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.

DVP-MVS++		98.92 1	99.18 1	98.61 4	99.47 5	99.61 2	99.39 3	97.82 1	98.80 1	96.86 8	98.90 2	99.92 1	98.67 17	99.02 2	98.20 19	99.43 45	99.82 1

DVP-MVS		98.86 4	98.97 3	98.75 2	99.43 12	99.63 1	99.25 12	97.81 2	98.62 2	97.69 1	97.59 20	99.90 2	98.93 5	98.99 4	98.42 11	99.37 57	99.62 4
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
SD-MVS		98.52 8	98.77 9	98.23 15	98.15 49	99.26 26	98.79 26	97.59 15	98.52 3	96.25 15	97.99 15	99.75 6	99.01 3	98.27 32	97.97 31	99.59 7	99.63 2
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
TSAR-MVS + ACMM		97.71 28	98.60 12	96.66 39	98.64 41	99.05 37	98.85 25	97.23 27	98.45 4	89.40 88	97.51 24	99.27 14	96.88 59	98.53 15	97.81 41	98.96 121	99.59 8

TSAR-MVS + MP.		98.49 9	98.78 8	98.15 19	98.14 50	99.17 33	99.34 6	97.18 29	98.44 5	95.72 19	97.84 16	99.28 12	98.87 7	99.05 1	98.05 26	99.66 2	99.60 7
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
ACMMPR		98.40 12	98.49 13	98.28 13	99.41 13	99.40 13	99.36 4	97.35 21	98.30 6	95.02 25	97.79 17	98.39 37	99.04 2	98.26 33	98.10 23	99.50 27	99.22 39

HFP-MVS		98.48 10	98.62 11	98.32 11	99.39 17	99.33 21	99.27 10	97.42 18	98.27 7	95.25 23	98.34 10	98.83 26	99.08 1	98.26 33	98.08 25	99.48 28	99.26 33

SED-MVS		98.90 2	99.07 2	98.69 3	99.38 18	99.61 2	99.33 8	97.80 4	98.25 8	97.60 2	98.87 4	99.89 3	98.67 17	99.02 2	98.26 17	99.36 59	99.61 6

DeepPCF-MVS	95.28 2	97.00 39	98.35 21	95.42 60	97.30 63	98.94 51	94.82 119	96.03 38	98.24 9	92.11 51	95.80 41	98.64 33	95.51 86	98.95 7	98.66 5	96.78 191	99.20 42

APDe-MVS		98.87 3	98.96 4	98.77 1	99.58 2	99.53 7	99.44 1	97.81 2	98.22 10	97.33 4	98.70 5	99.33 10	98.86 8	98.96 6	98.40 13	99.63 5	99.57 9

DeepC-MVS_fast	96.13 1	98.13 20	98.27 25	97.97 24	99.16 26	99.03 43	99.05 18	97.24 26	98.22 10	94.17 31	95.82 40	98.07 39	98.69 16	98.83 11	98.80 2	99.52 20	99.10 52
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MSP-MVS		98.73 6	98.93 5	98.50 6	99.44 11	99.57 4	99.36 4	97.65 9	98.14 12	96.51 14	98.49 7	99.65 8	98.67 17	98.60 14	98.42 11	99.40 51	99.63 2
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
OMC-MVS		97.00 39	96.92 48	97.09 34	98.69 39	98.66 72	97.85 47	95.02 42	98.09 13	94.47 27	93.15 60	96.90 46	97.38 46	97.16 70	96.82 72	99.13 99	97.65 142

NCCC		98.10 21	98.05 30	98.17 18	99.38 18	99.05 37	99.00 20	97.53 17	98.04 14	95.12 24	94.80 52	99.18 18	98.58 22	98.49 17	97.78 42	99.39 53	98.98 72

3Dnovator+	93.91 7	97.23 35	97.22 40	97.24 32	98.89 36	98.85 61	98.26 38	93.25 56	97.99 15	95.56 22	90.01 98	98.03 41	98.05 34	97.91 47	98.43 10	99.44 42	99.35 22

3Dnovator	93.79 8	97.08 37	97.20 41	96.95 37	99.09 28	99.03 43	98.20 39	93.33 52	97.99 15	93.82 32	90.61 92	96.80 49	97.82 37	97.90 48	98.78 3	99.47 31	99.26 33

TSAR-MVS + COLMAP		94.79 71	94.51 84	95.11 65	96.50 71	97.54 104	97.99 45	94.54 44	97.81 17	85.88 112	96.73 31	81.28 149	96.99 56	96.29 102	95.21 115	98.76 144	96.73 169

SMA-MVS		98.66 7	98.89 7	98.39 9	99.60 1	99.41 12	99.00 20	97.63 12	97.78 18	95.83 18	98.33 11	99.83 4	98.85 9	98.93 8	98.56 6	99.41 48	99.40 18
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
SF-MVS		98.39 13	98.45 17	98.33 10	99.45 9	99.05 37	98.27 37	97.65 9	97.73 19	97.02 7	98.18 12	99.25 15	98.11 32	98.15 38	97.62 46	99.45 36	99.19 43

CNVR-MVS		98.47 11	98.46 16	98.48 7	99.40 14	99.05 37	99.02 19	97.54 16	97.73 19	96.65 11	97.20 29	99.13 20	98.85 9	98.91 9	98.10 23	99.41 48	99.08 55

CNLPA		96.90 42	96.28 57	97.64 28	98.56 42	98.63 77	96.85 67	96.60 36	97.73 19	97.08 6	89.78 100	96.28 56	97.80 39	96.73 83	96.63 74	98.94 123	98.14 125

MVS_111021_HR		97.04 38	98.20 26	95.69 54	98.44 45	99.29 23	96.59 78	93.20 57	97.70 22	89.94 80	98.46 8	96.89 47	96.71 63	98.11 42	97.95 33	99.27 72	99.01 68

CSCG		97.44 32	97.18 43	97.75 27	99.47 5	99.52 8	98.55 31	95.41 40	97.69 23	95.72 19	94.29 55	95.53 63	98.10 33	96.20 107	97.38 55	99.24 76	99.62 4

HPM-MVS++		98.34 16	98.47 15	98.18 16	99.46 8	99.15 34	99.10 16	97.69 8	97.67 24	94.93 26	97.62 19	99.70 7	98.60 20	98.45 20	97.46 51	99.31 66	99.26 33

PLC	94.95 3	97.37 33	96.77 51	98.07 20	98.97 31	98.21 90	97.94 46	96.85 35	97.66 25	97.58 3	93.33 59	96.84 48	98.01 36	97.13 71	96.20 85	99.09 104	98.01 129
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MVS_111021_LR		97.16 36	98.01 31	96.16 46	98.47 43	98.98 48	96.94 64	93.89 48	97.64 26	91.44 55	98.89 3	96.41 52	97.20 49	98.02 45	97.29 60	99.04 115	98.85 87

TSAR-MVS + GP.		97.45 31	98.36 19	96.39 41	95.56 87	98.93 53	97.74 49	93.31 53	97.61 27	94.24 30	98.44 9	99.19 17	98.03 35	97.60 56	97.41 53	99.44 42	99.33 24

MCST-MVS		98.20 18	98.36 19	98.01 22	99.40 14	99.05 37	99.00 20	97.62 13	97.59 28	93.70 33	97.42 27	99.30 11	98.77 13	98.39 26	97.48 50	99.59 7	99.31 27

