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.

SED-MVS		99.44 1	99.69 21	99.15 1	99.61 12	99.95 13	99.81 5	96.94 7	99.97 9	98.73 2	99.53 28	100.00 1	99.91 4	99.90 8	98.52 57	99.87 29	100.00 1

SF-MVS		99.41 2	99.68 23	99.10 3	99.65 5	99.94 19	99.76 9	96.95 4	99.88 43	98.39 5	99.60 22	100.00 1	99.82 13	99.43 28	98.93 37	99.99 5	100.00 1

APDe-MVS		99.40 3	99.81 2	98.92 8	99.62 7	99.96 7	99.76 9	96.87 15	99.95 26	97.66 7	99.57 26	100.00 1	99.63 28	99.88 11	99.28 26	100.00 1	100.00 1

MSLP-MVS++		99.39 4	99.76 9	98.95 6	99.60 16	99.99 1	99.83 4	96.82 17	99.92 36	97.58 10	99.58 25	100.00 1	99.93 1	98.98 35	99.86 8	99.96 12	100.00 1

CNVR-MVS		99.39 4	99.75 12	98.98 4	99.69 1	99.95 13	99.76 9	96.91 10	99.98 3	97.59 9	99.64 19	100.00 1	99.93 1	99.94 2	98.75 50	99.97 11	99.97 87

DVP-MVS		99.38 6	99.57 36	99.15 1	99.62 7	99.94 19	99.72 22	96.99 2	99.98 3	98.85 1	98.21 75	100.00 1	99.88 7	99.88 11	98.96 35	99.85 33	100.00 1
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
MSP-MVS		99.38 6	99.78 5	98.91 11	99.61 12	99.96 7	99.85 2	96.94 7	99.96 20	97.38 13	99.60 22	100.00 1	99.70 20	99.96 1	98.96 35	100.00 1	100.00 1
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
DPE-MVS		99.37 8	99.74 15	98.94 7	99.60 16	99.94 19	99.87 1	96.95 4	99.94 29	97.42 11	99.62 21	100.00 1	99.80 16	99.91 5	98.78 48	99.98 9	100.00 1
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
DVP-MVS++		99.36 9	99.70 20	98.96 5	99.62 7	99.94 19	99.85 2	96.90 14	99.97 9	97.64 8	99.50 32	100.00 1	99.88 7	99.90 8	98.60 52	99.87 29	100.00 1

SMA-MVS		99.34 10	99.79 4	98.81 13	99.69 1	99.94 19	99.75 16	96.91 10	99.98 3	96.76 15	99.37 39	100.00 1	99.90 5	99.88 11	99.46 17	99.84 36	99.92 125
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
APD-MVS		99.33 11	99.85 1	98.73 14	99.61 12	99.92 40	99.77 8	96.91 10	99.93 32	96.31 19	99.59 24	99.95 40	99.84 11	99.73 18	99.84 9	99.95 14	100.00 1
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
NCCC		99.24 12	99.75 12	98.65 15	99.63 6	99.96 7	99.76 9	96.91 10	99.97 9	95.86 23	99.67 11	100.00 1	99.75 17	99.85 14	98.80 46	99.98 9	99.97 87

CNLPA		99.24 12	99.58 33	98.85 12	99.34 32	99.95 13	99.32 35	96.65 27	99.96 20	98.44 4	98.97 54	100.00 1	99.57 30	98.66 44	99.56 15	99.76 84	99.97 87

AdaColmap		99.21 14	99.45 39	98.92 8	99.67 4	99.95 13	99.65 27	96.77 22	99.97 9	97.67 6	100.00 1	99.69 54	99.93 1	99.26 31	97.25 98	99.85 33	100.00 1

HFP-MVS		99.19 15	99.77 8	98.51 18	99.55 20	99.94 19	99.76 9	96.84 16	99.88 43	95.27 27	99.67 11	100.00 1	99.85 10	99.56 23	99.36 21	99.79 67	99.97 87

PLC	98.06 1	99.17 16	99.38 41	98.92 8	99.47 22	99.90 48	99.48 32	96.47 32	99.96 20	98.73 2	99.52 31	100.00 1	99.55 32	98.54 57	97.73 85	99.84 36	99.99 59
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
SD-MVS		99.16 17	99.73 16	98.49 19	97.93 52	99.95 13	99.74 19	96.94 7	99.96 20	96.60 17	99.47 35	100.00 1	99.88 7	99.15 33	99.59 13	99.84 36	100.00 1
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
CP-MVS		99.14 18	99.67 24	98.53 17	99.45 24	99.94 19	99.63 29	96.62 29	99.82 56	95.92 22	99.65 16	100.00 1	99.71 19	99.76 17	98.56 54	99.83 42	100.00 1

ACMMPR		99.12 19	99.76 9	98.36 20	99.45 24	99.94 19	99.75 16	96.70 26	99.93 32	94.65 31	99.65 16	99.96 38	99.84 11	99.51 26	99.35 22	99.79 67	99.96 107

MCST-MVS		99.08 20	99.72 18	98.33 21	99.59 19	99.97 3	99.78 7	96.96 3	99.95 26	93.72 34	99.67 11	100.00 1	99.90 5	99.91 5	98.55 55	100.00 1	100.00 1

CPTT-MVS		99.08 20	99.53 38	98.57 16	99.44 26	99.93 34	99.60 30	95.92 37	99.77 64	97.01 14	99.67 11	100.00 1	99.72 18	99.56 23	97.76 82	99.70 114	99.98 75

DeepC-MVS_fast	98.03 2	99.05 22	99.78 5	98.21 24	99.47 22	99.97 3	99.75 16	96.80 18	99.97 9	93.58 36	98.68 62	99.94 41	99.69 21	99.93 4	99.95 3	99.96 12	99.98 75
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TSAR-MVS + MP.		98.99 23	99.61 30	98.27 22	97.88 53	99.92 40	99.71 24	96.80 18	99.96 20	95.58 25	98.71 61	100.00 1	99.68 23	99.91 5	98.78 48	99.99 5	100.00 1
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
HPM-MVS++		98.98 24	99.62 29	98.22 23	99.62 7	99.94 19	99.74 19	96.95 4	99.87 47	93.76 33	99.49 34	100.00 1	99.39 38	99.73 18	98.35 60	99.89 25	99.96 107

SteuartSystems-ACMMP		98.95 25	99.80 3	97.95 27	99.43 27	99.96 7	99.76 9	96.45 33	99.82 56	93.63 35	99.64 19	100.00 1	98.56 76	99.90 8	99.31 24	99.84 36	100.00 1
Skip Steuart: Steuart Systems R&D Blog.
PHI-MVS		98.85 26	99.67 24	97.89 28	98.63 47	99.93 34	98.95 46	95.20 39	99.84 54	94.94 28	99.74 10	100.00 1	99.69 21	98.40 64	99.75 11	99.93 17	99.99 59

MP-MVS		98.82 27	99.63 27	97.88 29	99.41 28	99.91 47	99.74 19	96.76 23	99.88 43	91.89 46	99.50 32	99.94 41	99.65 26	99.71 21	98.49 58	99.82 46	99.97 87
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
ACMMP_NAP		98.68 28	99.58 33	97.62 30	99.62 7	99.92 40	99.72 22	96.78 21	99.71 69	90.13 68	99.66 15	99.99 31	99.64 27	99.78 16	98.14 68	99.82 46	99.89 134

train_agg		98.62 29	99.76 9	97.28 32	99.03 40	99.93 34	99.65 27	96.37 34	99.98 3	89.24 79	99.53 28	99.83 46	99.59 29	99.85 14	99.19 29	99.80 60	100.00 1

