	95.71 70	97.03 55	90.65 102	93.39 51	94.08 42	95.29 91	94.15 137	93.21 34	95.22 100	94.92 92	95.82 119	95.75 51

NR-MVSNet		94.55 64	95.66 54	93.25 91	94.26 111	96.44 49	96.69 66	95.32 11	89.97 117	91.79 98	97.46 42	98.39 28	82.85 152	96.87 65	96.48 57	98.57 14	93.98 86

Vis-MVSNet		94.39 65	95.85 50	92.68 99	90.91 185	95.88 65	97.62 47	91.41 89	91.95 80	89.20 139	97.29 47	96.26 94	90.60 90	96.95 61	95.91 69	96.32 100	96.71 32
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
TSAR-MVS + GP.		94.25 66	94.81 71	93.60 81	96.52 46	95.80 68	94.37 125	92.47 59	90.89 105	88.92 141	95.34 89	94.38 135	92.85 44	96.36 78	95.62 77	96.47 92	95.28 60

CNVR-MVS		94.24 67	94.47 77	93.96 69	96.56 44	95.67 72	96.43 77	91.95 78	92.08 77	91.28 108	90.51 148	95.35 118	91.20 69	96.34 79	95.50 79	96.34 98	95.88 48

EC-MVSNet		94.23 68	93.81 100	94.71 50	94.85 93	96.23 53	97.14 51	93.40 46	81.79 180	91.58 102	93.29 123	95.21 126	93.13 35	97.73 38	96.95 42	98.20 32	95.45 55

v1192		93.98 69	93.94 94	94.01 65	93.91 123	94.63 101	97.00 57	89.75 120	91.01 103	96.50 10	97.93 26	98.26 33	91.74 59	92.06 148	92.05 138	95.18 133	91.66 135

v10		93.96 70	94.12 91	93.77 78	93.37 136	95.45 76	96.83 63	91.13 94	89.70 121	95.02 25	97.88 29	98.23 34	91.27 67	92.39 143	92.18 134	94.99 140	93.00 106

CDPH-MVS		93.96 70	93.86 96	94.08 62	96.31 52	95.84 66	96.92 59	91.85 81	87.21 143	91.25 110	92.83 129	96.06 102	91.05 75	95.57 91	94.81 94	97.12 75	94.72 69

MVS_0304		93.92 72	93.81 100	94.05 64	96.06 61	96.00 59	96.43 77	92.76 54	85.99 154	94.43 34	94.04 114	97.08 73	88.12 116	94.65 111	94.20 108	96.47 92	94.71 71

MSLP-MVS++		93.91 73	94.30 85	93.45 83	95.51 77	95.83 67	93.12 153	91.93 80	91.45 92	91.40 105	87.42 182	96.12 101	93.27 31	96.57 74	96.40 59	95.49 123	96.29 39

v1921920		93.90 74	93.82 98	94.00 66	93.74 128	94.31 108	97.12 52	89.33 131	91.13 100	96.77 9	97.90 27	98.06 41	91.95 54	91.93 152	91.54 147	95.10 136	91.85 129

train_agg		93.89 75	93.46 111	94.40 53	97.35 14	93.78 122	97.63 46	92.19 66	88.12 134	90.52 121	93.57 121	95.78 108	92.31 50	94.78 108	93.46 119	96.36 96	94.70 73

v144192		93.89 75	93.85 97	93.94 70	93.50 133	94.33 107	97.12 52	89.49 125	90.89 105	96.49 11	97.78 31	98.27 32	91.89 56	92.17 147	91.70 144	95.19 132	91.78 132

v1240		93.89 75	93.72 102	94.09 61	93.98 120	94.31 108	97.12 52	89.37 129	90.74 110	96.92 8	98.05 23	97.89 48	92.15 53	91.53 158	91.60 145	94.99 140	91.93 127

NCCC		93.87 78	93.42 112	94.40 53	96.84 34	95.42 77	96.47 75	92.62 55	92.36 71	92.05 88	83.83 202	95.55 111	91.84 58	95.89 84	95.23 83	96.56 89	95.63 52

v1144		93.83 79	93.87 95	93.78 77	93.72 129	94.57 105	96.85 62	89.98 114	91.31 97	95.90 17	97.89 28	98.40 27	91.13 72	92.01 151	92.01 139	95.10 136	90.94 141

MVS_111021_HR		93.82 80	94.26 88	93.31 86	95.01 88	93.97 118	95.73 93	89.75 120	92.06 78	92.49 77	94.01 116	96.05 103	90.61 89	95.95 83	94.78 97	96.28 101	93.04 105

thisisatest0515		93.79 81	94.41 80	93.06 96	94.14 112	92.50 143	95.56 99	88.55 138	91.61 85	92.45 78	96.84 59	95.71 109	90.62 87	94.58 112	95.07 86	97.05 78	94.58 75

TAPA-MVS	88.94 13	93.78 82	94.31 84	93.18 93	94.14 112	95.99 60	95.74 92	86.98 161	93.43 50	93.88 45	90.16 155	96.88 80	91.05 75	94.33 117	93.95 110	97.28 70	95.40 56
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
GeoE		93.72 83	93.62 106	93.84 72	94.75 97	94.90 95	97.24 50	91.81 83	86.97 147	92.74 69	93.83 118	97.24 70	90.46 92	95.10 104	94.09 109	96.08 110	93.18 103