CS-MVS-test		97.00 39	97.85 33	96.00 50	97.77 55	99.56 5	96.35 86	91.95 76	97.54 29	92.20 49	96.14 36	96.00 61	98.19 28	98.46 19	97.78 42	99.57 14	99.45 16

MSLP-MVS++		98.04 23	97.93 32	98.18 16	99.10 27	99.09 36	98.34 36	96.99 32	97.54 29	96.60 12	94.82 51	98.45 35	98.89 6	97.46 61	98.77 4	99.17 92	99.37 20

DPE-MVS		98.75 5	98.91 6	98.57 5	99.21 23	99.54 6	99.42 2	97.78 6	97.49 31	96.84 9	98.94 1	99.82 5	98.59 21	98.90 10	98.22 18	99.56 17	99.48 14
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
CP-MVS		98.32 17	98.34 22	98.29 12	99.34 20	99.30 22	99.15 14	97.35 21	97.49 31	95.58 21	97.72 18	98.62 34	98.82 11	98.29 28	97.67 45	99.51 25	99.28 28

ACMMP_NAP		98.20 18	98.49 13	97.85 25	99.50 4	99.40 13	99.26 11	97.64 11	97.47 33	92.62 46	97.59 20	99.09 22	98.71 15	98.82 12	97.86 38	99.40 51	99.19 43

X-MVS		97.84 24	98.19 27	97.42 30	99.40 14	99.35 17	99.06 17	97.25 25	97.38 34	90.85 62	96.06 37	98.72 30	98.53 24	98.41 24	98.15 22	99.46 32	99.28 28

SteuartSystems-ACMMP		98.38 14	98.71 10	97.99 23	99.34 20	99.46 10	99.34 6	97.33 24	97.31 35	94.25 29	98.06 13	99.17 19	98.13 31	98.98 5	98.46 9	99.55 18	99.54 11
Skip Steuart: Steuart Systems R&D Blog.
DeepC-MVS	94.87 4	96.76 48	96.50 54	97.05 35	98.21 48	99.28 24	98.67 27	97.38 20	97.31 35	90.36 74	89.19 102	93.58 72	98.19 28	98.31 27	98.50 7	99.51 25	99.36 21
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
QAPM		96.78 47	97.14 44	96.36 42	99.05 29	99.14 35	98.02 43	93.26 54	97.27 37	90.84 65	91.16 84	97.31 44	97.64 42	97.70 54	98.20 19	99.33 61	99.18 46

DPM-MVS		96.86 44	96.82 50	96.91 38	98.08 51	98.20 91	98.52 33	97.20 28	97.24 38	91.42 56	91.84 76	98.45 35	97.25 48	97.07 72	97.40 54	98.95 122	97.55 145

CS-MVS		96.87 43	97.41 39	96.24 45	97.42 60	99.48 9	97.30 56	91.83 81	97.17 39	93.02 40	94.80 52	94.45 67	98.16 30	98.61 13	97.85 39	99.69 1	99.50 12

TAPA-MVS	94.18 5	96.38 50	96.49 55	96.25 43	98.26 47	98.66 72	98.00 44	94.96 43	97.17 39	89.48 85	92.91 64	96.35 53	97.53 43	96.59 88	95.90 95	99.28 70	97.82 133
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CPTT-MVS		97.78 26	97.54 35	98.05 21	98.91 35	99.05 37	99.00 20	96.96 33	97.14 41	95.92 17	95.50 44	98.78 28	98.99 4	97.20 67	96.07 87	98.54 159	99.04 64

CANet		96.84 45	97.20 41	96.42 40	97.92 53	99.24 30	98.60 29	93.51 51	97.11 42	93.07 36	91.16 84	97.24 45	96.21 72	98.24 35	98.05 26	99.22 82	99.35 22

MP-MVS		98.09 22	98.30 24	97.84 26	99.34 20	99.19 32	99.23 13	97.40 19	97.09 43	93.03 39	97.58 22	98.85 25	98.57 23	98.44 22	97.69 44	99.48 28	99.23 37
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
LS3D		95.46 59	95.14 75	95.84 52	97.91 54	98.90 58	98.58 30	97.79 5	97.07 44	83.65 121	88.71 105	88.64 104	97.82 37	97.49 59	97.42 52	99.26 75	97.72 141

MVS_0304		96.31 51	96.91 49	95.62 55	97.21 65	99.20 31	98.55 31	93.10 59	97.04 45	89.73 82	90.30 94	96.35 53	95.71 79	98.14 39	97.93 36	99.38 54	99.40 18

ACMMP		97.37 33	97.48 37	97.25 31	98.88 37	99.28 24	98.47 34	96.86 34	97.04 45	92.15 50	97.57 23	96.05 60	97.67 40	97.27 65	95.99 92	99.46 32	99.14 51
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
baseline		94.83 68	95.82 63	93.68 94	94.75 111	97.80 100	96.51 81	88.53 124	97.02 47	89.34 90	92.93 63	92.18 79	94.69 99	95.78 119	96.08 86	98.27 170	98.97 76

tmp_tt				66.88 212	86.07 209	73.86 220	68.22 220	33.38 221	96.88 48	80.67 137	88.23 110	78.82 157	49.78 219	82.68 213	77.47 215	83.19 221

diffmvs		94.31 87	94.21 91	94.42 84	94.64 116	98.28 87	96.36 85	91.56 85	96.77 49	88.89 96	88.97 103	84.23 133	96.01 76	96.05 111	96.41 78	99.05 114	98.79 92
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
APD-MVS		98.36 15	98.32 23	98.41 8	99.47 5	99.26 26	99.12 15	97.77 7	96.73 50	96.12 16	97.27 28	98.88 24	98.46 25	98.47 18	98.39 14	99.52 20	99.22 39
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
train_agg		97.65 29	98.06 29	97.18 33	98.94 32	98.91 56	98.98 24	97.07 31	96.71 51	90.66 67	97.43 26	99.08 23	98.20 27	97.96 46	97.14 62	99.22 82	99.19 43

AdaColmap		97.53 30	96.93 47	98.24 14	99.21 23	98.77 65	98.47 34	97.34 23	96.68 52	96.52 13	95.11 49	96.12 58	98.72 14	97.19 69	96.24 83	99.17 92	98.39 113

PHI-MVS		97.78 26	98.44 18	97.02 36	98.73 38	99.25 28	98.11 40	95.54 39	96.66 53	92.79 43	98.52 6	99.38 9	97.50 44	97.84 49	98.39 14	99.45 36	99.03 65

canonicalmvs		95.25 65	95.45 69	95.00 68	95.27 95	98.72 68	96.89 65	89.82 107	96.51 54	90.84 65	93.72 58	86.01 119	97.66 41	95.78 119	97.94 34	99.54 19	99.50 12

CLD-MVS		94.79 71	94.36 88	95.30 62	95.21 97	97.46 107	97.23 57	92.24 72	96.43 55	91.77 54	92.69 66	84.31 132	96.06 73	95.52 125	95.03 120	99.31 66	99.06 60
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
ACMM	92.75 10	94.41 84	93.84 101	95.09 66	96.41 74	96.80 123	94.88 118	93.54 50	96.41 56	90.16 75	92.31 70	83.11 141	96.32 70	96.22 105	94.65 130	99.22 82	97.35 152
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EC-MVSNet		96.49 49	97.63 34	95.16 64	94.75 111	98.69 70	97.39 55	88.97 119	96.34 57	92.02 52	96.04 38	96.46 51	98.21 26	98.41 24	97.96 32	99.61 6	99.55 10

