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 56	99.87 29	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 34	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 50	99.97 12	99.97 84

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 73	100.00 1	99.88 7	99.88 10	98.96 35	99.85 31	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 27	97.42 11	99.62 21	100.00 1	99.80 16	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
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 40	100.00 1	99.90 5	99.88 10	99.46 17	99.84 34	99.92 123
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 30	96.31 20	99.59 26	99.95 41	99.84 10	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 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 46	99.98 10	99.97 84

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 52	100.00 1	99.57 31	98.66 44	99.56 15	99.76 80	99.97 84

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 93	99.85 31	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 62	99.97 84

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 81	99.84 34	99.99 55
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 34	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 53	99.83 40	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 87	96.47 18	99.62 21	100.00 1	99.63 28	99.74 17	97.97 69	99.77 73	99.94 116

ACMMPR		99.12 18	99.76 9	98.36 21	99.45 25	99.94 18	99.75 16	96.70 27	99.93 30	94.65 31	99.65 16	99.96 39	99.84 10	99.51 26	99.35 22	99.79 62	99.96 104

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 54	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 78	99.70 110	99.98 73

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 60	99.94 42	99.69 21	99.93 4	99.95 3	99.96 13	99.98 73
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 59	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 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 59	99.89 25	99.96 104

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 76	99.90 8	99.31 24	99.84 34	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 18	99.99 55

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 57	99.82 44	99.97 84
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 68	90.13 66	99.66 15	99.99 32	99.64 27	99.78 15	98.14 64	99.82 44	99.89 132

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 55	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 73	99.97 84

OMC-MVS		98.59 31	99.07 44	98.03 27	99.41 29	99.90 47	99.26 39	94.33 42	99.94 27	96.03 21	96.68 87	99.72 54	99.42 36	98.86 39	98.84 43	99.72 107	99.58 168

DPM-MVS		98.58 32	99.78 5	97.17 35	98.02 53	99.64 79	99.80 5	96.72 25	99.96 17	90.05 68	99.57 28	100.00 1	98.66 75	99.56 23	99.96 2	99.80 55	99.80 155

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 44	99.95 111

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 65	99.99 32	98.78 70	99.73 18	99.57 14	99.88 28	99.98 73

CSCG		98.22 35	98.37 66	98.04 26	99.60 16	99.82 58	99.45 34	93.59 43	99.16 103	96.46 19	98.22 72	95.86 102	99.41 38	96.33 129	99.22 28	99.75 89	99.94 116

3Dnovator+	95.21 7	98.17 36	99.08 43	97.12 36	99.28 36	99.78 67	98.61 55	89.93 62	99.93 30	95.36 26	95.50 96	100.00 1	99.56 32	98.58 51	99.80 10	99.95 15	99.97 84

ACMMP		98.16 37	99.01 46	97.18 34	98.86 44	99.92 39	98.77 52	95.73 39	99.31 99	91.15 54	100.00 1	99.81 49	98.82 68	98.11 79	95.91 128	99.77 73	99.97 84
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 61	99.44 27	99.08 32	99.85 31	99.95 111

EPNet		98.11 39	99.63 26	96.34 47	98.44 51	99.88 53	98.55 57	90.25 58	99.93 30	92.60 43	100.00 1	99.73 52	98.41 78	98.87 38	99.02 33	99.82 44	99.97 84
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 61	98.53 55	99.79 62	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	94.60 109	99.43 64	99.50 34	98.55 53	99.91 5	99.99 5	99.98 73

MVS_111021_HR		97.94 42	99.59 30	96.02 50	99.27 37	99.97 3	97.03 86	90.44 55	99.89 38	90.75 57	100.00 1	99.73 52	98.68 74	98.67 43	98.89 41	99.95 15	99.97 84

QAPM		97.90 43	98.89 50	96.74 41	99.35 32	99.80 65	98.84 49	90.20 59	99.94 27	92.85 38	94.17 112	99.78 50	99.42 36	98.71 41	99.87 7	99.79 62	99.98 73

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	85.33 106	98.62 62	99.45 63	99.06 57	99.29 31	99.94 4	99.81 51	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 95	99.51 61	98.72 73	98.53 58	99.00 34	99.90 23	99.99 55

TAPA-MVS	96.62 5	97.60 46	98.46 65	96.60 44	98.73 47	99.90 47	99.30 37	94.96 41	99.46 86	87.57 88	96.05 94	98.53 75	99.26 47	98.04 83	97.33 92	99.77 73	99.88 137
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	93.83 139	95.93 37	100.00 1	90.79 56	98.38 64	99.85 46	95.28 130	99.94 2	99.97 1	96.15 206	99.97 84

xxxxxxxxxxxxxcwj		97.57 48	96.26 117	99.10 3	99.65 5	99.94 18	99.76 8	96.95 4	99.88 41	98.39 5	99.60 23	76.05 169	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 71	96.66 43	97.62 57	99.92 39	98.93 48	96.64 29	98.53 130	88.31 86	94.04 115	99.58 59	98.94 59	97.53 99	97.79 76	99.54 139	99.97 84
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MSDG		97.29 50	97.55 86	97.00 38	98.66 48	99.71 73	99.03 45	96.15 36	99.59 75	89.67 73	92.77 126	94.86 105	98.75 71	98.22 73	97.94 70	99.72 107	99.76 159

CHOSEN 280x420		97.16 51	99.58 32	94.35 76	96.95 62	99.97 3	97.19 80	81.55 142	99.92 34	91.75 46	100.00 1	100.00 1	98.84 67	98.55 53	98.65 51	99.79 62	99.97 84

DELS-MVS		97.05 52	98.05 75	95.88 53	97.09 60	99.99 1	98.82 50	90.30 57	98.44 136	91.40 49	92.91 123	96.57 95	97.68 101	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 85	96.42 45	97.37 58	99.92 39	99.10 43	96.54 32	99.34 98	86.64 98	91.93 130	93.15 116	99.11 55	99.11 35	99.68 12	99.73 103	99.97 84
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 56	95.08 61	96.32 66	99.90 47	99.15 41	89.61 66	99.89 38	87.22 94	95.47 97	98.22 84	98.22 83	98.63 48	98.90 40	99.93 18	100.00 1