EPP-MVSNet		93.63 84	93.95 93	93.26 89	95.15 85	96.54 45	96.18 85	91.97 77	91.74 82	85.76 161	94.95 98	84.27 189	91.60 62	97.61 43	97.38 34	98.87 4	95.18 62

v8		93.60 85	93.82 98	93.34 84	93.13 143	95.06 88	96.39 79	90.75 100	89.90 119	94.03 43	97.70 35	98.21 36	91.08 74	92.36 144	91.47 148	94.63 148	92.07 123

MCST-MVS		93.60 85	93.40 114	93.83 73	95.30 79	95.40 79	96.49 74	90.87 98	90.08 114	91.72 99	90.28 153	95.99 104	91.69 60	93.94 126	92.99 125	96.93 82	95.13 63

PVSNet_Blended_VisFu		93.60 85	93.41 113	93.83 73	96.31 52	95.65 73	95.71 94	90.58 104	88.08 136	93.17 64	95.29 91	92.20 152	90.72 81	94.69 110	93.41 121	96.51 91	94.54 76

TransMVSNet (Re)		93.55 88	96.32 35	90.32 138	94.38 107	94.05 113	93.30 150	89.53 124	97.15 8	85.12 166	98.83 3	97.89 48	82.21 158	96.75 69	96.14 66	97.35 65	93.46 98

DCV-MVSNet		93.49 89	95.15 63	91.55 116	94.05 116	95.92 64	95.15 105	91.21 91	92.76 62	87.01 157	89.71 159	97.16 72	83.90 147	97.65 40	96.87 44	97.99 43	95.95 47

v2v482		93.42 90	93.49 110	93.32 85	93.44 135	94.05 113	96.36 82	89.76 119	91.41 94	95.24 22	97.63 38	98.34 30	90.44 93	91.65 156	91.76 143	94.69 145	89.62 153

canonicalmvs		93.38 91	94.36 82	92.24 105	93.94 122	96.41 51	94.18 134	90.47 105	93.07 57	88.47 147	88.66 169	93.78 142	88.80 106	95.74 87	95.75 75	97.57 55	97.13 18

3Dnovator	91.81 5	93.36 92	94.27 87	92.29 104	92.99 148	95.03 89	95.76 91	87.79 147	93.82 45	92.38 81	92.19 137	93.37 147	88.14 115	95.26 99	94.85 93	96.69 86	95.40 56

pm-mvs1		93.27 93	95.94 46	90.16 139	94.13 114	93.66 123	92.61 164	89.91 116	95.73 17	84.28 175	98.51 13	98.29 31	82.80 153	96.44 76	95.76 74	97.25 71	93.21 102

casdiffmvs_mvg		93.27 93	94.83 70	91.45 120	93.59 131	94.47 106	94.91 111	89.83 118	92.04 79	87.14 155	97.57 39	98.47 25	86.03 132	94.07 124	94.44 104	97.21 74	92.76 112
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 95	94.43 78	91.88 110	95.09 87	94.97 93	94.58 121	92.81 51	93.60 46	83.79 178	97.17 51	89.25 174	87.59 119	97.54 44	96.57 52	97.42 61	91.89 128

Anonymous20231211		93.19 96	95.50 57	90.49 135	93.77 127	95.29 82	94.36 129	90.04 113	91.44 93	84.59 170	96.72 62	97.65 60	82.45 157	97.25 51	96.32 61	97.74 49	93.79 89

TinyColmap		93.17 97	93.33 115	93.00 97	93.84 125	92.76 137	94.75 118	88.90 134	93.97 43	97.48 4	95.28 93	95.29 121	88.37 111	95.31 98	91.58 146	94.65 147	89.10 157

MVS_111021_LR		93.15 98	93.65 104	92.56 100	93.89 124	92.28 146	95.09 106	86.92 163	91.26 99	92.99 68	94.46 107	96.22 97	90.64 85	95.11 103	93.45 120	95.85 117	92.74 113

CNLPA		93.14 99	93.67 103	92.53 101	94.62 102	94.73 98	95.00 110	86.57 168	92.85 59	92.43 79	90.94 143	94.67 131	90.35 94	95.41 93	93.70 116	96.23 104	93.37 100

PLC	87.27 15	93.08 100	92.92 119	93.26 89	94.67 98	95.03 89	94.38 124	90.10 108	91.69 83	92.14 85	87.24 183	93.91 140	91.61 61	95.05 105	94.73 100	96.67 87	92.80 109
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CANet		93.07 101	93.05 118	93.10 94	95.90 66	95.41 78	95.88 88	91.94 79	84.77 163	93.36 57	94.05 113	95.25 124	86.25 130	94.33 117	93.94 111	95.30 126	93.58 95

TSAR-MVS + COLMAP		93.06 102	93.65 104	92.36 102	94.62 102	94.28 110	95.36 104	89.46 127	92.18 75	91.64 100	95.55 84	95.27 123	88.60 109	93.24 132	92.50 130	94.46 151	92.55 119

ECVR-MVS		93.05 103	94.25 89	91.65 113	94.76 95	95.23 83	94.26 132	92.80 52	92.49 65	83.90 176	96.75 61	89.99 165	86.84 124	97.62 41	96.72 48	97.32 67	90.92 142

Effi-MVS+		92.93 104	92.16 130	93.83 73	94.29 109	93.53 130	95.04 108	92.98 50	85.27 160	94.46 32	90.24 154	95.34 119	89.99 97	93.72 127	94.23 107	96.22 105	92.79 110

