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 20	99.15 1	99.61 12	99.95 12	99.81 4	96.94 8	99.97 8	98.73 2	99.53 30	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 22	99.10 3	99.65 5	99.94 18	99.76 8	96.95 4	99.88 41	98.39 5	99.60 23	100.00 1	99.82 12	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 8	96.87 15	99.95 23	97.66 8	99.57 28	100.00 1	99.63 28	99.88 10	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 3	96.82 17	99.92 33	97.58 10	99.58 27	100.00 1	99.93 1	98.98 36	99.86 8	99.96 13	100.00 1

CNVR-MVS		99.39 4	99.75 12	98.98 5	99.69 1	99.95 12	99.76 8	96.91 11	99.98 3	97.59 9	99.64 19	100.00 1	99.93 1	99.94 2	98.75 51	99.97 12	99.97 85

DVP-MVS		99.38 6	99.57 35	99.15 1	99.62 8	99.94 18	99.72 23	96.99 2	99.98 3	98.85 1	98.21 74	100.00 1	99.88 7	99.88 10	98.96 35	99.85 32	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 8	99.96 17	97.38 13	99.60 23	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 18	99.87 1	96.95 4	99.94 26	97.42 11	99.62 21	100.00 1	99.80 16	99.91 5	98.78 49	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
SMA-MVS		99.34 9	99.79 4	98.81 13	99.69 1	99.94 18	99.75 16	96.91 11	99.98 3	96.76 15	99.37 41	100.00 1	99.90 5	99.88 10	99.46 17	99.84 35	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 10	99.85 1	98.73 14	99.61 12	99.92 39	99.77 7	96.91 11	99.93 29	96.31 20	99.59 26	99.95 41	99.84 10	99.73 18	99.84 9	99.95 16	100.00 1
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
NCCC		99.24 11	99.75 12	98.65 15	99.63 7	99.96 7	99.76 8	96.91 11	99.97 8	95.86 23	99.67 11	100.00 1	99.75 17	99.85 13	98.80 47	99.98 10	99.97 85

CNLPA		99.24 11	99.58 32	98.85 12	99.34 33	99.95 12	99.32 36	96.65 28	99.96 17	98.44 4	98.97 54	100.00 1	99.57 31	98.66 44	99.56 15	99.76 81	99.97 85

AdaColmap		99.21 13	99.45 39	98.92 8	99.67 4	99.95 12	99.65 28	96.77 22	99.97 8	97.67 7	100.00 1	99.69 55	99.93 1	99.26 32	97.25 95	99.85 32	100.00 1

HFP-MVS		99.19 14	99.77 8	98.51 19	99.55 21	99.94 18	99.76 8	96.84 16	99.88 41	95.27 27	99.67 11	100.00 1	99.85 9	99.56 23	99.36 21	99.79 63	99.97 85

PLC	98.06 1	99.17 15	99.38 41	98.92 8	99.47 23	99.90 47	99.48 33	96.47 33	99.96 17	98.73 2	99.52 33	100.00 1	99.55 33	98.54 56	97.73 82	99.84 35	99.99 57
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
SD-MVS		99.16 16	99.73 16	98.49 20	97.93 54	99.95 12	99.74 19	96.94 8	99.96 17	96.60 17	99.47 36	100.00 1	99.88 7	99.15 34	99.59 13	99.84 35	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 17	99.67 23	98.53 18	99.45 25	99.94 18	99.63 30	96.62 30	99.82 55	95.92 22	99.65 16	100.00 1	99.71 19	99.76 16	98.56 54	99.83 41	100.00 1

zzz-MVS		99.12 18	99.52 38	98.65 15	99.58 20	99.93 33	99.74 19	96.72 25	99.44 88	96.47 18	99.62 21	100.00 1	99.63 28	99.74 17	97.97 70	99.77 74	99.94 118

ACMMPR		99.12 18	99.76 9	98.36 21	99.45 25	99.94 18	99.75 16	96.70 27	99.93 29	94.65 31	99.65 16	99.96 39	99.84 10	99.51 26	99.35 22	99.79 63	99.96 105

MCST-MVS		99.08 20	99.72 18	98.33 22	99.59 19	99.97 3	99.78 6	96.96 3	99.95 23	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 37	98.57 17	99.44 27	99.93 33	99.60 31	95.92 38	99.77 63	97.01 14	99.67 11	100.00 1	99.72 18	99.56 23	97.76 79	99.70 111	99.98 74

DeepC-MVS_fast	98.03 2	99.05 22	99.78 5	98.21 25	99.47 23	99.97 3	99.75 16	96.80 18	99.97 8	93.58 37	98.68 62	99.94 42	99.69 21	99.93 4	99.95 3	99.96 13	99.98 74
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 29	98.27 23	97.88 55	99.92 39	99.71 25	96.80 18	99.96 17	95.58 25	98.71 61	100.00 1	99.68 23	99.91 5	98.78 49	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 28	98.22 24	99.62 8	99.94 18	99.74 19	96.95 4	99.87 46	93.76 34	99.49 35	100.00 1	99.39 39	99.73 18	98.35 60	99.89 26	99.96 105

SteuartSystems-ACMMP		98.95 25	99.80 3	97.95 28	99.43 28	99.96 7	99.76 8	96.45 34	99.82 55	93.63 36	99.64 19	100.00 1	98.56 77	99.90 8	99.31 24	99.84 35	100.00 1
Skip Steuart: Steuart Systems R&D Blog.
PHI-MVS		98.85 26	99.67 23	97.89 29	98.63 49	99.93 33	98.95 47	95.20 40	99.84 53	94.94 28	99.74 10	100.00 1	99.69 21	98.40 63	99.75 11	99.93 19	99.99 57

MP-MVS		98.82 27	99.63 26	97.88 30	99.41 29	99.91 46	99.74 19	96.76 23	99.88 41	91.89 45	99.50 34	99.94 42	99.65 26	99.71 21	98.49 58	99.82 45	99.97 85
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 32	97.62 31	99.62 8	99.92 39	99.72 23	96.78 21	99.71 69	90.13 66	99.66 15	99.99 32	99.64 27	99.78 15	98.14 64	99.82 45	99.89 134

train_agg		98.62 29	99.76 9	97.28 33	99.03 42	99.93 33	99.65 28	96.37 35	99.98 3	89.24 76	99.53 30	99.83 47	99.59 30	99.85 13	99.19 29	99.80 56	100.00 1

X-MVS		98.62 29	99.75 12	97.29 32	99.50 22	99.94 18	99.71 25	96.55 31	99.85 50	88.58 81	99.65 16	99.98 34	99.67 24	99.60 22	99.26 27	99.77 74	99.97 85

OMC-MVS		98.59 31	99.07 45	98.03 27	99.41 29	99.90 47	99.26 39	94.33 42	99.94 26	96.03 21	96.68 88	99.72 54	99.42 36	98.86 39	98.84 43	99.72 108	99.58 170

DPM-MVS		98.58 32	99.78 5	97.17 35	98.02 53	99.64 78	99.80 5	96.72 25	99.96 17	90.05 68	99.57 28	100.00 1	98.66 76	99.56 23	99.96 2	99.80 56	99.80 157