DCV-MVSNet		94.76 74	95.12 77	94.35 85	95.10 101	95.81 157	96.46 83	89.49 113	96.33 58	90.16 75	92.55 68	90.26 90	95.83 78	95.52 125	96.03 90	99.06 110	99.33 24

CANet_DTU		93.92 95	96.57 53	90.83 127	95.63 85	98.39 85	96.99 61	87.38 135	96.26 59	71.97 181	96.31 34	93.02 74	94.53 103	97.38 63	96.83 71	98.49 162	97.79 134

UGNet		94.92 66	96.63 52	92.93 104	96.03 81	98.63 77	94.53 125	91.52 87	96.23 60	90.03 77	92.87 65	96.10 59	86.28 188	96.68 85	96.60 75	99.16 95	99.32 26
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
PGM-MVS		97.81 25	98.11 28	97.46 29	99.55 3	99.34 20	99.32 9	94.51 45	96.21 61	93.07 36	98.05 14	97.95 42	98.82 11	98.22 36	97.89 37	99.48 28	99.09 54

ETV-MVS		96.31 51	97.47 38	94.96 70	94.79 108	98.78 64	96.08 92	91.41 89	96.16 62	90.50 69	95.76 42	96.20 57	97.39 45	98.42 23	97.82 40	99.57 14	99.18 46

PVSNet_BlendedMVS		95.41 61	95.28 71	95.57 56	97.42 60	99.02 45	95.89 100	93.10 59	96.16 62	93.12 34	91.99 72	85.27 124	94.66 100	98.09 43	97.34 56	99.24 76	99.08 55

PVSNet_Blended		95.41 61	95.28 71	95.57 56	97.42 60	99.02 45	95.89 100	93.10 59	96.16 62	93.12 34	91.99 72	85.27 124	94.66 100	98.09 43	97.34 56	99.24 76	99.08 55

MVS_Test		94.82 69	95.66 64	93.84 92	94.79 108	98.35 86	96.49 82	89.10 118	96.12 65	87.09 108	92.58 67	90.61 88	96.48 68	96.51 95	96.89 69	99.11 102	98.54 102

CHOSEN 280x420		95.46 59	97.01 45	93.66 95	97.28 64	97.98 98	96.40 84	85.39 159	96.10 66	91.07 59	96.53 32	96.34 55	95.61 83	97.65 55	96.95 67	96.21 192	97.49 147

ADS-MVSNet		89.80 150	91.33 145	88.00 167	94.43 120	96.71 128	92.29 161	74.95 209	96.07 67	77.39 149	88.67 107	86.09 118	93.26 126	88.44 203	89.57 197	95.68 198	93.81 198

SCA		90.92 134	93.04 114	88.45 155	93.72 133	97.33 111	92.77 149	76.08 204	96.02 68	78.26 146	91.96 74	90.86 85	93.99 113	90.98 194	90.04 195	95.88 196	94.06 194

CDPH-MVS		96.84 45	97.49 36	96.09 47	98.92 34	98.85 61	98.61 28	95.09 41	96.00 69	87.29 106	95.45 46	97.42 43	97.16 50	97.83 50	97.94 34	99.44 42	98.92 78

PatchmatchNet		90.56 138	92.49 124	88.31 158	93.83 131	96.86 122	92.42 157	76.50 201	95.96 70	78.31 145	91.96 74	89.66 94	93.48 123	90.04 199	89.20 198	95.32 202	93.73 199
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
HQP-MVS		94.43 82	94.57 83	94.27 86	96.41 74	97.23 114	96.89 65	93.98 47	95.94 71	83.68 120	95.01 50	84.46 131	95.58 84	95.47 127	94.85 128	99.07 107	99.00 69

EPMVS		90.88 135	92.12 134	89.44 146	94.71 113	97.24 113	93.55 136	76.81 199	95.89 72	81.77 129	91.49 82	86.47 115	93.87 114	90.21 197	90.07 194	95.92 195	93.49 201

DI_MVS_plusplus_trai		94.01 91	93.63 105	94.44 83	94.54 117	98.26 89	97.51 52	90.63 97	95.88 73	89.34 90	80.54 161	89.36 96	95.48 87	96.33 101	96.27 82	99.17 92	98.78 93

EPNet		96.27 53	96.97 46	95.46 59	98.47 43	98.28 87	97.41 53	93.67 49	95.86 74	92.86 42	97.51 24	93.79 71	91.76 141	97.03 74	97.03 64	98.61 155	99.28 28
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MSDG		94.82 69	93.73 103	96.09 47	98.34 46	97.43 109	97.06 59	96.05 37	95.84 75	90.56 68	86.30 129	89.10 101	95.55 85	96.13 110	95.61 103	99.00 116	95.73 178

RPSCF		94.05 90	94.00 97	94.12 88	96.20 76	96.41 137	96.61 77	91.54 86	95.83 76	89.73 82	96.94 30	92.80 76	95.35 90	91.63 190	90.44 192	95.27 204	93.94 195

ACMP	92.88 9	94.43 82	94.38 87	94.50 82	96.01 82	97.69 102	95.85 103	92.09 73	95.74 77	89.12 94	95.14 48	82.62 144	94.77 96	95.73 121	94.67 129	99.14 98	99.06 60
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
casdiffmvs		94.38 85	94.15 96	94.64 81	94.70 115	98.51 81	96.03 95	91.66 84	95.70 78	89.36 89	86.48 124	85.03 129	96.60 66	97.40 62	97.30 58	99.52 20	98.67 95
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
MDTV_nov1_ep13		91.57 127	93.18 112	89.70 142	93.39 136	96.97 117	93.53 137	80.91 189	95.70 78	81.86 128	92.40 69	89.93 92	93.25 127	91.97 187	90.80 190	95.25 205	94.46 188

thisisatest0530		94.54 79	95.47 68	93.46 98	94.51 118	98.65 74	94.66 122	90.72 94	95.69 80	86.90 109	93.80 56	89.44 95	94.74 97	96.98 76	94.86 125	99.19 90	98.85 87

ET-MVSNet_ETH3D		93.34 107	94.33 89	92.18 111	83.26 214	97.66 103	96.72 74	89.89 106	95.62 81	87.17 107	96.00 39	83.69 138	96.99 56	93.78 156	95.34 110	99.06 110	98.18 124

tttt0517		94.52 80	95.44 70	93.44 99	94.51 118	98.68 71	94.61 124	90.72 94	95.61 82	86.84 110	93.78 57	89.26 98	94.74 97	97.02 75	94.86 125	99.20 89	98.87 85

PMMVS		94.61 76	95.56 66	93.50 97	94.30 122	96.74 127	94.91 117	89.56 112	95.58 83	87.72 103	96.15 35	92.86 75	96.06 73	95.47 127	95.02 121	98.43 167	97.09 158

COLMAP_ROB	90.49 14	93.27 109	92.71 118	93.93 90	97.75 57	97.44 108	96.07 93	93.17 58	95.40 84	83.86 119	83.76 144	88.72 103	93.87 114	94.25 152	94.11 146	98.87 130	95.28 184
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
FA-MVS(training)		93.94 93	95.16 74	92.53 107	94.87 106	98.57 80	95.42 108	79.49 192	95.37 85	90.98 60	86.54 122	94.26 69	95.44 88	97.80 53	95.19 116	98.97 119	98.38 114

