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 3	99.58 3	99.28 3	99.69 8	99.76 1	99.62 14	98.35 3	99.51 17	99.05 2	99.60 5	99.98 1	99.28 37	99.61 5	98.83 45	99.70 79	99.77 54

DVP-MVS		99.45 2	99.54 6	99.35 1	99.72 7	99.76 1	99.63 11	98.37 2	99.63 6	99.03 3	98.95 38	99.98 1	99.60 7	99.60 6	99.05 25	99.74 45	99.79 40
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
PVSNet_Blended_VisFu		97.41 74	98.49 65	96.15 89	97.49 71	99.76 1	96.02 146	93.75 77	99.26 41	93.38 86	93.73 146	99.35 57	96.47 138	98.96 43	98.46 62	99.77 34	99.90 4

MSP-MVS		99.34 6	99.52 9	99.14 8	99.68 12	99.75 4	99.64 8	98.31 8	99.44 21	98.10 14	99.28 17	99.98 1	99.30 35	99.34 22	99.05 25	99.81 17	99.79 40
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
APDe-MVS		99.49 1	99.64 1	99.32 2	99.74 4	99.74 5	99.75 1	98.34 4	99.56 11	98.72 7	99.57 6	99.97 7	99.53 16	99.65 2	99.25 14	99.84 7	99.77 54

CHOSEN 1792x2688		96.41 103	96.99 121	95.74 100	98.01 66	99.72 6	97.70 101	90.78 125	99.13 61	90.03 117	87.35 193	95.36 104	98.33 85	98.59 77	98.91 38	99.59 133	99.87 13

IS_MVSNet		97.86 59	98.86 54	96.68 74	96.02 100	99.72 6	98.35 76	93.37 85	98.75 111	94.01 72	96.88 100	98.40 69	98.48 82	99.09 36	99.42 5	99.83 10	99.80 33

CSCG		98.90 30	98.93 52	98.85 25	99.75 3	99.72 6	99.49 21	96.58 43	99.38 24	98.05 16	98.97 36	97.87 75	99.49 19	97.78 123	98.92 36	99.78 29	99.90 4

DPE-MVS		99.39 4	99.55 5	99.20 4	99.63 21	99.71 9	99.66 6	98.33 6	99.29 35	98.40 12	99.64 4	99.98 1	99.31 32	99.56 9	98.96 32	99.85 5	99.70 91
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
DELS-MVS		98.19 50	98.77 58	97.52 51	98.29 61	99.71 9	99.12 41	94.58 63	98.80 101	95.38 48	96.24 116	98.24 72	97.92 98	99.06 39	99.52 1	99.82 11	99.79 40
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
Vis-MVSNet		96.16 110	98.22 75	93.75 127	95.33 130	99.70 11	97.27 113	90.85 122	98.30 133	85.51 144	95.72 128	96.45 88	93.69 192	98.70 65	99.00 29	99.84 7	99.69 95
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
tfpn200view9		96.75 92	96.51 134	97.03 64	96.31 92	99.67 12	98.41 70	93.99 71	97.35 166	94.52 61	95.90 122	86.93 157	99.14 46	98.26 91	97.80 103	99.82 11	99.70 91

thres600view7		96.69 96	96.43 141	97.00 69	96.28 95	99.67 12	98.41 70	93.99 71	97.85 155	94.29 70	95.96 120	85.91 169	99.19 40	98.26 91	97.63 108	99.82 11	99.73 75

thres200		96.76 91	96.53 131	97.03 64	96.31 92	99.67 12	98.37 73	93.99 71	97.68 161	94.49 63	95.83 125	86.77 159	99.18 42	98.26 91	97.82 102	99.82 11	99.66 105

SteuartSystems-ACMMP		99.20 15	99.51 10	98.83 27	99.66 16	99.66 15	99.71 3	98.12 28	99.14 56	96.62 34	99.16 23	99.98 1	99.12 47	99.63 3	99.19 20	99.78 29	99.83 25
Skip Steuart: Steuart Systems R&D Blog.
EIA-MVS		97.70 65	98.78 57	96.44 84	95.72 111	99.65 16	98.14 85	93.72 78	98.30 133	92.31 101	98.63 54	97.90 74	98.97 57	98.92 48	98.30 77	99.78 29	99.80 33

zzz-MVS		99.31 8	99.44 16	99.16 6	99.73 5	99.65 16	99.63 11	98.26 13	99.27 38	98.01 18	99.27 18	99.97 7	99.60 7	99.59 7	98.58 57	99.71 70	99.73 75

UGNet		97.66 66	99.07 42	96.01 94	97.19 80	99.65 16	97.09 124	93.39 83	99.35 29	94.40 68	98.79 46	99.59 53	94.24 183	98.04 109	98.29 78	99.73 52	99.80 33
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
HyFIR lowres test		95.99 113	96.56 129	95.32 105	97.99 67	99.65 16	96.54 135	88.86 150	98.44 127	89.77 120	84.14 203	97.05 85	99.03 54	98.55 79	98.19 83	99.73 52	99.86 17

DeepC-MVS	97.63 4	98.33 46	98.57 61	98.04 42	98.62 57	99.65 16	99.45 25	98.15 24	99.51 17	92.80 95	95.74 126	96.44 90	99.46 21	99.37 19	99.50 2	99.78 29	99.81 31
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CANet		98.46 42	99.16 36	97.64 49	98.48 58	99.64 21	99.35 31	94.71 58	99.53 14	95.17 51	97.63 84	99.59 53	98.38 84	98.88 52	98.99 30	99.74 45	99.86 17

ACMMPR		99.30 9	99.54 6	99.03 16	99.66 16	99.64 21	99.68 4	98.25 14	99.56 11	97.12 30	99.19 21	99.95 17	99.72 1	99.43 16	99.25 14	99.72 59	99.77 54

ACMMP		98.74 34	99.03 47	98.40 33	99.36 39	99.64 21	99.20 36	97.75 38	98.82 98	95.24 50	98.85 44	99.87 36	99.17 44	98.74 63	97.50 114	99.71 70	99.76 60
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_0304		98.14 52	99.03 47	97.10 60	98.05 65	99.63 24	99.27 34	94.33 65	99.63 6	93.06 90	97.32 87	99.05 62	98.09 91	98.82 55	98.87 40	99.81 17	99.89 7

thres400		96.71 95	96.45 139	97.02 66	96.28 95	99.63 24	98.41 70	94.00 70	97.82 156	94.42 67	95.74 126	86.26 166	99.18 42	98.20 95	97.79 104	99.81 17	99.70 91

PGM-MVS		98.86 31	99.35 25	98.29 35	99.77 1	99.63 24	99.67 5	95.63 46	98.66 114	95.27 49	99.11 27	99.82 42	99.67 4	99.33 23	99.19 20	99.73 52	99.74 71

LS3D		97.79 60	98.25 71	97.26 57	98.40 59	99.63 24	99.53 18	98.63 1	99.25 43	88.13 124	96.93 98	94.14 120	99.19 40	99.14 33	99.23 17	99.69 82	99.42 143