Fast-Effi-MVS+		92.93 104	92.64 123	93.27 88	93.81 126	93.88 119	95.90 87	90.61 103	83.98 169	92.71 70	92.81 130	96.22 97	90.67 83	94.90 107	93.92 112	95.92 115	92.77 111

HQP-MVS		92.87 106	92.49 124	93.31 86	95.75 72	95.01 92	95.64 97	91.06 96	88.54 131	91.62 101	88.16 174	96.25 95	89.47 101	92.26 146	91.81 141	96.34 98	95.40 56

FMVSNet1		92.86 107	95.26 61	90.06 141	92.40 162	95.16 85	94.37 125	92.22 63	93.18 55	82.16 188	96.76 60	97.48 65	81.85 162	95.32 95	94.98 89	97.34 66	93.93 87

CLD-MVS		92.81 108	94.32 83	91.05 126	95.39 78	95.31 81	95.82 90	81.44 202	89.40 124	91.94 90	95.86 75	97.36 66	85.83 133	95.35 94	94.59 102	95.85 117	92.34 120
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
IS_MVSNet		92.76 109	93.25 116	92.19 106	94.91 92	95.56 74	95.86 89	92.12 70	88.10 135	82.71 183	93.15 126	88.30 177	88.86 105	97.29 48	96.95 42	98.66 10	93.38 99

FC-MVSNet-train		92.75 110	95.40 59	89.66 149	95.21 83	94.82 96	97.00 57	89.40 128	91.13 100	81.71 189	97.72 34	96.43 91	77.57 188	96.89 63	96.72 48	97.05 78	94.09 83

V42		92.67 111	93.50 109	91.71 112	91.41 176	92.96 135	95.71 94	85.00 180	89.67 122	93.22 62	97.67 36	98.01 45	91.02 77	92.65 139	92.12 136	93.86 159	91.42 136

PM-MVS		92.65 112	93.20 117	92.00 108	92.11 170	90.16 172	95.99 86	84.81 184	91.31 97	92.41 80	95.87 74	96.64 88	92.35 49	93.65 129	92.91 126	94.34 154	91.85 129

QAPM		92.57 113	93.51 108	91.47 119	92.91 150	94.82 96	93.01 155	87.51 151	91.49 89	91.21 111	92.24 135	91.70 155	88.74 107	94.54 114	94.39 106	95.41 124	95.37 59

MIMVSNet1		92.52 114	94.88 68	89.77 145	96.09 57	91.99 152	96.92 59	89.68 122	95.92 16	84.55 171	96.64 64	98.21 36	78.44 181	96.08 81	95.10 85	92.91 174	90.22 150

tfpnnormal		92.45 115	94.77 72	89.74 146	93.95 121	93.44 133	93.25 151	88.49 140	95.27 23	83.20 181	96.51 65	96.23 96	83.17 151	95.47 92	94.52 103	96.38 95	91.97 126

PCF-MVS	87.46 14	92.44 116	91.80 132	93.19 92	94.66 99	95.80 68	96.37 80	90.19 107	87.57 140	92.23 84	89.26 164	93.97 139	89.24 102	91.32 160	90.82 156	96.46 94	93.86 88
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
casdiffmvs		92.42 117	93.99 92	90.60 133	93.25 139	93.82 121	94.28 131	88.73 136	91.53 87	84.53 173	97.74 32	98.64 20	86.60 127	93.21 134	91.20 151	96.21 106	91.76 134
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		92.41 118	91.49 136	93.48 82	95.96 64	95.02 91	95.37 103	91.73 85	87.97 138	91.28 108	82.82 206	91.04 159	90.62 87	95.82 86	95.07 86	95.95 114	92.67 114

v148		92.38 119	92.78 121	91.91 109	92.86 151	92.13 149	94.84 113	87.03 160	91.47 91	93.07 67	96.92 57	98.89 11	90.10 96	92.05 149	89.69 164	93.56 162	88.27 166

pmmvs-eth3d		92.34 120	92.33 125	92.34 103	92.67 155	90.67 166	96.37 80	89.06 132	90.98 104	93.60 54	97.13 53	97.02 76	88.29 112	90.20 167	91.42 149	94.07 157	88.89 161

DELS-MVS		92.33 121	93.61 107	90.83 129	92.84 152	95.13 87	94.76 117	87.22 159	87.78 139	88.42 149	95.78 78	95.28 122	85.71 136	94.44 115	93.91 113	96.01 112	92.97 107
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		92.32 122	91.66 134	93.09 95	95.13 86	94.73 98	94.57 122	92.14 68	81.74 181	90.33 125	88.13 175	95.91 105	89.24 102	94.23 122	93.65 118	97.12 75	93.23 101

UGNet		92.31 123	94.70 73	89.53 151	90.99 184	95.53 75	96.19 84	92.10 72	91.35 96	85.76 161	95.31 90	95.48 114	76.84 193	95.22 100	94.79 96	95.32 125	95.19 61
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
USDC		92.17 124	92.17 129	92.18 107	92.93 149	92.22 147	93.66 141	87.41 154	93.49 48	97.99 1	94.10 112	96.68 87	86.46 128	92.04 150	89.18 170	94.61 149	87.47 169

ETV-MVS		92.12 125	90.44 144	94.08 62	96.36 50	93.63 125	96.27 83	92.00 75	78.90 200	92.13 86	85.29 196	89.85 168	90.26 95	97.07 56	96.29 63	97.46 57	92.04 124