X-MVS		98.62 29	99.75 12	97.29 31	99.50 21	99.94 19	99.71 24	96.55 30	99.85 51	88.58 84	99.65 16	99.98 33	99.67 24	99.60 22	99.26 27	99.77 78	99.97 87

OMC-MVS		98.59 31	99.07 46	98.03 26	99.41 28	99.90 48	99.26 38	94.33 41	99.94 29	96.03 20	96.68 88	99.72 53	99.42 35	98.86 38	98.84 43	99.72 111	99.58 169

DPM-MVS		98.58 32	99.78 5	97.17 34	98.02 51	99.64 82	99.80 6	96.72 25	99.96 20	90.05 70	99.57 26	100.00 1	98.66 75	99.56 23	99.96 2	99.80 60	99.80 157

PGM-MVS		98.47 33	99.73 16	97.00 36	99.68 3	99.94 19	99.76 9	91.74 46	99.84 54	91.17 55	100.00 1	99.69 54	99.81 14	99.38 29	99.30 25	99.82 46	99.95 114

TSAR-MVS + ACMM		98.30 34	99.64 26	96.74 39	99.08 39	99.94 19	99.67 26	96.73 24	99.97 9	86.30 104	98.30 67	99.99 31	98.78 69	99.73 18	99.57 14	99.88 28	99.98 75

CSCG		98.22 35	98.37 68	98.04 25	99.60 16	99.82 59	99.45 33	93.59 42	99.16 103	96.46 18	98.22 74	95.86 104	99.41 37	96.33 135	99.22 28	99.75 93	99.94 119

3Dnovator+	95.21 7	98.17 36	99.08 45	97.12 35	99.28 35	99.78 70	98.61 53	89.93 60	99.93 32	95.36 26	95.50 97	100.00 1	99.56 31	98.58 52	99.80 10	99.95 14	99.97 87

ACMMP		98.16 37	99.01 47	97.18 33	98.86 42	99.92 40	98.77 51	95.73 38	99.31 99	91.15 56	100.00 1	99.81 48	98.82 67	98.11 84	95.91 133	99.77 78	99.97 87
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
MVS_111021_LR		98.15 38	99.69 21	96.36 44	99.23 37	99.93 34	97.79 65	91.84 45	99.87 47	90.53 64	100.00 1	99.57 59	98.93 61	99.44 27	99.08 32	99.85 33	99.95 114

EPNet		98.11 39	99.63 27	96.34 45	98.44 49	99.88 54	98.55 54	90.25 56	99.93 32	92.60 43	100.00 1	99.73 51	98.41 78	98.87 37	99.02 33	99.82 46	99.97 87
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TSAR-MVS + GP.		98.06 40	99.55 37	96.32 46	94.72 80	99.92 40	99.22 39	89.98 58	99.97 9	94.77 30	99.94 9	100.00 1	99.43 34	98.52 61	98.53 56	99.79 67	100.00 1

3Dnovator	95.01 8	97.98 41	98.89 52	96.92 38	99.36 30	99.76 73	98.72 52	89.98 58	99.98 3	93.99 32	94.60 110	99.43 64	99.50 33	98.55 54	99.91 5	99.99 5	99.98 75

MVS_111021_HR		97.94 42	99.59 31	96.02 48	99.27 36	99.97 3	97.03 90	90.44 53	99.89 40	90.75 59	100.00 1	99.73 51	98.68 74	98.67 43	98.89 40	99.95 14	99.97 87

QAPM		97.90 43	98.89 52	96.74 39	99.35 31	99.80 65	98.84 48	90.20 57	99.94 29	92.85 38	94.17 113	99.78 49	99.42 35	98.71 41	99.87 7	99.79 67	99.98 75

CDPH-MVS		97.88 44	99.59 31	95.89 49	98.90 41	99.95 13	99.40 34	92.86 44	99.86 50	85.33 110	98.62 63	99.45 63	99.06 57	99.29 30	99.94 4	99.81 55	100.00 1

CANet		97.62 45	98.94 50	96.08 47	97.19 57	99.93 34	99.29 37	90.38 54	99.87 47	91.00 57	95.79 96	99.51 60	98.72 73	98.53 58	99.00 34	99.90 23	99.99 59

TAPA-MVS	96.62 5	97.60 46	98.46 67	96.60 42	98.73 45	99.90 48	99.30 36	94.96 40	99.46 87	87.57 92	96.05 95	98.53 76	99.26 47	98.04 89	97.33 97	99.77 78	99.88 139
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
DeepPCF-MVS	97.16 4	97.58 47	99.72 18	95.07 65	98.45 48	99.96 7	93.83 143	95.93 36	100.00 1	90.79 58	98.38 66	99.85 45	95.28 134	99.94 2	99.97 1	96.15 210	99.97 87

CS-MVS-test		97.51 48	99.18 44	95.56 54	97.16 58	99.96 7	97.39 78	89.82 62	100.00 1	89.88 71	99.16 46	98.38 82	99.23 49	98.85 39	97.93 75	99.87 29	100.00 1

PCF-MVS	97.20 3	97.49 49	98.20 73	96.66 41	97.62 55	99.92 40	98.93 47	96.64 28	98.53 134	88.31 90	94.04 116	99.58 58	98.94 59	97.53 105	97.79 80	99.54 144	99.97 87
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
CS-MVS		97.46 50	98.98 48	95.68 53	96.74 62	99.93 34	97.62 71	89.69 63	99.98 3	91.33 52	98.53 65	97.50 91	98.77 70	98.60 51	98.35 60	99.92 20	100.00 1

MSDG		97.29 51	97.55 89	97.00 36	98.66 46	99.71 77	99.03 44	96.15 35	99.59 76	89.67 77	92.77 127	94.86 107	98.75 71	98.22 75	97.94 73	99.72 111	99.76 161

CHOSEN 280x420		97.16 52	99.58 33	94.35 79	96.95 61	99.97 3	97.19 84	81.55 145	99.92 36	91.75 47	100.00 1	100.00 1	98.84 66	98.55 54	98.65 51	99.79 67	99.97 87

DELS-MVS		97.05 53	98.05 78	95.88 51	97.09 59	99.99 1	98.82 49	90.30 55	98.44 140	91.40 50	92.91 124	96.57 97	97.68 106	98.56 53	99.88 6	100.00 1	100.00 1
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
DeepC-MVS	96.33 6	97.05 53	97.59 88	96.42 43	97.37 56	99.92 40	99.10 42	96.54 31	99.34 98	86.64 101	91.93 132	93.15 118	99.11 55	99.11 34	99.68 12	99.73 107	99.97 87
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
test2506		97.04 55	98.09 77	95.81 52	94.12 85	99.80 65	97.33 80	89.48 68	98.90 120	95.99 21	99.11 49	92.84 120	98.14 88	98.14 80	98.32 64	99.82 46	99.51 174

MVS_0304		97.04 55	98.72 58	95.08 64	96.32 66	99.90 48	99.15 40	89.61 66	99.89 40	87.22 98	95.47 98	98.22 85	98.22 84	98.63 48	98.90 39	99.93 17	100.00 1