SMA-MVS		99.38 5	99.60 2	99.12 9	99.76 2	99.62 28	99.39 29	98.23 19	99.52 16	98.03 17	99.45 10	99.98 1	99.64 5	99.58 8	99.30 11	99.68 91	99.76 60
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
XVS					97.42 73	99.62 28	98.59 65			93.81 78		99.95 17				99.69 82

X-MVStestdata					97.42 73	99.62 28	98.59 65			93.81 78		99.95 17				99.69 82

X-MVS		98.93 29	99.37 21	98.42 32	99.67 13	99.62 28	99.60 15	98.15 24	99.08 66	93.81 78	98.46 60	99.95 17	99.59 10	99.49 13	99.21 19	99.68 91	99.75 67

Vis-MVSNet (Re-imp)		97.40 75	98.89 53	95.66 102	95.99 103	99.62 28	97.82 95	93.22 89	98.82 98	91.40 110	96.94 97	98.56 67	95.70 155	99.14 33	99.41 6	99.79 26	99.75 67

ETV-MVS		98.05 54	99.25 32	96.65 76	95.61 116	99.61 33	98.26 81	93.52 81	98.90 87	93.74 81	99.32 15	99.20 59	98.90 62	99.21 29	98.72 50	99.87 3	99.79 40

CHOSEN 280x420		97.99 56	99.24 33	96.53 80	98.34 60	99.61 33	98.36 75	89.80 141	99.27 38	95.08 53	99.81 1	98.58 66	98.64 74	99.02 40	98.92 36	98.93 185	99.48 139

PVSNet_BlendedMVS		97.51 71	97.71 93	97.28 55	98.06 63	99.61 33	97.31 111	95.02 52	99.08 66	95.51 45	98.05 71	90.11 141	98.07 92	98.91 49	98.40 66	99.72 59	99.78 46

PVSNet_Blended		97.51 71	97.71 93	97.28 55	98.06 63	99.61 33	97.31 111	95.02 52	99.08 66	95.51 45	98.05 71	90.11 141	98.07 92	98.91 49	98.40 66	99.72 59	99.78 46

MVS_111021_HR		98.59 41	99.36 22	97.68 48	99.42 35	99.61 33	98.14 85	94.81 55	99.31 32	95.00 54	99.51 8	99.79 45	99.00 56	98.94 45	98.83 45	99.69 82	99.57 123

tttt0517		97.23 78	98.24 74	96.04 92	95.60 118	99.60 38	96.94 129	93.23 87	99.15 53	92.56 99	98.74 51	96.12 97	98.17 86	98.21 94	96.10 155	99.73 52	99.78 46

ACMMP_NAP		99.05 25	99.45 13	98.58 31	99.73 5	99.60 38	99.64 8	98.28 12	99.23 44	94.57 60	99.35 14	99.97 7	99.55 14	99.63 3	98.66 52	99.70 79	99.74 71

thisisatest0530		97.23 78	98.25 71	96.05 91	95.60 118	99.59 40	96.96 128	93.23 87	99.17 52	92.60 98	98.75 50	96.19 94	98.17 86	98.19 96	96.10 155	99.72 59	99.77 54

CS-MVS-test		97.90 58	99.30 28	96.26 86	95.44 126	99.59 40	98.63 63	91.99 100	99.57 9	92.31 101	99.37 13	98.60 64	99.33 29	99.11 35	98.93 34	99.87 3	99.93 1

HFP-MVS		99.32 7	99.53 8	99.07 13	99.69 8	99.59 40	99.63 11	98.31 8	99.56 11	97.37 26	99.27 18	99.97 7	99.70 3	99.35 21	99.24 16	99.71 70	99.76 60

PHI-MVS		99.08 22	99.43 18	98.67 29	99.15 46	99.59 40	99.11 42	97.35 40	99.14 56	97.30 27	99.44 11	99.96 12	99.32 31	98.89 51	99.39 7	99.79 26	99.58 118

APD-MVS		99.25 12	99.38 20	99.09 11	99.69 8	99.58 44	99.56 17	98.32 7	98.85 91	97.87 20	98.91 41	99.92 28	99.30 35	99.45 15	99.38 8	99.79 26	99.58 118
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CP-MVS		99.27 10	99.44 16	99.08 12	99.62 23	99.58 44	99.53 18	98.16 22	99.21 47	97.79 21	99.15 24	99.96 12	99.59 10	99.54 11	98.86 41	99.78 29	99.74 71

MP-MVS		99.07 23	99.36 22	98.74 28	99.63 21	99.57 46	99.66 6	98.25 14	99.00 78	95.62 43	98.97 36	99.94 25	99.54 15	99.51 12	98.79 49	99.71 70	99.73 75
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CS-MVS		98.21 49	99.34 26	96.89 72	95.51 123	99.56 47	98.85 55	93.31 86	99.01 77	94.48 64	99.31 16	99.46 56	99.31 32	99.02 40	99.19 20	99.90 1	99.87 13

EPP-MVSNet		97.75 63	98.71 59	96.63 78	95.68 114	99.56 47	97.51 105	93.10 92	99.22 45	94.99 55	97.18 93	97.30 82	98.65 73	98.83 54	98.93 34	99.84 7	99.92 2

SD-MVS		99.25 12	99.50 11	98.96 21	98.79 53	99.55 49	99.33 32	98.29 11	99.75 1	97.96 19	99.15 24	99.95 17	99.61 6	99.17 31	99.06 24	99.81 17	99.84 21
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
Anonymous202405211			97.40 105		96.45 88	99.54 50	98.08 90	93.79 74	98.24 137		93.55 147	94.41 116	98.88 65	98.04 109	98.24 80	99.75 40	99.76 60

TSAR-MVS + MP.		99.27 10	99.57 4	98.92 23	98.78 54	99.53 51	99.72 2	98.11 29	99.73 2	97.43 25	99.15 24	99.96 12	99.59 10	99.73 1	99.07 23	99.88 2	99.82 26
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
CPTT-MVS		99.14 19	99.20 35	99.06 14	99.58 26	99.53 51	99.45 25	97.80 37	99.19 50	98.32 13	98.58 55	99.95 17	99.60 7	99.28 25	98.20 82	99.64 112	99.69 95

MVS_111021_LR		98.67 37	99.41 19	97.81 47	99.37 37	99.53 51	98.51 67	95.52 48	99.27 38	94.85 56	99.56 7	99.69 50	99.04 53	99.36 20	98.88 39	99.60 127	99.58 118

thres100view900		96.72 94	96.47 137	97.00 69	96.31 92	99.52 54	98.28 79	94.01 69	97.35 166	94.52 61	95.90 122	86.93 157	99.09 51	98.07 104	97.87 99	99.81 17	99.63 114

Anonymous20231211		97.10 82	97.06 119	97.14 59	96.32 91	99.52 54	98.16 84	93.76 75	98.84 95	95.98 40	90.92 165	94.58 115	98.90 62	97.72 128	98.10 88	99.71 70	99.75 67

casdiffmvs		96.93 87	97.43 104	96.34 85	95.70 112	99.50 56	97.75 99	93.22 89	98.98 80	92.64 96	94.97 133	91.71 136	98.93 58	98.62 70	98.52 61	99.82 11	99.72 86

MSLP-MVS++		99.15 18	99.24 33	99.04 15	99.52 32	99.49 57	99.09 44	98.07 30	99.37 26	98.47 9	97.79 78	99.89 34	99.50 17	98.93 46	99.45 4	99.61 119	99.76 60