EPP-MVSNet		95.27 64	96.18 60	94.20 87	94.88 105	98.64 75	94.97 115	90.70 96	95.34 86	89.67 84	91.66 79	93.84 70	95.42 89	97.32 64	97.00 65	99.58 11	99.47 15

CHOSEN 1792x2688		92.66 114	92.49 124	92.85 105	97.13 66	98.89 59	95.90 98	88.50 125	95.32 87	83.31 122	71.99 197	88.96 102	94.10 111	96.69 84	96.49 76	98.15 172	99.10 52

NP-MVS									95.32 87

PatchMatch-RL		94.69 75	94.41 86	95.02 67	97.63 59	98.15 94	94.50 126	91.99 74	95.32 87	91.31 58	95.47 45	83.44 139	96.02 75	96.56 89	95.23 114	98.69 148	96.67 170

GBi-Net		93.81 97	94.18 92	93.38 100	91.34 157	95.86 153	96.22 87	88.68 121	95.23 90	90.40 70	86.39 125	91.16 82	94.40 106	96.52 92	96.30 79	99.21 86	97.79 134

test1		93.81 97	94.18 92	93.38 100	91.34 157	95.86 153	96.22 87	88.68 121	95.23 90	90.40 70	86.39 125	91.16 82	94.40 106	96.52 92	96.30 79	99.21 86	97.79 134

FMVSNet3		93.79 99	94.17 94	93.35 102	91.21 160	95.99 146	96.62 76	88.68 121	95.23 90	90.40 70	86.39 125	91.16 82	94.11 110	95.96 112	96.67 73	99.07 107	97.79 134

test2506		94.32 86	93.00 115	95.87 51	96.16 77	99.39 15	96.96 62	92.80 64	95.22 93	94.47 27	91.55 81	70.45 195	95.25 91	98.29 28	97.98 29	99.59 7	98.10 127

ECVR-MVS		94.14 88	92.96 116	95.52 58	96.16 77	99.39 15	96.96 62	92.80 64	95.22 93	92.38 48	81.48 154	80.31 150	95.25 91	98.29 28	97.98 29	99.59 7	98.05 128

MVSTER		94.89 67	95.07 78	94.68 80	94.71 113	96.68 129	97.00 60	90.57 98	95.18 95	93.05 38	95.21 47	86.41 116	93.72 119	97.59 57	95.88 96	99.00 116	98.50 105

FMVSNet2		93.30 108	93.36 111	93.22 103	91.34 157	95.86 153	96.22 87	88.24 127	95.15 96	89.92 81	81.64 152	89.36 96	94.40 106	96.77 81	96.98 66	99.21 86	97.79 134

test1111		93.94 93	92.78 117	95.29 63	96.14 79	99.42 11	96.79 71	92.85 63	95.08 97	91.39 57	80.69 159	79.86 153	95.00 95	98.28 31	98.00 28	99.58 11	98.11 126

EPNet_dtu		92.45 117	95.02 79	89.46 145	98.02 52	95.47 168	94.79 120	92.62 66	94.97 98	70.11 192	94.76 54	92.61 78	84.07 202	95.94 113	95.56 104	97.15 188	95.82 177
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet_Blended_VisFu		94.77 73	95.54 67	93.87 91	96.48 72	98.97 49	94.33 128	91.84 79	94.93 99	90.37 73	85.04 135	94.99 64	90.87 156	98.12 41	97.30 58	99.30 68	99.45 16

Anonymous20231211		93.49 105	92.33 132	94.84 75	94.78 110	98.00 97	96.11 91	91.85 78	94.86 100	90.91 61	74.69 179	89.18 99	96.73 62	94.82 140	95.51 106	98.67 149	99.24 36

baseline1		94.59 77	94.47 85	94.72 78	95.16 98	97.97 99	96.07 93	91.94 77	94.86 100	89.98 78	91.60 80	85.87 121	95.64 81	97.07 72	96.90 68	99.52 20	97.06 162

LGP-MVS_train		94.12 89	94.62 82	93.53 96	96.44 73	97.54 104	97.40 54	91.84 79	94.66 102	81.09 134	95.70 43	83.36 140	95.10 93	96.36 100	95.71 101	99.32 63	99.03 65

OpenMVS	92.33 11	95.50 56	95.22 73	95.82 53	98.98 30	98.97 49	97.67 50	93.04 62	94.64 103	89.18 93	84.44 140	94.79 65	96.79 60	97.23 66	97.61 47	99.24 76	98.88 83

tpmrst		88.86 165	89.62 157	87.97 168	94.33 121	95.98 147	92.62 153	76.36 202	94.62 104	76.94 153	85.98 130	82.80 143	92.80 131	86.90 209	87.15 205	94.77 209	93.93 196

MAR-MVS		95.50 56	95.60 65	95.39 61	98.67 40	98.18 93	95.89 100	89.81 108	94.55 105	91.97 53	92.99 62	90.21 91	97.30 47	96.79 80	97.49 49	98.72 145	98.99 70
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
EIA-MVS		95.50 56	96.19 59	94.69 79	94.83 107	98.88 60	95.93 97	91.50 88	94.47 106	89.43 86	93.14 61	92.72 77	97.05 55	97.82 52	97.13 63	99.43 45	99.15 49

DELS-MVS		96.06 54	96.04 61	96.07 49	97.77 55	99.25 28	98.10 41	93.26 54	94.42 107	92.79 43	88.52 109	93.48 73	95.06 94	98.51 16	98.83 1	99.45 36	99.28 28
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 (Re-imp)		94.46 81	96.24 58	92.40 108	95.23 96	98.64 75	95.56 106	90.99 93	94.42 107	85.02 115	90.88 90	94.65 66	88.01 178	98.17 37	98.37 16	99.57 14	98.53 103

GG-mvs-BLEND		66.17 213	94.91 81	32.63 218	1.32 227	96.64 130	91.40 176	0.85 224	94.39 109	2.20 228	90.15 97	95.70 62	2.27 224	96.39 96	95.44 108	97.78 180	95.68 179

casdiffmvs_mvg		94.55 78	94.26 90	94.88 72	94.96 103	98.51 81	97.11 58	91.82 82	94.28 110	89.20 92	86.60 120	86.85 112	96.56 67	97.47 60	97.25 61	99.64 4	98.83 89
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
Effi-MVS+		92.93 111	93.86 100	91.86 113	94.07 126	98.09 96	95.59 105	85.98 151	94.27 111	79.54 142	91.12 87	81.81 146	96.71 63	96.67 86	96.06 88	99.27 72	98.98 72

IterMVS-LS		92.56 115	93.18 112	91.84 114	93.90 128	94.97 182	94.99 114	86.20 148	94.18 112	82.68 124	85.81 131	87.36 111	94.43 104	95.31 131	96.02 91	98.87 130	98.60 99
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IS_MVSNet		95.28 63	96.43 56	93.94 89	95.30 93	99.01 47	95.90 98	91.12 92	94.13 113	87.50 105	91.23 83	94.45 67	94.17 109	98.45 20	98.50 7	99.65 3	99.23 37