PGM-MVS		98.47 33	99.73 16	97.00 38	99.68 3	99.94 18	99.76 8	91.74 47	99.84 53	91.17 53	100.00 1	99.69 55	99.81 14	99.38 30	99.30 25	99.82 45	99.95 113

TSAR-MVS + ACMM		98.30 34	99.64 25	96.74 41	99.08 41	99.94 18	99.67 27	96.73 24	99.97 8	86.30 101	98.30 66	99.99 32	98.78 71	99.73 18	99.57 14	99.88 29	99.98 74

CSCG		98.22 35	98.37 67	98.04 26	99.60 16	99.82 58	99.45 34	93.59 43	99.16 104	96.46 19	98.22 73	95.86 103	99.41 38	96.33 131	99.22 28	99.75 90	99.94 118

3Dnovator+	95.21 7	98.17 36	99.08 44	97.12 36	99.28 36	99.78 67	98.61 55	89.93 62	99.93 29	95.36 26	95.50 97	100.00 1	99.56 32	98.58 51	99.80 10	99.95 16	99.97 85

ACMMP		98.16 37	99.01 46	97.18 34	98.86 44	99.92 39	98.77 52	95.73 39	99.31 100	91.15 54	100.00 1	99.81 49	98.82 69	98.11 80	95.91 130	99.77 74	99.97 85
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 20	96.36 46	99.23 38	99.93 33	97.79 67	91.84 46	99.87 46	90.53 62	100.00 1	99.57 60	98.93 63	99.44 27	99.08 32	99.85 32	99.95 113

EPNet		98.11 39	99.63 26	96.34 47	98.44 51	99.88 53	98.55 56	90.25 58	99.93 29	92.60 43	100.00 1	99.73 52	98.41 79	98.87 38	99.02 33	99.82 45	99.97 85
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TSAR-MVS + GP.		98.06 40	99.55 36	96.32 48	94.72 80	99.92 39	99.22 40	89.98 60	99.97 8	94.77 30	99.94 9	100.00 1	99.43 35	98.52 60	98.53 56	99.79 63	100.00 1

3Dnovator	95.01 8	97.98 41	98.89 50	96.92 40	99.36 31	99.76 69	98.72 53	89.98 60	99.98 3	93.99 33	94.60 110	99.43 64	99.50 34	98.55 53	99.91 5	99.99 5	99.98 74

MVS_111021_HR		97.94 42	99.59 30	96.02 50	99.27 37	99.97 3	97.03 87	90.44 55	99.89 37	90.75 57	100.00 1	99.73 52	98.68 75	98.67 43	98.89 41	99.95 16	99.97 85

QAPM		97.90 43	98.89 50	96.74 41	99.35 32	99.80 65	98.84 49	90.20 59	99.94 26	92.85 38	94.17 113	99.78 50	99.42 36	98.71 41	99.87 7	99.79 63	99.98 74

CDPH-MVS		97.88 44	99.59 30	95.89 51	98.90 43	99.95 12	99.40 35	92.86 45	99.86 49	85.33 107	98.62 63	99.45 63	99.06 58	99.29 31	99.94 4	99.81 52	100.00 1

CANet		97.62 45	98.94 48	96.08 49	97.19 59	99.93 33	99.29 38	90.38 56	99.87 46	91.00 55	95.79 96	99.51 61	98.72 74	98.53 57	99.00 34	99.90 24	99.99 57

TAPA-MVS	96.62 5	97.60 46	98.46 66	96.60 44	98.73 47	99.90 47	99.30 37	94.96 41	99.46 87	87.57 88	96.05 95	98.53 76	99.26 47	98.04 84	97.33 94	99.77 74	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 62	98.45 50	99.96 7	93.83 141	95.93 37	100.00 1	90.79 56	98.38 65	99.85 46	95.28 132	99.94 2	99.97 1	96.15 208	99.97 85

xxxxxxxxxxxxxcwj		97.57 48	96.26 118	99.10 3	99.65 5	99.94 18	99.76 8	96.95 4	99.88 41	98.39 5	99.60 23	76.05 171	99.82 12	99.43 28	98.93 37	99.99 5	100.00 1

PCF-MVS	97.20 3	97.49 49	98.20 72	96.66 43	97.62 57	99.92 39	98.93 48	96.64 29	98.53 132	88.31 86	94.04 116	99.58 59	98.94 61	97.53 101	97.79 77	99.54 141	99.97 85
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MSDG		97.29 50	97.55 87	97.00 38	98.66 48	99.71 73	99.03 45	96.15 36	99.59 76	89.67 74	92.77 127	94.86 106	98.75 72	98.22 74	97.94 71	99.72 108	99.76 161

CHOSEN 280x420		97.16 51	99.58 32	94.35 77	96.95 62	99.97 3	97.19 81	81.55 143	99.92 33	91.75 46	100.00 1	100.00 1	98.84 68	98.55 53	98.65 52	99.79 63	99.97 85

DELS-MVS		97.05 52	98.05 76	95.88 53	97.09 60	99.99 1	98.82 50	90.30 57	98.44 138	91.40 49	92.91 124	96.57 96	97.68 103	98.56 52	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 52	97.59 86	96.42 45	97.37 58	99.92 39	99.10 43	96.54 32	99.34 99	86.64 98	91.93 131	93.15 117	99.11 56	99.11 35	99.68 12	99.73 104	99.97 85
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MVS_0304		97.04 54	98.72 57	95.08 61	96.32 66	99.90 47	99.15 41	89.61 66	99.89 37	87.22 94	95.47 98	98.22 84	98.22 85	98.63 48	98.90 40	99.93 19	100.00 1

MAR-MVS		97.03 55	98.00 78	95.89 51	99.32 34	99.74 72	96.76 94	84.89 106	99.97 8	94.86 29	98.29 67	90.58 125	99.67 24	98.02 86	99.50 16	99.82 45	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
CS-MVS		97.02 56	99.29 42	94.37 74	91.49 128	99.42 96	98.18 63	87.66 87	99.89 37	89.71 72	99.40 37	98.98 71	99.24 48	98.52 60	98.84 43	99.96 13	100.00 1

MVSTER		97.00 57	98.85 52	94.83 69	92.71 99	97.43 150	99.03 45	85.52 98	99.82 55	92.74 41	99.15 47	99.94 42	99.19 52	98.66 44	96.99 109	99.79 63	99.98 74

baseline1		96.87 58	98.55 60	94.91 64	92.89 98	99.45 92	96.34 98	88.54 77	98.88 121	92.82 39	98.93 56	96.58 95	99.07 57	98.19 76	98.04 67	99.80 56	99.78 158

OpenMVS	94.03 11	96.87 58	98.10 75	95.44 57	99.29 35	99.78 67	98.46 61	89.92 63	99.47 86	85.78 105	91.05 134	98.50 77	99.30 44	98.49 62	99.41 18	99.89 26	99.98 74

PatchMatch-RL		96.84 60	98.03 77	95.47 54	98.84 45	99.81 63	95.61 111	89.20 69	99.65 73	91.28 51	99.39 38	93.46 115	98.18 86	98.05 82	96.28 119	99.69 116	99.55 172