MAR-MVS		97.03 55	98.00 77	95.89 51	99.32 34	99.74 72	96.76 93	84.89 105	99.97 8	94.86 29	98.29 66	90.58 124	99.67 24	98.02 85	99.50 16	99.82 44	99.92 123
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 56	98.85 52	94.83 69	92.71 99	97.43 148	99.03 45	85.52 98	99.82 55	92.74 41	99.15 46	99.94 42	99.19 51	98.66 44	96.99 107	99.79 62	99.98 73

CS-MVS		96.88 57	99.06 45	94.34 77	92.11 111	99.67 76	98.58 56	87.30 93	99.95 23	89.40 75	98.68 60	98.41 81	98.85 65	98.54 56	98.15 63	99.80 55	99.99 55

baseline1		96.87 58	98.55 59	94.91 64	92.89 98	99.45 93	96.34 97	88.54 77	98.88 120	92.82 39	98.93 54	96.58 94	99.07 56	98.19 75	98.04 66	99.80 55	99.78 156

OpenMVS	94.03 11	96.87 58	98.10 74	95.44 57	99.29 35	99.78 67	98.46 62	89.92 63	99.47 85	85.78 104	91.05 133	98.50 76	99.30 44	98.49 62	99.41 18	99.89 25	99.98 73

PatchMatch-RL		96.84 60	98.03 76	95.47 54	98.84 45	99.81 63	95.61 110	89.20 69	99.65 72	91.28 51	99.39 37	93.46 114	98.18 84	98.05 81	96.28 117	99.69 115	99.55 170

ETV-MVS		96.79 61	99.19 42	94.00 79	91.78 120	99.63 81	97.15 82	88.00 83	99.95 23	88.34 85	99.32 41	98.71 72	98.82 68	98.69 42	98.01 67	99.90 23	100.00 1

IS_MVSNet		96.66 62	98.62 58	94.38 73	92.41 105	99.70 74	97.19 80	87.67 86	99.05 111	91.27 52	95.09 102	98.46 80	97.95 92	98.64 46	99.37 19	99.79 62	100.00 1

PMMVS		96.45 63	98.24 70	94.36 75	92.58 100	99.01 111	97.08 85	87.42 91	99.88 41	90.06 67	99.39 37	94.63 106	99.33 42	97.85 91	96.99 107	99.70 110	99.96 104

LS3D		96.44 64	97.31 92	95.41 58	97.06 61	99.87 54	99.51 32	97.48 1	99.57 76	79.00 124	95.39 98	89.19 130	99.81 14	98.55 53	98.84 43	99.62 128	99.78 156

EIA-MVS		96.34 65	98.55 59	93.76 83	91.93 116	99.66 77	97.14 83	88.33 81	99.51 80	85.98 103	98.82 58	96.08 100	99.33 42	98.38 65	97.40 90	99.81 51	100.00 1

EPP-MVSNet		96.29 66	98.34 67	93.90 80	91.77 121	99.38 98	95.45 115	87.25 94	99.38 94	91.36 50	94.86 108	98.49 78	97.83 95	98.01 86	98.23 61	99.75 89	99.99 55

UGNet		96.05 67	98.55 59	93.13 91	94.64 81	99.65 78	94.70 127	87.78 84	99.40 93	89.69 72	98.25 69	99.25 68	92.12 162	96.50 121	97.08 102	99.84 34	99.72 161
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 68	96.83 104	95.11 60	97.87 56	99.52 85	98.81 51	91.40 50	99.42 90	84.97 107	90.46 136	96.82 93	98.05 87	96.46 125	96.19 120	99.54 139	98.92 185
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 69	96.48 112	95.46 55	96.47 64	99.89 51	95.64 107	91.23 51	99.75 65	91.59 47	96.80 84	82.44 149	98.05 87	98.53 58	97.92 72	99.80 55	100.00 1

PVSNet_Blended		96.01 69	96.48 112	95.46 55	96.47 64	99.89 51	95.64 107	91.23 51	99.75 65	91.59 47	96.80 84	82.44 149	98.05 87	98.53 58	97.92 72	99.80 55	100.00 1

thisisatest0530		95.89 71	98.32 68	93.06 94	91.76 122	99.75 70	94.94 122	87.60 88	99.91 36	86.66 97	98.28 67	99.98 34	97.72 97	97.10 111	93.24 163	99.65 120	99.95 111

tttt0517		95.88 72	98.31 69	93.04 95	91.75 124	99.75 70	94.90 123	87.60 88	99.91 36	86.63 99	98.28 67	99.98 34	97.72 97	97.10 111	93.24 163	99.65 120	99.95 111

thres100view900		95.86 73	96.62 106	94.97 63	93.10 90	99.83 56	97.76 68	89.15 70	98.62 126	90.69 58	99.00 49	84.86 140	99.30 44	97.57 98	96.48 112	99.81 51	100.00 1

RPSCF		95.86 73	96.94 102	94.61 71	96.52 63	98.67 125	98.54 58	88.43 79	99.56 77	90.51 64	99.39 37	98.70 73	97.72 97	93.77 172	92.00 179	95.93 207	96.50 202

DCV-MVSNet		95.85 75	97.53 87	93.89 81	93.20 89	97.01 154	97.14 83	84.77 106	99.16 103	90.38 65	98.96 53	93.73 111	98.23 82	96.57 120	97.37 91	99.64 124	99.93 119

baseline		95.85 75	98.13 73	93.20 90	92.29 108	99.58 83	97.49 74	84.33 113	99.44 87	87.28 92	97.00 83	94.04 110	97.93 93	98.36 67	98.47 58	99.87 29	99.99 55

canonicalmvs		95.80 77	97.02 97	94.37 74	92.96 96	99.47 90	97.49 74	84.58 108	99.44 87	92.05 44	98.54 63	86.65 135	99.37 40	96.18 132	98.93 37	99.77 73	99.92 123

tfpn200view9		95.78 78	96.54 109	94.89 66	93.10 90	99.82 58	97.67 69	88.85 73	98.62 126	90.69 58	99.00 49	84.86 140	99.28 46	97.41 105	96.10 121	99.76 80	99.99 55

thres200		95.77 79	96.55 108	94.86 67	93.09 92	99.82 58	97.63 72	88.85 73	98.49 131	90.66 60	98.99 51	84.86 140	99.20 49	97.41 105	96.28 117	99.76 80	100.00 1

