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 13	99.95 14	99.81 6	96.94 7	99.97 9	98.73 2	99.53 29	100.00 1	99.91 4	99.90 8	98.52 57	99.87 30	100.00 1

SF-MVS		99.41 2	99.68 23	99.10 3	99.65 6	99.94 20	99.76 10	96.95 4	99.88 43	98.39 5	99.60 23	100.00 1	99.82 14	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 8	99.96 7	99.76 10	96.87 15	99.95 26	97.66 7	99.57 27	100.00 1	99.63 29	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 17	99.99 1	99.83 4	96.82 17	99.92 36	97.58 10	99.58 26	100.00 1	99.93 1	98.98 35	99.86 8	99.96 13	100.00 1

CNVR-MVS		99.39 4	99.75 12	98.98 4	99.69 1	99.95 14	99.76 10	96.91 10	99.98 3	97.59 9	99.64 20	100.00 1	99.93 1	99.94 2	98.75 50	99.97 12	99.97 88

DVP-MVS		99.38 6	99.57 36	99.15 1	99.62 8	99.94 20	99.72 23	96.99 2	99.98 3	98.85 1	98.21 76	100.00 1	99.88 8	99.88 11	98.96 35	99.85 34	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 13	99.96 7	99.85 2	96.94 7	99.96 20	97.38 13	99.60 23	100.00 1	99.70 21	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 17	99.94 20	99.87 1	96.95 4	99.94 29	97.42 11	99.62 22	100.00 1	99.80 17	99.91 5	98.78 48	99.98 10	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 8	99.94 20	99.85 2	96.90 14	99.97 9	97.64 8	99.50 33	100.00 1	99.88 8	99.90 8	98.60 52	99.87 30	100.00 1

SMA-MVS		99.34 10	99.79 4	98.81 13	99.69 1	99.94 20	99.75 17	96.91 10	99.98 3	96.76 15	99.37 40	100.00 1	99.90 5	99.88 11	99.46 17	99.84 37	99.92 126
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 13	99.92 41	99.77 9	96.91 10	99.93 32	96.31 19	99.59 25	99.95 41	99.84 12	99.73 18	99.84 9	99.95 15	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 7	99.96 7	99.76 10	96.91 10	99.97 9	95.86 23	99.67 11	100.00 1	99.75 18	99.85 14	98.80 46	99.98 10	99.97 88

CNLPA		99.24 12	99.58 33	98.85 12	99.34 33	99.95 14	99.32 36	96.65 27	99.96 20	98.44 4	98.97 55	100.00 1	99.57 31	98.66 44	99.56 15	99.76 85	99.97 88

AdaColmap		99.21 14	99.45 39	98.92 8	99.67 4	99.95 14	99.65 28	96.77 22	99.97 9	97.67 6	100.00 1	99.69 55	99.93 1	99.26 31	97.25 98	99.85 34	100.00 1

HFP-MVS		99.19 15	99.77 8	98.51 18	99.55 21	99.94 20	99.76 10	96.84 16	99.88 43	95.27 27	99.67 11	100.00 1	99.85 11	99.56 23	99.36 21	99.79 68	99.97 88

PLC	98.06 1	99.17 16	99.38 41	98.92 8	99.47 23	99.90 49	99.48 33	96.47 32	99.96 20	98.73 2	99.52 32	100.00 1	99.55 33	98.54 57	97.73 85	99.84 37	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 53	99.95 14	99.74 20	96.94 7	99.96 20	96.60 17	99.47 36	100.00 1	99.88 8	99.15 33	99.59 13	99.84 37	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 25	99.94 20	99.63 30	96.62 29	99.82 56	95.92 22	99.65 16	100.00 1	99.71 20	99.76 17	98.56 54	99.83 43	100.00 1

ACMMPR		99.12 19	99.76 9	98.36 20	99.45 25	99.94 20	99.75 17	96.70 26	99.93 32	94.65 31	99.65 16	99.96 39	99.84 12	99.51 26	99.35 22	99.79 68	99.96 108

MCST-MVS		99.08 20	99.72 18	98.33 21	99.59 20	99.97 3	99.78 8	96.96 3	99.95 26	93.72 35	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 27	99.93 35	99.60 31	95.92 37	99.77 64	97.01 14	99.67 11	100.00 1	99.72 19	99.56 23	97.76 82	99.70 116	99.98 75

DeepC-MVS_fast	98.03 2	99.05 22	99.78 5	98.21 24	99.47 23	99.97 3	99.75 17	96.80 18	99.97 9	93.58 37	98.68 63	99.94 42	99.69 22	99.93 4	99.95 3	99.96 13	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 54	99.92 41	99.71 25	96.80 18	99.96 20	95.58 25	98.71 62	100.00 1	99.68 24	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 8	99.94 20	99.74 20	96.95 4	99.87 47	93.76 34	99.49 35	100.00 1	99.39 39	99.73 18	98.35 60	99.89 26	99.96 108

SteuartSystems-ACMMP		98.95 25	99.80 3	97.95 27	99.43 28	99.96 7	99.76 10	96.45 33	99.82 56	93.63 36	99.64 20	100.00 1	98.56 77	99.90 8	99.31 24	99.84 37	100.00 1
Skip Steuart: Steuart Systems R&D Blog.
PHI-MVS		98.85 26	99.67 24	97.89 28	98.63 48	99.93 35	98.95 47	95.20 39	99.84 54	94.94 28	99.74 10	100.00 1	99.69 22	98.40 64	99.75 11	99.93 18	99.99 59

MP-MVS		98.82 27	99.63 27	97.88 29	99.41 29	99.91 48	99.74 20	96.76 23	99.88 43	91.89 47	99.50 33	99.94 42	99.65 27	99.71 21	98.49 58	99.82 47	99.97 88
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 8	99.92 41	99.72 23	96.78 21	99.71 69	90.13 69	99.66 15	99.99 32	99.64 28	99.78 16	98.14 68	99.82 47	99.89 135

train_agg		98.62 29	99.76 9	97.28 32	99.03 41	99.93 35	99.65 28	96.37 34	99.98 3	89.24 80	99.53 29	99.83 47	99.59 30	99.85 14	99.19 29	99.80 61	100.00 1

X-MVS		98.62 29	99.75 12	97.29 31	99.50 22	99.94 20	99.71 25	96.55 30	99.85 51	88.58 85	99.65 16	99.98 34	99.67 25	99.60 22	99.26 27	99.77 79	99.97 88

OMC-MVS		98.59 31	99.07 46	98.03 26	99.41 29	99.90 49	99.26 39	94.33 41	99.94 29	96.03 20	96.68 90	99.72 54	99.42 36	98.86 38	98.84 43	99.72 113	99.58 171

DPM-MVS		98.58 32	99.78 5	97.17 34	98.02 52	99.64 83	99.80 7	96.72 25	99.96 20	90.05 71	99.57 27	100.00 1	98.66 76	99.56 23	99.96 2	99.80 61	99.80 158

PGM-MVS		98.47 33	99.73 16	97.00 36	99.68 3	99.94 20	99.76 10	91.74 46	99.84 54	91.17 56	100.00 1	99.69 55	99.81 15	99.38 29	99.30 25	99.82 47	99.95 115