MAR-MVS		97.03 57	98.00 80	95.89 49	99.32 33	99.74 76	96.76 97	84.89 109	99.97 9	94.86 29	98.29 68	90.58 128	99.67 24	98.02 91	99.50 16	99.82 46	99.92 125
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
MVSTER		97.00 58	98.85 54	94.83 72	92.71 102	97.43 153	99.03 44	85.52 102	99.82 56	92.74 41	99.15 47	99.94 41	99.19 52	98.66 44	96.99 112	99.79 67	99.98 75

DROMVSNet		96.90 59	99.32 42	94.07 80	91.64 132	99.30 105	98.18 61	85.61 101	99.97 9	89.79 72	99.33 40	99.31 67	99.28 45	98.48 63	98.86 41	99.91 21	100.00 1

baseline1		96.87 60	98.55 61	94.91 67	92.89 101	99.45 96	96.34 101	88.54 80	98.88 123	92.82 39	98.93 56	96.58 96	99.07 56	98.19 77	98.04 70	99.80 60	99.78 158

OpenMVS	94.03 11	96.87 60	98.10 76	95.44 58	99.29 34	99.78 70	98.46 59	89.92 61	99.47 86	85.78 108	91.05 136	98.50 77	99.30 43	98.49 62	99.41 18	99.89 25	99.98 75

PatchMatch-RL		96.84 62	98.03 79	95.47 55	98.84 43	99.81 63	95.61 115	89.20 72	99.65 73	91.28 53	99.39 36	93.46 116	98.18 85	98.05 87	96.28 122	99.69 119	99.55 171

ETV-MVS		96.79 63	99.19 43	94.00 82	91.78 123	99.63 84	97.15 86	88.00 86	99.95 26	88.34 89	99.32 41	98.71 73	98.82 67	98.69 42	98.01 71	99.90 23	100.00 1

IS_MVSNet		96.66 64	98.62 60	94.38 76	92.41 108	99.70 78	97.19 84	87.67 89	99.05 111	91.27 54	95.09 103	98.46 81	97.95 96	98.64 46	99.37 19	99.79 67	100.00 1

PMMVS		96.45 65	98.24 72	94.36 78	92.58 103	99.01 116	97.08 89	87.42 95	99.88 43	90.06 69	99.39 36	94.63 108	99.33 41	97.85 97	96.99 112	99.70 114	99.96 107

LS3D		96.44 66	97.31 96	95.41 59	97.06 60	99.87 55	99.51 31	97.48 1	99.57 77	79.00 128	95.39 99	89.19 134	99.81 14	98.55 54	98.84 43	99.62 133	99.78 158

EIA-MVS		96.34 67	98.55 61	93.76 86	91.93 119	99.66 80	97.14 87	88.33 84	99.51 81	85.98 107	98.82 60	96.08 102	99.33 41	98.38 67	97.40 95	99.81 55	100.00 1

EPP-MVSNet		96.29 68	98.34 69	93.90 83	91.77 124	99.38 102	95.45 120	87.25 97	99.38 94	91.36 51	94.86 109	98.49 79	97.83 100	98.01 92	98.23 66	99.75 93	99.99 59

UGNet		96.05 69	98.55 61	93.13 95	94.64 81	99.65 81	94.70 131	87.78 87	99.40 93	89.69 76	98.25 71	99.25 69	92.12 166	96.50 127	97.08 107	99.84 36	99.72 163
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
COLMAP_ROB	93.56 12	96.03 70	96.83 108	95.11 63	97.87 54	99.52 88	98.81 50	91.40 49	99.42 90	84.97 111	90.46 139	96.82 95	98.05 91	96.46 131	96.19 125	99.54 144	98.92 189
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
PVSNet_BlendedMVS		96.01 71	96.48 116	95.46 56	96.47 64	99.89 52	95.64 112	91.23 50	99.75 66	91.59 48	96.80 85	82.44 155	98.05 91	98.53 58	97.92 76	99.80 60	100.00 1

PVSNet_Blended		96.01 71	96.48 116	95.46 56	96.47 64	99.89 52	95.64 112	91.23 50	99.75 66	91.59 48	96.80 85	82.44 155	98.05 91	98.53 58	97.92 76	99.80 60	100.00 1

thisisatest0530		95.89 73	98.32 70	93.06 98	91.76 125	99.75 74	94.94 126	87.60 92	99.91 38	86.66 100	98.28 69	99.98 33	97.72 102	97.10 117	93.24 168	99.65 125	99.95 114

tttt0517		95.88 74	98.31 71	93.04 99	91.75 127	99.75 74	94.90 127	87.60 92	99.91 38	86.63 102	98.28 69	99.98 33	97.72 102	97.10 117	93.24 168	99.65 125	99.95 114

thres100view900		95.86 75	96.62 110	94.97 66	93.10 93	99.83 57	97.76 66	89.15 73	98.62 130	90.69 60	99.00 51	84.86 146	99.30 43	97.57 104	96.48 117	99.81 55	100.00 1

RPSCF		95.86 75	96.94 106	94.61 74	96.52 63	98.67 130	98.54 55	88.43 82	99.56 78	90.51 66	99.39 36	98.70 74	97.72 102	93.77 178	92.00 184	95.93 211	96.50 206

DCV-MVSNet		95.85 77	97.53 90	93.89 84	93.20 92	97.01 159	97.14 87	84.77 110	99.16 103	90.38 67	98.96 55	93.73 113	98.23 83	96.57 126	97.37 96	99.64 129	99.93 121

baseline		95.85 77	98.13 75	93.20 94	92.29 111	99.58 86	97.49 73	84.33 117	99.44 88	87.28 96	97.00 84	94.04 112	97.93 97	98.36 69	98.47 59	99.87 29	99.99 59

canonicalmvs		95.80 79	97.02 101	94.37 77	92.96 99	99.47 93	97.49 73	84.58 112	99.44 88	92.05 45	98.54 64	86.65 139	99.37 39	96.18 138	98.93 37	99.77 78	99.92 125

tfpn200view9		95.78 80	96.54 113	94.89 69	93.10 93	99.82 59	97.67 67	88.85 76	98.62 130	90.69 60	99.00 51	84.86 146	99.28 45	97.41 111	96.10 126	99.76 84	99.99 59

thres200		95.77 81	96.55 112	94.86 70	93.09 95	99.82 59	97.63 70	88.85 76	98.49 135	90.66 62	98.99 53	84.86 146	99.20 50	97.41 111	96.28 122	99.76 84	100.00 1

MVS_Test		95.74 82	98.18 74	92.90 101	92.16 112	99.49 92	97.36 79	84.30 118	99.79 61	84.94 112	96.65 89	93.63 115	98.85 65	98.61 50	99.10 31	99.81 55	100.00 1

thres400		95.72 83	96.48 116	94.84 71	93.00 98	99.83 57	97.55 72	88.93 74	98.49 135	90.61 63	98.86 57	84.63 150	99.20 50	97.45 107	96.10 126	99.77 78	99.99 59

thres600view7		95.64 84	96.38 120	94.79 73	92.96 99	99.82 59	97.48 77	88.85 76	98.38 141	90.52 65	98.84 59	84.61 151	99.15 53	97.41 111	95.60 138	99.76 84	99.99 59

Vis-MVSNet (Re-imp)		95.60 85	98.52 66	92.19 105	92.37 109	99.56 87	96.37 100	87.41 96	98.95 115	84.77 115	94.88 108	98.48 80	92.44 163	98.63 48	99.37 19	99.76 84	99.77 160