MVS_Test		95.74 80	98.18 72	92.90 97	92.16 109	99.49 89	97.36 79	84.30 114	99.79 60	84.94 108	96.65 88	93.63 113	98.85 65	98.61 50	99.10 31	99.81 51	100.00 1

thres400		95.72 81	96.48 112	94.84 68	93.00 95	99.83 56	97.55 73	88.93 71	98.49 131	90.61 61	98.86 55	84.63 144	99.20 49	97.45 101	96.10 121	99.77 73	99.99 55

thres600view7		95.64 82	96.38 115	94.79 70	92.96 96	99.82 58	97.48 78	88.85 73	98.38 137	90.52 63	98.84 57	84.61 145	99.15 53	97.41 105	95.60 133	99.76 80	99.99 55

Vis-MVSNet (Re-imp)		95.60 83	98.52 64	92.19 101	92.37 106	99.56 84	96.37 96	87.41 92	98.95 114	84.77 111	94.88 107	98.48 79	92.44 159	98.63 48	99.37 19	99.76 80	99.77 158

FMVSNet3		95.59 84	97.51 88	93.34 88	89.48 144	96.57 161	97.67 69	84.17 115	99.48 82	89.76 69	95.09 102	94.35 107	99.14 54	98.37 66	98.86 42	99.82 44	99.89 132

PVSNet_Blended_VisFu		95.37 85	97.44 90	92.95 96	95.20 73	99.80 65	92.68 147	88.41 80	99.12 106	87.64 87	88.31 147	99.10 69	94.07 146	98.27 70	97.51 87	99.73 103	100.00 1

DI_MVS_plusplus_trai		95.29 86	97.02 97	93.28 89	91.76 122	99.52 85	97.84 66	85.67 97	99.08 110	87.29 91	87.76 150	97.46 90	97.31 105	97.83 92	97.48 88	99.83 40	100.00 1

ET-MVSNet_ETH3D		95.20 87	97.82 82	92.15 103	80.77 205	98.13 136	97.65 71	86.93 95	99.72 67	88.56 84	99.29 44	97.01 92	99.24 48	94.58 160	95.98 126	99.75 89	99.99 55

TSAR-MVS + COLMAP		95.20 87	95.03 134	95.41 58	96.17 67	98.69 124	99.11 42	93.40 44	99.97 8	84.89 109	98.23 71	75.01 171	99.34 41	97.27 110	96.37 116	99.58 132	99.64 166

GBi-Net		95.19 89	96.99 100	93.09 92	89.11 145	96.47 164	96.90 87	84.17 115	99.48 82	89.76 69	95.09 102	94.35 107	98.87 62	96.50 121	97.21 94	99.74 93	99.81 152

test1		95.19 89	96.99 100	93.09 92	89.11 145	96.47 164	96.90 87	84.17 115	99.48 82	89.76 69	95.09 102	94.35 107	98.87 62	96.50 121	97.21 94	99.74 93	99.81 152

test0.0.03 1		95.15 91	97.87 81	91.99 104	91.69 126	98.82 122	93.04 145	83.60 120	99.65 72	88.80 79	94.15 113	97.67 88	94.97 132	96.62 119	98.16 62	99.83 40	100.00 1

baseline2		95.13 92	98.55 59	91.15 109	90.29 140	99.00 112	94.49 131	82.00 136	99.68 70	84.82 110	96.47 89	99.30 67	95.71 124	98.24 72	97.14 100	99.57 134	100.00 1

EPNet_dtu		95.10 93	98.81 54	90.78 110	98.38 52	98.47 127	96.54 95	89.36 67	99.78 62	65.65 178	99.31 42	98.24 83	94.79 135	98.28 69	99.35 22	99.93 18	98.27 189
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Anonymous20231211		94.96 94	94.99 135	94.91 64	93.01 94	99.44 96	96.85 91	88.49 78	98.78 121	92.61 42	83.94 163	90.25 126	98.94 59	95.87 140	96.77 109	99.58 132	99.89 132

UA-Net		94.95 95	98.66 57	90.63 112	94.60 83	98.94 118	96.03 100	85.28 100	98.01 147	78.92 125	97.42 81	99.96 39	89.09 186	98.95 37	98.80 46	99.82 44	98.57 187

CANet_DTU		94.90 96	98.98 47	90.13 119	94.74 79	99.81 63	98.53 59	82.23 135	99.97 8	66.76 175	100.00 1	98.50 76	98.74 72	97.52 100	97.19 99	99.76 80	99.88 137

FC-MVSNet-train		94.61 97	96.27 116	92.68 99	92.35 107	97.14 152	93.45 143	87.73 85	98.93 115	87.31 90	96.42 90	89.35 128	95.67 125	96.06 138	96.01 125	99.56 136	99.98 73

diffmvs		94.60 98	95.63 128	93.41 86	91.98 115	99.30 101	96.86 90	87.62 87	99.30 100	86.07 102	94.12 114	81.63 154	98.16 85	97.43 102	97.60 85	99.76 80	100.00 1

casdiffmvs		94.54 99	95.56 129	93.36 87	91.84 118	99.46 92	95.92 102	87.54 90	98.45 134	86.57 100	90.51 135	84.72 143	98.49 77	97.97 87	97.80 75	99.77 73	100.00 1

CLD-MVS		94.53 100	94.45 142	94.61 71	93.85 86	98.36 129	98.12 64	89.68 64	99.35 97	89.62 74	95.19 100	77.08 161	96.66 114	95.51 144	95.67 131	99.74 93	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		94.48 101	95.95 122	92.77 98	89.11 145	96.47 164	96.90 87	83.38 123	99.11 107	88.64 80	87.50 155	92.26 118	98.87 62	97.91 89	98.60 52	99.74 93	99.81 152

HQP-MVS		94.48 101	95.39 131	93.42 85	95.10 74	98.35 130	98.19 63	91.41 49	99.77 63	79.79 121	99.30 43	77.08 161	96.25 118	96.93 113	96.28 117	99.76 80	99.99 55