Anonymous202405211			92.18 133		95.04 102	98.20 91	96.14 90	91.79 83	93.93 114		74.60 180	88.38 107	96.48 68	95.17 135	95.82 100	99.00 116	99.15 49

USDC		90.69 136	90.52 153	90.88 126	94.17 124	96.43 136	95.82 104	86.76 141	93.92 115	76.27 159	86.49 123	74.30 178	93.67 121	95.04 138	93.36 161	98.61 155	94.13 191

test0.0.03 1		91.97 119	93.91 98	89.72 141	93.31 138	96.40 138	91.34 178	87.06 139	93.86 116	81.67 130	91.15 86	89.16 100	86.02 190	95.08 136	95.09 117	98.91 127	96.64 172

MS-PatchMatch		91.82 122	92.51 122	91.02 123	95.83 84	96.88 119	95.05 113	84.55 172	93.85 117	82.01 127	82.51 150	91.71 80	90.52 163	95.07 137	93.03 168	98.13 173	94.52 186

FC-MVSNet-train		93.85 96	93.91 98	93.78 93	94.94 104	96.79 126	94.29 129	91.13 91	93.84 118	88.26 100	90.40 93	85.23 126	94.65 102	96.54 91	95.31 111	99.38 54	99.28 28

FC-MVSNet-test		91.63 125	93.82 102	89.08 149	92.02 151	96.40 138	93.26 143	87.26 136	93.72 119	77.26 150	88.61 108	89.86 93	85.50 192	95.72 123	95.02 121	99.16 95	97.44 149

baseline2		93.01 110	94.17 94	91.64 117	92.83 144	97.49 106	93.40 140	87.53 133	93.67 120	86.07 111	91.83 77	86.58 113	91.36 145	96.38 97	95.06 119	98.67 149	98.20 123

Fast-Effi-MVS+		91.87 120	92.08 135	91.62 119	92.91 142	97.21 115	94.93 116	84.60 170	93.61 121	81.49 132	83.50 145	78.95 156	96.62 65	96.55 90	96.22 84	99.16 95	98.51 104

HyFIR lowres test		92.03 118	91.55 143	92.58 106	97.13 66	98.72 68	94.65 123	86.54 144	93.58 122	82.56 125	67.75 208	90.47 89	95.67 80	95.87 115	95.54 105	98.91 127	98.93 77

CostFormer		90.69 136	90.48 154	90.93 125	94.18 123	96.08 145	94.03 131	78.20 195	93.47 123	89.96 79	90.97 89	80.30 151	93.72 119	87.66 207	88.75 199	95.51 201	96.12 174

pmmvs4		90.55 139	89.91 156	91.30 122	90.26 169	94.95 183	92.73 151	87.94 130	93.44 124	85.35 114	82.28 151	76.09 170	93.02 130	93.56 161	92.26 184	98.51 161	96.77 168

GeoE		92.52 116	92.64 119	92.39 109	93.96 127	97.76 101	96.01 96	85.60 156	93.23 125	83.94 118	81.56 153	84.80 130	95.63 82	96.22 105	95.83 99	99.19 90	99.07 59

IB-MVS	89.56 15	91.71 124	92.50 123	90.79 129	95.94 83	98.44 84	87.05 200	91.38 90	93.15 126	92.98 41	84.78 136	85.14 127	78.27 207	92.47 178	94.44 141	99.10 103	99.08 55
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
thres600view7		93.49 105	92.37 131	94.79 77	95.42 88	98.93 53	96.58 79	92.31 68	93.04 127	87.88 102	86.62 119	76.94 168	97.09 54	96.82 77	95.63 102	99.45 36	98.63 97

IterMVS		90.20 144	92.43 128	87.61 175	92.82 145	94.31 197	94.11 130	81.54 186	92.97 128	69.90 194	84.71 137	88.16 110	89.96 170	95.25 132	94.17 145	97.31 186	97.46 148
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
thres400		93.56 103	92.43 128	94.87 74	95.40 89	98.91 56	96.70 75	92.38 67	92.93 129	88.19 101	86.69 118	77.35 165	97.13 51	96.75 82	95.85 97	99.42 47	98.56 100

thres200		93.62 101	92.54 121	94.88 72	95.36 90	98.93 53	96.75 73	92.31 68	92.84 130	88.28 99	86.99 115	77.81 164	97.13 51	96.82 77	95.92 93	99.45 36	98.49 106

testgi		89.42 153	91.50 144	87.00 184	92.40 149	95.59 164	89.15 194	85.27 163	92.78 131	72.42 179	91.75 78	76.00 171	84.09 201	94.38 149	93.82 156	98.65 153	96.15 173

IterMVS-SCA-FT		90.24 143	92.48 126	87.63 174	92.85 143	94.30 198	93.79 134	81.47 188	92.66 132	69.95 193	84.66 138	88.38 107	89.99 169	95.39 130	94.34 142	97.74 184	97.63 143

thres100view900		93.55 104	92.47 127	94.81 76	95.33 91	98.74 66	96.78 72	92.30 71	92.63 133	88.29 97	87.21 113	78.01 161	96.78 61	96.38 97	95.92 93	99.38 54	98.40 112

tfpn200view9		93.64 100	92.57 120	94.89 71	95.33 91	98.94 51	96.82 68	92.31 68	92.63 133	88.29 97	87.21 113	78.01 161	97.12 53	96.82 77	95.85 97	99.45 36	98.56 100

Fast-Effi-MVS+-dtu		91.19 131	93.64 104	88.33 157	92.19 150	96.46 135	93.99 132	81.52 187	92.59 135	71.82 182	92.17 71	85.54 122	91.68 142	95.73 121	94.64 131	98.80 139	98.34 116

tpm cat1		88.90 163	87.78 179	90.22 135	93.88 130	95.39 171	93.79 134	78.11 196	92.55 136	89.43 86	81.31 155	79.84 154	91.40 144	84.95 210	86.34 208	94.68 211	94.09 192

PCF-MVS	93.95 6	95.65 55	95.14 75	96.25 43	97.73 58	98.73 67	97.59 51	97.13 30	92.50 137	89.09 95	89.85 99	96.65 50	96.90 58	94.97 139	94.89 124	99.08 105	98.38 114
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Effi-MVS+-dtu		91.78 123	93.59 107	89.68 144	92.44 148	97.11 116	94.40 127	84.94 166	92.43 138	75.48 163	91.09 88	83.75 137	93.55 122	96.61 87	95.47 107	97.24 187	98.67 95

ACMH+	90.88 12	91.41 130	91.13 146	91.74 116	95.11 100	96.95 118	93.13 145	89.48 114	92.42 139	79.93 139	85.13 134	78.02 160	93.82 117	93.49 163	93.88 152	98.94 123	97.99 130

CR-MVSNet		90.16 146	91.96 138	88.06 163	93.32 137	95.95 150	93.36 141	75.99 205	92.40 140	75.19 167	83.18 146	85.37 123	92.05 136	95.21 133	94.56 135	98.47 164	97.08 160

RPMNet		90.19 145	92.03 137	88.05 164	93.46 134	95.95 150	93.41 139	74.59 210	92.40 140	75.91 161	84.22 141	86.41 116	92.49 132	94.42 148	93.85 154	98.44 165	96.96 163