FMVSNet3		95.59 86	97.51 92	93.34 92	89.48 148	96.57 166	97.67 67	84.17 119	99.48 83	89.76 73	95.09 103	94.35 109	99.14 54	98.37 68	98.86 41	99.82 46	99.89 134

ECVR-MVS		95.46 87	95.58 133	95.31 61	94.12 85	99.80 65	97.33 80	89.48 68	98.90 120	92.99 37	87.97 151	86.41 141	98.14 88	98.14 80	98.32 64	99.82 46	99.52 173

PVSNet_Blended_VisFu		95.37 88	97.44 94	92.95 100	95.20 73	99.80 65	92.68 151	88.41 83	99.12 106	87.64 91	88.31 150	99.10 70	94.07 150	98.27 72	97.51 91	99.73 107	100.00 1

DI_MVS_plusplus_trai		95.29 89	97.02 101	93.28 93	91.76 125	99.52 88	97.84 64	85.67 100	99.08 110	87.29 95	87.76 154	97.46 92	97.31 110	97.83 98	97.48 92	99.83 42	100.00 1

ET-MVSNet_ETH3D		95.20 90	97.82 85	92.15 106	80.77 209	98.13 141	97.65 69	86.93 98	99.72 68	88.56 87	99.29 44	97.01 94	99.24 48	94.58 166	95.98 131	99.75 93	99.99 59

TSAR-MVS + COLMAP		95.20 90	95.03 140	95.41 59	96.17 67	98.69 129	99.11 41	93.40 43	99.97 9	84.89 113	98.23 73	75.01 175	99.34 40	97.27 116	96.37 121	99.58 137	99.64 168

GBi-Net		95.19 92	96.99 104	93.09 96	89.11 149	96.47 168	96.90 91	84.17 119	99.48 83	89.76 73	95.09 103	94.35 109	98.87 62	96.50 127	97.21 99	99.74 97	99.81 154

test1		95.19 92	96.99 104	93.09 96	89.11 149	96.47 168	96.90 91	84.17 119	99.48 83	89.76 73	95.09 103	94.35 109	98.87 62	96.50 127	97.21 99	99.74 97	99.81 154

test1111		95.15 94	95.18 138	95.12 62	94.07 87	99.80 65	97.20 83	89.53 67	98.80 124	92.22 44	85.44 162	86.24 142	97.89 98	98.12 82	98.34 63	99.80 60	99.51 174

test0.0.03 1		95.15 94	97.87 84	91.99 107	91.69 129	98.82 127	93.04 149	83.60 124	99.65 73	88.80 82	94.15 114	97.67 89	94.97 136	96.62 125	98.16 67	99.83 42	100.00 1

baseline2		95.13 96	98.55 61	91.15 112	90.29 144	99.00 117	94.49 135	82.00 139	99.68 71	84.82 114	96.47 90	99.30 68	95.71 128	98.24 74	97.14 105	99.57 139	100.00 1

casdiffmvs_mvg		95.10 97	96.45 119	93.53 88	92.05 115	99.42 100	97.25 82	87.66 90	97.17 159	86.09 105	91.79 134	91.27 122	98.31 81	98.06 86	97.42 94	99.81 55	100.00 1
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
EPNet_dtu		95.10 97	98.81 56	90.78 113	98.38 50	98.47 132	96.54 99	89.36 70	99.78 63	65.65 182	99.31 42	98.24 84	94.79 139	98.28 71	99.35 22	99.93 17	98.27 193
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Anonymous20231211		94.96 99	94.99 141	94.91 67	93.01 97	99.44 99	96.85 95	88.49 81	98.78 125	92.61 42	83.94 168	90.25 130	98.94 59	95.87 146	96.77 114	99.58 137	99.89 134

UA-Net		94.95 100	98.66 59	90.63 115	94.60 83	98.94 123	96.03 105	85.28 104	98.01 151	78.92 129	97.42 83	99.96 38	89.09 190	98.95 36	98.80 46	99.82 46	98.57 191

CANet_DTU		94.90 101	98.98 48	90.13 123	94.74 79	99.81 63	98.53 56	82.23 138	99.97 9	66.76 179	100.00 1	98.50 77	98.74 72	97.52 106	97.19 104	99.76 84	99.88 139

FC-MVSNet-train		94.61 102	96.27 121	92.68 103	92.35 110	97.14 157	93.45 147	87.73 88	98.93 116	87.31 94	96.42 91	89.35 132	95.67 129	96.06 144	96.01 130	99.56 141	99.98 75

diffmvs		94.60 103	95.63 132	93.41 90	91.98 118	99.30 105	96.86 94	87.62 91	99.30 100	86.07 106	94.12 115	81.63 160	98.16 86	97.43 108	97.60 89	99.76 84	100.00 1
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
casdiffmvs		94.54 104	95.56 134	93.36 91	91.84 121	99.46 95	95.92 107	87.54 94	98.45 138	86.57 103	90.51 138	84.72 149	98.49 77	97.97 93	97.80 79	99.77 78	100.00 1
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
CLD-MVS		94.53 105	94.45 148	94.61 74	93.85 89	98.36 134	98.12 62	89.68 64	99.35 97	89.62 78	95.19 101	77.08 166	96.66 119	95.51 150	95.67 136	99.74 97	100.00 1
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
FMVSNet2		94.48 106	95.95 126	92.77 102	89.11 149	96.47 168	96.90 91	83.38 127	99.11 107	88.64 83	87.50 159	92.26 121	98.87 62	97.91 95	98.60 52	99.74 97	99.81 154

HQP-MVS		94.48 106	95.39 136	93.42 89	95.10 74	98.35 135	98.19 60	91.41 48	99.77 64	79.79 125	99.30 43	77.08 166	96.25 122	96.93 119	96.28 122	99.76 84	99.99 59

FA-MVS(training)		94.33 108	97.52 91	90.60 117	92.42 107	99.77 72	96.13 104	68.75 195	99.05 111	88.49 88	91.95 130	99.48 61	98.12 90	98.39 65	94.02 161	99.68 121	99.98 75

MDTV_nov1_ep13		94.32 109	98.77 57	89.14 133	91.70 128	99.52 88	95.21 122	72.09 193	99.80 59	78.91 130	96.32 92	99.62 56	97.71 105	98.39 65	97.71 86	99.22 193	100.00 1

CDS-MVSNet		94.32 109	97.00 103	91.19 111	89.82 147	98.71 128	95.51 117	85.14 108	96.85 160	82.33 120	92.48 128	96.40 100	94.71 140	96.86 122	97.76 82	99.63 131	99.92 125
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
dps		94.29 111	97.33 95	90.75 114	92.02 116	99.21 109	94.31 137	66.97 200	99.50 82	95.61 24	96.22 94	98.64 75	96.08 124	93.71 180	94.03 160	99.52 148	99.98 75

ACMM	94.44 10	94.26 112	94.62 145	93.84 85	94.86 78	97.73 148	93.48 146	90.76 52	99.27 101	87.46 93	99.04 50	76.60 168	96.76 117	96.37 134	93.76 163	99.74 97	99.55 171
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMP	94.49 9	94.19 113	94.74 144	93.56 87	94.25 84	98.32 137	96.02 106	89.35 71	98.90 120	87.28 96	99.14 48	76.41 171	94.94 137	96.07 143	94.35 157	99.49 155	99.99 59
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
EPMVS		94.08 114	98.54 65	88.87 134	92.51 105	99.47 93	94.18 139	66.53 201	99.68 71	82.40 119	95.24 100	99.40 65	97.86 99	98.12 82	97.99 72	99.75 93	99.88 139

test-LLR		93.71 115	97.23 97	89.60 127	91.69 129	99.10 113	94.68 133	83.60 124	99.36 95	71.94 156	93.82 118	96.51 98	95.96 126	97.42 109	94.37 154	99.74 97	99.99 59