TSAR-MVS + ACMM		98.30 34	99.64 26	96.74 39	99.08 40	99.94 20	99.67 27	96.73 24	99.97 9	86.30 105	98.30 68	99.99 32	98.78 70	99.73 18	99.57 14	99.88 29	99.98 75

CSCG		98.22 35	98.37 68	98.04 25	99.60 17	99.82 60	99.45 34	93.59 42	99.16 103	96.46 18	98.22 75	95.86 105	99.41 38	96.33 135	99.22 28	99.75 94	99.94 120

3Dnovator+	95.21 7	98.17 36	99.08 45	97.12 35	99.28 36	99.78 71	98.61 54	89.93 60	99.93 32	95.36 26	95.50 99	100.00 1	99.56 32	98.58 52	99.80 10	99.95 15	99.97 88

ACMMP		98.16 37	99.01 47	97.18 33	98.86 43	99.92 41	98.77 52	95.73 38	99.31 99	91.15 57	100.00 1	99.81 49	98.82 68	98.11 84	95.91 133	99.77 79	99.97 88
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 38	99.93 35	97.79 66	91.84 45	99.87 47	90.53 65	100.00 1	99.57 60	98.93 62	99.44 27	99.08 32	99.85 34	99.95 115

EPNet		98.11 39	99.63 27	96.34 45	98.44 50	99.88 55	98.55 55	90.25 56	99.93 32	92.60 44	100.00 1	99.73 52	98.41 79	98.87 37	99.02 33	99.82 47	99.97 88
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 81	99.92 41	99.22 40	89.98 58	99.97 9	94.77 30	99.94 9	100.00 1	99.43 35	98.52 61	98.53 56	99.79 68	100.00 1

3Dnovator	95.01 8	97.98 41	98.89 52	96.92 38	99.36 31	99.76 74	98.72 53	89.98 58	99.98 3	93.99 33	94.60 112	99.43 65	99.50 34	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 37	99.97 3	97.03 91	90.44 53	99.89 40	90.75 60	100.00 1	99.73 52	98.68 75	98.67 43	98.89 40	99.95 15	99.97 88

QAPM		97.90 43	98.89 52	96.74 39	99.35 32	99.80 66	98.84 49	90.20 57	99.94 29	92.85 39	94.17 115	99.78 50	99.42 36	98.71 41	99.87 7	99.79 68	99.98 75

CDPH-MVS		97.88 44	99.59 31	95.89 49	98.90 42	99.95 14	99.40 35	92.86 44	99.86 50	85.33 111	98.62 64	99.45 64	99.06 58	99.29 30	99.94 4	99.81 56	100.00 1

CANet		97.62 45	98.94 50	96.08 47	97.19 58	99.93 35	99.29 38	90.38 54	99.87 47	91.00 58	95.79 98	99.51 61	98.72 74	98.53 58	99.00 34	99.90 24	99.99 59

TAPA-MVS	96.62 5	97.60 46	98.46 67	96.60 42	98.73 46	99.90 49	99.30 37	94.96 40	99.46 87	87.57 93	96.05 97	98.53 77	99.26 48	98.04 89	97.33 97	99.77 79	99.88 140
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 49	99.96 7	93.83 145	95.93 36	100.00 1	90.79 59	98.38 67	99.85 46	95.28 136	99.94 2	99.97 1	96.15 212	99.97 88

CS-MVS-test		97.51 48	99.18 44	95.56 54	97.16 59	99.96 7	97.39 79	89.82 62	100.00 1	89.88 72	99.16 47	98.38 83	99.23 50	98.85 39	97.93 75	99.87 30	100.00 1

PCF-MVS	97.20 3	97.49 49	98.20 73	96.66 41	97.62 56	99.92 41	98.93 48	96.64 28	98.53 134	88.31 91	94.04 118	99.58 59	98.94 60	97.53 105	97.79 80	99.54 146	99.97 88
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 63	99.93 35	97.62 72	89.69 63	99.98 3	91.33 53	98.53 66	97.50 92	98.77 71	98.60 51	98.35 60	99.92 21	100.00 1

MSDG		97.29 51	97.55 89	97.00 36	98.66 47	99.71 78	99.03 45	96.15 35	99.59 76	89.67 78	92.77 129	94.86 108	98.75 72	98.22 75	97.94 73	99.72 113	99.76 163

CHOSEN 280x420		97.16 52	99.58 33	94.35 79	96.95 62	99.97 3	97.19 85	81.55 146	99.92 36	91.75 48	100.00 1	100.00 1	98.84 67	98.55 54	98.65 51	99.79 68	99.97 88

DELS-MVS		97.05 53	98.05 78	95.88 51	97.09 60	99.99 1	98.82 50	90.30 55	98.44 140	91.40 51	92.91 126	96.57 98	97.68 107	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 57	99.92 41	99.10 43	96.54 31	99.34 98	86.64 102	91.93 134	93.15 119	99.11 56	99.11 34	99.68 12	99.73 108	99.97 88
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 86	99.80 66	97.33 81	89.48 68	98.90 120	95.99 21	99.11 50	92.84 121	98.14 89	98.14 80	98.32 64	99.82 47	99.51 176

MVS_0304		97.04 55	98.72 58	95.08 64	96.32 67	99.90 49	99.15 41	89.61 66	99.89 40	87.22 99	95.47 100	98.22 86	98.22 85	98.63 48	98.90 39	99.93 18	100.00 1

MAR-MVS		97.03 57	98.00 80	95.89 49	99.32 34	99.74 77	96.76 98	84.89 110	99.97 9	94.86 29	98.29 69	90.58 129	99.67 25	98.02 91	99.50 16	99.82 47	99.92 126
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	92.71 103	97.43 155	99.03 45	85.52 103	99.82 56	92.74 42	99.15 48	99.94 42	99.19 53	98.66 44	96.99 112	99.79 68	99.98 75

EC-MVSNet		96.90 59	99.32 42	94.07 80	91.64 133	99.30 106	98.18 62	85.61 102	99.97 9	89.79 73	99.33 41	99.31 68	99.28 46	98.48 63	98.86 41	99.91 22	100.00 1

baseline1		96.87 60	98.55 61	94.91 67	92.89 102	99.45 97	96.34 102	88.54 81	98.88 123	92.82 40	98.93 57	96.58 97	99.07 57	98.19 77	98.04 70	99.80 61	99.78 160

OpenMVS	94.03 11	96.87 60	98.10 76	95.44 58	99.29 35	99.78 71	98.46 60	89.92 61	99.47 86	85.78 109	91.05 138	98.50 78	99.30 44	98.49 62	99.41 18	99.89 26	99.98 75

PatchMatch-RL		96.84 62	98.03 79	95.47 55	98.84 44	99.81 64	95.61 116	89.20 72	99.65 73	91.28 54	99.39 37	93.46 117	98.18 86	98.05 87	96.28 122	99.69 121	99.55 173