MDTV_nov1_ep13		94.32 103	98.77 55	89.14 129	91.70 125	99.52 85	95.21 118	72.09 190	99.80 58	78.91 126	96.32 91	99.62 57	97.71 100	98.39 64	97.71 82	99.22 188	100.00 1

CDS-MVSNet		94.32 103	97.00 99	91.19 108	89.82 143	98.71 123	95.51 112	85.14 104	96.85 155	82.33 116	92.48 127	96.40 98	94.71 136	96.86 116	97.76 78	99.63 126	99.92 123
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
dps		94.29 105	97.33 91	90.75 111	92.02 113	99.21 104	94.31 133	66.97 196	99.50 81	95.61 24	96.22 93	98.64 74	96.08 120	93.71 174	94.03 156	99.52 143	99.98 73

ACMM	94.44 10	94.26 106	94.62 139	93.84 82	94.86 78	97.73 143	93.48 142	90.76 54	99.27 101	87.46 89	99.04 48	76.60 163	96.76 112	96.37 128	93.76 158	99.74 93	99.55 170
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMP	94.49 9	94.19 107	94.74 138	93.56 84	94.25 84	98.32 132	96.02 101	89.35 68	98.90 119	87.28 92	99.14 47	76.41 166	94.94 133	96.07 137	94.35 153	99.49 150	99.99 55
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
EPMVS		94.08 108	98.54 63	88.87 130	92.51 102	99.47 90	94.18 135	66.53 197	99.68 70	82.40 115	95.24 99	99.40 65	97.86 94	98.12 78	97.99 68	99.75 89	99.88 137

test-LLR		93.71 109	97.23 93	89.60 123	91.69 126	99.10 108	94.68 129	83.60 120	99.36 95	71.94 152	93.82 117	96.51 96	95.96 122	97.42 103	94.37 150	99.74 93	99.99 55

CHOSEN 1792x2688		93.69 110	94.89 137	92.28 100	96.17 67	99.84 55	95.69 106	83.17 126	98.54 129	82.04 117	77.58 192	91.15 120	96.90 107	98.36 67	98.82 45	99.73 103	99.98 73

LGP-MVS_train		93.60 111	95.05 133	91.90 105	94.90 77	98.29 133	97.93 65	88.06 82	99.14 105	74.83 141	99.26 45	76.50 164	96.07 121	96.31 130	95.90 130	99.59 130	99.97 84

SCA		93.53 112	98.90 49	87.27 147	92.01 114	99.30 101	93.43 144	65.72 201	99.80 58	75.20 140	97.66 79	99.74 51	97.44 103	98.21 74	97.62 84	99.84 34	100.00 1

FMVSNet5		93.53 112	96.09 121	90.56 114	86.74 159	92.84 204	92.64 148	77.50 168	99.41 92	88.97 78	98.02 75	97.81 86	98.00 90	94.85 156	95.43 135	99.50 149	94.25 206

OPM-MVS		93.50 114	93.00 152	94.07 78	95.82 70	98.26 134	98.49 61	91.62 48	94.69 176	81.93 118	92.82 125	76.18 168	96.82 109	96.12 134	94.57 144	99.74 93	98.39 188

CostFormer		93.50 114	96.50 111	90.00 120	91.69 126	98.65 126	93.88 138	67.64 194	98.97 112	89.16 77	97.79 77	88.92 131	97.97 91	95.14 153	96.06 123	99.63 126	100.00 1

IterMVS-LS		93.50 114	96.22 118	90.33 117	90.93 131	95.50 189	94.83 125	80.54 146	98.92 116	79.11 123	90.64 134	93.70 112	96.79 110	96.93 113	97.85 74	99.78 69	99.99 55
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PatchmatchNet		93.48 117	98.84 53	87.22 148	91.93 116	99.39 97	92.55 149	66.06 199	99.71 68	75.61 137	98.24 70	99.59 58	97.35 104	97.87 90	97.64 83	99.83 40	99.43 173
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MS-PatchMatch		93.46 118	95.91 124	90.61 113	95.48 71	99.31 100	95.62 109	77.23 170	99.42 90	81.88 119	88.92 144	96.06 101	93.80 148	96.45 127	93.11 167	99.65 120	98.10 193

tpm cat1		93.29 119	96.53 110	89.50 125	91.84 118	99.18 106	94.70 127	67.70 193	98.38 137	86.67 96	89.16 141	99.38 66	96.66 114	94.33 161	95.30 136	99.43 166	100.00 1

Effi-MVS+-dtu		93.13 120	97.13 95	88.47 138	88.86 151	99.19 105	96.79 92	79.08 159	99.64 74	70.01 162	97.51 80	89.38 127	96.53 116	97.60 96	96.55 111	99.57 134	100.00 1

HyFIR lowres test		93.13 120	94.48 141	91.56 106	96.12 69	99.68 75	93.52 141	79.98 150	97.24 153	81.73 120	72.66 201	95.74 103	98.29 81	98.27 70	97.79 76	99.70 110	100.00 1

Vis-MVSNet		93.08 122	96.76 105	88.78 134	91.14 130	99.63 81	94.85 124	83.34 124	97.19 154	74.78 142	91.92 131	93.15 116	88.81 189	97.59 97	98.35 59	99.78 69	99.49 172
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
Effi-MVS+		93.06 123	95.94 123	89.70 122	90.82 132	99.45 93	95.71 105	78.94 160	98.72 122	74.71 143	97.92 76	80.73 156	98.35 79	97.72 94	97.05 105	99.70 110	100.00 1

ADS-MVSNet		92.91 124	97.97 78	87.01 150	92.07 112	99.27 103	92.70 146	65.39 204	99.85 50	75.40 138	94.93 106	98.26 82	96.86 108	96.09 135	97.52 86	99.65 120	99.84 148

GeoE		92.88 125	95.20 132	90.18 118	90.59 136	99.18 106	96.31 98	78.36 164	97.52 152	78.53 128	87.11 156	88.01 132	97.63 102	97.79 93	96.76 110	99.66 118	100.00 1

TESTMET0.1,1		92.87 126	97.23 93	87.79 144	86.96 158	99.10 108	94.68 129	77.46 169	99.36 95	71.94 152	93.82 117	96.51 96	95.96 122	97.42 103	94.37 150	99.74 93	99.99 55