IterMVS-LS		92.10 126	92.33 125	91.82 111	93.18 140	93.66 123	92.80 162	92.27 62	90.82 107	90.59 120	97.19 49	90.97 160	87.76 118	89.60 174	90.94 155	94.34 154	93.16 104
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MSDG		92.09 127	92.84 120	91.22 125	92.55 157	92.97 134	93.42 146	85.43 178	90.24 113	91.83 95	94.70 101	94.59 132	88.48 110	94.91 106	93.31 123	95.59 122	89.15 156

EIA-MVS		91.95 128	90.36 146	93.81 76	96.54 45	94.65 100	95.38 102	90.40 106	78.01 205	93.72 50	86.70 190	91.95 154	89.93 98	95.67 90	94.72 101	96.89 83	90.79 144

MAR-MVS		91.86 129	91.14 140	92.71 98	94.29 109	94.24 111	94.91 111	91.82 82	81.66 182	93.32 58	84.51 199	93.42 146	86.86 123	95.16 102	94.44 104	95.05 139	94.53 77
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
EU-MVSNet		91.63 130	92.73 122	90.35 137	88.36 203	87.89 183	96.53 72	81.51 201	92.45 68	91.82 96	96.44 68	97.05 75	93.26 32	94.10 123	88.94 175	90.61 181	92.24 121

FC-MVSNet-test		91.49 131	94.43 78	88.07 167	94.97 89	90.53 169	95.42 101	91.18 93	93.24 53	72.94 212	98.37 15	93.86 141	78.78 175	97.82 32	96.13 67	95.13 134	91.05 139

FA-MVS(training)		91.38 132	91.18 139	91.62 115	93.49 134	92.38 144	95.03 109	90.81 99	87.20 144	91.46 104	93.00 128	89.47 171	84.19 144	93.20 136	92.08 137	94.74 144	90.90 143

OpenMVS	89.22 12	91.09 133	91.42 137	90.71 131	92.79 154	93.61 127	92.74 163	85.47 177	86.10 153	90.73 115	85.71 195	93.07 150	86.69 126	94.07 124	93.34 122	95.86 116	94.02 85

FPMVS		90.81 134	91.60 135	89.88 144	92.52 158	88.18 179	93.31 149	83.62 190	91.59 86	88.45 148	88.96 167	89.73 170	86.96 121	96.42 77	95.69 76	94.43 152	90.65 145

DI_MVS_plusplus_trai		90.68 135	90.40 145	91.00 127	92.43 161	92.61 141	94.17 135	88.98 133	88.32 133	88.76 145	93.67 119	87.58 179	86.44 129	89.74 172	90.33 159	95.24 129	90.56 148

Vis-MVSNet (Re-imp)		90.68 135	92.18 128	88.92 156	94.63 100	92.75 138	92.91 158	91.20 92	89.21 126	75.01 208	93.96 117	89.07 175	82.72 155	95.88 85	95.30 81	97.08 77	89.08 158

DPM-MVS		90.67 137	89.86 150	91.63 114	95.29 81	94.16 112	94.52 123	89.63 123	89.59 123	89.67 133	81.95 208	88.64 176	85.75 135	90.46 165	90.43 158	94.91 142	93.77 90

diffmvs		90.44 138	92.23 127	88.35 163	91.36 178	91.38 158	92.45 168	84.84 183	89.88 120	85.09 167	96.69 63	97.71 57	83.33 150	90.01 171	88.96 174	93.03 172	91.00 140
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		90.28 139	92.04 131	88.23 165	91.22 180	94.05 113	92.88 159	90.69 101	86.53 150	79.89 196	94.38 108	92.73 151	78.54 178	91.64 157	92.26 133	96.17 107	92.67 114

IterMVS-SCA-FT		90.24 140	89.37 156	91.26 124	92.50 159	92.11 150	91.69 179	87.48 152	87.05 146	91.82 96	95.76 79	87.25 180	91.36 65	89.02 179	85.53 190	92.68 175	88.90 160

MVS_Test		90.19 141	90.58 141	89.74 146	92.12 169	91.74 154	92.51 165	88.54 139	82.80 175	87.50 153	94.62 102	95.02 129	83.97 145	88.69 182	89.32 168	93.79 160	91.85 129

EPNet		90.17 142	89.07 158	91.45 120	97.25 19	90.62 168	94.84 113	93.54 44	80.96 184	91.85 94	86.98 186	85.88 185	77.79 185	92.30 145	92.58 129	93.41 164	94.20 82
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet_BlendedMVS		90.09 143	90.12 148	90.05 142	92.40 162	92.74 139	91.74 175	85.89 173	80.54 187	90.30 126	88.54 170	95.51 112	84.69 140	92.64 140	90.25 160	95.28 127	90.61 146

PVSNet_Blended		90.09 143	90.12 148	90.05 142	92.40 162	92.74 139	91.74 175	85.89 173	80.54 187	90.30 126	88.54 170	95.51 112	84.69 140	92.64 140	90.25 160	95.28 127	90.61 146

pmmvs4		89.95 145	89.32 157	90.69 132	91.60 175	89.17 176	94.37 125	87.63 148	88.07 137	91.02 113	94.50 105	90.50 163	86.13 131	86.33 196	89.40 167	93.39 165	87.29 172

MDA-MVSNet-bldmvs		89.75 146	91.67 133	87.50 172	74.25 222	90.88 163	94.68 119	85.89 173	91.64 84	91.03 112	95.86 75	94.35 136	89.10 104	96.87 65	86.37 186	90.04 182	85.72 177