ETV-MVS		96.79 63	99.19 43	94.00 82	91.78 124	99.63 85	97.15 87	88.00 87	99.95 26	88.34 90	99.32 42	98.71 74	98.82 68	98.69 42	98.01 71	99.90 24	100.00 1

IS_MVSNet		96.66 64	98.62 60	94.38 76	92.41 109	99.70 79	97.19 85	87.67 90	99.05 111	91.27 55	95.09 105	98.46 82	97.95 97	98.64 46	99.37 19	99.79 68	100.00 1

PMMVS		96.45 65	98.24 72	94.36 78	92.58 104	99.01 118	97.08 90	87.42 96	99.88 43	90.06 70	99.39 37	94.63 109	99.33 42	97.85 97	96.99 112	99.70 116	99.96 108

LS3D		96.44 66	97.31 96	95.41 59	97.06 61	99.87 56	99.51 32	97.48 1	99.57 77	79.00 130	95.39 101	89.19 135	99.81 15	98.55 54	98.84 43	99.62 135	99.78 160

EIA-MVS		96.34 67	98.55 61	93.76 86	91.93 120	99.66 81	97.14 88	88.33 85	99.51 81	85.98 108	98.82 61	96.08 103	99.33 42	98.38 67	97.40 95	99.81 56	100.00 1

EPP-MVSNet		96.29 68	98.34 69	93.90 83	91.77 125	99.38 103	95.45 121	87.25 98	99.38 94	91.36 52	94.86 111	98.49 80	97.83 101	98.01 92	98.23 66	99.75 94	99.99 59

UGNet		96.05 69	98.55 61	93.13 95	94.64 82	99.65 82	94.70 133	87.78 88	99.40 93	89.69 77	98.25 72	99.25 70	92.12 168	96.50 127	97.08 107	99.84 37	99.72 165
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 55	99.52 89	98.81 51	91.40 49	99.42 90	84.97 112	90.46 141	96.82 96	98.05 92	96.46 131	96.19 125	99.54 146	98.92 191
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 65	99.89 53	95.64 113	91.23 50	99.75 66	91.59 49	96.80 87	82.44 156	98.05 92	98.53 58	97.92 76	99.80 61	100.00 1

PVSNet_Blended		96.01 71	96.48 116	95.46 56	96.47 65	99.89 53	95.64 113	91.23 50	99.75 66	91.59 49	96.80 87	82.44 156	98.05 92	98.53 58	97.92 76	99.80 61	100.00 1

thisisatest0530		95.89 73	98.32 70	93.06 98	91.76 126	99.75 75	94.94 128	87.60 93	99.91 38	86.66 101	98.28 70	99.98 34	97.72 103	97.10 117	93.24 169	99.65 127	99.95 115

tttt0517		95.88 74	98.31 71	93.04 99	91.75 128	99.75 75	94.90 129	87.60 93	99.91 38	86.63 103	98.28 70	99.98 34	97.72 103	97.10 117	93.24 169	99.65 127	99.95 115

thres100view900		95.86 75	96.62 110	94.97 66	93.10 94	99.83 58	97.76 67	89.15 73	98.62 130	90.69 61	99.00 52	84.86 147	99.30 44	97.57 104	96.48 117	99.81 56	100.00 1

RPSCF		95.86 75	96.94 106	94.61 74	96.52 64	98.67 132	98.54 56	88.43 83	99.56 78	90.51 67	99.39 37	98.70 75	97.72 103	93.77 179	92.00 185	95.93 213	96.50 208

DCV-MVSNet		95.85 77	97.53 90	93.89 84	93.20 93	97.01 161	97.14 88	84.77 111	99.16 103	90.38 68	98.96 56	93.73 114	98.23 84	96.57 126	97.37 96	99.64 131	99.93 122

baseline		95.85 77	98.13 75	93.20 94	92.29 112	99.58 87	97.49 74	84.33 118	99.44 88	87.28 97	97.00 86	94.04 113	97.93 98	98.36 69	98.47 59	99.87 30	99.99 59

canonicalmvs		95.80 79	97.02 101	94.37 77	92.96 100	99.47 94	97.49 74	84.58 113	99.44 88	92.05 46	98.54 65	86.65 140	99.37 40	96.18 138	98.93 37	99.77 79	99.92 126

tfpn200view9		95.78 80	96.54 113	94.89 69	93.10 94	99.82 60	97.67 68	88.85 76	98.62 130	90.69 61	99.00 52	84.86 147	99.28 46	97.41 111	96.10 126	99.76 85	99.99 59

thres200		95.77 81	96.55 112	94.86 70	93.09 96	99.82 60	97.63 71	88.85 76	98.49 135	90.66 63	98.99 54	84.86 147	99.20 51	97.41 111	96.28 122	99.76 85	100.00 1

MVS_Test		95.74 82	98.18 74	92.90 101	92.16 113	99.49 93	97.36 80	84.30 119	99.79 61	84.94 113	96.65 91	93.63 116	98.85 66	98.61 50	99.10 31	99.81 56	100.00 1

thres400		95.72 83	96.48 116	94.84 71	93.00 99	99.83 58	97.55 73	88.93 74	98.49 135	90.61 64	98.86 58	84.63 151	99.20 51	97.45 107	96.10 126	99.77 79	99.99 59

thres600view7		95.64 84	96.38 120	94.79 73	92.96 100	99.82 60	97.48 78	88.85 76	98.38 141	90.52 66	98.84 60	84.61 152	99.15 54	97.41 111	95.60 138	99.76 85	99.99 59

Vis-MVSNet (Re-imp)		95.60 85	98.52 66	92.19 105	92.37 110	99.56 88	96.37 101	87.41 97	98.95 115	84.77 116	94.88 110	98.48 81	92.44 165	98.63 48	99.37 19	99.76 85	99.77 162

FMVSNet3		95.59 86	97.51 92	93.34 92	89.48 149	96.57 168	97.67 68	84.17 120	99.48 83	89.76 74	95.09 105	94.35 110	99.14 55	98.37 68	98.86 41	99.82 47	99.89 135

ECVR-MVS		95.46 87	95.58 133	95.31 61	94.12 86	99.80 66	97.33 81	89.48 68	98.90 120	92.99 38	87.97 153	86.41 142	98.14 89	98.14 80	98.32 64	99.82 47	99.52 175

PVSNet_Blended_VisFu		95.37 88	97.44 94	92.95 100	95.20 74	99.80 66	92.68 153	88.41 84	99.12 106	87.64 92	88.31 152	99.10 71	94.07 152	98.27 72	97.51 91	99.73 108	100.00 1

DI_MVS_plusplus_trai		95.29 89	97.02 101	93.28 93	91.76 126	99.52 89	97.84 65	85.67 101	99.08 110	87.29 96	87.76 156	97.46 93	97.31 111	97.83 98	97.48 92	99.83 43	100.00 1

ET-MVSNet_ETH3D		95.20 90	97.82 85	92.15 106	80.77 211	98.13 143	97.65 70	86.93 99	99.72 68	88.56 88	99.29 45	97.01 95	99.24 49	94.58 167	95.98 131	99.75 94	99.99 59