CHOSEN 1792x2688		93.69 116	94.89 143	92.28 104	96.17 67	99.84 56	95.69 111	83.17 130	98.54 133	82.04 121	77.58 196	91.15 124	96.90 112	98.36 69	98.82 45	99.73 107	99.98 75

LGP-MVS_train		93.60 117	95.05 139	91.90 108	94.90 77	98.29 138	97.93 63	88.06 85	99.14 105	74.83 145	99.26 45	76.50 169	96.07 125	96.31 136	95.90 135	99.59 135	99.97 87

SCA		93.53 118	98.90 51	87.27 151	92.01 117	99.30 105	93.43 148	65.72 205	99.80 59	75.20 144	97.66 81	99.74 50	97.44 108	98.21 76	97.62 88	99.84 36	100.00 1

FMVSNet5		93.53 118	96.09 125	90.56 118	86.74 163	92.84 208	92.64 152	77.50 171	99.41 92	88.97 81	98.02 77	97.81 87	98.00 94	94.85 162	95.43 140	99.50 154	94.25 210

OPM-MVS		93.50 120	93.00 158	94.07 80	95.82 70	98.26 139	98.49 58	91.62 47	94.69 180	81.93 122	92.82 126	76.18 173	96.82 114	96.12 140	94.57 148	99.74 97	98.39 192
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
CostFormer		93.50 120	96.50 115	90.00 124	91.69 129	98.65 131	93.88 142	67.64 198	98.97 113	89.16 80	97.79 79	88.92 135	97.97 95	95.14 159	96.06 128	99.63 131	100.00 1

IterMVS-LS		93.50 120	96.22 122	90.33 121	90.93 135	95.50 193	94.83 129	80.54 149	98.92 117	79.11 127	90.64 137	93.70 114	96.79 115	96.93 119	97.85 78	99.78 74	99.99 59
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PatchmatchNet		93.48 123	98.84 55	87.22 152	91.93 119	99.39 101	92.55 153	66.06 203	99.71 69	75.61 141	98.24 72	99.59 57	97.35 109	97.87 96	97.64 87	99.83 42	99.43 177
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MS-PatchMatch		93.46 124	95.91 128	90.61 116	95.48 71	99.31 104	95.62 114	77.23 173	99.42 90	81.88 123	88.92 147	96.06 103	93.80 152	96.45 133	93.11 172	99.65 125	98.10 197

tpm cat1		93.29 125	96.53 114	89.50 129	91.84 121	99.18 111	94.70 131	67.70 197	98.38 141	86.67 99	89.16 144	99.38 66	96.66 119	94.33 167	95.30 141	99.43 171	100.00 1

Effi-MVS+-dtu		93.13 126	97.13 99	88.47 142	88.86 155	99.19 110	96.79 96	79.08 162	99.64 75	70.01 166	97.51 82	89.38 131	96.53 121	97.60 102	96.55 116	99.57 139	100.00 1

HyFIR lowres test		93.13 126	94.48 147	91.56 109	96.12 69	99.68 79	93.52 145	79.98 153	97.24 157	81.73 124	72.66 205	95.74 105	98.29 82	98.27 72	97.79 80	99.70 114	100.00 1

Vis-MVSNet		93.08 128	96.76 109	88.78 138	91.14 134	99.63 84	94.85 128	83.34 128	97.19 158	74.78 146	91.92 133	93.15 118	88.81 193	97.59 103	98.35 60	99.78 74	99.49 176
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
Effi-MVS+		93.06 129	95.94 127	89.70 126	90.82 136	99.45 96	95.71 110	78.94 163	98.72 126	74.71 147	97.92 78	80.73 161	98.35 79	97.72 100	97.05 110	99.70 114	100.00 1

ADS-MVSNet		92.91 130	97.97 81	87.01 154	92.07 114	99.27 108	92.70 150	65.39 208	99.85 51	75.40 142	94.93 107	98.26 83	96.86 113	96.09 141	97.52 90	99.65 125	99.84 150

GeoE		92.88 131	95.20 137	90.18 122	90.59 140	99.18 111	96.31 102	78.36 167	97.52 156	78.53 132	87.11 160	88.01 136	97.63 107	97.79 99	96.76 115	99.66 123	100.00 1

TESTMET0.1,1		92.87 132	97.23 97	87.79 148	86.96 162	99.10 113	94.68 133	77.46 172	99.36 95	71.94 156	93.82 118	96.51 98	95.96 126	97.42 109	94.37 154	99.74 97	99.99 59

FC-MVSNet-test		92.78 133	96.19 124	88.80 137	88.00 159	97.54 150	93.60 144	82.36 137	98.16 145	79.71 126	91.55 135	95.41 106	89.65 185	96.09 141	95.23 142	99.49 155	99.31 180

Fast-Effi-MVS+-dtu		92.73 134	97.62 87	87.02 153	88.91 153	98.83 126	95.79 108	73.98 187	99.89 40	68.62 171	97.73 80	93.30 117	95.21 135	97.67 101	95.96 132	99.59 135	100.00 1

IB-MVS	90.59 15	92.70 135	95.70 130	89.21 132	94.62 82	99.45 96	83.77 201	88.92 75	99.53 79	92.82 39	98.86 57	86.08 143	75.24 212	92.81 194	93.17 170	99.89 25	100.00 1
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
test-mter		92.67 136	97.13 99	87.47 150	86.72 164	99.07 115	94.28 138	76.90 174	99.21 102	71.53 160	93.63 120	96.32 101	95.67 129	97.32 114	94.36 156	99.74 97	99.99 59

RPMNet		92.64 137	97.88 83	86.53 159	90.79 137	98.95 121	95.13 123	64.44 212	99.09 108	72.36 152	93.58 121	99.01 71	96.74 118	98.05 87	96.45 119	99.71 113	100.00 1

FMVSNet1		92.55 138	93.66 153	91.26 110	87.91 160	96.12 175	94.75 130	81.69 144	97.67 154	85.63 109	80.56 181	87.88 138	98.15 87	96.50 127	97.21 99	99.41 176	99.71 164

tpmrst		92.52 139	97.45 93	86.77 157	92.15 113	99.36 103	92.53 154	65.95 204	99.53 79	72.50 150	92.22 129	99.83 46	97.81 101	95.18 158	96.05 129	99.69 119	100.00 1

testgi		92.47 140	95.68 131	88.73 139	90.68 138	98.35 135	91.67 161	79.50 158	98.96 114	77.12 137	95.17 102	85.84 144	93.95 151	95.75 148	96.47 118	99.45 166	99.21 183

TAMVS		92.43 141	94.21 151	90.35 120	88.68 156	98.85 125	94.15 140	81.53 146	95.58 170	83.61 117	87.05 161	86.45 140	94.71 140	96.27 137	95.91 133	99.42 174	99.38 179