WB-MVS		89.70 147	94.13 90	84.54 192	88.16 205	92.57 142	88.90 199	88.32 141	96.67 11	73.61 211	98.29 18	98.80 15	80.60 168	95.73 88	92.18 134	87.66 194	84.64 180

tttt0517		89.64 148	88.05 169	91.49 118	93.52 132	91.65 155	93.67 140	87.53 149	82.77 176	89.39 138	90.37 152	70.05 214	88.21 113	93.71 128	93.79 114	96.63 88	94.04 84

PatchMatch-RL		89.59 149	88.80 162	90.51 134	92.20 168	88.00 182	91.72 177	86.64 165	84.75 164	88.25 150	87.10 185	90.66 162	89.85 100	93.23 133	92.28 132	94.41 153	85.60 178

Fast-Effi-MVS+-dtu		89.57 150	88.42 166	90.92 128	93.35 137	91.57 156	93.01 155	95.71 9	78.94 199	87.65 152	84.68 198	93.14 149	82.00 160	90.84 163	91.01 154	93.78 161	88.77 162

thisisatest0530		89.54 151	87.99 171	91.35 123	93.17 141	91.31 159	93.45 145	87.53 149	82.96 174	89.17 140	90.45 149	70.32 213	88.21 113	93.37 131	93.79 114	96.54 90	93.71 92

test2506		89.51 152	87.77 174	91.55 116	94.76 95	95.23 83	94.26 132	92.80 52	92.49 65	83.31 180	89.97 157	50.93 228	86.84 124	97.62 41	96.72 48	97.32 67	91.42 136

GBi-Net		89.35 153	90.58 141	87.91 168	91.22 180	94.05 113	92.88 159	90.05 110	79.40 191	78.60 199	90.58 145	87.05 181	78.54 178	95.32 95	94.98 89	96.17 107	92.67 114

test1		89.35 153	90.58 141	87.91 168	91.22 180	94.05 113	92.88 159	90.05 110	79.40 191	78.60 199	90.58 145	87.05 181	78.54 178	95.32 95	94.98 89	96.17 107	92.67 114

thres600view7		89.14 155	88.83 160	89.51 152	93.71 130	93.55 128	93.93 138	88.02 145	87.30 142	82.40 184	81.18 209	80.63 200	82.69 156	94.27 119	95.90 70	96.27 102	88.94 159

CVMVSNet		88.97 156	89.73 152	88.10 166	87.33 210	85.22 193	94.68 119	78.68 203	88.94 128	86.98 158	95.55 84	85.71 186	89.87 99	91.19 161	89.69 164	91.05 179	91.78 132

CANet_DTU		88.95 157	89.51 155	88.29 164	93.12 144	91.22 161	93.61 142	83.47 193	80.07 190	90.71 119	89.19 165	93.68 144	76.27 197	91.44 159	91.17 153	92.59 176	89.83 152

GA-MVS		88.76 158	88.04 170	89.59 150	92.32 165	91.46 157	92.28 170	86.62 166	83.82 171	89.84 129	92.51 134	81.94 194	83.53 149	89.41 176	89.27 169	92.95 173	87.90 167

pmmvs5		88.63 159	89.70 153	87.39 173	89.24 196	90.64 167	91.87 174	82.13 197	83.34 172	87.86 151	94.58 103	96.15 100	79.87 172	87.33 191	89.07 173	93.39 165	86.76 173

thres400		88.54 160	88.15 168	88.98 154	93.17 141	92.84 136	93.56 143	86.93 162	86.45 151	82.37 185	79.96 211	81.46 197	81.83 163	93.21 134	94.76 98	96.04 111	88.39 164

CDS-MVSNet		88.41 161	89.79 151	86.79 178	94.55 105	90.82 164	92.50 166	89.85 117	83.26 173	80.52 193	91.05 141	89.93 167	69.11 208	93.17 137	92.71 128	94.21 156	87.63 168
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
gg-mvs-nofinetune		88.32 162	88.81 161	87.75 170	93.07 145	89.37 175	89.06 198	95.94 8	95.29 21	87.15 154	97.38 44	76.38 203	68.05 211	91.04 162	89.10 172	93.24 168	83.10 187

IterMVS		88.32 162	88.25 167	88.41 162	90.83 186	91.24 160	93.07 154	81.69 199	86.77 148	88.55 146	95.61 80	86.91 184	87.01 120	87.38 190	83.77 192	89.29 184	86.06 176
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
thres200		88.29 164	87.88 172	88.76 158	92.50 159	93.55 128	92.47 167	88.02 145	84.80 162	81.44 190	79.28 213	82.20 193	81.83 163	94.27 119	93.67 117	96.27 102	87.40 170

IB-MVS	86.01 17	88.24 165	87.63 175	88.94 155	92.03 171	91.77 153	92.40 169	85.58 176	78.24 202	84.85 168	71.99 218	93.45 145	83.96 146	93.48 130	92.33 131	94.84 143	92.15 122
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		88.22 166	87.85 173	88.65 160	91.40 177	86.75 187	94.07 136	84.97 181	88.86 130	93.20 63	96.11 73	96.21 99	83.70 148	87.29 192	80.29 199	84.56 203	79.46 200

test20.03		88.20 167	91.26 138	84.63 190	96.64 42	89.39 174	90.73 186	89.97 115	91.07 102	72.02 214	94.98 97	95.45 115	69.35 207	92.70 138	91.19 152	89.06 186	84.02 181