TSAR-MVS + COLMAP		95.20 90	95.03 140	95.41 59	96.17 68	98.69 131	99.11 42	93.40 43	99.97 9	84.89 114	98.23 74	75.01 177	99.34 41	97.27 116	96.37 121	99.58 139	99.64 170

GBi-Net		95.19 92	96.99 104	93.09 96	89.11 150	96.47 170	96.90 92	84.17 120	99.48 83	89.76 74	95.09 105	94.35 110	98.87 63	96.50 127	97.21 99	99.74 98	99.81 155

test1		95.19 92	96.99 104	93.09 96	89.11 150	96.47 170	96.90 92	84.17 120	99.48 83	89.76 74	95.09 105	94.35 110	98.87 63	96.50 127	97.21 99	99.74 98	99.81 155

test1111		95.15 94	95.18 138	95.12 62	94.07 88	99.80 66	97.20 84	89.53 67	98.80 124	92.22 45	85.44 164	86.24 143	97.89 99	98.12 82	98.34 63	99.80 61	99.51 176

test0.0.03 1		95.15 94	97.87 84	91.99 107	91.69 130	98.82 129	93.04 151	83.60 125	99.65 73	88.80 83	94.15 116	97.67 90	94.97 138	96.62 125	98.16 67	99.83 43	100.00 1

baseline2		95.13 96	98.55 61	91.15 113	90.29 145	99.00 119	94.49 137	82.00 140	99.68 71	84.82 115	96.47 92	99.30 69	95.71 130	98.24 74	97.14 105	99.57 141	100.00 1

casdiffmvs_mvg		95.10 97	96.45 119	93.53 88	92.05 116	99.42 101	97.25 83	87.66 91	97.17 160	86.09 106	91.79 136	91.27 123	98.31 82	98.06 86	97.42 94	99.81 56	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	90.78 114	98.38 51	98.47 134	96.54 100	89.36 70	99.78 63	65.65 184	99.31 43	98.24 85	94.79 141	98.28 71	99.35 22	99.93 18	98.27 195
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 98	99.44 100	96.85 96	88.49 82	98.78 125	92.61 43	83.94 170	90.25 131	98.94 60	95.87 146	96.77 114	99.58 139	99.89 135

UA-Net		94.95 100	98.66 59	90.63 116	94.60 84	98.94 125	96.03 106	85.28 105	98.01 152	78.92 131	97.42 84	99.96 39	89.09 192	98.95 36	98.80 46	99.82 47	98.57 193

CANet_DTU		94.90 101	98.98 48	90.13 124	94.74 80	99.81 64	98.53 57	82.23 139	99.97 9	66.76 181	100.00 1	98.50 78	98.74 73	97.52 106	97.19 104	99.76 85	99.88 140

FC-MVSNet-train		94.61 102	96.27 121	92.68 103	92.35 111	97.14 159	93.45 149	87.73 89	98.93 116	87.31 95	96.42 93	89.35 133	95.67 131	96.06 144	96.01 130	99.56 143	99.98 75

diffmvs		94.60 103	95.63 132	93.41 90	91.98 119	99.30 106	96.86 95	87.62 92	99.30 100	86.07 107	94.12 117	81.63 161	98.16 87	97.43 108	97.60 89	99.76 85	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	91.84 122	99.46 96	95.92 108	87.54 95	98.45 138	86.57 104	90.51 140	84.72 150	98.49 78	97.97 93	97.80 79	99.77 79	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	94.45 149	94.61 74	93.85 90	98.36 136	98.12 63	89.68 64	99.35 97	89.62 79	95.19 103	77.08 168	96.66 121	95.51 150	95.67 136	99.74 98	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	89.11 150	96.47 170	96.90 92	83.38 128	99.11 107	88.64 84	87.50 161	92.26 122	98.87 63	97.91 95	98.60 52	99.74 98	99.81 155

HQP-MVS		94.48 106	95.39 136	93.42 89	95.10 75	98.35 137	98.19 61	91.41 48	99.77 64	79.79 127	99.30 44	77.08 168	96.25 124	96.93 119	96.28 122	99.76 85	99.99 59

FA-MVS(training)		94.33 108	97.52 91	90.60 118	92.42 108	99.77 73	96.13 105	68.75 196	99.05 111	88.49 89	91.95 132	99.48 62	98.12 91	98.39 65	94.02 161	99.68 123	99.98 75

MDTV_nov1_ep13		94.32 109	98.77 57	89.14 134	91.70 129	99.52 89	95.21 124	72.09 194	99.80 59	78.91 132	96.32 94	99.62 57	97.71 106	98.39 65	97.71 86	99.22 195	100.00 1

CDS-MVSNet		94.32 109	97.00 103	91.19 112	89.82 148	98.71 130	95.51 118	85.14 109	96.85 161	82.33 122	92.48 130	96.40 101	94.71 142	96.86 122	97.76 82	99.63 133	99.92 126
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	90.75 115	92.02 117	99.21 110	94.31 139	66.97 201	99.50 82	95.61 24	96.22 96	98.64 76	96.08 126	93.71 181	94.03 160	99.52 150	99.98 75

ACMM	94.44 10	94.26 112	94.62 145	93.84 85	94.86 79	97.73 150	93.48 148	90.76 52	99.27 101	87.46 94	99.04 51	76.60 170	96.76 119	96.37 134	93.76 164	99.74 98	99.55 173
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 85	98.32 139	96.02 107	89.35 71	98.90 120	87.28 97	99.14 49	76.41 173	94.94 139	96.07 143	94.35 157	99.49 157	99.99 59
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
EPMVS		94.08 114	98.54 65	88.87 135	92.51 106	99.47 94	94.18 141	66.53 202	99.68 71	82.40 121	95.24 102	99.40 66	97.86 100	98.12 82	97.99 72	99.75 94	99.88 140

test-LLR		93.71 115	97.23 97	89.60 128	91.69 130	99.10 115	94.68 135	83.60 125	99.36 95	71.94 158	93.82 120	96.51 99	95.96 128	97.42 109	94.37 154	99.74 98	99.99 59

CHOSEN 1792x2688		93.69 116	94.89 143	92.28 104	96.17 68	99.84 57	95.69 112	83.17 131	98.54 133	82.04 123	77.58 198	91.15 125	96.90 114	98.36 69	98.82 45	99.73 108	99.98 75

LGP-MVS_train		93.60 117	95.05 139	91.90 108	94.90 78	98.29 140	97.93 64	88.06 86	99.14 105	74.83 147	99.26 46	76.50 171	96.07 127	96.31 136	95.90 135	99.59 137	99.97 88

SCA		93.53 118	98.90 51	87.27 152	92.01 118	99.30 106	93.43 150	65.72 206	99.80 59	75.20 146	97.66 82	99.74 51	97.44 109	98.21 76	97.62 88	99.84 37	100.00 1

FMVSNet5		93.53 118	96.09 125	90.56 119	86.74 165	92.84 210	92.64 154	77.50 172	99.41 92	88.97 82	98.02 78	97.81 88	98.00 95	94.85 162	95.43 140	99.50 156	94.25 212