Vis-MVSNet		92.77 112	95.00 80	90.16 136	94.10 125	98.79 63	94.76 121	88.26 126	92.37 142	79.95 138	88.19 111	91.58 81	84.38 199	97.59 57	97.58 48	99.52 20	98.91 81
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test-LLR		91.62 126	93.56 108	89.35 148	93.31 138	96.57 132	92.02 169	87.06 139	92.34 143	75.05 170	90.20 95	88.64 104	90.93 152	96.19 108	94.07 147	97.75 182	96.90 166

TESTMET0.1,1		91.07 132	93.56 108	88.17 159	90.43 164	96.57 132	92.02 169	82.83 181	92.34 143	75.05 170	90.20 95	88.64 104	90.93 152	96.19 108	94.07 147	97.75 182	96.90 166

TinyColmap		89.42 153	88.58 165	90.40 133	93.80 132	95.45 169	93.96 133	86.54 144	92.24 145	76.49 156	80.83 157	70.44 196	93.37 124	94.45 147	93.30 164	98.26 171	93.37 202

FMVSNet5		90.36 141	90.93 149	89.70 142	87.99 202	92.25 207	92.03 168	83.51 176	92.20 146	84.13 117	85.59 132	86.48 114	92.43 133	94.61 142	94.52 138	98.13 173	90.85 208

test-mter		90.95 133	93.54 110	87.93 169	90.28 168	96.80 123	91.44 175	82.68 182	92.15 147	74.37 174	89.57 101	88.23 109	90.88 155	96.37 99	94.31 143	97.93 179	97.37 151

dps		90.11 148	89.37 161	90.98 124	93.89 129	96.21 142	93.49 138	77.61 197	91.95 148	92.74 45	88.85 104	78.77 158	92.37 134	87.71 206	87.71 203	95.80 197	94.38 189

PatchT		89.13 160	91.71 139	86.11 191	92.92 141	95.59 164	83.64 208	75.09 208	91.87 149	75.19 167	82.63 149	85.06 128	92.05 136	95.21 133	94.56 135	97.76 181	97.08 160

pmnet_mix02		86.12 193	87.12 187	84.96 195	89.82 174	94.12 199	84.88 206	86.63 143	91.78 150	65.60 204	80.76 158	76.98 167	86.61 186	87.29 208	84.80 211	96.21 192	94.09 192

thisisatest0515		90.12 147	92.06 136	87.85 170	90.03 171	96.17 143	87.83 197	87.45 134	91.71 151	77.15 151	85.40 133	84.01 135	85.74 191	95.41 129	93.30 164	98.88 129	98.43 108

ACMH	90.77 13	91.51 129	91.63 141	91.38 120	95.62 86	96.87 121	91.76 173	89.66 110	91.58 152	78.67 144	86.73 117	78.12 159	93.77 118	94.59 143	94.54 137	98.78 142	98.98 72
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tpm		87.95 173	89.44 160	86.21 190	92.53 147	94.62 192	91.40 176	76.36 202	91.46 153	69.80 196	87.43 112	75.14 173	91.55 143	89.85 201	90.60 191	95.61 199	96.96 163

FMVSNet1		91.54 128	90.93 149	92.26 110	90.35 167	95.27 175	95.22 111	87.16 138	91.37 154	87.62 104	75.45 174	83.84 136	94.43 104	96.52 92	96.30 79	98.82 134	97.74 140

DU-MVS		89.67 152	88.84 163	90.63 131	89.26 186	95.61 162	92.48 155	89.91 104	91.22 155	79.57 140	77.72 169	71.18 192	93.21 128	92.53 176	94.57 134	99.35 60	99.05 62

NR-MVSNet		89.34 155	88.66 164	90.13 139	90.40 165	95.61 162	93.04 147	89.91 104	91.22 155	78.96 143	77.72 169	68.90 204	89.16 174	94.24 153	93.95 150	99.32 63	98.99 70

UA-Net		93.96 92	95.95 62	91.64 117	96.06 80	98.59 79	95.29 109	90.00 103	91.06 157	82.87 123	90.64 91	98.06 40	86.06 189	98.14 39	98.20 19	99.58 11	96.96 163

dmvs_re		91.84 121	91.60 142	92.12 112	91.60 153	97.26 112	95.14 112	91.96 75	91.02 158	80.98 135	86.56 121	77.96 163	93.84 116	94.71 141	95.08 118	99.22 82	98.62 98

UniMVSNet (Re)		90.03 149	89.61 158	90.51 132	89.97 173	96.12 144	92.32 159	89.26 115	90.99 159	80.95 136	78.25 168	75.08 175	91.14 148	93.78 156	93.87 153	99.41 48	99.21 41

UniMVSNet_NR-MVSNet		90.35 142	89.96 155	90.80 128	89.66 176	95.83 156	92.48 155	90.53 99	90.96 160	79.57 140	79.33 165	77.14 166	93.21 128	92.91 172	94.50 140	99.37 57	99.05 62

Baseline_NR-MVSNet		89.27 157	88.01 173	90.73 130	89.26 186	93.71 202	92.71 152	89.78 109	90.73 161	81.28 133	73.53 189	72.85 184	92.30 135	92.53 176	93.84 155	99.07 107	98.88 83

DeepMVS_CX						86.86 215	79.50 214	70.43 215	90.73 161	63.66 207	80.36 163	60.83 216	79.68 205	76.23 214		89.46 216	86.53 214

MIMVSNet		88.99 162	91.07 147	86.57 187	86.78 208	95.62 161	91.20 181	75.40 207	90.65 163	76.57 155	84.05 142	82.44 145	91.01 151	95.84 116	95.38 109	98.48 163	93.50 200

GA-MVS		89.28 156	90.75 152	87.57 176	91.77 152	96.48 134	92.29 161	87.58 132	90.61 164	65.77 203	84.48 139	76.84 169	89.46 172	95.84 116	93.68 157	98.52 160	97.34 153

MDTV_nov1_ep13_2view		86.30 191	88.27 168	84.01 197	87.71 205	94.67 190	88.08 196	76.78 200	90.59 165	68.66 200	80.46 162	80.12 152	87.58 182	89.95 200	88.20 201	95.25 205	93.90 197

TranMVSNet+NR-MVSNet		89.23 158	88.48 167	90.11 140	89.07 192	95.25 176	92.91 148	90.43 100	90.31 166	77.10 152	76.62 172	71.57 190	91.83 140	92.12 182	94.59 133	99.32 63	98.92 78

CDS-MVSNet		92.77 112	93.60 106	91.80 115	92.63 146	96.80 123	95.24 110	89.14 117	90.30 167	84.58 116	86.76 116	90.65 87	90.42 164	95.89 114	96.49 76	98.79 141	98.32 119
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
SixPastTwentyTwo		88.37 168	89.47 159	87.08 182	90.01 172	95.93 152	87.41 198	85.32 160	90.26 168	70.26 190	86.34 128	71.95 188	90.93 152	92.89 173	91.72 187	98.55 158	97.22 155

TDRefinement		89.07 161	88.15 170	90.14 138	95.16 98	96.88 119	95.55 107	90.20 101	89.68 169	76.42 157	76.67 171	74.30 178	84.85 196	93.11 168	91.91 186	98.64 154	94.47 187