FC-MVSNet-test		92.78 127	96.19 120	88.80 133	88.00 155	97.54 145	93.60 140	82.36 134	98.16 141	79.71 122	91.55 132	95.41 104	89.65 181	96.09 135	95.23 137	99.49 150	99.31 176

Fast-Effi-MVS+-dtu		92.73 128	97.62 84	87.02 149	88.91 149	98.83 121	95.79 103	73.98 184	99.89 38	68.62 167	97.73 78	93.30 115	95.21 131	97.67 95	95.96 127	99.59 130	100.00 1

IB-MVS	90.59 15	92.70 129	95.70 126	89.21 128	94.62 82	99.45 93	83.77 197	88.92 72	99.53 78	92.82 39	98.86 55	86.08 137	75.24 208	92.81 189	93.17 165	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		92.67 130	97.13 95	87.47 146	86.72 160	99.07 110	94.28 134	76.90 171	99.21 102	71.53 156	93.63 119	96.32 99	95.67 125	97.32 108	94.36 152	99.74 93	99.99 55

RPMNet		92.64 131	97.88 80	86.53 155	90.79 133	98.95 116	95.13 119	64.44 208	99.09 108	72.36 148	93.58 120	99.01 70	96.74 113	98.05 81	96.45 114	99.71 109	100.00 1

FMVSNet1		92.55 132	93.66 147	91.26 107	87.91 156	96.12 171	94.75 126	81.69 141	97.67 150	85.63 105	80.56 176	87.88 134	98.15 86	96.50 121	97.21 94	99.41 171	99.71 162

tpmrst		92.52 133	97.45 89	86.77 153	92.15 110	99.36 99	92.53 150	65.95 200	99.53 78	72.50 146	92.22 128	99.83 47	97.81 96	95.18 152	96.05 124	99.69 115	100.00 1

testgi		92.47 134	95.68 127	88.73 135	90.68 134	98.35 130	91.67 157	79.50 155	98.96 113	77.12 133	95.17 101	85.84 138	93.95 147	95.75 142	96.47 113	99.45 161	99.21 179

TAMVS		92.43 135	94.21 145	90.35 116	88.68 152	98.85 120	94.15 136	81.53 143	95.58 165	83.61 113	87.05 157	86.45 136	94.71 136	96.27 131	95.91 128	99.42 169	99.38 175

CR-MVSNet		92.32 136	97.97 78	85.74 164	90.63 135	98.95 116	95.46 113	65.50 202	99.09 108	67.51 171	94.20 111	98.18 85	95.59 128	98.16 76	97.20 97	99.74 93	100.00 1

CVMVSNet		92.13 137	95.40 130	88.32 141	91.29 129	97.29 150	91.85 154	86.42 96	96.71 157	71.84 154	89.56 139	91.18 119	88.98 188	96.17 133	97.76 78	99.51 147	99.14 181

Fast-Effi-MVS+		92.11 138	94.33 143	89.52 124	89.06 148	99.00 112	95.13 119	76.72 173	98.59 128	78.21 130	89.99 137	77.35 160	98.34 80	97.97 87	97.44 89	99.67 117	99.96 104

test_part1		91.82 139	91.53 160	92.16 102	92.47 103	96.48 163	95.41 116	83.13 127	95.13 173	87.04 95	80.43 178	80.96 155	96.28 117	93.11 186	95.01 141	98.63 200	99.61 167

ACMH+	92.61 13	91.80 140	93.03 150	90.37 115	93.03 93	98.17 135	94.00 137	84.13 118	98.12 143	77.39 132	91.95 129	74.62 172	94.36 143	94.62 159	93.82 157	99.32 180	99.87 142

IterMVS-SCA-FT		91.75 141	96.87 103	85.78 162	90.34 138	95.93 178	95.06 121	73.85 185	98.91 117	61.01 191	89.21 140	98.87 71	94.66 139	98.09 80	97.12 101	99.76 80	99.99 55

IterMVS		91.65 142	96.62 106	85.85 161	90.27 141	95.80 180	95.32 117	74.15 181	98.91 117	60.95 192	88.79 146	97.76 87	94.69 138	98.04 83	97.07 103	99.73 103	100.00 1
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ACMH	92.34 14	91.59 143	93.02 151	89.92 121	93.97 85	97.98 140	90.10 172	84.70 107	98.46 133	76.80 134	93.38 121	71.94 183	94.39 141	95.34 148	94.04 155	99.54 139	100.00 1
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs4		91.41 144	93.05 149	89.49 126	85.85 168	96.52 162	91.70 156	82.49 132	98.14 142	83.17 114	87.57 152	81.76 153	94.39 141	95.47 145	92.62 173	99.33 178	99.29 177

PatchT		91.06 145	97.66 83	83.36 186	90.32 139	98.96 115	82.30 201	64.72 207	98.45 134	67.51 171	93.28 122	97.60 89	95.59 128	98.16 76	97.20 97	99.70 110	100.00 1

MIMVSNet		91.01 146	96.22 118	84.93 172	85.24 172	98.09 137	90.40 167	64.96 206	97.55 151	72.65 144	96.23 92	90.81 122	96.79 110	96.69 117	97.06 104	99.52 143	97.09 199

UniMVSNet_NR-MVSNet		90.50 147	92.31 155	88.38 139	85.04 178	96.34 167	90.94 159	85.32 99	95.87 164	75.69 135	87.68 151	78.49 157	93.78 149	93.21 183	94.60 143	99.53 142	99.97 84

UniMVSNet (Re)		90.41 148	91.96 157	88.59 137	85.71 169	96.73 158	90.82 161	84.11 119	95.23 171	78.54 127	88.91 145	76.41 166	92.84 156	93.40 181	93.05 168	99.55 138	100.00 1

GA-MVS		90.38 149	94.59 140	85.46 168	88.30 154	98.44 128	92.18 151	83.30 125	97.89 149	58.05 200	92.86 124	84.25 147	91.27 172	96.65 118	92.61 174	99.66 118	99.43 173

USDC		90.36 150	91.68 158	88.82 132	92.58 100	98.02 138	96.27 99	79.83 151	98.37 139	70.61 161	89.05 142	67.50 199	94.17 144	95.77 141	94.43 148	99.46 158	98.62 186

thisisatest0515		90.28 151	94.32 144	85.57 167	85.23 173	97.23 151	85.44 193	83.09 128	96.80 156	72.41 147	89.82 138	90.87 121	87.93 194	95.27 151	90.39 196	99.33 178	99.88 137