ETV-MVS		96.79 61	99.19 43	94.00 79	91.78 119	99.63 80	97.15 83	88.00 83	99.95 23	88.34 85	99.32 42	98.71 73	98.82 69	98.69 42	98.01 68	99.90 24	100.00 1

IS_MVSNet		96.66 62	98.62 59	94.38 73	92.41 105	99.70 74	97.19 81	87.67 86	99.05 112	91.27 52	95.09 103	98.46 81	97.95 94	98.64 46	99.37 19	99.79 63	100.00 1

PMMVS		96.45 63	98.24 71	94.36 76	92.58 100	99.01 112	97.08 86	87.42 92	99.88 41	90.06 67	99.39 38	94.63 107	99.33 42	97.85 93	96.99 109	99.70 111	99.96 105

LS3D		96.44 64	97.31 93	95.41 58	97.06 61	99.87 54	99.51 32	97.48 1	99.57 77	79.00 125	95.39 99	89.19 131	99.81 14	98.55 53	98.84 43	99.62 130	99.78 158

EIA-MVS		96.34 65	98.55 60	93.76 83	91.93 115	99.66 76	97.14 84	88.33 81	99.51 81	85.98 103	98.82 60	96.08 101	99.33 42	98.38 66	97.40 92	99.81 52	100.00 1

EPP-MVSNet		96.29 66	98.34 68	93.90 80	91.77 120	99.38 98	95.45 116	87.25 94	99.38 95	91.36 50	94.86 109	98.49 79	97.83 97	98.01 87	98.23 62	99.75 90	99.99 57

CS-MVS-test		96.17 67	98.83 54	93.08 94	91.29 129	99.27 103	97.39 79	85.23 101	99.77 63	85.81 104	99.00 50	97.27 91	98.95 60	98.40 63	98.07 66	99.80 56	100.00 1

UGNet		96.05 68	98.55 60	93.13 91	94.64 81	99.65 77	94.70 129	87.78 84	99.40 94	89.69 73	98.25 70	99.25 68	92.12 164	96.50 123	97.08 104	99.84 35	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 69	96.83 105	95.11 60	97.87 56	99.52 84	98.81 51	91.40 50	99.42 91	84.97 108	90.46 137	96.82 94	98.05 89	96.46 127	96.19 122	99.54 141	98.92 187
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 70	96.48 113	95.46 55	96.47 64	99.89 51	95.64 108	91.23 51	99.75 66	91.59 47	96.80 85	82.44 150	98.05 89	98.53 57	97.92 73	99.80 56	100.00 1

PVSNet_Blended		96.01 70	96.48 113	95.46 55	96.47 64	99.89 51	95.64 108	91.23 51	99.75 66	91.59 47	96.80 85	82.44 150	98.05 89	98.53 57	97.92 73	99.80 56	100.00 1

thisisatest0530		95.89 72	98.32 69	93.06 95	91.76 121	99.75 70	94.94 124	87.60 89	99.91 35	86.66 97	98.28 68	99.98 34	97.72 99	97.10 113	93.24 165	99.65 122	99.95 113

tttt0517		95.88 73	98.31 70	93.04 96	91.75 123	99.75 70	94.90 125	87.60 89	99.91 35	86.63 99	98.28 68	99.98 34	97.72 99	97.10 113	93.24 165	99.65 122	99.95 113

thres100view900		95.86 74	96.62 107	94.97 63	93.10 90	99.83 56	97.76 68	89.15 70	98.62 127	90.69 58	99.00 50	84.86 141	99.30 44	97.57 100	96.48 114	99.81 52	100.00 1

RPSCF		95.86 74	96.94 103	94.61 71	96.52 63	98.67 127	98.54 57	88.43 79	99.56 78	90.51 64	99.39 38	98.70 74	97.72 99	93.77 174	92.00 181	95.93 209	96.50 204

DCV-MVSNet		95.85 76	97.53 88	93.89 81	93.20 89	97.01 156	97.14 84	84.77 107	99.16 104	90.38 65	98.96 55	93.73 112	98.23 84	96.57 122	97.37 93	99.64 126	99.93 121

baseline		95.85 76	98.13 74	93.20 90	92.29 108	99.58 82	97.49 74	84.33 114	99.44 88	87.28 92	97.00 84	94.04 111	97.93 95	98.36 68	98.47 59	99.87 30	99.99 57

canonicalmvs		95.80 78	97.02 98	94.37 74	92.96 96	99.47 89	97.49 74	84.58 109	99.44 88	92.05 44	98.54 64	86.65 136	99.37 40	96.18 134	98.93 37	99.77 74	99.92 125

tfpn200view9		95.78 79	96.54 110	94.89 66	93.10 90	99.82 58	97.67 69	88.85 73	98.62 127	90.69 58	99.00 50	84.86 141	99.28 46	97.41 107	96.10 123	99.76 81	99.99 57

thres200		95.77 80	96.55 109	94.86 67	93.09 92	99.82 58	97.63 72	88.85 73	98.49 133	90.66 60	98.99 53	84.86 141	99.20 50	97.41 107	96.28 119	99.76 81	100.00 1

MVS_Test		95.74 81	98.18 73	92.90 98	92.16 109	99.49 88	97.36 80	84.30 115	99.79 60	84.94 109	96.65 89	93.63 114	98.85 67	98.61 50	99.10 31	99.81 52	100.00 1

thres400		95.72 82	96.48 113	94.84 68	93.00 95	99.83 56	97.55 73	88.93 71	98.49 133	90.61 61	98.86 57	84.63 145	99.20 50	97.45 103	96.10 123	99.77 74	99.99 57

thres600view7		95.64 83	96.38 116	94.79 70	92.96 96	99.82 58	97.48 78	88.85 73	98.38 139	90.52 63	98.84 59	84.61 146	99.15 54	97.41 107	95.60 135	99.76 81	99.99 57

Vis-MVSNet (Re-imp)		95.60 84	98.52 65	92.19 102	92.37 106	99.56 83	96.37 97	87.41 93	98.95 115	84.77 112	94.88 108	98.48 80	92.44 161	98.63 48	99.37 19	99.76 81	99.77 160

FMVSNet3		95.59 85	97.51 89	93.34 88	89.48 145	96.57 163	97.67 69	84.17 116	99.48 83	89.76 69	95.09 103	94.35 108	99.14 55	98.37 67	98.86 42	99.82 45	99.89 134

PVSNet_Blended_VisFu		95.37 86	97.44 91	92.95 97	95.20 73	99.80 65	92.68 149	88.41 80	99.12 107	87.64 87	88.31 149	99.10 69	94.07 148	98.27 71	97.51 88	99.73 104	100.00 1

DI_MVS_plusplus_trai		95.29 87	97.02 98	93.28 89	91.76 121	99.52 84	97.84 66	85.67 97	99.08 111	87.29 91	87.76 152	97.46 90	97.31 107	97.83 94	97.48 89	99.83 41	100.00 1