HPM-MVS++		99.10 21	99.30 28	98.86 24	99.69 8	99.48 58	99.59 16	98.34 4	99.26 41	96.55 37	99.10 30	99.96 12	99.36 27	99.25 26	98.37 70	99.64 112	99.66 105

QAPM		98.62 40	99.04 46	98.13 39	99.57 27	99.48 58	99.17 38	94.78 56	99.57 9	96.16 38	96.73 102	99.80 43	99.33 29	98.79 57	99.29 13	99.75 40	99.64 112

TSAR-MVS + ACMM		98.77 33	99.45 13	97.98 44	99.37 37	99.46 60	99.44 27	98.13 27	99.65 4	92.30 103	98.91 41	99.95 17	99.05 52	99.42 17	98.95 33	99.58 137	99.82 26

TSAR-MVS + GP.		98.66 39	99.36 22	97.85 46	97.16 81	99.46 60	99.03 48	94.59 62	99.09 64	97.19 29	99.73 3	99.95 17	99.39 26	98.95 44	98.69 51	99.75 40	99.65 108

canonicalmvs		97.31 76	97.81 92	96.72 73	96.20 98	99.45 62	98.21 82	91.60 108	99.22 45	95.39 47	98.48 58	90.95 138	99.16 45	97.66 130	99.05 25	99.76 36	99.90 4

baseline1		97.58 68	98.05 82	97.02 66	96.21 97	99.45 62	97.71 100	93.71 79	98.47 126	95.75 42	98.78 47	93.20 130	98.91 61	98.52 81	98.44 63	99.81 17	99.53 128

3Dnovator	96.92 7	98.67 37	99.05 43	98.23 38	99.57 27	99.45 62	99.11 42	94.66 59	99.69 3	96.80 33	96.55 111	99.61 52	99.40 25	98.87 53	99.49 3	99.85 5	99.66 105

COLMAP_ROB	96.15 12	97.78 61	98.17 77	97.32 53	98.84 51	99.45 62	99.28 33	95.43 49	99.48 19	91.80 108	94.83 136	98.36 70	98.90 62	98.09 101	97.85 100	99.68 91	99.15 160
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
EPNet		98.05 54	98.86 54	97.10 60	99.02 49	99.43 66	98.47 68	94.73 57	99.05 72	95.62 43	98.93 39	97.62 79	95.48 163	98.59 77	98.55 58	99.29 175	99.84 21
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CANet_DTU		96.64 99	99.08 40	93.81 126	97.10 82	99.42 67	98.85 55	90.01 135	99.31 32	79.98 177	99.78 2	99.10 61	97.42 113	98.35 88	98.05 90	99.47 158	99.53 128

OpenMVS	96.23 11	97.95 57	98.45 66	97.35 52	99.52 32	99.42 67	98.91 52	94.61 60	98.87 88	92.24 105	94.61 137	99.05 62	99.10 49	98.64 68	99.05 25	99.74 45	99.51 135

xxxxxxxxxxxxxcwj		98.14 52	97.38 106	99.03 16	99.65 18	99.41 69	98.87 53	98.24 17	99.14 56	98.73 5	99.11 27	86.38 165	98.92 59	99.22 27	98.84 43	99.76 36	99.56 124

SF-MVS		99.18 16	99.32 27	99.03 16	99.65 18	99.41 69	98.87 53	98.24 17	99.14 56	98.73 5	99.11 27	99.92 28	98.92 59	99.22 27	98.84 43	99.76 36	99.56 124

DI_MVS_plusplus_trai		96.90 88	97.49 99	96.21 88	95.61 116	99.40 71	98.72 61	92.11 96	99.14 56	92.98 94	93.08 157	95.14 106	98.13 90	98.05 108	97.91 97	99.74 45	99.73 75

GeoE		95.98 115	97.24 115	94.51 114	95.02 135	99.38 72	98.02 92	87.86 164	98.37 130	87.86 128	92.99 159	93.54 125	98.56 78	98.61 71	97.92 95	99.73 52	99.85 20

UA-Net		97.13 81	99.14 37	94.78 110	97.21 79	99.38 72	97.56 104	92.04 98	98.48 125	88.03 125	98.39 63	99.91 31	94.03 186	99.33 23	99.23 17	99.81 17	99.25 155

NCCC		99.05 25	99.08 40	99.02 19	99.62 23	99.38 72	99.43 28	98.21 20	99.36 28	97.66 23	97.79 78	99.90 32	99.45 22	99.17 31	98.43 65	99.77 34	99.51 135

DeepC-MVS_fast	98.34 1	99.17 17	99.45 13	98.85 25	99.55 29	99.37 75	99.64 8	98.05 32	99.53 14	96.58 35	98.93 39	99.92 28	99.49 19	99.46 14	99.32 10	99.80 25	99.64 112
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CDPH-MVS		98.41 43	99.10 39	97.61 50	99.32 43	99.36 76	99.49 21	96.15 45	98.82 98	91.82 107	98.41 61	99.66 51	99.10 49	98.93 46	98.97 31	99.75 40	99.58 118

baseline		97.45 73	98.70 60	95.99 95	95.89 105	99.36 76	98.29 78	91.37 114	99.21 47	92.99 93	98.40 62	96.87 87	97.96 96	98.60 74	98.60 56	99.42 165	99.86 17

PCF-MVS	97.50 6	98.18 51	98.35 69	97.99 43	98.65 56	99.36 76	98.94 51	98.14 26	98.59 116	93.62 82	96.61 107	99.76 48	99.03 54	97.77 124	97.45 119	99.57 141	98.89 174
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Effi-MVS+		95.81 116	97.31 113	94.06 121	95.09 133	99.35 79	97.24 115	88.22 159	98.54 120	85.38 145	98.52 56	88.68 148	98.70 70	98.32 89	97.93 94	99.74 45	99.84 21

train_agg		98.73 35	99.11 38	98.28 36	99.36 39	99.35 79	99.48 23	97.96 34	98.83 96	93.86 77	98.70 53	99.86 37	99.44 23	99.08 38	98.38 68	99.61 119	99.58 118

diffmvs		96.83 89	97.33 109	96.25 87	95.76 109	99.34 81	98.06 91	93.22 89	99.43 22	92.30 103	96.90 99	89.83 146	98.55 79	98.00 112	98.14 84	99.64 112	99.70 91

CNVR-MVS		99.23 14	99.28 30	99.17 5	99.65 18	99.34 81	99.46 24	98.21 20	99.28 36	98.47 9	98.89 43	99.94 25	99.50 17	99.42 17	98.61 55	99.73 52	99.52 131

3Dnovator+	96.92 7	98.71 36	99.05 43	98.32 34	99.53 30	99.34 81	99.06 46	94.61 60	99.65 4	97.49 24	96.75 101	99.86 37	99.44 23	98.78 58	99.30 11	99.81 17	99.67 101

MCST-MVS		99.11 20	99.27 31	98.93 22	99.67 13	99.33 84	99.51 20	98.31 8	99.28 36	96.57 36	99.10 30	99.90 32	99.71 2	99.19 30	98.35 71	99.82 11	99.71 89