TinyColmap		89.94 152	90.88 165	88.84 131	92.43 104	97.91 141	95.59 111	80.10 149	98.12 143	71.33 158	84.56 159	67.46 200	94.15 145	95.57 143	94.27 154	99.43 166	98.26 190

pm-mvs1		89.68 153	92.00 156	86.96 151	86.23 164	96.62 160	90.36 168	83.05 129	93.97 184	72.15 151	81.77 171	82.10 151	90.69 178	95.38 147	94.50 146	99.29 184	99.65 164

tpm		89.60 154	94.93 136	83.39 184	89.94 142	97.11 153	90.09 173	65.28 205	98.67 124	60.03 196	96.79 86	84.38 146	95.66 127	91.90 193	95.65 132	99.32 180	99.98 73

NR-MVSNet		89.52 155	90.71 166	88.14 143	86.19 165	96.20 169	92.07 152	84.58 108	95.54 166	75.27 139	87.52 153	67.96 197	91.24 173	94.33 161	93.45 161	99.49 150	99.97 84

DU-MVS		89.49 156	90.60 167	88.19 142	84.71 182	96.20 169	90.94 159	84.58 108	95.54 166	75.69 135	87.52 153	68.74 196	93.78 149	91.10 197	95.13 139	99.47 156	99.97 84

Baseline_NR-MVSNet		89.13 157	89.53 179	88.66 136	84.71 182	94.43 197	91.79 155	84.49 111	95.54 166	78.28 129	78.52 189	72.46 182	93.29 153	91.10 197	94.82 142	99.42 169	99.86 145

tfpnnormal		89.09 158	89.71 173	88.38 139	87.37 157	96.78 157	91.46 158	85.20 102	90.33 204	72.35 149	83.45 164	69.30 194	94.45 140	95.29 149	92.86 170	99.44 165	99.93 119

TranMVSNet+NR-MVSNet		88.88 159	89.90 172	87.69 145	84.06 194	95.68 181	91.88 153	85.23 101	95.16 172	72.54 145	83.06 167	70.14 191	92.93 155	90.81 200	94.53 145	99.48 154	99.89 132

WR-MVS_H		88.47 160	90.55 168	86.04 157	85.13 175	96.07 173	89.86 179	79.80 152	94.37 181	72.32 150	83.12 166	74.44 175	89.60 182	93.52 178	92.40 175	99.51 147	99.96 104

SixPastTwentyTwo		88.35 161	91.51 161	84.66 174	85.39 171	96.96 155	86.57 189	79.62 154	96.57 158	63.73 185	87.86 149	75.18 170	93.43 152	94.03 165	90.37 197	99.24 187	99.58 168

TransMVSNet (Re)		88.33 162	89.55 178	86.91 152	86.65 161	95.56 186	90.48 165	84.44 112	92.02 203	71.07 160	80.13 179	72.48 181	89.41 183	95.05 155	94.44 147	99.39 173	97.14 198

MVS-HIRNet		88.27 163	94.05 146	81.51 192	88.90 150	98.93 119	83.38 199	60.52 215	98.06 145	63.78 184	80.67 175	90.36 125	92.94 154	97.29 109	96.41 115	99.56 136	96.66 201

WR-MVS		88.23 164	90.15 170	86.00 159	84.39 189	95.64 182	89.96 176	81.80 138	94.46 179	71.60 155	82.10 169	74.36 176	88.76 190	92.48 190	92.20 177	99.46 158	99.83 150

CP-MVSNet		88.09 165	89.57 176	86.36 156	84.63 185	95.46 191	89.48 181	80.53 147	93.42 191	71.26 159	81.25 173	69.90 192	92.78 157	93.30 182	93.69 159	99.47 156	99.96 104

pmnet_mix02		88.07 166	92.32 154	83.10 187	86.14 166	96.23 168	81.90 204	83.05 129	98.04 146	57.59 202	84.93 158	82.02 152	90.87 177	93.54 177	91.53 188	99.06 195	99.97 84

UniMVSNet_ETH3D		88.05 167	87.01 197	89.27 127	88.53 153	97.49 146	90.35 169	83.48 122	94.57 177	77.87 131	70.08 205	61.75 211	96.22 119	90.17 201	95.21 138	99.16 192	99.82 151

anonymousdsp		87.98 168	92.38 153	82.85 188	83.68 198	96.79 156	90.78 162	74.06 183	95.29 170	57.91 201	83.33 165	83.12 148	91.15 175	95.96 139	92.37 176	99.52 143	99.76 159

LTVRE_ROB	88.65 16	87.87 169	91.11 164	84.10 181	86.64 162	97.47 147	94.40 132	78.41 163	96.13 162	52.02 209	87.95 148	65.92 205	93.59 151	95.29 149	95.09 140	99.52 143	99.95 111
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		87.84 170	89.42 181	85.99 160	85.16 174	96.01 175	90.52 164	81.78 140	94.43 180	67.59 169	81.32 172	71.87 184	91.48 170	91.25 196	91.16 192	99.43 166	99.92 123

TDRefinement		87.79 171	88.76 188	86.66 154	93.54 87	98.02 138	95.76 104	85.18 103	96.57 158	67.90 168	80.51 177	66.51 204	78.37 205	93.20 184	89.73 198	99.22 188	96.75 200

MDTV_nov1_ep13_2view		87.75 172	93.32 148	81.26 194	83.74 197	96.64 159	85.66 192	66.20 198	98.36 140	61.61 189	84.34 161	87.95 133	91.12 176	94.01 166	92.66 172	99.22 188	99.27 178

v8		87.54 173	89.33 182	85.45 169	85.41 170	95.50 189	90.32 170	78.94 160	94.35 182	66.93 174	81.90 170	70.99 188	91.62 168	91.49 195	91.22 191	99.48 154	99.87 142

v1144		87.49 174	89.64 174	84.97 171	84.73 181	95.84 179	90.17 171	79.30 156	93.96 185	64.65 182	78.83 186	73.38 180	91.51 169	93.77 172	91.77 183	99.45 161	99.93 119

v2v482		87.46 175	88.90 186	85.78 162	84.58 186	95.95 177	89.90 178	82.43 133	94.19 183	65.65 178	79.80 181	69.12 195	92.67 158	91.88 194	91.46 189	99.45 161	99.93 119

v10		87.40 176	89.62 175	84.80 173	84.93 179	95.07 195	90.44 166	75.63 177	94.51 178	66.52 176	78.87 185	73.47 179	91.86 166	93.69 175	91.87 182	99.45 161	99.86 145

pmmvs5		87.33 177	90.01 171	84.20 179	84.31 191	96.04 174	87.63 187	76.59 174	93.17 196	65.35 181	84.30 162	71.68 185	91.91 165	95.41 146	91.37 190	99.39 173	98.13 191