ET-MVSNet_ETH3D		95.20 88	97.82 83	92.15 104	80.77 207	98.13 138	97.65 71	86.93 95	99.72 68	88.56 84	99.29 45	97.01 93	99.24 48	94.58 162	95.98 128	99.75 90	99.99 57

TSAR-MVS + COLMAP		95.20 88	95.03 135	95.41 58	96.17 67	98.69 126	99.11 42	93.40 44	99.97 8	84.89 110	98.23 72	75.01 173	99.34 41	97.27 112	96.37 118	99.58 134	99.64 168

GBi-Net		95.19 90	96.99 101	93.09 92	89.11 146	96.47 166	96.90 88	84.17 116	99.48 83	89.76 69	95.09 103	94.35 108	98.87 64	96.50 123	97.21 96	99.74 94	99.81 154

test1		95.19 90	96.99 101	93.09 92	89.11 146	96.47 166	96.90 88	84.17 116	99.48 83	89.76 69	95.09 103	94.35 108	98.87 64	96.50 123	97.21 96	99.74 94	99.81 154

test0.0.03 1		95.15 92	97.87 82	91.99 105	91.69 125	98.82 124	93.04 147	83.60 121	99.65 73	88.80 79	94.15 114	97.67 88	94.97 134	96.62 121	98.16 63	99.83 41	100.00 1

baseline2		95.13 93	98.55 60	91.15 110	90.29 141	99.00 113	94.49 133	82.00 137	99.68 71	84.82 111	96.47 90	99.30 67	95.71 126	98.24 73	97.14 102	99.57 136	100.00 1

EPNet_dtu		95.10 94	98.81 55	90.78 111	98.38 52	98.47 129	96.54 96	89.36 67	99.78 62	65.65 180	99.31 43	98.24 83	94.79 137	98.28 70	99.35 22	99.93 19	98.27 191
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Anonymous20231211		94.96 95	94.99 136	94.91 64	93.01 94	99.44 95	96.85 92	88.49 78	98.78 122	92.61 42	83.94 165	90.25 127	98.94 61	95.87 142	96.77 111	99.58 134	99.89 134

UA-Net		94.95 96	98.66 58	90.63 113	94.60 83	98.94 120	96.03 101	85.28 100	98.01 149	78.92 126	97.42 82	99.96 39	89.09 188	98.95 37	98.80 47	99.82 45	98.57 189

CANet_DTU		94.90 97	98.98 47	90.13 120	94.74 79	99.81 63	98.53 58	82.23 136	99.97 8	66.76 177	100.00 1	98.50 77	98.74 73	97.52 102	97.19 101	99.76 81	99.88 139

FC-MVSNet-train		94.61 98	96.27 117	92.68 100	92.35 107	97.14 154	93.45 145	87.73 85	98.93 116	87.31 90	96.42 91	89.35 129	95.67 127	96.06 140	96.01 127	99.56 138	99.98 74

diffmvs		94.60 99	95.63 129	93.41 86	91.98 114	99.30 101	96.86 91	87.62 88	99.30 101	86.07 102	94.12 115	81.63 155	98.16 87	97.43 104	97.60 86	99.76 81	100.00 1

casdiffmvs		94.54 100	95.56 130	93.36 87	91.84 117	99.46 91	95.92 103	87.54 91	98.45 136	86.57 100	90.51 136	84.72 144	98.49 78	97.97 88	97.80 76	99.77 74	100.00 1

CLD-MVS		94.53 101	94.45 143	94.61 71	93.85 86	98.36 131	98.12 64	89.68 64	99.35 98	89.62 75	95.19 101	77.08 163	96.66 116	95.51 146	95.67 133	99.74 94	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		94.48 102	95.95 123	92.77 99	89.11 146	96.47 166	96.90 88	83.38 124	99.11 108	88.64 80	87.50 157	92.26 119	98.87 64	97.91 91	98.60 53	99.74 94	99.81 154

HQP-MVS		94.48 102	95.39 132	93.42 85	95.10 74	98.35 132	98.19 62	91.41 49	99.77 63	79.79 122	99.30 44	77.08 163	96.25 120	96.93 115	96.28 119	99.76 81	99.99 57

MDTV_nov1_ep13		94.32 104	98.77 56	89.14 131	91.70 124	99.52 84	95.21 119	72.09 192	99.80 58	78.91 127	96.32 92	99.62 57	97.71 102	98.39 65	97.71 83	99.22 190	100.00 1

CDS-MVSNet		94.32 104	97.00 100	91.19 109	89.82 144	98.71 125	95.51 113	85.14 105	96.85 157	82.33 117	92.48 128	96.40 99	94.71 138	96.86 118	97.76 79	99.63 128	99.92 125
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
dps		94.29 106	97.33 92	90.75 112	92.02 112	99.21 105	94.31 135	66.97 198	99.50 82	95.61 24	96.22 94	98.64 75	96.08 122	93.71 176	94.03 158	99.52 145	99.98 74

ACMM	94.44 10	94.26 107	94.62 140	93.84 82	94.86 78	97.73 145	93.48 144	90.76 54	99.27 102	87.46 89	99.04 49	76.60 165	96.76 114	96.37 130	93.76 160	99.74 94	99.55 172
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMP	94.49 9	94.19 108	94.74 139	93.56 84	94.25 84	98.32 134	96.02 102	89.35 68	98.90 120	87.28 92	99.14 48	76.41 168	94.94 135	96.07 139	94.35 155	99.49 152	99.99 57
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
EPMVS		94.08 109	98.54 64	88.87 132	92.51 102	99.47 89	94.18 137	66.53 199	99.68 71	82.40 116	95.24 100	99.40 65	97.86 96	98.12 79	97.99 69	99.75 90	99.88 139

test-LLR		93.71 110	97.23 94	89.60 124	91.69 125	99.10 109	94.68 131	83.60 121	99.36 96	71.94 154	93.82 118	96.51 97	95.96 124	97.42 105	94.37 152	99.74 94	99.99 57

CHOSEN 1792x2688		93.69 111	94.89 138	92.28 101	96.17 67	99.84 55	95.69 107	83.17 127	98.54 131	82.04 118	77.58 194	91.15 121	96.90 109	98.36 68	98.82 46	99.73 104	99.98 74

LGP-MVS_train		93.60 112	95.05 134	91.90 106	94.90 77	98.29 135	97.93 65	88.06 82	99.14 106	74.83 143	99.26 46	76.50 166	96.07 123	96.31 132	95.90 132	99.59 132	99.97 85

SCA		93.53 113	98.90 49	87.27 149	92.01 113	99.30 101	93.43 146	65.72 203	99.80 58	75.20 142	97.66 80	99.74 51	97.44 105	98.21 75	97.62 85	99.84 35	100.00 1

FMVSNet5		93.53 113	96.09 122	90.56 115	86.74 161	92.84 206	92.64 150	77.50 169	99.41 93	88.97 78	98.02 76	97.81 86	98.00 92	94.85 158	95.43 137	99.50 151	94.25 208

OPM-MVS		93.50 115	93.00 154	94.07 78	95.82 70	98.26 136	98.49 60	91.62 48	94.69 178	81.93 119	92.82 126	76.18 170	96.82 111	96.12 136	94.57 146	99.74 94	98.39 190