OPM-MVS		93.50 120	93.00 159	94.07 80	95.82 71	98.26 141	98.49 59	91.62 47	94.69 181	81.93 124	92.82 128	76.18 175	96.82 116	96.12 140	94.57 148	99.74 98	98.39 194
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
CostFormer		93.50 120	96.50 115	90.00 125	91.69 130	98.65 133	93.88 144	67.64 199	98.97 113	89.16 81	97.79 80	88.92 136	97.97 96	95.14 159	96.06 128	99.63 133	100.00 1

IterMVS-LS		93.50 120	96.22 122	90.33 122	90.93 136	95.50 195	94.83 131	80.54 150	98.92 117	79.11 129	90.64 139	93.70 115	96.79 117	96.93 119	97.85 78	99.78 75	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	87.22 153	91.93 120	99.39 102	92.55 155	66.06 204	99.71 69	75.61 143	98.24 73	99.59 58	97.35 110	97.87 96	97.64 87	99.83 43	99.43 179
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MS-PatchMatch		93.46 124	95.91 128	90.61 117	95.48 72	99.31 105	95.62 115	77.23 174	99.42 90	81.88 125	88.92 149	96.06 104	93.80 154	96.45 133	93.11 173	99.65 127	98.10 199

dmvs_re		93.34 125	94.59 146	91.88 109	87.97 161	99.14 114	95.29 123	88.61 79	98.09 149	82.71 120	97.34 85	78.96 163	96.98 112	94.62 165	93.98 162	99.73 108	99.98 75

tpm cat1		93.29 126	96.53 114	89.50 130	91.84 122	99.18 112	94.70 133	67.70 198	98.38 141	86.67 100	89.16 146	99.38 67	96.66 121	94.33 168	95.30 141	99.43 173	100.00 1

Effi-MVS+-dtu		93.13 127	97.13 99	88.47 143	88.86 156	99.19 111	96.79 97	79.08 163	99.64 75	70.01 168	97.51 83	89.38 132	96.53 123	97.60 102	96.55 116	99.57 141	100.00 1

HyFIR lowres test		93.13 127	94.48 148	91.56 110	96.12 70	99.68 80	93.52 147	79.98 154	97.24 158	81.73 126	72.66 207	95.74 106	98.29 83	98.27 72	97.79 80	99.70 116	100.00 1

Vis-MVSNet		93.08 129	96.76 109	88.78 139	91.14 135	99.63 85	94.85 130	83.34 129	97.19 159	74.78 148	91.92 135	93.15 119	88.81 195	97.59 103	98.35 60	99.78 75	99.49 178
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
Effi-MVS+		93.06 130	95.94 127	89.70 127	90.82 137	99.45 97	95.71 111	78.94 164	98.72 126	74.71 149	97.92 79	80.73 162	98.35 80	97.72 100	97.05 110	99.70 116	100.00 1

ADS-MVSNet		92.91 131	97.97 81	87.01 155	92.07 115	99.27 109	92.70 152	65.39 209	99.85 51	75.40 144	94.93 109	98.26 84	96.86 115	96.09 141	97.52 90	99.65 127	99.84 151

GeoE		92.88 132	95.20 137	90.18 123	90.59 141	99.18 112	96.31 103	78.36 168	97.52 157	78.53 134	87.11 162	88.01 137	97.63 108	97.79 99	96.76 115	99.66 125	100.00 1

TESTMET0.1,1		92.87 133	97.23 97	87.79 149	86.96 164	99.10 115	94.68 135	77.46 173	99.36 95	71.94 158	93.82 120	96.51 99	95.96 128	97.42 109	94.37 154	99.74 98	99.99 59

FC-MVSNet-test		92.78 134	96.19 124	88.80 138	88.00 160	97.54 152	93.60 146	82.36 138	98.16 145	79.71 128	91.55 137	95.41 107	89.65 187	96.09 141	95.23 142	99.49 157	99.31 182

Fast-Effi-MVS+-dtu		92.73 135	97.62 87	87.02 154	88.91 154	98.83 128	95.79 109	73.98 188	99.89 40	68.62 173	97.73 81	93.30 118	95.21 137	97.67 101	95.96 132	99.59 137	100.00 1

IB-MVS	90.59 15	92.70 136	95.70 130	89.21 133	94.62 83	99.45 97	83.77 203	88.92 75	99.53 79	92.82 40	98.86 58	86.08 144	75.24 214	92.81 195	93.17 171	99.89 26	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		92.67 137	97.13 99	87.47 151	86.72 166	99.07 117	94.28 140	76.90 175	99.21 102	71.53 162	93.63 122	96.32 102	95.67 131	97.32 114	94.36 156	99.74 98	99.99 59

RPMNet		92.64 138	97.88 83	86.53 160	90.79 138	98.95 123	95.13 125	64.44 213	99.09 108	72.36 154	93.58 123	99.01 72	96.74 120	98.05 87	96.45 119	99.71 115	100.00 1

FMVSNet1		92.55 139	93.66 154	91.26 111	87.91 162	96.12 177	94.75 132	81.69 145	97.67 155	85.63 110	80.56 183	87.88 139	98.15 88	96.50 127	97.21 99	99.41 178	99.71 166

tpmrst		92.52 140	97.45 93	86.77 158	92.15 114	99.36 104	92.53 156	65.95 205	99.53 79	72.50 152	92.22 131	99.83 47	97.81 102	95.18 158	96.05 129	99.69 121	100.00 1

testgi		92.47 141	95.68 131	88.73 140	90.68 139	98.35 137	91.67 163	79.50 159	98.96 114	77.12 139	95.17 104	85.84 145	93.95 153	95.75 148	96.47 118	99.45 168	99.21 185

TAMVS		92.43 142	94.21 152	90.35 121	88.68 157	98.85 127	94.15 142	81.53 147	95.58 171	83.61 118	87.05 163	86.45 141	94.71 142	96.27 137	95.91 133	99.42 176	99.38 181

CR-MVSNet		92.32 143	97.97 81	85.74 169	90.63 140	98.95 123	95.46 119	65.50 207	99.09 108	67.51 177	94.20 114	98.18 87	95.59 134	98.16 78	97.20 102	99.74 98	100.00 1

CVMVSNet		92.13 144	95.40 135	88.32 146	91.29 134	97.29 157	91.85 160	86.42 100	96.71 163	71.84 160	89.56 144	91.18 124	88.98 194	96.17 139	97.76 82	99.51 154	99.14 187

Fast-Effi-MVS+		92.11 145	94.33 150	89.52 129	89.06 153	99.00 119	95.13 125	76.72 177	98.59 132	78.21 136	89.99 142	77.35 167	98.34 81	97.97 93	97.44 93	99.67 124	99.96 108

ACMH+	92.61 13	91.80 146	93.03 157	90.37 120	93.03 97	98.17 142	94.00 143	84.13 123	98.12 147	77.39 138	91.95 132	74.62 178	94.36 149	94.62 165	93.82 163	99.32 187	99.87 145

IterMVS-SCA-FT		91.75 147	96.87 107	85.78 167	90.34 143	95.93 184	95.06 127	73.85 189	98.91 118	61.01 197	89.21 145	98.87 73	94.66 145	98.09 85	97.12 106	99.76 85	99.99 59