N_pmnet		87.31 178	91.51 161	82.41 191	85.13 175	95.57 185	80.59 206	81.79 139	96.20 160	58.52 199	78.62 187	85.66 139	89.36 184	94.64 158	92.14 178	99.08 194	97.72 197

PS-CasMVS		87.24 179	88.52 191	85.73 165	84.58 186	95.35 193	89.03 184	80.17 148	93.11 197	68.86 166	77.71 191	66.89 201	92.30 160	93.13 185	93.50 160	99.46 158	99.96 104

EU-MVSNet		87.20 180	90.47 169	83.38 185	85.11 177	93.85 202	86.10 191	79.76 153	93.30 195	65.39 180	84.41 160	78.43 158	85.04 201	92.20 192	93.03 169	98.86 197	98.05 194

PEN-MVS		87.20 180	88.22 192	86.01 158	84.01 196	94.93 196	90.00 175	81.52 145	93.46 190	69.29 164	79.69 182	65.51 206	91.72 167	91.01 199	93.12 166	99.49 150	99.84 148

EG-PatchMatch MVS		86.96 182	89.56 177	83.93 182	86.29 163	97.61 144	90.75 163	73.31 188	95.43 169	66.08 177	75.88 198	71.31 186	87.55 196	94.79 157	92.74 171	99.61 129	99.13 182

v1192		86.93 183	89.01 184	84.50 175	84.46 188	95.51 188	89.93 177	78.65 162	93.75 186	62.29 187	77.19 193	70.88 189	92.28 161	93.84 169	91.96 180	99.38 175	99.90 129

v1921920		86.81 184	88.93 185	84.33 178	84.23 192	95.41 192	90.09 173	78.10 165	93.74 187	62.17 188	76.98 195	71.14 187	92.05 163	93.69 175	91.69 186	99.32 180	99.88 137

v144192		86.80 185	88.90 186	84.35 176	84.33 190	95.56 186	89.34 182	77.74 167	93.60 188	64.03 183	77.82 190	70.76 190	91.28 171	92.91 188	91.74 185	99.37 176	99.90 129

DTE-MVSNet		86.70 186	87.66 196	85.58 166	83.30 199	94.29 198	89.74 180	81.53 143	92.77 199	68.93 165	80.13 179	64.00 209	90.62 179	89.45 202	93.34 162	99.32 180	99.67 163

gg-mvs-nofinetune		86.69 187	91.30 163	81.30 193	90.42 137	99.64 79	98.50 60	61.68 213	79.23 213	40.35 216	66.58 207	97.14 91	96.92 106	98.64 46	97.94 70	99.91 22	99.97 84

v148		86.63 188	87.79 194	85.28 170	84.65 184	95.97 176	86.46 190	82.84 131	92.91 198	71.52 157	78.99 184	66.74 203	86.83 198	89.28 203	90.69 194	99.41 171	99.94 116

v1240		86.24 189	88.56 190	83.54 183	84.05 195	95.21 194	89.27 183	76.76 172	93.42 191	60.68 195	75.99 197	69.80 193	91.21 174	93.83 171	91.76 184	99.29 184	99.91 128

pmmvs6		85.75 190	86.97 198	84.34 177	84.88 180	95.59 184	87.41 188	79.19 158	87.81 209	67.56 170	63.05 210	77.76 159	89.15 185	93.45 180	91.90 181	97.83 204	99.21 179

v7n		85.39 191	87.70 195	82.70 189	82.77 201	95.64 182	88.27 186	74.83 179	92.30 201	62.58 186	76.37 196	64.80 208	88.38 192	94.29 163	90.61 195	99.34 177	99.87 142

gm-plane-assit		84.93 192	91.61 159	77.14 202	84.14 193	91.29 208	66.18 216	69.70 191	85.22 212	47.95 213	78.58 188	89.24 129	94.90 134	98.82 40	98.12 65	99.99 5	100.00 1

CMPMVS	65.66 17	84.62 193	85.02 200	84.15 180	95.40 72	97.79 142	88.35 185	79.22 157	89.66 207	60.71 194	72.20 202	73.94 177	87.32 197	86.73 205	84.55 207	93.90 209	90.31 210
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test_method		84.44 194	89.04 183	79.08 197	81.15 204	92.82 205	82.06 203	61.92 211	96.17 161	59.38 197	74.47 200	67.52 198	91.96 164	96.92 115	95.53 134	97.98 203	99.85 147

Anonymous20231206		84.28 195	89.53 179	78.17 199	82.31 203	94.16 200	82.57 200	76.51 175	93.38 194	52.98 207	79.47 183	73.74 178	75.45 207	95.07 154	94.41 149	99.18 191	96.46 203

new_pmnet		84.12 196	87.89 193	79.72 196	80.43 206	94.14 201	80.26 207	74.14 182	96.01 163	56.30 206	74.94 199	76.45 165	88.59 191	93.11 186	89.31 199	98.59 201	91.27 209

test20.03		83.86 197	88.73 189	78.16 200	82.60 202	93.00 203	81.61 205	74.68 180	92.36 200	57.50 203	83.01 168	74.48 174	73.30 209	92.40 191	91.14 193	99.29 184	94.75 205

pmmvs-eth3d		82.92 198	83.31 203	82.47 190	76.97 208	91.76 207	83.79 196	76.10 176	90.33 204	69.95 163	71.04 204	48.09 214	89.02 187	93.85 168	89.14 200	99.02 196	98.96 184