CostFormer		93.50 115	96.50 112	90.00 121	91.69 125	98.65 128	93.88 140	67.64 196	98.97 113	89.16 77	97.79 78	88.92 132	97.97 93	95.14 155	96.06 125	99.63 128	100.00 1

IterMVS-LS		93.50 115	96.22 119	90.33 118	90.93 132	95.50 191	94.83 127	80.54 147	98.92 117	79.11 124	90.64 135	93.70 113	96.79 112	96.93 115	97.85 75	99.78 70	99.99 57
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PatchmatchNet		93.48 118	98.84 53	87.22 150	91.93 115	99.39 97	92.55 151	66.06 201	99.71 69	75.61 139	98.24 71	99.59 58	97.35 106	97.87 92	97.64 84	99.83 41	99.43 175
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MS-PatchMatch		93.46 119	95.91 125	90.61 114	95.48 71	99.31 100	95.62 110	77.23 171	99.42 91	81.88 120	88.92 146	96.06 102	93.80 150	96.45 129	93.11 169	99.65 122	98.10 195

tpm cat1		93.29 120	96.53 111	89.50 127	91.84 117	99.18 107	94.70 129	67.70 195	98.38 139	86.67 96	89.16 143	99.38 66	96.66 116	94.33 163	95.30 138	99.43 168	100.00 1

Effi-MVS+-dtu		93.13 121	97.13 96	88.47 140	88.86 153	99.19 106	96.79 93	79.08 160	99.64 75	70.01 164	97.51 81	89.38 128	96.53 118	97.60 98	96.55 113	99.57 136	100.00 1

HyFIR lowres test		93.13 121	94.48 142	91.56 107	96.12 69	99.68 75	93.52 143	79.98 151	97.24 155	81.73 121	72.66 203	95.74 104	98.29 83	98.27 71	97.79 77	99.70 111	100.00 1

Vis-MVSNet		93.08 123	96.76 106	88.78 136	91.14 131	99.63 80	94.85 126	83.34 125	97.19 156	74.78 144	91.92 132	93.15 117	88.81 191	97.59 99	98.35 60	99.78 70	99.49 174
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
Effi-MVS+		93.06 124	95.94 124	89.70 123	90.82 133	99.45 92	95.71 106	78.94 161	98.72 123	74.71 145	97.92 77	80.73 157	98.35 80	97.72 96	97.05 107	99.70 111	100.00 1

ADS-MVSNet		92.91 125	97.97 79	87.01 152	92.07 111	99.27 103	92.70 148	65.39 206	99.85 50	75.40 140	94.93 107	98.26 82	96.86 110	96.09 137	97.52 87	99.65 122	99.84 150

GeoE		92.88 126	95.20 133	90.18 119	90.59 137	99.18 107	96.31 99	78.36 165	97.52 154	78.53 129	87.11 158	88.01 133	97.63 104	97.79 95	96.76 112	99.66 120	100.00 1

TESTMET0.1,1		92.87 127	97.23 94	87.79 146	86.96 160	99.10 109	94.68 131	77.46 170	99.36 96	71.94 154	93.82 118	96.51 97	95.96 124	97.42 105	94.37 152	99.74 94	99.99 57

FC-MVSNet-test		92.78 128	96.19 121	88.80 135	88.00 157	97.54 147	93.60 142	82.36 135	98.16 143	79.71 123	91.55 133	95.41 105	89.65 183	96.09 137	95.23 139	99.49 152	99.31 178

Fast-Effi-MVS+-dtu		92.73 129	97.62 85	87.02 151	88.91 151	98.83 123	95.79 104	73.98 186	99.89 37	68.62 169	97.73 79	93.30 116	95.21 133	97.67 97	95.96 129	99.59 132	100.00 1

IB-MVS	90.59 15	92.70 130	95.70 127	89.21 130	94.62 82	99.45 92	83.77 199	88.92 72	99.53 79	92.82 39	98.86 57	86.08 138	75.24 210	92.81 191	93.17 167	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		92.67 131	97.13 96	87.47 148	86.72 162	99.07 111	94.28 136	76.90 172	99.21 103	71.53 158	93.63 120	96.32 100	95.67 127	97.32 110	94.36 154	99.74 94	99.99 57

RPMNet		92.64 132	97.88 81	86.53 157	90.79 134	98.95 118	95.13 120	64.44 210	99.09 109	72.36 150	93.58 121	99.01 70	96.74 115	98.05 82	96.45 116	99.71 110	100.00 1

FMVSNet1		92.55 133	93.66 149	91.26 108	87.91 158	96.12 173	94.75 128	81.69 142	97.67 152	85.63 106	80.56 178	87.88 135	98.15 88	96.50 123	97.21 96	99.41 173	99.71 164

tpmrst		92.52 134	97.45 90	86.77 155	92.15 110	99.36 99	92.53 152	65.95 202	99.53 79	72.50 148	92.22 129	99.83 47	97.81 98	95.18 154	96.05 126	99.69 116	100.00 1

testgi		92.47 135	95.68 128	88.73 137	90.68 135	98.35 132	91.67 159	79.50 156	98.96 114	77.12 135	95.17 102	85.84 139	93.95 149	95.75 144	96.47 115	99.45 163	99.21 181

TAMVS		92.43 136	94.21 147	90.35 117	88.68 154	98.85 122	94.15 138	81.53 144	95.58 167	83.61 114	87.05 159	86.45 137	94.71 138	96.27 133	95.91 130	99.42 171	99.38 177

CR-MVSNet		92.32 137	97.97 79	85.74 166	90.63 136	98.95 118	95.46 114	65.50 204	99.09 109	67.51 173	94.20 112	98.18 85	95.59 130	98.16 77	97.20 99	99.74 94	100.00 1

CVMVSNet		92.13 138	95.40 131	88.32 143	91.29 129	97.29 152	91.85 156	86.42 96	96.71 159	71.84 156	89.56 141	91.18 120	88.98 190	96.17 135	97.76 79	99.51 149	99.14 183

Fast-Effi-MVS+		92.11 139	94.33 144	89.52 125	89.06 149	99.00 113	95.13 120	76.72 174	98.59 129	78.21 131	89.99 138	77.35 161	98.34 81	97.97 88	97.44 90	99.67 118	99.96 105

DROMVSNet		92.11 139	94.33 144	89.52 125	89.06 149	99.00 113	95.13 120	76.72 174	98.59 129	78.21 131	89.99 138	77.35 161	98.34 81	97.97 88	97.44 90	99.67 118	99.96 105

test_part1		91.82 141	91.53 162	92.16 103	92.47 103	96.48 165	95.41 117	83.13 128	95.13 175	87.04 95	80.43 180	80.96 156	96.28 119	93.11 188	95.01 143	98.63 202	99.61 169

ACMH+	92.61 13	91.80 142	93.03 152	90.37 116	93.03 93	98.17 137	94.00 139	84.13 119	98.12 145	77.39 134	91.95 130	74.62 174	94.36 145	94.62 161	93.82 159	99.32 182	99.87 144