DeepPCF-MVS	97.74 3	98.34 45	99.46 12	97.04 63	98.82 52	99.33 84	96.28 142	97.47 39	99.58 8	94.70 59	98.99 35	99.85 40	97.24 116	99.55 10	99.34 9	97.73 200	99.56 124

TAPA-MVS	97.53 5	98.41 43	98.84 56	97.91 45	99.08 48	99.33 84	99.15 39	97.13 41	99.34 30	93.20 87	97.75 80	99.19 60	99.20 39	98.66 66	98.13 85	99.66 104	99.48 139
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
AdaColmap		99.06 24	98.98 50	99.15 7	99.60 25	99.30 87	99.38 30	98.16 22	99.02 75	98.55 8	98.71 52	99.57 55	99.58 13	99.09 36	97.84 101	99.64 112	99.36 149

MVS_Test		97.30 77	98.54 62	95.87 96	95.74 110	99.28 88	98.19 83	91.40 113	99.18 51	91.59 109	98.17 69	96.18 95	98.63 75	98.61 71	98.55 58	99.66 104	99.78 46

IB-MVS	93.96 15	95.02 133	96.44 140	93.36 140	97.05 83	99.28 88	90.43 200	93.39 83	98.02 144	96.02 39	94.92 135	92.07 134	83.52 209	95.38 186	95.82 163	99.72 59	99.59 117
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
gg-mvs-nofinetune		90.85 193	94.14 172	87.02 199	94.89 138	99.25 90	98.64 62	76.29 213	88.24 214	57.50 218	79.93 209	95.45 103	95.18 172	98.77 59	98.07 89	99.62 117	99.24 156

PatchMatch-RL		97.77 62	98.25 71	97.21 58	99.11 47	99.25 90	97.06 126	94.09 68	98.72 112	95.14 52	98.47 59	96.29 92	98.43 83	98.65 67	97.44 120	99.45 160	98.94 169

PLC	97.93 2	99.02 28	98.94 51	99.11 10	99.46 34	99.24 92	99.06 46	97.96 34	99.31 32	99.16 1	97.90 76	99.79 45	99.36 27	98.71 64	98.12 86	99.65 108	99.52 131
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
OMC-MVS		98.84 32	99.01 49	98.65 30	99.39 36	99.23 93	99.22 35	96.70 42	99.40 23	97.77 22	97.89 77	99.80 43	99.21 38	99.02 40	98.65 53	99.57 141	99.07 166

abl_6				98.09 40	99.33 42	99.22 94	98.79 59	94.96 54	98.52 124	97.00 32	97.30 88	99.86 37	98.76 67			99.69 82	99.41 144

CNLPA		99.03 27	99.05 43	99.01 20	99.27 44	99.22 94	99.03 48	97.98 33	99.34 30	99.00 4	98.25 67	99.71 49	99.31 32	98.80 56	98.82 47	99.48 156	99.17 159

MSDG		98.27 48	98.29 70	98.24 37	99.20 45	99.22 94	99.20 36	97.82 36	99.37 26	94.43 66	95.90 122	97.31 81	99.12 47	98.76 60	98.35 71	99.67 99	99.14 163

Effi-MVS+-dtu		95.74 118	98.04 83	93.06 144	93.92 147	99.16 97	97.90 93	88.16 161	99.07 71	82.02 165	98.02 74	94.32 118	96.74 128	98.53 80	97.56 111	99.61 119	99.62 115

SCA		94.95 134	97.44 103	92.04 156	95.55 120	99.16 97	96.26 143	79.30 202	99.02 75	85.73 142	98.18 68	97.13 84	97.69 106	96.03 179	94.91 183	97.69 201	97.65 194

Fast-Effi-MVS+		95.38 126	96.52 132	94.05 122	94.15 145	99.14 99	97.24 115	86.79 170	98.53 121	87.62 130	94.51 138	87.06 153	98.76 67	98.60 74	98.04 91	99.72 59	99.77 54

DROMVSNet		95.38 126	96.52 132	94.05 122	94.15 145	99.14 99	97.24 115	86.79 170	98.53 121	87.62 130	94.51 138	87.06 153	98.76 67	98.60 74	98.04 91	99.72 59	99.77 54

baseline2		96.36 105	97.82 91	94.65 112	94.60 142	99.09 101	96.45 139	89.63 143	98.36 131	91.29 112	97.60 85	94.13 121	96.37 139	98.45 84	97.70 106	99.54 150	99.41 144

TAMVS		95.53 122	96.50 136	94.39 117	93.86 150	99.03 102	96.67 132	89.55 145	97.33 168	90.64 114	93.02 158	91.58 137	96.21 142	97.72 128	97.43 121	99.43 163	99.36 149

testgi		95.67 119	97.48 100	93.56 133	95.07 134	99.00 103	95.33 159	88.47 156	98.80 101	86.90 135	97.30 88	92.33 132	95.97 150	97.66 130	97.91 97	99.60 127	99.38 148

RPSCF		97.61 67	98.16 78	96.96 71	98.10 62	99.00 103	98.84 57	93.76 75	99.45 20	94.78 58	99.39 12	99.31 58	98.53 81	96.61 159	95.43 169	97.74 198	97.93 192

ADS-MVSNet		94.65 141	97.04 120	91.88 164	95.68 114	98.99 105	95.89 147	79.03 205	99.15 53	85.81 141	96.96 96	98.21 73	97.10 118	94.48 198	94.24 192	97.74 198	97.21 198

test0.0.03 1		96.69 96	98.12 80	95.01 108	95.49 124	98.99 105	95.86 148	90.82 123	98.38 129	92.54 100	96.66 105	97.33 80	95.75 153	97.75 126	98.34 73	99.60 127	99.40 147

MDTV_nov1_ep13		95.57 120	97.48 100	93.35 141	95.43 127	98.97 107	97.19 119	83.72 191	98.92 86	87.91 127	97.75 80	96.12 97	97.88 102	96.84 158	95.64 167	97.96 196	98.10 188

CDS-MVSNet		96.59 102	98.02 85	94.92 109	94.45 143	98.96 108	97.46 107	91.75 104	97.86 154	90.07 116	96.02 119	97.25 83	96.21 142	98.04 109	98.38 68	99.60 127	99.65 108
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
EPMVS		95.05 132	96.86 125	92.94 146	95.84 107	98.96 108	96.68 131	79.87 198	99.05 72	90.15 115	97.12 94	95.99 99	97.49 111	95.17 190	94.75 188	97.59 202	96.96 202

MAR-MVS		97.71 64	98.04 83	97.32 53	99.35 41	98.91 110	97.65 102	91.68 106	98.00 145	97.01 31	97.72 82	94.83 110	98.85 66	98.44 86	98.86 41	99.41 166	99.52 131
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
Fast-Effi-MVS+-dtu		95.38 126	98.20 76	92.09 155	93.91 148	98.87 111	97.35 110	85.01 184	99.08 66	81.09 169	98.10 70	96.36 91	95.62 158	98.43 87	97.03 128	99.55 146	99.50 137