IterMVS		91.65 148	96.62 110	85.85 166	90.27 146	95.80 186	95.32 122	74.15 185	98.91 118	60.95 198	88.79 151	97.76 89	94.69 144	98.04 89	97.07 108	99.73 108	100.00 1
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ACMH	92.34 14	91.59 149	93.02 158	89.92 126	93.97 89	97.98 147	90.10 178	84.70 112	98.46 137	76.80 140	93.38 124	71.94 189	94.39 147	95.34 154	94.04 159	99.54 146	100.00 1
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs4		91.41 150	93.05 156	89.49 131	85.85 174	96.52 169	91.70 162	82.49 136	98.14 146	83.17 119	87.57 158	81.76 160	94.39 147	95.47 151	92.62 179	99.33 185	99.29 183

PatchT		91.06 151	97.66 86	83.36 191	90.32 144	98.96 122	82.30 207	64.72 212	98.45 138	67.51 177	93.28 125	97.60 91	95.59 134	98.16 78	97.20 102	99.70 116	100.00 1

MIMVSNet		91.01 152	96.22 122	84.93 177	85.24 178	98.09 144	90.40 173	64.96 211	97.55 156	72.65 150	96.23 95	90.81 127	96.79 117	96.69 123	97.06 109	99.52 150	97.09 205

UniMVSNet_NR-MVSNet		90.50 153	92.31 162	88.38 144	85.04 184	96.34 173	90.94 165	85.32 104	95.87 170	75.69 141	87.68 157	78.49 164	93.78 155	93.21 190	94.60 147	99.53 149	99.97 88

UniMVSNet (Re)		90.41 154	91.96 164	88.59 142	85.71 175	96.73 165	90.82 167	84.11 124	95.23 177	78.54 133	88.91 150	76.41 173	92.84 162	93.40 188	93.05 174	99.55 145	100.00 1

GA-MVS		90.38 155	94.59 146	85.46 173	88.30 159	98.44 135	92.18 157	83.30 130	97.89 154	58.05 206	92.86 127	84.25 154	91.27 178	96.65 124	92.61 180	99.66 125	99.43 179

USDC		90.36 156	91.68 165	88.82 137	92.58 104	98.02 145	96.27 104	79.83 155	98.37 143	70.61 167	89.05 147	67.50 205	94.17 150	95.77 147	94.43 152	99.46 165	98.62 192

thisisatest0515		90.28 157	94.32 151	85.57 172	85.23 179	97.23 158	85.44 199	83.09 132	96.80 162	72.41 153	89.82 143	90.87 126	87.93 200	95.27 157	90.39 202	99.33 185	99.88 140

TinyColmap		89.94 158	90.88 171	88.84 136	92.43 107	97.91 148	95.59 117	80.10 153	98.12 147	71.33 164	84.56 166	67.46 206	94.15 151	95.57 149	94.27 158	99.43 173	98.26 196

pm-mvs1		89.68 159	92.00 163	86.96 156	86.23 170	96.62 167	90.36 174	83.05 133	93.97 189	72.15 157	81.77 178	82.10 158	90.69 184	95.38 153	94.50 150	99.29 191	99.65 168

tpm		89.60 160	94.93 142	83.39 189	89.94 147	97.11 160	90.09 179	65.28 210	98.67 128	60.03 202	96.79 89	84.38 153	95.66 133	91.90 199	95.65 137	99.32 187	99.98 75

NR-MVSNet		89.52 161	90.71 172	88.14 148	86.19 171	96.20 175	92.07 158	84.58 113	95.54 172	75.27 145	87.52 159	67.96 203	91.24 179	94.33 168	93.45 167	99.49 157	99.97 88

DU-MVS		89.49 162	90.60 173	88.19 147	84.71 188	96.20 175	90.94 165	84.58 113	95.54 172	75.69 141	87.52 159	68.74 202	93.78 155	91.10 203	95.13 144	99.47 163	99.97 88

Baseline_NR-MVSNet		89.13 163	89.53 185	88.66 141	84.71 188	94.43 203	91.79 161	84.49 116	95.54 172	78.28 135	78.52 195	72.46 188	93.29 159	91.10 203	94.82 146	99.42 176	99.86 148

tfpnnormal		89.09 164	89.71 179	88.38 144	87.37 163	96.78 164	91.46 164	85.20 107	90.33 209	72.35 155	83.45 171	69.30 200	94.45 146	95.29 155	92.86 176	99.44 172	99.93 122

TranMVSNet+NR-MVSNet		88.88 165	89.90 178	87.69 150	84.06 200	95.68 187	91.88 159	85.23 106	95.16 178	72.54 151	83.06 174	70.14 197	92.93 161	90.81 206	94.53 149	99.48 161	99.89 135

WR-MVS_H		88.47 166	90.55 174	86.04 162	85.13 181	96.07 179	89.86 185	79.80 156	94.37 186	72.32 156	83.12 173	74.44 181	89.60 188	93.52 185	92.40 181	99.51 154	99.96 108

SixPastTwentyTwo		88.35 167	91.51 167	84.66 179	85.39 177	96.96 162	86.57 195	79.62 158	96.57 164	63.73 191	87.86 155	75.18 176	93.43 158	94.03 172	90.37 203	99.24 194	99.58 171

TransMVSNet (Re)		88.33 168	89.55 184	86.91 157	86.65 167	95.56 192	90.48 171	84.44 117	92.02 208	71.07 166	80.13 185	72.48 187	89.41 189	95.05 161	94.44 151	99.39 180	97.14 204

MVS-HIRNet		88.27 169	94.05 153	81.51 197	88.90 155	98.93 126	83.38 205	60.52 220	98.06 150	63.78 190	80.67 182	90.36 130	92.94 160	97.29 115	96.41 120	99.56 143	96.66 207

WR-MVS		88.23 170	90.15 176	86.00 164	84.39 195	95.64 188	89.96 182	81.80 142	94.46 184	71.60 161	82.10 176	74.36 182	88.76 196	92.48 196	92.20 183	99.46 165	99.83 153

CP-MVSNet		88.09 171	89.57 182	86.36 161	84.63 191	95.46 197	89.48 187	80.53 151	93.42 196	71.26 165	81.25 180	69.90 198	92.78 163	93.30 189	93.69 165	99.47 163	99.96 108

pmnet_mix02		88.07 172	92.32 161	83.10 192	86.14 172	96.23 174	81.90 210	83.05 133	98.04 151	57.59 208	84.93 165	82.02 159	90.87 183	93.54 184	91.53 194	99.06 202	99.97 88

UniMVSNet_ETH3D		88.05 173	87.01 203	89.27 132	88.53 158	97.49 153	90.35 175	83.48 127	94.57 182	77.87 137	70.08 211	61.75 217	96.22 125	90.17 207	95.21 143	99.16 199	99.82 154

anonymousdsp		87.98 174	92.38 160	82.85 193	83.68 204	96.79 163	90.78 168	74.06 187	95.29 176	57.91 207	83.33 172	83.12 155	91.15 181	95.96 145	92.37 182	99.52 150	99.76 163