IterMVS-SCA-FT		91.75 143	96.87 104	85.78 164	90.34 139	95.93 180	95.06 123	73.85 187	98.91 118	61.01 193	89.21 142	98.87 72	94.66 141	98.09 81	97.12 103	99.76 81	99.99 57

IterMVS		91.65 144	96.62 107	85.85 163	90.27 142	95.80 182	95.32 118	74.15 183	98.91 118	60.95 194	88.79 148	97.76 87	94.69 140	98.04 84	97.07 105	99.73 104	100.00 1
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ACMH	92.34 14	91.59 145	93.02 153	89.92 122	93.97 85	97.98 142	90.10 174	84.70 108	98.46 135	76.80 136	93.38 122	71.94 185	94.39 143	95.34 150	94.04 157	99.54 141	100.00 1
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs4		91.41 146	93.05 151	89.49 128	85.85 170	96.52 164	91.70 158	82.49 133	98.14 144	83.17 115	87.57 154	81.76 154	94.39 143	95.47 147	92.62 175	99.33 180	99.29 179

PatchT		91.06 147	97.66 84	83.36 188	90.32 140	98.96 117	82.30 203	64.72 209	98.45 136	67.51 173	93.28 123	97.60 89	95.59 130	98.16 77	97.20 99	99.70 111	100.00 1

MIMVSNet		91.01 148	96.22 119	84.93 174	85.24 174	98.09 139	90.40 169	64.96 208	97.55 153	72.65 146	96.23 93	90.81 123	96.79 112	96.69 119	97.06 106	99.52 145	97.09 201

UniMVSNet_NR-MVSNet		90.50 149	92.31 157	88.38 141	85.04 180	96.34 169	90.94 161	85.32 99	95.87 166	75.69 137	87.68 153	78.49 158	93.78 151	93.21 185	94.60 145	99.53 144	99.97 85

UniMVSNet (Re)		90.41 150	91.96 159	88.59 139	85.71 171	96.73 160	90.82 163	84.11 120	95.23 173	78.54 128	88.91 147	76.41 168	92.84 158	93.40 183	93.05 170	99.55 140	100.00 1

GA-MVS		90.38 151	94.59 141	85.46 170	88.30 156	98.44 130	92.18 153	83.30 126	97.89 151	58.05 202	92.86 125	84.25 148	91.27 174	96.65 120	92.61 176	99.66 120	99.43 175

USDC		90.36 152	91.68 160	88.82 134	92.58 100	98.02 140	96.27 100	79.83 152	98.37 141	70.61 163	89.05 144	67.50 201	94.17 146	95.77 143	94.43 150	99.46 160	98.62 188

thisisatest0515		90.28 153	94.32 146	85.57 169	85.23 175	97.23 153	85.44 195	83.09 129	96.80 158	72.41 149	89.82 140	90.87 122	87.93 196	95.27 153	90.39 198	99.33 180	99.88 139

TinyColmap		89.94 154	90.88 167	88.84 133	92.43 104	97.91 143	95.59 112	80.10 150	98.12 145	71.33 160	84.56 161	67.46 202	94.15 147	95.57 145	94.27 156	99.43 168	98.26 192

pm-mvs1		89.68 155	92.00 158	86.96 153	86.23 166	96.62 162	90.36 170	83.05 130	93.97 186	72.15 153	81.77 173	82.10 152	90.69 180	95.38 149	94.50 148	99.29 186	99.65 166

tpm		89.60 156	94.93 137	83.39 186	89.94 143	97.11 155	90.09 175	65.28 207	98.67 125	60.03 198	96.79 87	84.38 147	95.66 129	91.90 195	95.65 134	99.32 182	99.98 74

NR-MVSNet		89.52 157	90.71 168	88.14 145	86.19 167	96.20 171	92.07 154	84.58 109	95.54 168	75.27 141	87.52 155	67.96 199	91.24 175	94.33 163	93.45 163	99.49 152	99.97 85

DU-MVS		89.49 158	90.60 169	88.19 144	84.71 184	96.20 171	90.94 161	84.58 109	95.54 168	75.69 137	87.52 155	68.74 198	93.78 151	91.10 199	95.13 141	99.47 158	99.97 85

Baseline_NR-MVSNet		89.13 159	89.53 181	88.66 138	84.71 184	94.43 199	91.79 157	84.49 112	95.54 168	78.28 130	78.52 191	72.46 184	93.29 155	91.10 199	94.82 144	99.42 171	99.86 147

tfpnnormal		89.09 160	89.71 175	88.38 141	87.37 159	96.78 159	91.46 160	85.20 103	90.33 206	72.35 151	83.45 166	69.30 196	94.45 142	95.29 151	92.86 172	99.44 167	99.93 121

TranMVSNet+NR-MVSNet		88.88 161	89.90 174	87.69 147	84.06 196	95.68 183	91.88 155	85.23 101	95.16 174	72.54 147	83.06 169	70.14 193	92.93 157	90.81 202	94.53 147	99.48 156	99.89 134

WR-MVS_H		88.47 162	90.55 170	86.04 159	85.13 177	96.07 175	89.86 181	79.80 153	94.37 183	72.32 152	83.12 168	74.44 177	89.60 184	93.52 180	92.40 177	99.51 149	99.96 105

SixPastTwentyTwo		88.35 163	91.51 163	84.66 176	85.39 173	96.96 157	86.57 191	79.62 155	96.57 160	63.73 187	87.86 151	75.18 172	93.43 154	94.03 167	90.37 199	99.24 189	99.58 170

TransMVSNet (Re)		88.33 164	89.55 180	86.91 154	86.65 163	95.56 188	90.48 167	84.44 113	92.02 205	71.07 162	80.13 181	72.48 183	89.41 185	95.05 157	94.44 149	99.39 175	97.14 200

MVS-HIRNet		88.27 165	94.05 148	81.51 194	88.90 152	98.93 121	83.38 201	60.52 217	98.06 147	63.78 186	80.67 177	90.36 126	92.94 156	97.29 111	96.41 117	99.56 138	96.66 203

WR-MVS		88.23 166	90.15 172	86.00 161	84.39 191	95.64 184	89.96 178	81.80 139	94.46 181	71.60 157	82.10 171	74.36 178	88.76 192	92.48 192	92.20 179	99.46 160	99.83 152

CP-MVSNet		88.09 167	89.57 178	86.36 158	84.63 187	95.46 193	89.48 183	80.53 148	93.42 193	71.26 161	81.25 175	69.90 194	92.78 159	93.30 184	93.69 161	99.47 158	99.96 105

pmnet_mix02		88.07 168	92.32 156	83.10 189	86.14 168	96.23 170	81.90 206	83.05 130	98.04 148	57.59 204	84.93 160	82.02 153	90.87 179	93.54 179	91.53 190	99.06 197	99.97 85

UniMVSNet_ETH3D		88.05 169	87.01 199	89.27 129	88.53 155	97.49 148	90.35 171	83.48 123	94.57 179	77.87 133	70.08 207	61.75 213	96.22 121	90.17 203	95.21 140	99.16 194	99.82 153

anonymousdsp		87.98 170	92.38 155	82.85 190	83.68 200	96.79 158	90.78 164	74.06 185	95.29 172	57.91 203	83.33 167	83.12 149	91.15 177	95.96 141	92.37 178	99.52 145	99.76 161