PatchmatchNet		94.70 139	97.08 118	91.92 161	95.53 121	98.85 112	95.77 149	79.54 200	98.95 81	85.98 139	98.52 56	96.45 88	97.39 114	95.32 187	94.09 193	97.32 204	97.38 197
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test-mter		94.86 137	97.32 110	92.00 158	92.41 167	98.82 113	96.18 145	86.35 177	98.05 143	82.28 163	96.48 112	94.39 117	95.46 165	98.17 97	96.20 151	99.32 173	99.13 164

GA-MVS		93.93 156	96.31 143	91.16 176	93.61 155	98.79 114	95.39 158	90.69 129	98.25 136	73.28 202	96.15 117	88.42 149	94.39 181	97.76 125	95.35 171	99.58 137	99.45 141

ACMH+	95.51 13	95.40 125	96.00 144	94.70 111	96.33 90	98.79 114	96.79 130	91.32 115	98.77 107	87.18 133	95.60 130	85.46 172	96.97 121	97.15 150	96.59 140	99.59 133	99.65 108

ACMH	95.42 14	95.27 130	95.96 146	94.45 116	96.83 85	98.78 116	94.72 173	91.67 107	98.95 81	86.82 136	96.42 113	83.67 182	97.00 120	97.48 139	96.68 136	99.69 82	99.76 60
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
DPM-MVS		98.31 47	98.53 63	98.05 41	98.76 55	98.77 117	99.13 40	98.07 30	99.10 63	94.27 71	96.70 103	99.84 41	98.70 70	97.90 117	98.11 87	99.40 168	99.28 152

LGP-MVS_train		96.23 107	96.89 123	95.46 104	97.32 75	98.77 117	98.81 58	93.60 80	98.58 117	85.52 143	99.08 32	86.67 161	97.83 105	97.87 119	97.51 113	99.69 82	99.73 75

TDRefinement		93.04 169	93.57 186	92.41 149	96.58 87	98.77 117	97.78 98	91.96 102	98.12 141	80.84 170	89.13 179	79.87 205	87.78 205	96.44 164	94.50 191	99.54 150	98.15 187

MS-PatchMatch		95.99 113	97.26 114	94.51 114	97.46 72	98.76 120	97.27 113	86.97 169	99.09 64	89.83 119	93.51 149	97.78 76	96.18 144	97.53 137	95.71 166	99.35 171	98.41 182

ACMP	96.25 10	96.62 101	96.72 126	96.50 83	96.96 84	98.75 121	97.80 96	94.30 66	98.85 91	93.12 89	98.78 47	86.61 162	97.23 117	97.73 127	96.61 139	99.62 117	99.71 89
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test-LLR		95.50 123	97.32 110	93.37 139	95.49 124	98.74 122	96.44 140	90.82 123	98.18 138	82.75 160	96.60 108	94.67 113	95.54 161	98.09 101	96.00 157	99.20 179	98.93 170

TESTMET0.1,1		94.95 134	97.32 110	92.20 153	92.62 162	98.74 122	96.44 140	86.67 173	98.18 138	82.75 160	96.60 108	94.67 113	95.54 161	98.09 101	96.00 157	99.20 179	98.93 170

PMMVS		97.52 70	98.39 67	96.51 82	95.82 108	98.73 124	97.80 96	93.05 93	98.76 108	94.39 69	99.07 33	97.03 86	98.55 79	98.31 90	97.61 109	99.43 163	99.21 158

dps		94.63 142	95.31 157	93.84 125	95.53 121	98.71 125	96.54 135	80.12 197	97.81 158	97.21 28	96.98 95	92.37 131	96.34 141	92.46 205	91.77 205	97.26 206	97.08 200

MIMVSNet		94.49 147	97.59 97	90.87 181	91.74 182	98.70 126	94.68 175	78.73 207	97.98 146	83.71 153	97.71 83	94.81 111	96.96 122	97.97 113	97.92 95	99.40 168	98.04 189

FC-MVSNet-test		96.07 112	97.94 88	93.89 124	93.60 156	98.67 127	96.62 134	90.30 134	98.76 108	88.62 121	95.57 131	97.63 78	94.48 179	97.97 113	97.48 117	99.71 70	99.52 131

PatchT		93.96 155	97.36 107	90.00 188	94.76 141	98.65 128	90.11 203	78.57 208	97.96 149	80.42 173	96.07 118	94.10 122	96.85 125	98.10 99	97.49 115	99.26 177	99.15 160

LTVRE_ROB	93.20 16	92.84 171	94.92 158	90.43 185	92.83 160	98.63 129	97.08 125	87.87 163	97.91 151	68.42 211	93.54 148	79.46 207	96.62 133	97.55 136	97.40 122	99.74 45	99.92 2
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
tpmrst		93.86 158	95.88 148	91.50 168	95.69 113	98.62 130	95.64 152	79.41 201	98.80 101	83.76 152	95.63 129	96.13 96	97.25 115	92.92 202	92.31 201	97.27 205	96.74 203

CLD-MVS		96.74 93	96.51 134	97.01 68	96.71 86	98.62 130	98.73 60	94.38 64	98.94 83	94.46 65	97.33 86	87.03 155	98.07 92	97.20 149	96.87 132	99.72 59	99.54 127
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
UniMVSNet_ETH3D		93.15 166	92.33 199	94.11 120	93.91 148	98.61 132	94.81 170	90.98 120	97.06 175	87.51 132	82.27 207	76.33 213	97.87 103	94.79 196	97.47 118	99.56 144	99.81 31

CR-MVSNet		94.57 146	97.34 108	91.33 172	94.90 137	98.59 133	97.15 120	79.14 203	97.98 146	80.42 173	96.59 110	93.50 127	96.85 125	98.10 99	97.49 115	99.50 155	99.15 160

Patchmtry						98.59 133	97.15 120	79.14 203		80.42 173

RPMNet		94.66 140	97.16 116	91.75 165	94.98 136	98.59 133	97.00 127	78.37 209	97.98 146	83.78 150	96.27 115	94.09 123	96.91 123	97.36 142	96.73 134	99.48 156	99.09 165

ET-MVSNet_ETH3D		96.17 109	96.99 121	95.21 106	88.53 207	98.54 136	98.28 79	92.61 94	98.85 91	93.60 83	99.06 34	90.39 140	98.63 75	95.98 181	96.68 136	99.61 119	99.41 144

OPM-MVS		96.22 108	95.85 150	96.65 76	97.75 68	98.54 136	99.00 50	95.53 47	96.88 179	89.88 118	95.95 121	86.46 164	98.07 92	97.65 132	96.63 138	99.67 99	98.83 176

tpm cat1		94.06 151	94.90 159	93.06 144	95.42 129	98.52 138	96.64 133	80.67 194	97.82 156	92.63 97	93.39 151	95.00 108	96.06 148	91.36 208	91.58 207	96.98 208	96.66 205

MVSTER		97.16 80	97.71 93	96.52 81	95.97 104	98.48 139	98.63 63	92.10 97	98.68 113	95.96 41	99.23 20	91.79 135	96.87 124	98.76 60	97.37 124	99.57 141	99.68 100

thisisatest0515		94.61 143	96.89 123	91.95 160	92.00 174	98.47 140	92.01 195	90.73 127	98.18 138	83.96 147	94.51 138	95.13 107	93.38 193	97.38 141	94.74 189	99.61 119	99.79 40