LTVRE_ROB	88.65 16	87.87 175	91.11 170	84.10 186	86.64 168	97.47 154	94.40 138	78.41 167	96.13 168	52.02 215	87.95 154	65.92 211	93.59 157	95.29 155	95.09 145	99.52 150	99.95 115
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		87.84 176	89.42 187	85.99 165	85.16 180	96.01 181	90.52 170	81.78 144	94.43 185	67.59 175	81.32 179	71.87 190	91.48 176	91.25 202	91.16 198	99.43 173	99.92 126

TDRefinement		87.79 177	88.76 194	86.66 159	93.54 91	98.02 145	95.76 110	85.18 108	96.57 164	67.90 174	80.51 184	66.51 210	78.37 211	93.20 191	89.73 204	99.22 195	96.75 206

MDTV_nov1_ep13_2view		87.75 178	93.32 155	81.26 199	83.74 203	96.64 166	85.66 198	66.20 203	98.36 144	61.61 195	84.34 168	87.95 138	91.12 182	94.01 173	92.66 178	99.22 195	99.27 184

v8		87.54 179	89.33 188	85.45 174	85.41 176	95.50 195	90.32 176	78.94 164	94.35 187	66.93 180	81.90 177	70.99 194	91.62 174	91.49 201	91.22 197	99.48 161	99.87 145

v1144		87.49 180	89.64 180	84.97 176	84.73 187	95.84 185	90.17 177	79.30 160	93.96 190	64.65 188	78.83 192	73.38 186	91.51 175	93.77 179	91.77 189	99.45 168	99.93 122

v2v482		87.46 181	88.90 192	85.78 167	84.58 192	95.95 183	89.90 184	82.43 137	94.19 188	65.65 184	79.80 187	69.12 201	92.67 164	91.88 200	91.46 195	99.45 168	99.93 122

v10		87.40 182	89.62 181	84.80 178	84.93 185	95.07 201	90.44 172	75.63 181	94.51 183	66.52 182	78.87 191	73.47 185	91.86 172	93.69 182	91.87 188	99.45 168	99.86 148

pmmvs5		87.33 183	90.01 177	84.20 184	84.31 197	96.04 180	87.63 193	76.59 178	93.17 201	65.35 187	84.30 169	71.68 191	91.91 171	95.41 152	91.37 196	99.39 180	98.13 197

N_pmnet		87.31 184	91.51 167	82.41 196	85.13 181	95.57 191	80.59 212	81.79 143	96.20 166	58.52 205	78.62 193	85.66 146	89.36 190	94.64 164	92.14 184	99.08 201	97.72 203

PS-CasMVS		87.24 185	88.52 197	85.73 170	84.58 192	95.35 199	89.03 190	80.17 152	93.11 202	68.86 172	77.71 197	66.89 207	92.30 166	93.13 192	93.50 166	99.46 165	99.96 108

EU-MVSNet		87.20 186	90.47 175	83.38 190	85.11 183	93.85 208	86.10 197	79.76 157	93.30 200	65.39 186	84.41 167	78.43 165	85.04 207	92.20 198	93.03 175	98.86 204	98.05 200

PEN-MVS		87.20 186	88.22 198	86.01 163	84.01 202	94.93 202	90.00 181	81.52 149	93.46 195	69.29 170	79.69 188	65.51 212	91.72 173	91.01 205	93.12 172	99.49 157	99.84 151

EG-PatchMatch MVS		86.96 188	89.56 183	83.93 187	86.29 169	97.61 151	90.75 169	73.31 192	95.43 175	66.08 183	75.88 204	71.31 192	87.55 202	94.79 163	92.74 177	99.61 136	99.13 188

v1192		86.93 189	89.01 190	84.50 180	84.46 194	95.51 194	89.93 183	78.65 166	93.75 191	62.29 193	77.19 199	70.88 195	92.28 167	93.84 176	91.96 186	99.38 182	99.90 132

v1921920		86.81 190	88.93 191	84.33 183	84.23 198	95.41 198	90.09 179	78.10 169	93.74 192	62.17 194	76.98 201	71.14 193	92.05 169	93.69 182	91.69 192	99.32 187	99.88 140

v144192		86.80 191	88.90 192	84.35 181	84.33 196	95.56 192	89.34 188	77.74 171	93.60 193	64.03 189	77.82 196	70.76 196	91.28 177	92.91 194	91.74 191	99.37 183	99.90 132

DTE-MVSNet		86.70 192	87.66 202	85.58 171	83.30 205	94.29 204	89.74 186	81.53 147	92.77 204	68.93 171	80.13 185	64.00 215	90.62 185	89.45 208	93.34 168	99.32 187	99.67 167

gg-mvs-nofinetune		86.69 193	91.30 169	81.30 198	90.42 142	99.64 83	98.50 58	61.68 218	79.23 218	40.35 222	66.58 213	97.14 94	96.92 113	98.64 46	97.94 73	99.91 22	99.97 88

v148		86.63 194	87.79 200	85.28 175	84.65 190	95.97 182	86.46 196	82.84 135	92.91 203	71.52 163	78.99 190	66.74 209	86.83 204	89.28 209	90.69 200	99.41 178	99.94 120

v1240		86.24 195	88.56 196	83.54 188	84.05 201	95.21 200	89.27 189	76.76 176	93.42 196	60.68 201	75.99 203	69.80 199	91.21 180	93.83 178	91.76 190	99.29 191	99.91 131

pmmvs6		85.75 196	86.97 204	84.34 182	84.88 186	95.59 190	87.41 194	79.19 162	87.81 214	67.56 176	63.05 216	77.76 166	89.15 191	93.45 187	91.90 187	97.83 210	99.21 185

v7n		85.39 197	87.70 201	82.70 194	82.77 207	95.64 188	88.27 192	74.83 183	92.30 206	62.58 192	76.37 202	64.80 214	88.38 198	94.29 170	90.61 201	99.34 184	99.87 145

gm-plane-assit		84.93 198	91.61 166	77.14 207	84.14 199	91.29 214	66.18 222	69.70 195	85.22 217	47.95 219	78.58 194	89.24 134	94.90 140	98.82 40	98.12 69	99.99 5	100.00 1

CMPMVS	65.66 17	84.62 199	85.02 206	84.15 185	95.40 73	97.79 149	88.35 191	79.22 161	89.66 212	60.71 200	72.20 208	73.94 183	87.32 203	86.73 211	84.55 213	93.90 215	90.31 216
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test_method		84.44 200	89.04 189	79.08 202	81.15 210	92.82 211	82.06 209	61.92 216	96.17 167	59.38 203	74.47 206	67.52 204	91.96 170	96.92 121	95.53 139	97.98 209	99.85 150