CR-MVSNet		92.32 142	97.97 81	85.74 168	90.63 139	98.95 121	95.46 118	65.50 206	99.09 108	67.51 175	94.20 112	98.18 86	95.59 132	98.16 78	97.20 102	99.74 97	100.00 1

CVMVSNet		92.13 143	95.40 135	88.32 145	91.29 133	97.29 155	91.85 158	86.42 99	96.71 162	71.84 158	89.56 142	91.18 123	88.98 192	96.17 139	97.76 82	99.51 152	99.14 185

Fast-Effi-MVS+		92.11 144	94.33 149	89.52 128	89.06 152	99.00 117	95.13 123	76.72 176	98.59 132	78.21 134	89.99 140	77.35 165	98.34 80	97.97 93	97.44 93	99.67 122	99.96 107

ACMH+	92.61 13	91.80 145	93.03 156	90.37 119	93.03 96	98.17 140	94.00 141	84.13 122	98.12 147	77.39 136	91.95 130	74.62 176	94.36 147	94.62 165	93.82 162	99.32 185	99.87 144

IterMVS-SCA-FT		91.75 146	96.87 107	85.78 166	90.34 142	95.93 182	95.06 125	73.85 188	98.91 118	61.01 195	89.21 143	98.87 72	94.66 143	98.09 85	97.12 106	99.76 84	99.99 59

IterMVS		91.65 147	96.62 110	85.85 165	90.27 145	95.80 184	95.32 121	74.15 184	98.91 118	60.95 196	88.79 149	97.76 88	94.69 142	98.04 89	97.07 108	99.73 107	100.00 1
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ACMH	92.34 14	91.59 148	93.02 157	89.92 125	93.97 88	97.98 145	90.10 176	84.70 111	98.46 137	76.80 138	93.38 122	71.94 187	94.39 145	95.34 154	94.04 159	99.54 144	100.00 1
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs4		91.41 149	93.05 155	89.49 130	85.85 172	96.52 167	91.70 160	82.49 135	98.14 146	83.17 118	87.57 156	81.76 159	94.39 145	95.47 151	92.62 178	99.33 183	99.29 181

PatchT		91.06 150	97.66 86	83.36 190	90.32 143	98.96 120	82.30 205	64.72 211	98.45 138	67.51 175	93.28 123	97.60 90	95.59 132	98.16 78	97.20 102	99.70 114	100.00 1

MIMVSNet		91.01 151	96.22 122	84.93 176	85.24 176	98.09 142	90.40 171	64.96 210	97.55 155	72.65 148	96.23 93	90.81 126	96.79 115	96.69 123	97.06 109	99.52 148	97.09 203

UniMVSNet_NR-MVSNet		90.50 152	92.31 161	88.38 143	85.04 182	96.34 171	90.94 163	85.32 103	95.87 169	75.69 139	87.68 155	78.49 162	93.78 153	93.21 189	94.60 147	99.53 147	99.97 87

UniMVSNet (Re)		90.41 153	91.96 163	88.59 141	85.71 173	96.73 163	90.82 165	84.11 123	95.23 176	78.54 131	88.91 148	76.41 171	92.84 160	93.40 187	93.05 173	99.55 143	100.00 1

GA-MVS		90.38 154	94.59 146	85.46 172	88.30 158	98.44 133	92.18 155	83.30 129	97.89 153	58.05 204	92.86 125	84.25 153	91.27 176	96.65 124	92.61 179	99.66 123	99.43 177

USDC		90.36 155	91.68 164	88.82 136	92.58 103	98.02 143	96.27 103	79.83 154	98.37 143	70.61 165	89.05 145	67.50 203	94.17 148	95.77 147	94.43 152	99.46 163	98.62 190

thisisatest0515		90.28 156	94.32 150	85.57 171	85.23 177	97.23 156	85.44 197	83.09 131	96.80 161	72.41 151	89.82 141	90.87 125	87.93 198	95.27 157	90.39 201	99.33 183	99.88 139

TinyColmap		89.94 157	90.88 170	88.84 135	92.43 106	97.91 146	95.59 116	80.10 152	98.12 147	71.33 162	84.56 164	67.46 204	94.15 149	95.57 149	94.27 158	99.43 171	98.26 194

pm-mvs1		89.68 158	92.00 162	86.96 155	86.23 168	96.62 165	90.36 172	83.05 132	93.97 188	72.15 155	81.77 176	82.10 157	90.69 182	95.38 153	94.50 150	99.29 189	99.65 166

tpm		89.60 159	94.93 142	83.39 188	89.94 146	97.11 158	90.09 177	65.28 209	98.67 128	60.03 200	96.79 87	84.38 152	95.66 131	91.90 198	95.65 137	99.32 185	99.98 75

NR-MVSNet		89.52 160	90.71 171	88.14 147	86.19 169	96.20 173	92.07 156	84.58 112	95.54 171	75.27 143	87.52 157	67.96 201	91.24 177	94.33 167	93.45 166	99.49 155	99.97 87

DU-MVS		89.49 161	90.60 172	88.19 146	84.71 186	96.20 173	90.94 163	84.58 112	95.54 171	75.69 139	87.52 157	68.74 200	93.78 153	91.10 202	95.13 144	99.47 161	99.97 87

Baseline_NR-MVSNet		89.13 162	89.53 184	88.66 140	84.71 186	94.43 201	91.79 159	84.49 115	95.54 171	78.28 133	78.52 193	72.46 186	93.29 157	91.10 202	94.82 146	99.42 174	99.86 147

tfpnnormal		89.09 163	89.71 178	88.38 143	87.37 161	96.78 162	91.46 162	85.20 106	90.33 208	72.35 153	83.45 169	69.30 198	94.45 144	95.29 155	92.86 175	99.44 170	99.93 121

TranMVSNet+NR-MVSNet		88.88 164	89.90 177	87.69 149	84.06 198	95.68 185	91.88 157	85.23 105	95.16 177	72.54 149	83.06 172	70.14 195	92.93 159	90.81 205	94.53 149	99.48 159	99.89 134

WR-MVS_H		88.47 165	90.55 173	86.04 161	85.13 179	96.07 177	89.86 183	79.80 155	94.37 185	72.32 154	83.12 171	74.44 179	89.60 186	93.52 184	92.40 180	99.51 152	99.96 107

SixPastTwentyTwo		88.35 166	91.51 166	84.66 178	85.39 175	96.96 160	86.57 193	79.62 157	96.57 163	63.73 189	87.86 153	75.18 174	93.43 156	94.03 171	90.37 202	99.24 192	99.58 169

TransMVSNet (Re)		88.33 167	89.55 183	86.91 156	86.65 165	95.56 190	90.48 169	84.44 116	92.02 207	71.07 164	80.13 183	72.48 185	89.41 187	95.05 161	94.44 151	99.39 178	97.14 202

MVS-HIRNet		88.27 168	94.05 152	81.51 196	88.90 154	98.93 124	83.38 203	60.52 219	98.06 149	63.78 188	80.67 180	90.36 129	92.94 158	97.29 115	96.41 120	99.56 141	96.66 205

WR-MVS		88.23 169	90.15 175	86.00 163	84.39 193	95.64 186	89.96 180	81.80 141	94.46 183	71.60 159	82.10 174	74.36 180	88.76 194	92.48 195	92.20 182	99.46 163	99.83 152