TSAR-MVS + COLMAP		96.79 90	96.55 130	97.06 62	97.70 70	98.46 141	99.07 45	96.23 44	99.38 24	91.32 111	98.80 45	85.61 171	98.69 72	97.64 133	96.92 131	99.37 170	99.06 167

ACMM	96.26 9	96.67 98	96.69 127	96.66 75	97.29 78	98.46 141	96.48 138	95.09 51	99.21 47	93.19 88	98.78 47	86.73 160	98.17 86	97.84 121	96.32 147	99.74 45	99.49 138
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
HQP-MVS		96.37 104	96.58 128	96.13 90	97.31 77	98.44 143	98.45 69	95.22 50	98.86 89	88.58 122	98.33 65	87.00 156	97.67 107	97.23 147	96.56 141	99.56 144	99.62 115

USDC		94.26 149	94.83 161	93.59 132	96.02 100	98.44 143	97.84 94	88.65 154	98.86 89	82.73 162	94.02 143	80.56 198	96.76 127	97.28 146	96.15 154	99.55 146	98.50 180

EG-PatchMatch MVS		92.45 180	93.92 181	90.72 182	92.56 164	98.43 145	94.88 167	84.54 187	97.18 171	79.55 179	86.12 200	83.23 186	93.15 196	97.22 148	96.00 157	99.67 99	99.27 154

FC-MVSNet-train		97.04 83	97.91 89	96.03 93	96.00 102	98.41 146	96.53 137	93.42 82	99.04 74	93.02 92	98.03 73	94.32 118	97.47 112	97.93 115	97.77 105	99.75 40	99.88 11

SixPastTwentyTwo		93.44 163	95.32 156	91.24 174	92.11 172	98.40 147	92.77 191	88.64 155	98.09 142	77.83 186	93.51 149	85.74 170	96.52 137	96.91 156	94.89 186	99.59 133	99.73 75

CVMVSNet		95.33 129	97.09 117	93.27 142	95.23 131	98.39 148	95.49 155	92.58 95	97.71 160	83.00 159	94.44 141	93.28 128	93.92 189	97.79 122	98.54 60	99.41 166	99.45 141

MVS-HIRNet		92.51 179	95.97 145	88.48 196	93.73 154	98.37 149	90.33 201	75.36 215	98.32 132	77.78 187	89.15 178	94.87 109	95.14 173	97.62 134	96.39 145	98.51 188	97.11 199

DCV-MVSNet		97.56 69	98.36 68	96.62 79	96.44 89	98.36 150	98.37 73	91.73 105	99.11 62	94.80 57	98.36 64	96.28 93	98.60 77	98.12 98	98.44 63	99.76 36	99.87 13

CostFormer		94.25 150	94.88 160	93.51 136	95.43 127	98.34 151	96.21 144	80.64 195	97.94 150	94.01 72	98.30 66	86.20 168	97.52 109	92.71 203	92.69 199	97.23 207	98.02 190

TinyColmap		94.00 153	94.35 170	93.60 131	95.89 105	98.26 152	97.49 106	88.82 151	98.56 119	83.21 156	91.28 164	80.48 200	96.68 130	97.34 143	96.26 150	99.53 152	98.24 186

anonymousdsp		93.12 167	95.86 149	89.93 190	91.09 199	98.25 153	95.12 160	85.08 182	97.44 164	73.30 201	90.89 166	90.78 139	95.25 171	97.91 116	95.96 161	99.71 70	99.82 26

tfpnnormal		93.85 159	94.12 174	93.54 135	93.22 159	98.24 154	95.45 156	91.96 102	94.61 205	83.91 148	90.74 167	81.75 195	97.04 119	97.49 138	96.16 153	99.68 91	99.84 21

EPNet_dtu		96.30 106	98.53 63	93.70 130	98.97 50	98.24 154	97.36 109	94.23 67	98.85 91	79.18 181	99.19 21	98.47 68	94.09 185	97.89 118	98.21 81	98.39 191	98.85 175
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
GG-mvs-BLEND		69.11 210	98.13 79	35.26 215	3.49 224	98.20 156	94.89 166	2.38 221	98.42 128	5.82 225	96.37 114	98.60 64	5.97 220	98.75 62	97.98 93	99.01 184	98.61 177

test_part1		95.56 121	95.38 154	95.78 97	96.07 99	98.16 157	97.57 103	90.78 125	97.43 165	93.04 91	89.12 180	89.41 147	97.93 97	96.38 167	97.38 123	99.29 175	99.78 46

pm-mvs1		94.27 148	95.57 152	92.75 147	92.58 163	98.13 158	94.87 168	90.71 128	96.70 185	83.78 150	89.94 173	89.85 145	94.96 176	97.58 135	97.07 127	99.61 119	99.72 86

UniMVSNet (Re)		94.58 145	95.34 155	93.71 129	92.25 171	98.08 159	94.97 163	91.29 119	97.03 177	87.94 126	93.97 145	86.25 167	96.07 147	96.27 173	95.97 160	99.72 59	99.79 40

GBi-Net		96.98 85	98.00 86	95.78 97	93.81 151	97.98 160	98.09 87	91.32 115	98.80 101	93.92 74	97.21 90	95.94 100	97.89 99	98.07 104	98.34 73	99.68 91	99.67 101

test1		96.98 85	98.00 86	95.78 97	93.81 151	97.98 160	98.09 87	91.32 115	98.80 101	93.92 74	97.21 90	95.94 100	97.89 99	98.07 104	98.34 73	99.68 91	99.67 101

FMVSNet3		97.02 84	98.12 80	95.73 101	93.59 157	97.98 160	98.34 77	91.32 115	98.80 101	93.92 74	97.21 90	95.94 100	97.63 108	98.61 71	98.62 54	99.61 119	99.65 108

FMVSNet2		96.64 99	97.50 98	95.63 103	93.81 151	97.98 160	98.09 87	90.87 121	98.99 79	93.48 84	93.17 154	95.25 105	97.89 99	98.63 69	98.80 48	99.68 91	99.67 101

MDTV_nov1_ep13_2view		92.44 181	95.66 151	88.68 194	91.05 200	97.92 164	92.17 194	79.64 199	98.83 96	76.20 191	91.45 162	93.51 126	95.04 174	95.68 185	93.70 196	97.96 196	98.53 179

WR-MVS_H		93.54 161	94.67 165	92.22 151	91.95 175	97.91 165	94.58 179	88.75 152	96.64 186	83.88 149	90.66 169	85.13 175	94.40 180	96.54 163	95.91 162	99.73 52	99.89 7

UniMVSNet_NR-MVSNet		94.59 144	95.47 153	93.55 134	91.85 179	97.89 166	95.03 161	92.00 99	97.33 168	86.12 137	93.19 153	87.29 152	96.60 134	96.12 176	96.70 135	99.72 59	99.80 33

FMVSNet1		95.77 117	96.41 142	95.03 107	93.42 158	97.86 167	97.11 123	89.89 138	98.53 121	92.00 106	89.17 177	93.23 129	98.15 89	98.07 104	98.34 73	99.61 119	99.69 95

DU-MVS		93.98 154	94.44 169	93.44 137	91.66 184	97.77 168	95.03 161	91.57 109	97.17 172	86.12 137	93.13 155	81.13 197	96.60 134	95.10 192	97.01 130	99.67 99	99.80 33