OPM-MVS		93.61 102	92.43 128	95.00 68	96.94 68	97.34 110	97.78 48	94.23 46	89.64 170	85.53 113	88.70 106	82.81 142	96.28 71	96.28 103	95.00 123	99.24 76	97.22 155
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
CVMVSNet		89.77 151	91.66 140	87.56 177	93.21 140	95.45 169	91.94 172	89.22 116	89.62 171	69.34 198	83.99 143	85.90 120	84.81 197	94.30 151	95.28 112	96.85 190	97.09 158

v10		88.00 172	87.96 174	88.05 164	89.44 181	94.68 189	92.36 158	83.35 177	89.37 172	72.96 178	73.98 186	72.79 185	91.35 146	93.59 158	92.88 171	98.81 137	98.42 110

v8		88.21 171	87.94 176	88.51 154	89.62 177	95.01 181	92.31 160	84.99 165	88.94 173	74.70 172	75.03 176	73.51 182	90.67 160	92.11 183	92.74 176	98.80 139	98.24 121

WR-MVS		87.93 174	88.09 171	87.75 171	89.26 186	95.28 173	90.81 184	86.69 142	88.90 174	75.29 166	74.31 184	73.72 181	85.19 195	92.26 179	93.32 163	99.27 72	98.81 91

V42		88.31 169	87.95 175	88.73 152	89.44 181	95.34 172	92.23 163	87.21 137	88.83 175	74.49 173	74.89 178	73.43 183	90.41 166	92.08 185	92.77 175	98.60 157	98.33 117

N_pmnet		84.80 197	85.10 201	84.45 196	89.25 189	92.86 205	84.04 207	86.21 146	88.78 176	66.73 202	72.41 196	74.87 177	85.21 194	88.32 204	86.45 206	95.30 203	92.04 205

anonymousdsp		88.90 163	91.00 148	86.44 188	88.74 199	95.97 148	90.40 188	82.86 180	88.77 177	67.33 201	81.18 156	81.44 148	90.22 167	96.23 104	94.27 144	99.12 101	99.16 48

v2v482		88.25 170	87.71 180	88.88 150	89.23 190	95.28 173	92.10 165	87.89 131	88.69 178	73.31 177	75.32 175	71.64 189	91.89 138	92.10 184	92.92 170	98.86 132	97.99 130

v1144		87.92 176	87.79 178	88.07 161	89.27 185	95.15 178	92.17 164	85.62 155	88.52 179	71.52 183	73.80 187	72.40 187	91.06 150	93.54 162	92.80 173	98.81 137	98.33 117

v1192		87.51 181	87.31 182	87.74 172	89.04 193	94.87 187	92.07 166	85.03 164	88.49 180	70.32 189	72.65 194	70.35 197	91.21 147	93.59 158	92.80 173	98.78 142	98.42 110

v1921920		87.31 185	87.13 186	87.52 178	88.87 196	94.72 188	91.96 171	84.59 171	88.28 181	69.86 195	72.50 195	70.03 200	91.10 149	93.33 165	92.61 178	98.71 146	98.44 107

MVS-HIRNet		85.36 196	86.89 189	83.57 198	90.13 170	94.51 193	83.57 209	72.61 212	88.27 182	71.22 186	68.97 204	81.81 146	88.91 176	93.08 169	91.94 185	94.97 208	89.64 211

test_method		72.96 210	78.68 210	66.28 213	50.17 224	64.90 222	75.45 218	50.90 220	87.89 183	62.54 211	62.98 213	68.34 206	70.45 212	91.90 188	82.41 212	88.19 218	92.35 203

new_pmnet		81.53 204	82.68 206	80.20 203	83.47 213	89.47 214	82.21 212	78.36 193	87.86 184	60.14 217	67.90 207	69.43 202	82.03 204	89.22 202	87.47 204	94.99 207	87.39 213

v144192		87.40 183	87.20 185	87.64 173	88.89 194	94.88 186	91.65 174	84.70 169	87.80 185	71.17 187	73.20 192	70.91 193	90.75 158	92.69 174	92.49 179	98.71 146	98.43 108

TAMVS		90.54 140	90.87 151	90.16 136	91.48 155	96.61 131	93.26 143	86.08 149	87.71 186	81.66 131	83.11 148	84.04 134	90.42 164	94.54 144	94.60 132	98.04 177	95.48 182

PM-MVS		84.72 199	84.47 203	85.03 194	84.67 210	91.57 209	86.27 202	82.31 184	87.65 187	70.62 188	76.54 173	56.41 221	88.75 177	92.59 175	89.85 196	97.54 185	96.66 171

v148		87.51 181	86.79 190	88.36 156	89.39 183	95.21 177	89.84 191	88.20 128	87.61 188	77.56 148	73.38 191	70.32 198	86.80 184	90.70 195	92.31 182	98.37 168	97.98 132

v1240		86.89 187	86.75 192	87.06 183	88.75 198	94.65 191	91.30 180	84.05 173	87.49 189	68.94 199	71.96 198	68.86 205	90.65 161	93.33 165	92.72 177	98.67 149	98.24 121

CP-MVSNet		87.89 177	87.27 183	88.62 153	89.30 184	95.06 179	90.60 186	85.78 153	87.43 190	75.98 160	74.60 180	68.14 207	90.76 157	93.07 170	93.60 158	99.30 68	98.98 72

WR-MVS_H		87.93 174	87.85 177	88.03 166	89.62 177	95.58 166	90.47 187	85.55 157	87.20 191	76.83 154	74.42 183	72.67 186	86.37 187	93.22 167	93.04 167	99.33 61	98.83 89

EU-MVSNet		85.62 195	87.65 181	83.24 200	88.54 200	92.77 206	87.12 199	85.32 160	86.71 192	64.54 206	78.52 167	75.11 174	78.35 206	92.25 180	92.28 183	95.58 200	95.93 175

v7n		86.43 190	86.52 194	86.33 189	87.91 203	94.93 184	90.15 190	83.05 178	86.57 193	70.21 191	71.48 199	66.78 211	87.72 179	94.19 155	92.96 169	98.92 125	98.76 94

PEN-MVS		87.22 186	86.50 195	88.07 161	88.88 195	94.44 194	90.99 183	86.21 146	86.53 194	73.66 176	74.97 177	66.56 214	89.42 173	91.20 193	93.48 160	99.24 76	98.31 120

PS-CasMVS		87.33 184	86.68 193	88.10 160	89.22 191	94.93 184	90.35 189	85.70 154	86.44 195	74.01 175	73.43 190	66.59 213	90.04 168	92.92 171	93.52 159	99.28 70	98.91 81

pm-mvs1		89.19 159	89.02 162	89.38 147	90.40 165	95.74 160	92.05 167	88.10 129	86.13 196	77.70 147	73.72 188	79.44 155	88.97 175	95.81 118	94.51 139	99.08 105	97.78 139

DTE-MVSNet		86.67 189	86.09 196	87.35 180	88.45 201	94.08 200	90.65 185	86.05 150	86.13 196	72.19 180	74.58 182	66.77 212	87.61 181	90.31 196	93.12 166	99.13 99	97.62 144

pmmvs5		87.83 178	88.09 171	87.51 179	89.59 179	95.48 167	89.75 192	84.73 168	86.07 198	71.44 184	80.57 160	70.09 199	90.74 159	94.47 146	92.87 172	98.82 134	97.10 157

Anonymous20231206		83.84 201	85.19 200	82.26 201	87.38 206	92.87 204	85.49 204	83.65 175	86.07 198	63.44 210	68.42 205	69.01 203	75.45 210	93.34 164	92.44 180	98.12 175	94.20 190