PM-MVS		82.79 199	84.51 201	80.77 195	77.22 207	92.13 206	83.61 198	73.31 188	93.50 189	61.06 190	77.15 194	46.52 217	90.55 180	94.14 164	89.05 202	98.85 198	99.12 183

pmmvs3		80.91 200	85.62 199	75.42 204	75.01 210	89.09 211	75.31 210	68.70 192	86.99 210	46.74 215	81.18 174	62.91 210	87.95 193	93.84 169	89.06 201	98.80 199	96.23 204

MIMVSNet1		80.64 201	83.97 202	76.76 203	68.91 213	91.15 210	78.32 209	75.47 178	89.58 208	56.64 205	65.10 208	65.17 207	82.14 202	93.51 179	91.64 187	99.10 193	91.66 208

MDA-MVSNet-bldmvs		80.30 202	82.83 204	77.34 201	69.16 212	94.29 198	72.16 211	81.97 137	90.14 206	57.32 204	94.01 116	47.97 215	86.81 199	68.74 212	86.82 204	96.63 205	97.86 195

new-patchmatchnet		78.17 203	80.82 205	75.07 205	76.93 209	91.20 209	71.90 212	73.32 187	86.59 211	48.91 210	67.11 206	47.85 216	81.19 203	88.18 204	87.02 203	98.19 202	97.79 196

FPMVS		73.80 204	74.62 206	72.84 206	83.09 200	84.44 213	83.89 195	73.64 186	92.20 202	48.50 211	72.19 203	59.51 212	63.16 211	69.13 211	66.26 215	84.74 214	78.59 216

Gipuma		71.02 205	72.60 209	69.19 207	71.31 211	75.11 216	66.36 215	61.65 214	94.93 174	47.29 214	38.74 215	38.52 218	75.52 206	86.09 206	85.92 206	93.01 210	88.87 212
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
GG-mvs-BLEND		69.85 206	99.39 40	35.39 215	3.67 222	99.94 18	99.10 43	1.69 219	99.85 50	3.19 223	98.13 74	99.46 62	4.92 218	99.23 33	99.14 30	99.80 55	100.00 1

PMMVS2		65.18 207	68.25 210	61.59 208	61.37 216	79.72 215	59.18 219	61.80 212	64.72 216	37.33 217	53.82 212	35.59 219	54.46 216	73.94 210	80.52 208	95.40 208	89.43 211

PMVS	60.14 18	62.67 208	64.05 211	61.06 209	68.32 214	53.27 222	52.23 220	67.63 195	75.07 215	48.30 212	58.27 211	57.43 213	49.99 217	67.20 213	62.42 216	79.87 217	74.68 217
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
testmvs		61.76 209	72.90 208	48.76 212	21.21 220	68.61 217	66.11 217	37.38 217	94.83 175	33.06 218	64.31 209	29.72 220	86.08 200	74.44 209	78.71 209	48.74 219	99.65 164

E-PMN		55.33 210	55.79 213	54.81 211	59.81 218	57.23 220	38.83 221	63.59 209	64.06 218	24.66 220	35.33 217	26.40 222	58.69 213	55.41 215	70.54 212	83.26 215	81.56 215

EMVS		55.14 211	55.29 214	54.97 210	60.87 217	57.52 219	38.58 222	63.57 210	64.54 217	23.36 221	36.96 216	27.99 221	60.69 212	51.17 216	66.61 214	82.73 216	82.25 214

MVE	58.81 19	52.07 212	55.15 215	48.48 213	42.45 219	62.35 218	36.41 223	54.70 216	49.88 219	27.65 219	29.98 218	18.08 223	54.87 215	65.93 214	77.26 210	74.79 218	82.59 213
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test123		48.14 213	58.11 212	36.51 214	8.71 221	56.81 221	59.55 218	24.08 218	77.50 214	14.41 222	49.20 213	11.94 225	80.98 204	41.62 217	69.81 213	31.32 220	99.90 129

uanet_test		0.00 214	0.00 216	0.00 216	0.00 223	0.00 223	0.00 224	0.00 220	0.00 220	0.00 224	0.00 219	0.00 226	0.00 219	0.00 218	0.00 217	0.00 221	0.00 218

sosnet-low-res		0.00 214	0.00 216	0.00 216	0.00 223	0.00 223	0.00 224	0.00 220	0.00 220	0.00 224	0.00 219	0.00 226	0.00 219	0.00 218	0.00 217	0.00 221	0.00 218

sosnet		0.00 214	0.00 216	0.00 216	0.00 223	0.00 223	0.00 224	0.00 220	0.00 220	0.00 224	0.00 219	0.00 226	0.00 219	0.00 218	0.00 217	0.00 221	0.00 218

RE-MVS-def										52.74 208

9.14												100.00 1

SR-MVS					99.61 12			96.80 18				100.00 1

Anonymous202405211			95.78 125		93.26 88	99.52 85	96.70 94	88.55 76	97.93 148		88.99 143	90.68 123	98.99 58	96.46 125	97.02 106	99.64 124	99.89 132

our_test_3					85.89 167	96.09 172	82.15 202

ambc			74.33 207		66.84 215	84.26 214	84.17 194		93.39 193	58.99 198	45.93 214	18.06 224	70.61 210	93.94 167	86.62 205	92.61 212	98.13 191

MTAPA										96.61 16		100.00 1

MTMP										97.42 11		100.00 1

Patchmatch-RL test							68.01 214

tmp_tt				78.81 198	98.80 46	85.73 212	70.08 213	77.87 166	98.68 123	83.71 112	99.53 30	74.55 173	54.97 214	78.28 208	72.43 211	87.45 213

XVS					95.09 75	99.94 18	97.49 74			88.58 81		99.98 34				99.78 69

X-MVStestdata					95.09 75	99.94 18	97.49 74			88.58 81		99.98 34				99.78 69

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 82	100.00 1	99.17 52			99.93 18	99.99 55

mPP-MVS					99.23 38							99.87 45

NP-MVS									99.79 60

Patchmtry						99.00 112	95.46 113	65.50 202		67.51 171

DeepMVS_CX						97.31 149	79.48 208	89.65 65	98.66 125	60.89 193	94.40 110	66.89 201	87.65 195	81.69 207		92.76 211	94.24 207

High-resolution multi-view benchmark