NR-MVSNet		94.01 152	94.51 167	93.44 137	92.56 164	97.77 168	95.67 150	91.57 109	97.17 172	85.84 140	93.13 155	80.53 199	95.29 169	97.01 154	96.17 152	99.69 82	99.75 67

pmmvs5		92.71 178	94.27 171	90.90 180	91.42 193	97.74 170	93.23 188	86.66 174	95.99 198	78.96 183	91.45 162	83.44 184	95.55 160	97.30 145	95.05 180	99.58 137	98.93 170

IterMVS-SCA-FT		94.89 136	97.87 90	91.42 169	94.86 139	97.70 171	97.24 115	84.88 185	98.93 84	75.74 193	94.26 142	98.25 71	96.69 129	98.52 81	97.68 107	99.10 183	99.73 75

pmmvs4		95.09 131	95.90 147	94.14 119	92.29 169	97.70 171	95.45 156	90.31 132	98.60 115	90.70 113	93.25 152	89.90 144	96.67 131	97.13 151	95.42 170	99.44 162	99.28 152

v7n		91.61 192	92.95 193	90.04 187	90.56 202	97.69 173	93.74 187	85.59 180	95.89 200	76.95 188	86.60 198	78.60 210	93.76 191	97.01 154	94.99 181	99.65 108	99.87 13

WR-MVS		93.43 164	94.48 168	92.21 152	91.52 191	97.69 173	94.66 177	89.98 136	96.86 180	83.43 154	90.12 171	85.03 176	93.94 188	96.02 180	95.82 163	99.71 70	99.82 26

v148		92.36 187	92.88 194	91.75 165	91.63 187	97.66 175	92.64 192	90.55 130	96.09 194	83.34 155	88.19 186	80.00 202	92.74 197	93.98 200	94.58 190	99.58 137	99.69 95

V42		93.05 168	93.90 182	92.04 156	91.91 176	97.66 175	94.91 165	89.91 137	96.85 181	80.58 172	89.66 174	83.43 185	95.37 167	95.03 194	94.90 184	99.59 133	99.78 46

pmnet_mix02		92.44 181	94.68 164	89.83 191	92.46 166	97.65 177	89.92 205	90.49 131	98.76 108	73.05 204	91.78 160	90.08 143	94.86 177	94.53 197	91.94 204	98.21 194	98.01 191

pmmvs6		91.90 191	92.53 198	91.17 175	91.81 180	97.63 178	93.23 188	88.37 158	93.43 210	80.61 171	77.32 211	87.47 151	94.12 184	96.58 161	95.72 165	98.88 187	99.53 128

v2v482		92.77 175	93.52 189	91.90 163	91.59 189	97.63 178	94.57 180	90.31 132	96.80 183	79.22 180	88.74 183	81.55 196	96.04 149	95.26 188	94.97 182	99.66 104	99.69 95

IterMVS		94.81 138	97.71 93	91.42 169	94.83 140	97.63 178	97.38 108	85.08 182	98.93 84	75.67 194	94.02 143	97.64 77	96.66 132	98.45 84	97.60 110	98.90 186	99.72 86
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TransMVSNet (Re)		93.45 162	94.08 175	92.72 148	92.83 160	97.62 181	94.94 164	91.54 111	95.65 202	83.06 158	88.93 181	83.53 183	94.25 182	97.41 140	97.03 128	99.67 99	98.40 185

v1144		92.81 172	94.03 177	91.40 171	91.68 183	97.60 182	94.73 172	88.40 157	96.71 184	78.48 184	88.14 188	84.46 180	95.45 166	96.31 172	95.22 175	99.65 108	99.76 60

our_test_3					92.30 168	97.58 183	90.09 204

TranMVSNet+NR-MVSNet		93.67 160	94.14 172	93.13 143	91.28 198	97.58 183	95.60 153	91.97 101	97.06 175	84.05 146	90.64 170	82.22 192	96.17 145	94.94 195	96.78 133	99.69 82	99.78 46

CP-MVSNet		93.25 165	94.00 178	92.38 150	91.65 186	97.56 185	94.38 182	89.20 147	96.05 196	83.16 157	89.51 175	81.97 193	96.16 146	96.43 165	96.56 141	99.71 70	99.89 7

IterMVS-LS		96.12 111	97.48 100	94.53 113	95.19 132	97.56 185	97.15 120	89.19 148	99.08 66	88.23 123	94.97 133	94.73 112	97.84 104	97.86 120	98.26 79	99.60 127	99.88 11
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PS-CasMVS		92.72 176	93.36 190	91.98 159	91.62 188	97.52 187	94.13 186	88.98 149	95.94 199	81.51 168	87.35 193	79.95 204	95.91 151	96.37 168	96.49 143	99.70 79	99.89 7

v8		92.87 170	93.87 183	91.72 167	92.05 173	97.50 188	94.79 171	88.20 160	96.85 181	80.11 176	90.01 172	82.86 189	95.48 163	95.15 191	94.90 184	99.66 104	99.80 33

tpm		92.38 185	94.79 162	89.56 192	94.30 144	97.50 188	94.24 185	78.97 206	97.72 159	74.93 198	97.97 75	82.91 187	96.60 134	93.65 201	94.81 187	98.33 192	98.98 168

v144192		92.38 185	93.55 188	91.00 178	91.44 192	97.47 190	94.27 183	87.41 167	96.52 189	78.03 185	87.50 192	82.65 191	95.32 168	95.82 184	95.15 177	99.55 146	99.78 46

v1192		92.43 183	93.61 185	91.05 177	91.53 190	97.43 191	94.61 178	87.99 162	96.60 187	76.72 189	87.11 195	82.74 190	95.85 152	96.35 170	95.30 173	99.60 127	99.74 71

CMPMVS	70.31 18	90.74 194	91.06 202	90.36 186	97.32 75	97.43 191	92.97 190	87.82 165	93.50 209	75.34 197	83.27 205	84.90 177	92.19 200	92.64 204	91.21 208	96.50 211	94.46 209
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
v1921920		92.36 187	93.57 186	90.94 179	91.39 194	97.39 193	94.70 174	87.63 166	96.60 187	76.63 190	86.98 196	82.89 188	95.75 153	96.26 174	95.14 178	99.55 146	99.73 75

N_pmnet		92.21 189	94.60 166	89.42 193	91.88 177	97.38 194	89.15 207	89.74 142	97.89 152	73.75 200	87.94 190	92.23 133	93.85 190	96.10 177	93.20 198	98.15 195	97.43 196

PEN-MVS		92.72 176	93.20 192	92.15 154	91.29 196	97.31 195	94.67 176	89.81 139	96.19 192	81.83 166	88.58 184	79.06 208	95.61 159	95.21 189	96.27 148	99.72 59	99.82 26

v1240		91.99 190	93.33 191	90.44 184	91.29 196	97.30 196	94.25 184	86.79 170	96.43 190	75.49 196	86.34 199	81.85 194	95.29 169	96.42 166	95.22 175	99.52 153	99.73 75

v10		92.79 174	94.06 176	91.31 173	91.78 181	97.29 197	94.87 168	86.10 178	96.97 178	79.82 178	88.16 187	84.56 179	95.63 157	96.33 171	95.31 172	99.65 108	99.80 33