HyFIR lowres test		88.19 168	86.56 182	90.09 140	91.24 179	92.17 148	94.30 130	88.79 135	84.06 166	85.45 164	89.52 162	85.64 187	88.64 108	85.40 199	87.28 180	92.14 178	81.87 190

ET-MVSNet_ETH3D		88.06 169	85.75 187	90.74 130	92.82 153	90.68 165	93.77 139	88.59 137	81.22 183	89.78 131	89.15 166	66.79 221	84.29 143	91.72 155	91.34 150	95.22 130	89.36 155

tfpn200view9		87.94 170	87.51 177	88.44 161	92.28 166	93.63 125	93.35 148	88.11 143	80.90 185	80.89 191	78.25 214	82.25 191	79.65 174	94.27 119	94.76 98	96.36 96	88.48 163

FMVSNet3		87.90 171	88.63 164	87.04 174	89.78 194	93.46 132	91.62 180	90.05 110	79.40 191	78.60 199	90.58 145	87.05 181	77.07 192	88.03 187	89.86 163	95.12 135	92.04 124

MS-PatchMatch		87.72 172	88.62 165	86.66 179	90.81 187	88.18 179	90.92 183	82.25 196	85.86 155	80.40 194	90.14 156	89.29 173	84.93 137	89.39 177	89.12 171	90.67 180	88.34 165

Anonymous20231206		87.45 173	89.66 154	84.87 187	94.00 117	87.73 185	91.36 181	86.41 170	88.89 129	75.03 207	92.59 133	96.82 82	72.48 205	89.72 173	88.06 177	89.93 183	83.81 183

EPNet_dtu		87.40 174	86.27 183	88.72 159	95.68 74	83.37 199	92.09 172	90.08 109	78.11 204	91.29 107	86.33 191	89.74 169	75.39 200	89.07 178	87.89 178	87.81 191	89.38 154
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
baseline1		86.96 175	87.58 176	86.24 181	93.07 145	90.44 170	89.24 197	86.85 164	85.14 161	77.26 205	90.45 149	76.09 205	75.79 198	91.80 154	91.81 141	95.20 131	87.35 171

baseline		86.71 176	88.89 159	84.16 193	87.85 206	85.23 192	89.82 191	77.69 206	84.03 168	84.75 169	94.91 99	94.59 132	77.19 191	86.57 195	86.51 185	87.66 194	90.36 149

CHOSEN 1792x2688		86.64 177	86.62 180	86.65 180	90.33 190	87.86 184	93.19 152	83.30 194	83.95 170	82.32 186	87.93 177	89.34 172	86.92 122	85.64 198	84.95 191	83.85 207	86.68 174

dmvs_re		86.51 178	86.14 185	86.95 176	93.07 145	86.11 189	92.01 173	86.04 172	72.70 215	79.10 197	75.37 217	89.99 165	78.10 184	94.56 113	93.01 124	93.35 167	91.26 138

testgi		86.49 179	90.31 147	82.03 197	95.63 75	88.18 179	93.47 144	84.89 182	93.23 54	69.54 218	87.16 184	97.96 47	60.66 215	91.90 153	89.90 162	87.99 189	83.84 182

thres100view900		86.46 180	86.00 186	86.99 175	92.28 166	91.03 162	91.09 182	84.49 186	80.90 185	80.89 191	78.25 214	82.25 191	77.57 188	90.17 168	92.84 127	95.63 121	86.57 175

gm-plane-assit		86.15 181	82.51 195	90.40 136	95.81 70	92.29 145	97.99 34	84.66 185	92.15 76	93.15 65	97.84 30	44.65 229	78.60 177	88.02 188	85.95 187	92.20 177	76.69 208

CMPMVS	66.55 18	85.55 182	87.46 178	83.32 194	84.99 212	81.97 204	79.19 219	75.93 208	79.32 194	88.82 143	85.09 197	91.07 158	82.12 159	92.56 142	89.63 166	88.84 187	92.56 118
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
CR-MVSNet		85.32 183	81.58 197	89.69 148	90.36 189	84.79 195	86.72 210	92.22 63	75.38 210	90.73 115	90.41 151	67.88 218	84.86 138	83.76 202	85.74 188	93.24 168	83.14 185

baseline2		84.95 184	82.68 194	87.59 171	92.64 156	88.41 178	90.09 188	84.25 187	75.88 208	85.23 165	82.49 207	71.15 211	80.14 171	88.21 186	87.21 183	93.21 171	85.39 179

pmnet_mix02		84.85 185	86.58 181	82.83 195	90.19 191	81.10 207	88.52 202	78.58 204	91.50 88	80.32 195	96.48 66	95.86 106	75.42 199	85.17 200	76.44 208	83.91 206	79.51 199

MVSTER		84.79 186	83.79 190	85.96 183	89.14 197	89.80 173	89.39 195	82.99 195	74.16 214	82.78 182	85.97 193	66.81 220	76.84 193	90.77 164	88.83 176	94.66 146	90.19 151

MIMVSNet		84.76 187	86.75 179	82.44 196	91.71 174	85.95 190	89.74 193	89.49 125	85.28 159	69.69 217	87.93 177	90.88 161	64.85 213	88.26 185	87.74 179	89.18 185	81.24 191