UniMVSNet_ETH3D		88.47 167	86.00 197	91.35 121	91.55 154	96.29 140	92.53 154	88.81 120	85.58 200	82.33 126	67.63 209	66.87 210	94.04 112	91.49 191	95.24 113	98.84 133	98.92 78

EG-PatchMatch MVS		86.68 188	87.24 184	86.02 192	90.58 163	96.26 141	91.08 182	81.59 185	84.96 201	69.80 196	71.35 201	75.08 175	84.23 200	94.24 153	93.35 162	98.82 134	95.46 183

TransMVSNet (Re)		87.73 179	86.79 190	88.83 151	90.76 161	94.40 195	91.33 179	89.62 111	84.73 202	75.41 165	72.73 193	71.41 191	86.80 184	94.53 145	93.93 151	99.06 110	95.83 176

pmmvs-eth3d		84.33 200	82.94 205	85.96 193	84.16 211	90.94 210	86.55 201	83.79 174	84.25 203	75.85 162	70.64 202	56.43 220	87.44 183	92.20 181	90.41 193	97.97 178	95.68 179

ambc			73.83 213		76.23 218	85.13 217	82.27 211		84.16 204	65.58 205	52.82 217	23.31 228	73.55 211	91.41 192	85.26 210	92.97 214	94.70 185

tfpnnormal		88.50 166	87.01 188	90.23 134	91.36 156	95.78 159	92.74 150	90.09 102	83.65 205	76.33 158	71.46 200	69.58 201	91.84 139	95.54 124	94.02 149	99.06 110	99.03 65

LTVRE_ROB	87.32 16	87.55 180	88.25 169	86.73 185	90.66 162	95.80 158	93.05 146	84.77 167	83.35 206	60.32 215	83.12 147	67.39 208	93.32 125	94.36 150	94.86 125	98.28 169	98.87 85
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
Gipuma		68.35 211	66.71 214	70.27 210	74.16 219	68.78 221	63.93 222	71.77 214	83.34 207	54.57 221	34.37 219	31.88 225	68.69 213	83.30 212	85.53 209	88.48 217	79.78 217
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
FPMVS		75.84 209	74.59 212	77.29 209	86.92 207	83.89 218	85.01 205	80.05 191	82.91 208	60.61 214	65.25 211	60.41 217	63.86 215	75.60 215	73.60 217	87.29 219	80.47 216

MDA-MVSNet-bldmvs		80.11 205	80.24 208	79.94 204	77.01 217	93.21 203	78.86 215	85.94 152	82.71 209	60.86 212	79.71 164	51.77 223	83.71 203	75.60 215	86.37 207	93.28 213	92.35 203

test20.03		82.92 203	85.52 198	79.90 205	87.75 204	91.84 208	82.80 210	82.99 179	82.65 210	60.32 215	78.90 166	70.50 194	67.10 214	92.05 186	90.89 189	98.44 165	91.80 206

MIMVSNet1		80.03 206	80.93 207	78.97 206	72.46 220	90.73 211	80.81 213	82.44 183	80.39 211	63.64 208	57.57 215	64.93 215	76.37 208	91.66 189	91.55 188	98.07 176	89.70 210

CMPMVS	65.18 17	84.76 198	83.10 204	86.69 186	95.29 94	95.05 180	88.37 195	85.51 158	80.27 212	71.31 185	68.37 206	73.85 180	85.25 193	87.72 205	87.75 202	94.38 212	88.70 212
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
new-patchmatchnet		78.49 208	78.19 211	78.84 207	84.13 212	90.06 212	77.11 217	80.39 190	79.57 213	59.64 218	66.01 210	55.65 222	75.62 209	84.55 211	80.70 213	96.14 194	90.77 209

pmmvs6		85.98 194	84.89 202	87.25 181	88.83 197	94.35 196	89.36 193	85.30 162	78.51 214	75.44 164	62.71 214	75.41 172	87.65 180	93.58 160	92.40 181	96.89 189	97.29 154

gm-plane-assit		83.26 202	85.29 199	80.89 202	89.52 180	89.89 213	70.26 219	78.24 194	77.11 215	58.01 219	74.16 185	66.90 209	90.63 162	97.20 67	96.05 89	98.66 152	95.68 179

pmmvs3		79.16 207	80.12 209	78.05 208	79.36 215	86.59 216	78.13 216	73.87 211	76.42 216	57.51 220	70.59 203	57.02 219	84.66 198	90.10 198	88.32 200	94.75 210	91.77 207

gg-mvs-nofinetune		86.17 192	88.57 166	83.36 199	93.44 135	98.15 94	96.58 79	72.05 213	74.12 217	49.23 222	64.81 212	90.85 86	89.90 171	97.83 50	96.84 70	98.97 119	97.41 150

PMVS	63.12 18	67.27 212	66.39 215	68.30 211	77.98 216	60.24 223	59.53 223	76.82 198	66.65 218	60.74 213	54.39 216	59.82 218	51.24 218	73.92 218	70.52 218	83.48 220	79.17 218
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PMMVS2		64.36 214	65.94 216	62.52 214	67.37 221	77.44 219	64.39 221	69.32 218	61.47 219	34.59 223	46.09 218	41.03 224	48.02 221	74.56 217	78.23 214	91.43 215	82.76 215

EMVS		49.98 216	46.76 219	53.74 216	64.96 222	51.29 225	37.81 225	69.35 217	51.83 220	22.69 226	29.57 221	25.06 226	57.28 216	44.81 221	56.11 220	70.32 223	68.64 221

E-PMN		50.67 215	47.85 218	53.96 215	64.13 223	50.98 226	38.06 224	69.51 216	51.40 221	24.60 225	29.46 222	24.39 227	56.07 217	48.17 220	59.70 219	71.40 222	70.84 220

MVE	50.86 19	49.54 217	51.43 217	47.33 217	44.14 225	59.20 224	36.45 226	60.59 219	41.47 222	31.14 224	29.58 220	17.06 229	48.52 220	62.22 219	74.63 216	63.12 224	75.87 219
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		12.09 218	16.94 220	6.42 219	3.15 226	6.08 227	9.51 228	3.84 222	21.46 223	5.31 227	27.49 223	6.76 230	10.89 222	17.06 222	15.01 221	5.84 225	24.75 222

test123		9.58 219	13.53 221	4.97 220	1.31 228	5.47 228	8.32 229	2.95 223	18.14 224	2.03 229	20.82 224	2.34 231	10.60 223	10.00 223	14.16 222	4.60 226	23.77 223

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

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

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

TPM-MVS					98.94 32	98.47 83	98.04 42			92.62 46	96.51 33	98.76 29	95.94 77			98.92 125	97.55 145

RE-MVS-def										63.50 209

9.14												99.28 12

SR-MVS					99.45 9			97.61 14				99.20 16

our_test_3					89.78 175	93.84 201	85.59 203

MTAPA										96.83 10		99.12 21

MTMP										97.18 5		98.83 26

Patchmatch-RL test							34.61 227

XVS					96.60 69	99.35 17	96.82 68			90.85 62		98.72 30				99.46 32

X-MVStestdata					96.60 69	99.35 17	96.82 68			90.85 62		98.72 30				99.46 32

mPP-MVS					99.21 23							98.29 38

Patchmtry						95.96 149	93.36 141	75.99 205		75.19 167

High-resolution multi-view benchmark