Baseline_NR-MVSNet		93.87 157	93.98 179	93.75 127	91.66 184	97.02 198	95.53 154	91.52 112	97.16 174	87.77 129	87.93 191	83.69 181	96.35 140	95.10 192	97.23 125	99.68 91	99.73 75

DTE-MVSNet		92.42 184	92.85 195	91.91 162	90.87 201	96.97 199	94.53 181	89.81 139	95.86 201	81.59 167	88.83 182	77.88 211	95.01 175	94.34 199	96.35 146	99.64 112	99.73 75

DeepMVS_CX						96.85 200	87.43 210	89.27 146	98.30 133	75.55 195	95.05 132	79.47 206	92.62 199	89.48 209		95.18 214	95.96 207

Anonymous20231206		90.70 195	93.93 180	86.92 200	90.21 205	96.79 201	90.30 202	86.61 175	96.05 196	69.25 209	88.46 185	84.86 178	85.86 207	97.11 152	96.47 144	99.30 174	97.80 193

MDA-MVSNet-bldmvs		87.84 203	89.22 206	86.23 201	81.74 213	96.77 202	83.74 213	89.57 144	94.50 207	72.83 206	96.64 106	64.47 218	92.71 198	81.43 213	92.28 202	96.81 209	98.47 181

EU-MVSNet		92.80 173	94.76 163	90.51 183	91.88 177	96.74 203	92.48 193	88.69 153	96.21 191	79.00 182	91.51 161	87.82 150	91.83 201	95.87 183	96.27 148	99.21 178	98.92 173

test20.03		90.65 196	93.71 184	87.09 198	90.44 203	96.24 204	89.74 206	85.46 181	95.59 203	72.99 205	90.68 168	85.33 173	84.41 208	95.94 182	95.10 179	99.52 153	97.06 201

new_pmnet		90.45 197	92.84 196	87.66 197	88.96 206	96.16 205	88.71 208	84.66 186	97.56 162	71.91 208	85.60 201	86.58 163	93.28 194	96.07 178	93.54 197	98.46 189	94.39 210

FMVSNet5		95.42 124	96.47 137	94.20 118	92.26 170	95.99 206	95.66 151	87.15 168	97.87 153	93.46 85	96.68 104	93.79 124	97.52 109	97.10 153	97.21 126	99.11 182	96.62 206

PM-MVS		89.55 199	90.30 204	88.67 195	87.06 208	95.60 207	90.88 198	84.51 188	96.14 193	75.75 192	86.89 197	63.47 219	94.64 178	96.85 157	93.89 194	99.17 181	99.29 151

pmmvs-eth3d		89.81 198	89.65 205	90.00 188	86.94 209	95.38 208	91.08 196	86.39 176	94.57 206	82.27 164	83.03 206	64.94 216	93.96 187	96.57 162	93.82 195	99.35 171	99.24 156

gm-plane-assit		89.44 200	92.82 197	85.49 203	91.37 195	95.34 209	79.55 217	82.12 192	91.68 213	64.79 215	87.98 189	80.26 201	95.66 156	98.51 83	97.56 111	99.45 160	98.41 182

MIMVSNet1		88.61 201	90.68 203	86.19 202	81.56 214	95.30 210	87.78 209	85.98 179	94.19 208	72.30 207	78.84 210	78.90 209	90.06 202	96.59 160	95.47 168	99.46 159	95.49 208

new-patchmatchnet		86.12 205	87.30 207	84.74 204	86.92 210	95.19 211	83.57 214	84.42 189	92.67 211	65.66 212	80.32 208	64.72 217	89.41 203	92.33 207	89.21 209	98.43 190	96.69 204

pmmvs3		88.19 202	91.27 201	84.60 205	85.60 211	93.66 212	85.68 212	81.13 193	92.36 212	63.66 217	89.51 175	77.10 212	93.22 195	96.37 168	92.40 200	98.30 193	97.46 195

ambc			80.99 210		80.04 216	90.84 213	90.91 197		96.09 194	74.18 199	62.81 214	30.59 225	82.44 210	96.25 175	91.77 205	95.91 213	98.56 178

FPMVS		83.82 206	84.61 208	82.90 206	90.39 204	90.71 214	90.85 199	84.10 190	95.47 204	65.15 213	83.44 204	74.46 214	75.48 211	81.63 212	79.42 214	91.42 216	87.14 214

tmp_tt				82.25 207	97.73 69	88.71 215	80.18 215	68.65 218	99.15 53	86.98 134	99.47 9	85.31 174	68.35 216	87.51 210	83.81 212	91.64 215

PMMVS2		77.26 208	79.47 211	74.70 211	76.00 217	88.37 216	74.22 218	76.34 212	78.31 216	54.13 219	69.96 213	52.50 221	70.14 215	84.83 211	88.71 210	97.35 203	93.58 212

test_method		87.27 204	91.58 200	82.25 207	75.65 218	87.52 217	86.81 211	72.60 216	97.51 163	73.20 203	85.07 202	79.97 203	88.69 204	97.31 144	95.24 174	96.53 210	98.41 182

Gipuma		81.40 207	81.78 209	80.96 209	83.21 212	85.61 218	79.73 216	76.25 214	97.33 168	64.21 216	55.32 215	55.55 220	86.04 206	92.43 206	92.20 203	96.32 212	93.99 211
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MVE	67.97 19	65.53 213	67.43 215	63.31 214	59.33 221	74.20 219	53.09 223	70.43 217	66.27 219	43.13 220	45.98 219	30.62 224	70.65 214	79.34 215	86.30 211	83.25 220	89.33 213
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		68.12 212	68.11 214	68.14 213	75.51 219	71.76 220	55.38 222	77.20 211	77.78 217	37.79 222	53.59 216	43.61 222	74.72 212	67.05 217	76.70 216	88.27 219	86.24 215

E-PMN		68.30 211	68.43 213	68.15 212	74.70 220	71.56 221	55.64 221	77.24 210	77.48 218	39.46 221	51.95 218	41.68 223	73.28 213	70.65 216	79.51 213	88.61 218	86.20 216

PMVS	72.60 17	76.39 209	77.66 212	74.92 210	81.04 215	69.37 222	68.47 219	80.54 196	85.39 215	65.07 214	73.52 212	72.91 215	65.67 217	80.35 214	76.81 215	88.71 217	85.25 217
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
testmvs		31.24 214	40.15 216	20.86 216	12.61 222	17.99 223	25.16 224	13.30 219	48.42 220	24.82 223	53.07 217	30.13 226	28.47 218	42.73 218	37.65 217	20.79 221	51.04 218

test123		26.75 215	34.25 217	18.01 217	7.93 223	17.18 224	24.85 225	12.36 220	44.83 221	16.52 224	41.80 220	18.10 227	28.29 219	33.08 219	34.79 218	18.10 222	49.95 219

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										69.05 210

9.14												99.79 45

SR-MVS					99.67 13			98.25 14				99.94 25

MTAPA										98.09 15		99.97 7

MTMP										98.46 11		99.96 12

Patchmatch-RL test							66.86 220

mPP-MVS					99.53 30							99.89 34

NP-MVS									98.57 118

High-resolution multi-view benchmark