SCA		84.69 188	81.10 198	88.87 157	89.02 198	90.31 171	92.21 171	92.09 73	82.72 177	89.68 132	86.83 188	73.08 207	85.80 134	80.50 210	77.51 205	84.45 205	76.80 207

new-patchmatchnet		84.45 189	88.75 163	79.43 203	93.28 138	81.87 205	81.68 216	83.48 192	94.47 29	71.53 215	98.33 16	97.88 51	58.61 218	90.35 166	77.33 206	87.99 189	81.05 193

PatchT		83.44 190	81.10 198	86.18 182	77.92 220	82.58 203	89.87 190	87.39 155	75.88 208	90.73 115	89.86 158	66.71 222	84.86 138	83.76 202	85.74 188	86.33 200	83.14 185

RPMNet		83.42 191	78.40 207	89.28 153	89.79 193	84.79 195	90.64 187	92.11 71	75.38 210	87.10 156	79.80 212	61.99 227	82.79 154	81.88 208	82.07 196	93.23 170	82.87 188

TAMVS		82.96 192	86.15 184	79.24 206	90.57 188	83.12 202	87.29 206	75.12 210	84.06 166	65.81 219	92.22 136	88.27 178	69.11 208	88.72 180	87.26 182	87.56 196	79.38 201

PatchmatchNet		82.44 193	78.69 206	86.83 177	89.81 192	81.55 206	90.78 185	87.27 158	82.39 179	88.85 142	88.31 173	70.96 212	81.90 161	78.58 214	74.33 214	82.35 211	74.69 211
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MDTV_nov1_ep13		82.33 194	79.66 201	85.45 185	88.83 200	83.88 197	90.09 188	81.98 198	79.07 198	88.82 143	88.70 168	73.77 206	78.41 182	80.29 212	76.08 209	84.56 203	75.83 209

CostFormer		82.15 195	79.54 202	85.20 186	88.92 199	85.70 191	90.87 184	86.26 171	79.19 197	83.87 177	87.89 179	69.20 216	76.62 195	77.50 217	75.28 211	84.69 202	82.02 189

PMMVS		81.93 196	83.48 192	80.12 202	72.35 223	75.05 216	88.54 201	64.01 215	77.02 207	82.22 187	87.51 181	91.12 157	79.70 173	86.59 193	86.64 184	93.88 158	80.41 194

pmmvs3		81.69 197	83.83 189	79.19 207	78.33 219	78.57 210	89.53 194	58.71 218	78.88 201	84.34 174	88.36 172	91.96 153	77.69 187	87.48 189	82.42 195	86.54 199	79.18 202

tpm		81.58 198	78.84 204	84.79 189	91.11 183	79.50 208	89.79 192	83.75 188	79.30 195	92.05 88	90.98 142	64.78 224	74.54 201	80.50 210	76.67 207	77.49 216	80.15 197

test0.0.03 1		81.51 199	83.30 193	79.42 204	93.99 118	86.50 188	85.93 214	87.32 156	78.16 203	61.62 220	80.78 210	81.78 195	59.87 216	88.40 184	87.27 181	87.78 193	80.19 196

dps		81.42 200	77.88 212	85.56 184	87.67 208	85.17 194	88.37 204	87.46 153	74.37 213	84.55 171	86.80 189	62.18 226	80.20 170	81.13 209	77.52 204	85.10 201	77.98 205

test-LLR		80.62 201	77.20 215	84.62 191	93.99 118	75.11 214	87.04 207	87.32 156	70.11 218	78.59 202	83.17 204	71.60 209	73.88 203	82.32 206	79.20 201	86.91 197	78.87 203

tpm cat1		80.03 202	75.93 218	84.81 188	89.31 195	83.26 201	88.86 200	86.55 169	79.24 196	86.10 160	84.22 200	63.62 225	77.37 190	73.43 218	70.88 217	80.67 212	76.87 206

N_pmnet		79.33 203	84.22 188	73.62 213	91.72 173	73.72 217	86.11 212	76.36 207	92.38 69	53.38 221	95.54 86	95.62 110	59.14 217	84.23 201	74.84 213	75.03 219	73.25 215

EPMVS		79.26 204	78.20 210	80.49 200	87.04 211	78.86 209	86.08 213	83.51 191	82.63 178	73.94 210	89.59 160	68.67 217	72.03 206	78.17 215	75.08 212	80.37 213	74.37 212

CHOSEN 280x420		79.24 205	78.26 209	80.38 201	79.60 218	68.80 222	89.32 196	75.38 209	77.25 206	78.02 204	75.57 216	76.17 204	81.19 166	88.61 183	81.39 197	78.79 214	80.03 198

ADS-MVSNet		79.11 206	79.38 203	78.80 209	81.90 216	75.59 213	84.36 215	83.69 189	87.31 141	76.76 206	87.58 180	76.90 202	68.55 210	78.70 213	75.56 210	77.53 215	74.07 213

FMVSNet5		79.08 207	78.83 205	79.38 205	87.52 209	86.78 186	87.64 205	78.15 205	69.54 220	70.64 216	65.97 221	65.44 223	63.87 214	90.17 168	90.46 157	88.48 188	83.45 184

tpmrst		78.81 208	76.18 217	81.87 198	88.56 201	77.45 211	86.74 209	81.52 200	80.08 189	83.48 179	90.84 144	66.88 219	74.54 201	73.04 219	71.02 216	76.38 217	73.95 214

test-mter		78.71 209	78.35 208	79.12 208	84.03 213	76.58 212	88.51 203	59.06 217	71.06 216	78.87 198	83.73 203	71.83 208	76.44 196	83.41 205	80.61 198	87.79 192	81.24 191