CP-MVSNet		88.09 170	89.57 181	86.36 160	84.63 189	95.46 195	89.48 185	80.53 150	93.42 195	71.26 163	81.25 178	69.90 196	92.78 161	93.30 188	93.69 164	99.47 161	99.96 107

pmnet_mix02		88.07 171	92.32 160	83.10 191	86.14 170	96.23 172	81.90 208	83.05 132	98.04 150	57.59 206	84.93 163	82.02 158	90.87 181	93.54 183	91.53 193	99.06 200	99.97 87

UniMVSNet_ETH3D		88.05 172	87.01 202	89.27 131	88.53 157	97.49 151	90.35 173	83.48 126	94.57 181	77.87 135	70.08 209	61.75 215	96.22 123	90.17 206	95.21 143	99.16 197	99.82 153

anonymousdsp		87.98 173	92.38 159	82.85 192	83.68 202	96.79 161	90.78 166	74.06 186	95.29 175	57.91 205	83.33 170	83.12 154	91.15 179	95.96 145	92.37 181	99.52 148	99.76 161

LTVRE_ROB	88.65 16	87.87 174	91.11 169	84.10 185	86.64 166	97.47 152	94.40 136	78.41 166	96.13 167	52.02 213	87.95 152	65.92 209	93.59 155	95.29 155	95.09 145	99.52 148	99.95 114
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
V42		87.84 175	89.42 186	85.99 164	85.16 178	96.01 179	90.52 168	81.78 143	94.43 184	67.59 173	81.32 177	71.87 188	91.48 174	91.25 201	91.16 197	99.43 171	99.92 125

TDRefinement		87.79 176	88.76 193	86.66 158	93.54 90	98.02 143	95.76 109	85.18 107	96.57 163	67.90 172	80.51 182	66.51 208	78.37 209	93.20 190	89.73 203	99.22 193	96.75 204

MDTV_nov1_ep13_2view		87.75 177	93.32 154	81.26 198	83.74 201	96.64 164	85.66 196	66.20 202	98.36 144	61.61 193	84.34 166	87.95 137	91.12 180	94.01 172	92.66 177	99.22 193	99.27 182

v8		87.54 178	89.33 187	85.45 173	85.41 174	95.50 193	90.32 174	78.94 163	94.35 186	66.93 178	81.90 175	70.99 192	91.62 172	91.49 200	91.22 196	99.48 159	99.87 144

v1144		87.49 179	89.64 179	84.97 175	84.73 185	95.84 183	90.17 175	79.30 159	93.96 189	64.65 186	78.83 190	73.38 184	91.51 173	93.77 178	91.77 188	99.45 166	99.93 121

v2v482		87.46 180	88.90 191	85.78 166	84.58 190	95.95 181	89.90 182	82.43 136	94.19 187	65.65 182	79.80 185	69.12 199	92.67 162	91.88 199	91.46 194	99.45 166	99.93 121

v10		87.40 181	89.62 180	84.80 177	84.93 183	95.07 199	90.44 170	75.63 180	94.51 182	66.52 180	78.87 189	73.47 183	91.86 170	93.69 181	91.87 187	99.45 166	99.86 147

pmmvs5		87.33 182	90.01 176	84.20 183	84.31 195	96.04 178	87.63 191	76.59 177	93.17 200	65.35 185	84.30 167	71.68 189	91.91 169	95.41 152	91.37 195	99.39 178	98.13 195

N_pmnet		87.31 183	91.51 166	82.41 195	85.13 179	95.57 189	80.59 210	81.79 142	96.20 165	58.52 203	78.62 191	85.66 145	89.36 188	94.64 164	92.14 183	99.08 199	97.72 201

PS-CasMVS		87.24 184	88.52 196	85.73 169	84.58 190	95.35 197	89.03 188	80.17 151	93.11 201	68.86 170	77.71 195	66.89 205	92.30 164	93.13 191	93.50 165	99.46 163	99.96 107

EU-MVSNet		87.20 185	90.47 174	83.38 189	85.11 181	93.85 206	86.10 195	79.76 156	93.30 199	65.39 184	84.41 165	78.43 163	85.04 205	92.20 197	93.03 174	98.86 202	98.05 198

PEN-MVS		87.20 185	88.22 197	86.01 162	84.01 200	94.93 200	90.00 179	81.52 148	93.46 194	69.29 168	79.69 186	65.51 210	91.72 171	91.01 204	93.12 171	99.49 155	99.84 150

EG-PatchMatch MVS		86.96 187	89.56 182	83.93 186	86.29 167	97.61 149	90.75 167	73.31 191	95.43 174	66.08 181	75.88 202	71.31 190	87.55 200	94.79 163	92.74 176	99.61 134	99.13 186

v1192		86.93 188	89.01 189	84.50 179	84.46 192	95.51 192	89.93 181	78.65 165	93.75 190	62.29 191	77.19 197	70.88 193	92.28 165	93.84 175	91.96 185	99.38 180	99.90 131

v1921920		86.81 189	88.93 190	84.33 182	84.23 196	95.41 196	90.09 177	78.10 168	93.74 191	62.17 192	76.98 199	71.14 191	92.05 167	93.69 181	91.69 191	99.32 185	99.88 139

v144192		86.80 190	88.90 191	84.35 180	84.33 194	95.56 190	89.34 186	77.74 170	93.60 192	64.03 187	77.82 194	70.76 194	91.28 175	92.91 193	91.74 190	99.37 181	99.90 131

DTE-MVSNet		86.70 191	87.66 201	85.58 170	83.30 203	94.29 202	89.74 184	81.53 146	92.77 203	68.93 169	80.13 183	64.00 213	90.62 183	89.45 207	93.34 167	99.32 185	99.67 165

gg-mvs-nofinetune		86.69 192	91.30 168	81.30 197	90.42 141	99.64 82	98.50 57	61.68 217	79.23 217	40.35 220	66.58 211	97.14 93	96.92 111	98.64 46	97.94 73	99.91 21	99.97 87

v148		86.63 193	87.79 199	85.28 174	84.65 188	95.97 180	86.46 194	82.84 134	92.91 202	71.52 161	78.99 188	66.74 207	86.83 202	89.28 208	90.69 199	99.41 176	99.94 119

v1240		86.24 194	88.56 195	83.54 187	84.05 199	95.21 198	89.27 187	76.76 175	93.42 195	60.68 199	75.99 201	69.80 197	91.21 178	93.83 177	91.76 189	99.29 189	99.91 130

pmmvs6		85.75 195	86.97 203	84.34 181	84.88 184	95.59 188	87.41 192	79.19 161	87.81 213	67.56 174	63.05 214	77.76 164	89.15 189	93.45 186	91.90 186	97.83 208	99.21 183

v7n		85.39 196	87.70 200	82.70 193	82.77 205	95.64 186	88.27 190	74.83 182	92.30 205	62.58 190	76.37 200	64.80 212	88.38 196	94.29 169	90.61 200	99.34 182	99.87 144

gm-plane-assit		84.93 197	91.61 165	77.14 206	84.14 197	91.29 212	66.18 220	69.70 194	85.22 216	47.95 217	78.58 192	89.24 133	94.90 138	98.82 40	98.12 69	99.99 5	100.00 1

CMPMVS	65.66 17	84.62 198	85.02 205	84.15 184	95.40 72	97.79 147	88.35 189	79.22 160	89.66 211	60.71 198	72.20 206	73.94 181	87.32 201	86.73 210	84.55 212	93.90 213	90.31 214
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test_method		84.44 199	89.04 188	79.08 201	81.15 208	92.82 209	82.06 207	61.92 215	96.17 166	59.38 201	74.47 204	67.52 202	91.96 168	96.92 121	95.53 139	97.98 207	99.85 149