LTVRE_ROB	88.65 16	87.87 171	91.11 166	84.10 183	86.64 164	97.47 149	94.40 134	78.41 164	96.13 164	52.02 211	87.95 150	65.92 207	93.59 153	95.29 151	95.09 142	99.52 145	99.95 113
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		87.84 172	89.42 183	85.99 162	85.16 176	96.01 177	90.52 166	81.78 141	94.43 182	67.59 171	81.32 174	71.87 186	91.48 172	91.25 198	91.16 194	99.43 168	99.92 125

TDRefinement		87.79 173	88.76 190	86.66 156	93.54 87	98.02 140	95.76 105	85.18 104	96.57 160	67.90 170	80.51 179	66.51 206	78.37 207	93.20 186	89.73 200	99.22 190	96.75 202

MDTV_nov1_ep13_2view		87.75 174	93.32 150	81.26 196	83.74 199	96.64 161	85.66 194	66.20 200	98.36 142	61.61 191	84.34 163	87.95 134	91.12 178	94.01 168	92.66 174	99.22 190	99.27 180

v8		87.54 175	89.33 184	85.45 171	85.41 172	95.50 191	90.32 172	78.94 161	94.35 184	66.93 176	81.90 172	70.99 190	91.62 170	91.49 197	91.22 193	99.48 156	99.87 144

v1144		87.49 176	89.64 176	84.97 173	84.73 183	95.84 181	90.17 173	79.30 157	93.96 187	64.65 184	78.83 188	73.38 182	91.51 171	93.77 174	91.77 185	99.45 163	99.93 121

v2v482		87.46 177	88.90 188	85.78 164	84.58 188	95.95 179	89.90 180	82.43 134	94.19 185	65.65 180	79.80 183	69.12 197	92.67 160	91.88 196	91.46 191	99.45 163	99.93 121

v10		87.40 178	89.62 177	84.80 175	84.93 181	95.07 197	90.44 168	75.63 179	94.51 180	66.52 178	78.87 187	73.47 181	91.86 168	93.69 177	91.87 184	99.45 163	99.86 147

pmmvs5		87.33 179	90.01 173	84.20 181	84.31 193	96.04 176	87.63 189	76.59 176	93.17 198	65.35 183	84.30 164	71.68 187	91.91 167	95.41 148	91.37 192	99.39 175	98.13 193

N_pmnet		87.31 180	91.51 163	82.41 193	85.13 177	95.57 187	80.59 208	81.79 140	96.20 162	58.52 201	78.62 189	85.66 140	89.36 186	94.64 160	92.14 180	99.08 196	97.72 199

PS-CasMVS		87.24 181	88.52 193	85.73 167	84.58 188	95.35 195	89.03 186	80.17 149	93.11 199	68.86 168	77.71 193	66.89 203	92.30 162	93.13 187	93.50 162	99.46 160	99.96 105

EU-MVSNet		87.20 182	90.47 171	83.38 187	85.11 179	93.85 204	86.10 193	79.76 154	93.30 197	65.39 182	84.41 162	78.43 159	85.04 203	92.20 194	93.03 171	98.86 199	98.05 196

PEN-MVS		87.20 182	88.22 194	86.01 160	84.01 198	94.93 198	90.00 177	81.52 146	93.46 192	69.29 166	79.69 184	65.51 208	91.72 169	91.01 201	93.12 168	99.49 152	99.84 150

EG-PatchMatch MVS		86.96 184	89.56 179	83.93 184	86.29 165	97.61 146	90.75 165	73.31 190	95.43 171	66.08 179	75.88 200	71.31 188	87.55 198	94.79 159	92.74 173	99.61 131	99.13 184

v1192		86.93 185	89.01 186	84.50 177	84.46 190	95.51 190	89.93 179	78.65 163	93.75 188	62.29 189	77.19 195	70.88 191	92.28 163	93.84 171	91.96 182	99.38 177	99.90 131

v1921920		86.81 186	88.93 187	84.33 180	84.23 194	95.41 194	90.09 175	78.10 166	93.74 189	62.17 190	76.98 197	71.14 189	92.05 165	93.69 177	91.69 188	99.32 182	99.88 139

v144192		86.80 187	88.90 188	84.35 178	84.33 192	95.56 188	89.34 184	77.74 168	93.60 190	64.03 185	77.82 192	70.76 192	91.28 173	92.91 190	91.74 187	99.37 178	99.90 131

DTE-MVSNet		86.70 188	87.66 198	85.58 168	83.30 201	94.29 200	89.74 182	81.53 144	92.77 201	68.93 167	80.13 181	64.00 211	90.62 181	89.45 204	93.34 164	99.32 182	99.67 165

gg-mvs-nofinetune		86.69 189	91.30 165	81.30 195	90.42 138	99.64 78	98.50 59	61.68 215	79.23 215	40.35 218	66.58 209	97.14 92	96.92 108	98.64 46	97.94 71	99.91 23	99.97 85

v148		86.63 190	87.79 196	85.28 172	84.65 186	95.97 178	86.46 192	82.84 132	92.91 200	71.52 159	78.99 186	66.74 205	86.83 200	89.28 205	90.69 196	99.41 173	99.94 118

v1240		86.24 191	88.56 192	83.54 185	84.05 197	95.21 196	89.27 185	76.76 173	93.42 193	60.68 197	75.99 199	69.80 195	91.21 176	93.83 173	91.76 186	99.29 186	99.91 130

pmmvs6		85.75 192	86.97 200	84.34 179	84.88 182	95.59 186	87.41 190	79.19 159	87.81 211	67.56 172	63.05 212	77.76 160	89.15 187	93.45 182	91.90 183	97.83 206	99.21 181

v7n		85.39 193	87.70 197	82.70 191	82.77 203	95.64 184	88.27 188	74.83 181	92.30 203	62.58 188	76.37 198	64.80 210	88.38 194	94.29 165	90.61 197	99.34 179	99.87 144

gm-plane-assit		84.93 194	91.61 161	77.14 204	84.14 195	91.29 210	66.18 218	69.70 193	85.22 214	47.95 215	78.58 190	89.24 130	94.90 136	98.82 40	98.12 65	99.99 5	100.00 1

CMPMVS	65.66 17	84.62 195	85.02 202	84.15 182	95.40 72	97.79 144	88.35 187	79.22 158	89.66 209	60.71 196	72.20 204	73.94 179	87.32 199	86.73 207	84.55 209	93.90 211	90.31 212
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test_method		84.44 196	89.04 185	79.08 199	81.15 206	92.82 207	82.06 205	61.92 213	96.17 163	59.38 199	74.47 202	67.52 200	91.96 166	96.92 117	95.53 136	97.98 205	99.85 149