Anonymous20231206		84.28 201	89.53 185	78.17 204	82.31 209	94.16 206	82.57 206	76.51 179	93.38 199	52.98 213	79.47 189	73.74 184	75.45 213	95.07 160	94.41 153	99.18 198	96.46 209

new_pmnet		84.12 202	87.89 199	79.72 201	80.43 212	94.14 207	80.26 213	74.14 186	96.01 169	56.30 212	74.94 205	76.45 172	88.59 197	93.11 193	89.31 205	98.59 207	91.27 215

test20.03		83.86 203	88.73 195	78.16 205	82.60 208	93.00 209	81.61 211	74.68 184	92.36 205	57.50 209	83.01 175	74.48 180	73.30 215	92.40 197	91.14 199	99.29 191	94.75 211

pmmvs-eth3d		82.92 204	83.31 209	82.47 195	76.97 214	91.76 213	83.79 202	76.10 180	90.33 209	69.95 169	71.04 210	48.09 220	89.02 193	93.85 175	89.14 206	99.02 203	98.96 190

PM-MVS		82.79 205	84.51 207	80.77 200	77.22 213	92.13 212	83.61 204	73.31 192	93.50 194	61.06 196	77.15 200	46.52 223	90.55 186	94.14 171	89.05 208	98.85 205	99.12 189

pmmvs3		80.91 206	85.62 205	75.42 209	75.01 216	89.09 217	75.31 216	68.70 197	86.99 215	46.74 221	81.18 181	62.91 216	87.95 199	93.84 176	89.06 207	98.80 206	96.23 210

MIMVSNet1		80.64 207	83.97 208	76.76 208	68.91 219	91.15 216	78.32 215	75.47 182	89.58 213	56.64 211	65.10 214	65.17 213	82.14 208	93.51 186	91.64 193	99.10 200	91.66 214

MDA-MVSNet-bldmvs		80.30 208	82.83 210	77.34 206	69.16 218	94.29 204	72.16 217	81.97 141	90.14 211	57.32 210	94.01 119	47.97 221	86.81 205	68.74 218	86.82 210	96.63 211	97.86 201

new-patchmatchnet		78.17 209	80.82 211	75.07 210	76.93 215	91.20 215	71.90 218	73.32 191	86.59 216	48.91 216	67.11 212	47.85 222	81.19 209	88.18 210	87.02 209	98.19 208	97.79 202

FPMVS		73.80 210	74.62 212	72.84 211	83.09 206	84.44 219	83.89 201	73.64 190	92.20 207	48.50 217	72.19 209	59.51 218	63.16 217	69.13 217	66.26 221	84.74 220	78.59 222

Gipuma		71.02 211	72.60 215	69.19 212	71.31 217	75.11 222	66.36 221	61.65 219	94.93 179	47.29 220	38.74 221	38.52 224	75.52 212	86.09 212	85.92 212	93.01 216	88.87 218
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
GG-mvs-BLEND		69.85 212	99.39 40	35.39 220	3.67 228	99.94 20	99.10 43	1.69 224	99.85 51	3.19 229	98.13 77	99.46 63	4.92 224	99.23 32	99.14 30	99.80 61	100.00 1

PMMVS2		65.18 213	68.25 216	61.59 213	61.37 222	79.72 221	59.18 225	61.80 217	64.72 221	37.33 223	53.82 218	35.59 225	54.46 222	73.94 216	80.52 214	95.40 214	89.43 217

PMVS	60.14 18	62.67 214	64.05 217	61.06 214	68.32 220	53.27 228	52.23 226	67.63 200	75.07 220	48.30 218	58.27 217	57.43 219	49.99 223	67.20 219	62.42 222	79.87 223	74.68 223
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
testmvs		61.76 215	72.90 214	48.76 217	21.21 226	68.61 223	66.11 223	37.38 222	94.83 180	33.06 224	64.31 215	29.72 226	86.08 206	74.44 215	78.71 215	48.74 225	99.65 168

E-PMN		55.33 216	55.79 219	54.81 216	59.81 224	57.23 226	38.83 227	63.59 214	64.06 223	24.66 226	35.33 223	26.40 228	58.69 219	55.41 221	70.54 218	83.26 221	81.56 221

EMVS		55.14 217	55.29 220	54.97 215	60.87 223	57.52 225	38.58 228	63.57 215	64.54 222	23.36 227	36.96 222	27.99 227	60.69 218	51.17 222	66.61 220	82.73 222	82.25 220

MVE	58.81 19	52.07 218	55.15 221	48.48 218	42.45 225	62.35 224	36.41 229	54.70 221	49.88 224	27.65 225	29.98 224	18.08 229	54.87 221	65.93 220	77.26 216	74.79 224	82.59 219
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test123		48.14 219	58.11 218	36.51 219	8.71 227	56.81 227	59.55 224	24.08 223	77.50 219	14.41 228	49.20 219	11.94 231	80.98 210	41.62 223	69.81 219	31.32 226	99.90 132

uanet_test		0.00 220	0.00 222	0.00 221	0.00 229	0.00 229	0.00 230	0.00 225	0.00 225	0.00 230	0.00 225	0.00 232	0.00 225	0.00 224	0.00 223	0.00 227	0.00 224

sosnet-low-res		0.00 220	0.00 222	0.00 221	0.00 229	0.00 229	0.00 230	0.00 225	0.00 225	0.00 230	0.00 225	0.00 232	0.00 225	0.00 224	0.00 223	0.00 227	0.00 224

sosnet		0.00 220	0.00 222	0.00 221	0.00 229	0.00 229	0.00 230	0.00 225	0.00 225	0.00 230	0.00 225	0.00 232	0.00 225	0.00 224	0.00 223	0.00 227	0.00 224

TPM-MVS					99.67 4	99.96 7	99.82 5			94.63 32	99.65 16	100.00 1	99.90 5			99.99 5	99.80 158

RE-MVS-def										52.74 214

9.14												100.00 1

SR-MVS					99.61 13			96.80 18				100.00 1

Anonymous202405211			95.78 129		93.26 92	99.52 89	96.70 99	88.55 80	97.93 153		88.99 148	90.68 128	98.99 59	96.46 131	97.02 111	99.64 131	99.89 135

our_test_3					85.89 173	96.09 178	82.15 208

ambc			74.33 213		66.84 221	84.26 220	84.17 200		93.39 198	58.99 204	45.93 220	18.06 230	70.61 216	93.94 174	86.62 211	92.61 218	98.13 197

MTAPA										96.61 16		100.00 1

MTMP										97.42 11		100.00 1

Patchmatch-RL test							68.01 220

tmp_tt				78.81 203	98.80 45	85.73 218	70.08 219	77.87 170	98.68 127	83.71 117	99.53 29	74.55 179	54.97 220	78.28 214	72.43 217	87.45 219

XVS					95.09 76	99.94 20	97.49 74			88.58 85		99.98 34				99.78 75

X-MVStestdata					95.09 76	99.94 20	97.49 74			88.58 85		99.98 34				99.78 75

mPP-MVS					99.23 38							99.87 45

NP-MVS									99.79 61

Patchmtry						99.00 119	95.46 119	65.50 207		67.51 177

DeepMVS_CX						97.31 156	79.48 214	89.65 65	98.66 129	60.89 199	94.40 113	66.89 207	87.65 201	81.69 213		92.76 217	94.24 213

High-resolution multi-view benchmark