Anonymous20231206		84.28 200	89.53 184	78.17 203	82.31 207	94.16 204	82.57 204	76.51 178	93.38 198	52.98 211	79.47 187	73.74 182	75.45 211	95.07 160	94.41 153	99.18 196	96.46 207

new_pmnet		84.12 201	87.89 198	79.72 200	80.43 210	94.14 205	80.26 211	74.14 185	96.01 168	56.30 210	74.94 203	76.45 170	88.59 195	93.11 192	89.31 204	98.59 205	91.27 213

test20.03		83.86 202	88.73 194	78.16 204	82.60 206	93.00 207	81.61 209	74.68 183	92.36 204	57.50 207	83.01 173	74.48 178	73.30 213	92.40 196	91.14 198	99.29 189	94.75 209

pmmvs-eth3d		82.92 203	83.31 208	82.47 194	76.97 212	91.76 211	83.79 200	76.10 179	90.33 208	69.95 167	71.04 208	48.09 218	89.02 191	93.85 174	89.14 205	99.02 201	98.96 188

PM-MVS		82.79 204	84.51 206	80.77 199	77.22 211	92.13 210	83.61 202	73.31 191	93.50 193	61.06 194	77.15 198	46.52 221	90.55 184	94.14 170	89.05 207	98.85 203	99.12 187

pmmvs3		80.91 205	85.62 204	75.42 208	75.01 214	89.09 215	75.31 214	68.70 196	86.99 214	46.74 219	81.18 179	62.91 214	87.95 197	93.84 175	89.06 206	98.80 204	96.23 208

MIMVSNet1		80.64 206	83.97 207	76.76 207	68.91 217	91.15 214	78.32 213	75.47 181	89.58 212	56.64 209	65.10 212	65.17 211	82.14 206	93.51 185	91.64 192	99.10 198	91.66 212

MDA-MVSNet-bldmvs		80.30 207	82.83 209	77.34 205	69.16 216	94.29 202	72.16 215	81.97 140	90.14 210	57.32 208	94.01 117	47.97 219	86.81 203	68.74 217	86.82 209	96.63 209	97.86 199

new-patchmatchnet		78.17 208	80.82 210	75.07 209	76.93 213	91.20 213	71.90 216	73.32 190	86.59 215	48.91 214	67.11 210	47.85 220	81.19 207	88.18 209	87.02 208	98.19 206	97.79 200

FPMVS		73.80 209	74.62 211	72.84 210	83.09 204	84.44 217	83.89 199	73.64 189	92.20 206	48.50 215	72.19 207	59.51 216	63.16 215	69.13 216	66.26 220	84.74 218	78.59 220

Gipuma		71.02 210	72.60 214	69.19 211	71.31 215	75.11 220	66.36 219	61.65 218	94.93 178	47.29 218	38.74 219	38.52 222	75.52 210	86.09 211	85.92 211	93.01 214	88.87 216
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
GG-mvs-BLEND		69.85 211	99.39 40	35.39 219	3.67 226	99.94 19	99.10 42	1.69 223	99.85 51	3.19 227	98.13 76	99.46 62	4.92 222	99.23 32	99.14 30	99.80 60	100.00 1

PMMVS2		65.18 212	68.25 215	61.59 212	61.37 220	79.72 219	59.18 223	61.80 216	64.72 220	37.33 221	53.82 216	35.59 223	54.46 220	73.94 215	80.52 213	95.40 212	89.43 215

PMVS	60.14 18	62.67 213	64.05 216	61.06 213	68.32 218	53.27 226	52.23 224	67.63 199	75.07 219	48.30 216	58.27 215	57.43 217	49.99 221	67.20 218	62.42 221	79.87 221	74.68 221
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
testmvs		61.76 214	72.90 213	48.76 216	21.21 224	68.61 221	66.11 221	37.38 221	94.83 179	33.06 222	64.31 213	29.72 224	86.08 204	74.44 214	78.71 214	48.74 223	99.65 166

E-PMN		55.33 215	55.79 218	54.81 215	59.81 222	57.23 224	38.83 225	63.59 213	64.06 222	24.66 224	35.33 221	26.40 226	58.69 217	55.41 220	70.54 217	83.26 219	81.56 219

EMVS		55.14 216	55.29 219	54.97 214	60.87 221	57.52 223	38.58 226	63.57 214	64.54 221	23.36 225	36.96 220	27.99 225	60.69 216	51.17 221	66.61 219	82.73 220	82.25 218

MVE	58.81 19	52.07 217	55.15 220	48.48 217	42.45 223	62.35 222	36.41 227	54.70 220	49.88 223	27.65 223	29.98 222	18.08 227	54.87 219	65.93 219	77.26 215	74.79 222	82.59 217
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test123		48.14 218	58.11 217	36.51 218	8.71 225	56.81 225	59.55 222	24.08 222	77.50 218	14.41 226	49.20 217	11.94 229	80.98 208	41.62 222	69.81 218	31.32 224	99.90 131

uanet_test		0.00 219	0.00 221	0.00 220	0.00 227	0.00 227	0.00 228	0.00 224	0.00 224	0.00 228	0.00 223	0.00 230	0.00 223	0.00 223	0.00 222	0.00 225	0.00 222

sosnet-low-res		0.00 219	0.00 221	0.00 220	0.00 227	0.00 227	0.00 228	0.00 224	0.00 224	0.00 228	0.00 223	0.00 230	0.00 223	0.00 223	0.00 222	0.00 225	0.00 222

sosnet		0.00 219	0.00 221	0.00 220	0.00 227	0.00 227	0.00 228	0.00 224	0.00 224	0.00 228	0.00 223	0.00 230	0.00 223	0.00 223	0.00 222	0.00 225	0.00 222

RE-MVS-def										52.74 212

9.14												100.00 1

SR-MVS					99.61 12			96.80 18				100.00 1

Anonymous202405211			95.78 129		93.26 91	99.52 88	96.70 98	88.55 79	97.93 152		88.99 146	90.68 127	98.99 58	96.46 131	97.02 111	99.64 129	99.89 134

our_test_3					85.89 171	96.09 176	82.15 206

ambc			74.33 212		66.84 219	84.26 218	84.17 198		93.39 197	58.99 202	45.93 218	18.06 228	70.61 214	93.94 173	86.62 210	92.61 216	98.13 195

MTAPA										96.61 16		100.00 1

MTMP										97.42 11		100.00 1

Patchmatch-RL test							68.01 218

tmp_tt				78.81 202	98.80 44	85.73 216	70.08 217	77.87 169	98.68 127	83.71 116	99.53 28	74.55 177	54.97 218	78.28 213	72.43 216	87.45 217

XVS					95.09 75	99.94 19	97.49 73			88.58 84		99.98 33				99.78 74

X-MVStestdata					95.09 75	99.94 19	97.49 73			88.58 84		99.98 33				99.78 74

mPP-MVS					99.23 37							99.87 44

NP-MVS									99.79 61

Patchmtry						99.00 117	95.46 118	65.50 206		67.51 175

DeepMVS_CX						97.31 154	79.48 212	89.65 65	98.66 129	60.89 197	94.40 111	66.89 205	87.65 199	81.69 212		92.76 215	94.24 211

High-resolution multi-view benchmark