Anonymous20231206		84.28 197	89.53 181	78.17 201	82.31 205	94.16 202	82.57 202	76.51 177	93.38 196	52.98 209	79.47 185	73.74 180	75.45 209	95.07 156	94.41 151	99.18 193	96.46 205

new_pmnet		84.12 198	87.89 195	79.72 198	80.43 208	94.14 203	80.26 209	74.14 184	96.01 165	56.30 208	74.94 201	76.45 167	88.59 193	93.11 188	89.31 201	98.59 203	91.27 211

test20.03		83.86 199	88.73 191	78.16 202	82.60 204	93.00 205	81.61 207	74.68 182	92.36 202	57.50 205	83.01 170	74.48 176	73.30 211	92.40 193	91.14 195	99.29 186	94.75 207

pmmvs-eth3d		82.92 200	83.31 205	82.47 192	76.97 210	91.76 209	83.79 198	76.10 178	90.33 206	69.95 165	71.04 206	48.09 216	89.02 189	93.85 170	89.14 202	99.02 198	98.96 186

PM-MVS		82.79 201	84.51 203	80.77 197	77.22 209	92.13 208	83.61 200	73.31 190	93.50 191	61.06 192	77.15 196	46.52 219	90.55 182	94.14 166	89.05 204	98.85 200	99.12 185

pmmvs3		80.91 202	85.62 201	75.42 206	75.01 212	89.09 213	75.31 212	68.70 194	86.99 212	46.74 217	81.18 176	62.91 212	87.95 195	93.84 171	89.06 203	98.80 201	96.23 206

MIMVSNet1		80.64 203	83.97 204	76.76 205	68.91 215	91.15 212	78.32 211	75.47 180	89.58 210	56.64 207	65.10 210	65.17 209	82.14 204	93.51 181	91.64 189	99.10 195	91.66 210

MDA-MVSNet-bldmvs		80.30 204	82.83 206	77.34 203	69.16 214	94.29 200	72.16 213	81.97 138	90.14 208	57.32 206	94.01 117	47.97 217	86.81 201	68.74 214	86.82 206	96.63 207	97.86 197

new-patchmatchnet		78.17 205	80.82 207	75.07 207	76.93 211	91.20 211	71.90 214	73.32 189	86.59 213	48.91 212	67.11 208	47.85 218	81.19 205	88.18 206	87.02 205	98.19 204	97.79 198

FPMVS		73.80 206	74.62 208	72.84 208	83.09 202	84.44 215	83.89 197	73.64 188	92.20 204	48.50 213	72.19 205	59.51 214	63.16 213	69.13 213	66.26 217	84.74 216	78.59 218

Gipuma		71.02 207	72.60 211	69.19 209	71.31 213	75.11 218	66.36 217	61.65 216	94.93 176	47.29 216	38.74 217	38.52 220	75.52 208	86.09 208	85.92 208	93.01 212	88.87 214
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
GG-mvs-BLEND		69.85 208	99.39 40	35.39 217	3.67 224	99.94 18	99.10 43	1.69 221	99.85 50	3.19 225	98.13 75	99.46 62	4.92 220	99.23 33	99.14 30	99.80 56	100.00 1

PMMVS2		65.18 209	68.25 212	61.59 210	61.37 218	79.72 217	59.18 221	61.80 214	64.72 218	37.33 219	53.82 214	35.59 221	54.46 218	73.94 212	80.52 210	95.40 210	89.43 213

PMVS	60.14 18	62.67 210	64.05 213	61.06 211	68.32 216	53.27 224	52.23 222	67.63 197	75.07 217	48.30 214	58.27 213	57.43 215	49.99 219	67.20 215	62.42 218	79.87 219	74.68 219
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
testmvs		61.76 211	72.90 210	48.76 214	21.21 222	68.61 219	66.11 219	37.38 219	94.83 177	33.06 220	64.31 211	29.72 222	86.08 202	74.44 211	78.71 211	48.74 221	99.65 166

E-PMN		55.33 212	55.79 215	54.81 213	59.81 220	57.23 222	38.83 223	63.59 211	64.06 220	24.66 222	35.33 219	26.40 224	58.69 215	55.41 217	70.54 214	83.26 217	81.56 217

EMVS		55.14 213	55.29 216	54.97 212	60.87 219	57.52 221	38.58 224	63.57 212	64.54 219	23.36 223	36.96 218	27.99 223	60.69 214	51.17 218	66.61 216	82.73 218	82.25 216

MVE	58.81 19	52.07 214	55.15 217	48.48 215	42.45 221	62.35 220	36.41 225	54.70 218	49.88 221	27.65 221	29.98 220	18.08 225	54.87 217	65.93 216	77.26 212	74.79 220	82.59 215
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test123		48.14 215	58.11 214	36.51 216	8.71 223	56.81 223	59.55 220	24.08 220	77.50 216	14.41 224	49.20 215	11.94 227	80.98 206	41.62 219	69.81 215	31.32 222	99.90 131

uanet_test		0.00 216	0.00 218	0.00 218	0.00 225	0.00 225	0.00 226	0.00 222	0.00 222	0.00 226	0.00 221	0.00 228	0.00 221	0.00 220	0.00 219	0.00 223	0.00 220

sosnet-low-res		0.00 216	0.00 218	0.00 218	0.00 225	0.00 225	0.00 226	0.00 222	0.00 222	0.00 226	0.00 221	0.00 228	0.00 221	0.00 220	0.00 219	0.00 223	0.00 220

sosnet		0.00 216	0.00 218	0.00 218	0.00 225	0.00 225	0.00 226	0.00 222	0.00 222	0.00 226	0.00 221	0.00 228	0.00 221	0.00 220	0.00 219	0.00 223	0.00 220

RE-MVS-def										52.74 210

9.14												100.00 1

SR-MVS					99.61 12			96.80 18				100.00 1

Anonymous202405211			95.78 126		93.26 88	99.52 84	96.70 95	88.55 76	97.93 150		88.99 145	90.68 124	98.99 59	96.46 127	97.02 108	99.64 126	99.89 134

our_test_3					85.89 169	96.09 174	82.15 204

ambc			74.33 209		66.84 217	84.26 216	84.17 196		93.39 195	58.99 200	45.93 216	18.06 226	70.61 212	93.94 169	86.62 207	92.61 214	98.13 193

MTAPA										96.61 16		100.00 1

MTMP										97.42 11		100.00 1

Patchmatch-RL test							68.01 216

tmp_tt				78.81 200	98.80 46	85.73 214	70.08 215	77.87 167	98.68 124	83.71 113	99.53 30	74.55 175	54.97 216	78.28 210	72.43 213	87.45 215

XVS					95.09 75	99.94 18	97.49 74			88.58 81		99.98 34				99.78 70

X-MVStestdata					95.09 75	99.94 18	97.49 74			88.58 81		99.98 34				99.78 70

abl_6				97.06 37	99.17 40	99.82 58	98.68 54	90.86 53	100.00 1	94.53 32	97.40 83	100.00 1	99.17 53			99.93 19	99.99 57

mPP-MVS					99.23 38							99.87 45

NP-MVS									99.79 60

Patchmtry						99.00 113	95.46 114	65.50 204		67.51 173

DeepMVS_CX						97.31 151	79.48 210	89.65 65	98.66 126	60.89 195	94.40 111	66.89 203	87.65 197	81.69 209		92.76 213	94.24 209

High-resolution multi-view benchmark