MVS-HIRNet		78.28 210	75.28 219	81.79 199	80.33 217	69.38 221	76.83 220	86.59 167	70.76 217	86.66 159	89.57 161	81.04 198	77.74 186	77.81 216	71.65 215	82.62 209	66.73 219

E-PMN		77.81 211	77.88 212	77.73 212	88.26 204	70.48 220	80.19 218	71.20 212	86.66 149	72.89 213	88.09 176	81.74 196	78.75 176	90.02 170	68.30 218	75.10 218	59.85 220

EMVS		77.65 212	77.49 214	77.83 210	87.75 207	71.02 219	81.13 217	70.54 213	86.38 152	74.52 209	89.38 163	80.19 201	78.22 183	89.48 175	67.13 219	74.83 220	58.84 221

TESTMET0.1,1		77.47 213	77.20 215	77.78 211	81.94 215	75.11 214	87.04 207	58.33 219	70.11 218	78.59 202	83.17 204	71.60 209	73.88 203	82.32 206	79.20 201	86.91 197	78.87 203

new_pmnet		76.65 214	83.52 191	68.63 214	82.60 214	72.08 218	76.76 221	64.17 214	84.41 165	49.73 223	91.77 139	91.53 156	56.16 219	86.59 193	83.26 194	82.37 210	75.02 210

MVE	60.41 19	73.21 215	80.84 200	64.30 215	56.34 224	57.24 224	75.28 223	72.76 211	87.14 145	41.39 225	86.31 192	85.30 188	80.66 167	86.17 197	83.36 193	59.35 222	80.38 195
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMMVS2		69.86 216	82.14 196	55.52 216	75.19 221	63.08 223	75.52 222	60.97 216	88.50 132	25.11 227	91.77 139	96.44 90	25.43 221	88.70 181	79.34 200	70.93 221	67.17 218

GG-mvs-BLEND		54.28 217	77.89 211	26.72 219	0.37 229	83.31 200	70.04 224	0.39 225	74.71 212	5.36 228	68.78 219	83.06 190	0.62 225	83.73 204	78.99 203	83.55 208	72.68 217

test_method		43.16 218	51.13 220	33.85 217	7.35 226	12.38 227	51.70 226	11.91 221	62.51 222	47.64 224	62.49 222	80.78 199	28.84 220	59.55 222	34.48 221	55.68 223	45.72 222

testmvs		2.38 219	3.35 221	1.26 221	0.83 227	0.96 229	1.53 229	0.83 223	3.59 224	1.63 230	6.03 224	2.93 231	1.55 224	3.49 223	2.51 223	1.21 227	3.92 224

test123		2.16 220	2.82 222	1.41 220	0.62 228	1.18 228	1.53 229	0.82 224	2.78 225	2.27 229	4.18 225	1.98 232	1.64 223	2.58 224	3.01 222	1.56 226	4.00 223

uanet_test		0.00 221	0.00 223	0.00 222	0.00 230	0.00 230	0.00 231	0.00 226	0.00 226	0.00 231	0.00 226	0.00 233	0.00 226	0.00 225	0.00 224	0.00 228	0.00 225

sosnet-low-res		0.00 221	0.00 223	0.00 222	0.00 230	0.00 230	0.00 231	0.00 226	0.00 226	0.00 231	0.00 226	0.00 233	0.00 226	0.00 225	0.00 224	0.00 228	0.00 225

sosnet		0.00 221	0.00 223	0.00 222	0.00 230	0.00 230	0.00 231	0.00 226	0.00 226	0.00 231	0.00 226	0.00 233	0.00 226	0.00 225	0.00 224	0.00 228	0.00 225

TPM-MVS					94.35 108	93.52 131	92.94 157			89.43 137	84.20 201	90.07 164	80.21 169			94.56 150	93.77 90
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												93.19 148

SR-MVS					97.13 23			94.77 17				97.77 54

Anonymous202405211			94.63 74		94.51 106	94.96 94	93.94 137	91.35 90	90.82 107		95.60 82	95.85 107	81.74 165	96.47 75	95.84 73	97.39 63	92.85 108

our_test_3					91.78 172	88.87 177	94.37 125

ambc			94.61 75		98.09 4	95.14 86	91.71 178		94.18 38	96.46 12	96.26 69	96.30 93	91.26 68	94.70 109	92.00 140	93.45 163	93.67 93

MTAPA										94.88 28		96.88 80

MTMP										95.43 18		97.25 68

Patchmatch-RL test							8.96 228

tmp_tt				28.44 218	36.05 225	15.86 226	21.29 227	6.40 222	54.52 223	51.96 222	50.37 223	38.68 230	9.55 222	61.75 221	59.66 220	45.36 225

XVS					96.86 32	97.48 18	98.73 3			93.28 59		96.82 82				98.17 35

X-MVStestdata					96.86 32	97.48 18	98.73 3			93.28 59		96.82 82				98.17 35

mPP-MVS					98.24 2							97.65 60

NP-MVS									85.48 158

Patchmtry						83.74 198	86.72 210	92.22 63		90.73 115

DeepMVS_CX						47.68 225	53.20 225	19.21 220	63.24 221	26.96 226	66.50 220	69.82 215	66.91 212	64.27 220		54.91 224	72.72 216

High-resolution multi-view benchmark