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.

SMA-MVS		98.66 7	98.89 7	98.39 9	99.60 1	99.41 13	99.00 21	97.63 12	97.78 19	95.83 18	98.33 12	99.83 4	98.85 9	98.93 8	98.56 7	99.41 51	99.40 21
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
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 11	97.33 4	98.70 6	99.33 10	98.86 8	98.96 6	98.40 14	99.63 5	99.57 9
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
PGM-MVS		97.81 26	98.11 29	97.46 29	99.55 3	99.34 21	99.32 9	94.51 45	96.21 63	93.07 37	98.05 15	97.95 42	98.82 11	98.22 37	97.89 39	99.48 30	99.09 56

ACMMP_NAP		98.20 18	98.49 14	97.85 25	99.50 4	99.40 14	99.26 11	97.64 11	97.47 34	92.62 47	97.59 21	99.09 22	98.71 15	98.82 12	97.86 40	99.40 54	99.19 45

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 20	99.43 48	99.82 1

APD-MVS		98.36 15	98.32 24	98.41 8	99.47 5	99.26 27	99.12 15	97.77 7	96.73 50	96.12 16	97.27 29	98.88 24	98.46 25	98.47 19	98.39 15	99.52 22	99.22 41
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CSCG		97.44 33	97.18 44	97.75 27	99.47 5	99.52 8	98.55 32	95.41 40	97.69 24	95.72 19	94.29 56	95.53 63	98.10 33	96.20 108	97.38 57	99.24 79	99.62 4

HPM-MVS++		98.34 16	98.47 16	98.18 16	99.46 8	99.15 34	99.10 16	97.69 8	97.67 25	94.93 26	97.62 20	99.70 7	98.60 20	98.45 21	97.46 53	99.31 68	99.26 35

SF-MVS		98.39 13	98.45 18	98.33 10	99.45 9	99.05 37	98.27 37	97.65 9	97.73 20	97.02 7	98.18 13	99.25 15	98.11 32	98.15 39	97.62 48	99.45 38	99.19 45

SR-MVS					99.45 9			97.61 14				99.20 16

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 13	96.51 14	98.49 8	99.65 8	98.67 17	98.60 14	98.42 12	99.40 54	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
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 21	99.90 2	98.93 5	98.99 4	98.42 12	99.37 59	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
ACMMPR		98.40 12	98.49 14	98.28 13	99.41 13	99.40 14	99.36 4	97.35 21	98.30 7	95.02 25	97.79 18	98.39 37	99.04 2	98.26 34	98.10 24	99.50 29	99.22 41

X-MVS		97.84 25	98.19 28	97.42 30	99.40 14	99.35 18	99.06 17	97.25 25	97.38 35	90.85 63	96.06 38	98.72 30	98.53 24	98.41 25	98.15 23	99.46 34	99.28 30

MCST-MVS		98.20 18	98.36 20	98.01 22	99.40 14	99.05 37	99.00 21	97.62 13	97.59 29	93.70 34	97.42 28	99.30 11	98.77 13	98.39 27	97.48 52	99.59 7	99.31 29

CNVR-MVS		98.47 11	98.46 17	98.48 7	99.40 14	99.05 37	99.02 19	97.54 16	97.73 20	96.65 11	97.20 30	99.13 20	98.85 9	98.91 9	98.10 24	99.41 51	99.08 57

HFP-MVS		98.48 10	98.62 12	98.32 11	99.39 17	99.33 22	99.27 10	97.42 18	98.27 8	95.25 23	98.34 11	98.83 26	99.08 1	98.26 34	98.08 26	99.48 30	99.26 35

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 9	97.60 2	98.87 4	99.89 3	98.67 17	99.02 2	98.26 18	99.36 61	99.61 6

NCCC		98.10 21	98.05 31	98.17 18	99.38 18	99.05 37	99.00 21	97.53 17	98.04 15	95.12 24	94.80 53	99.18 18	98.58 22	98.49 18	97.78 44	99.39 56	98.98 74

MP-MVS		98.09 22	98.30 25	97.84 26	99.34 20	99.19 32	99.23 13	97.40 19	97.09 44	93.03 40	97.58 23	98.85 25	98.57 23	98.44 23	97.69 46	99.48 30	99.23 39
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CP-MVS		98.32 17	98.34 23	98.29 12	99.34 20	99.30 23	99.15 14	97.35 21	97.49 32	95.58 21	97.72 19	98.62 34	98.82 11	98.29 29	97.67 47	99.51 27	99.28 30

SteuartSystems-ACMMP		98.38 14	98.71 11	97.99 23	99.34 20	99.46 11	99.34 6	97.33 24	97.31 36	94.25 30	98.06 14	99.17 19	98.13 31	98.98 5	98.46 10	99.55 18	99.54 11
Skip Steuart: Steuart Systems R&D Blog.
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 32	96.84 9	98.94 1	99.82 5	98.59 21	98.90 10	98.22 19	99.56 17	99.48 17
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
mPP-MVS					99.21 23							98.29 38

AdaColmap		97.53 31	96.93 48	98.24 14	99.21 23	98.77 65	98.47 34	97.34 23	96.68 52	96.52 13	95.11 50	96.12 58	98.72 14	97.19 69	96.24 86	99.17 95	98.39 117

DeepC-MVS_fast	96.13 1	98.13 20	98.27 26	97.97 24	99.16 26	99.03 43	99.05 18	97.24 26	98.22 11	94.17 32	95.82 41	98.07 39	98.69 16	98.83 11	98.80 2	99.52 22	99.10 54
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MSLP-MVS++		98.04 23	97.93 33	98.18 16	99.10 27	99.09 36	98.34 36	96.99 32	97.54 30	96.60 12	94.82 52	98.45 35	98.89 6	97.46 61	98.77 4	99.17 95	99.37 22

3Dnovator	93.79 8	97.08 38	97.20 42	96.95 38	99.09 28	99.03 43	98.20 39	93.33 53	97.99 16	93.82 33	90.61 95	96.80 50	97.82 37	97.90 48	98.78 3	99.47 33	99.26 35

QAPM		96.78 48	97.14 45	96.36 43	99.05 29	99.14 35	98.02 43	93.26 55	97.27 38	90.84 66	91.16 87	97.31 45	97.64 43	97.70 54	98.20 20	99.33 63	99.18 48

OpenMVS	92.33 11	95.50 56	95.22 75	95.82 54	98.98 30	98.97 49	97.67 50	93.04 62	94.64 107	89.18 96	84.44 144	94.79 65	96.79 62	97.23 66	97.61 49	99.24 79	98.88 85

PLC	94.95 3	97.37 34	96.77 51	98.07 20	98.97 31	98.21 93	97.94 46	96.85 35	97.66 26	97.58 3	93.33 62	96.84 49	98.01 36	97.13 71	96.20 88	99.09 107	98.01 133
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
TPM-MVS					98.94 32	98.47 86	98.04 42			92.62 47	96.51 34	98.76 29	95.94 82			98.92 129	97.55 149
Ray Leroy Khuboni and Hongjun Xu: Textureless Resilient Propagation Matching in Multiple View Stereosis (TPM-MVS). SATNAC 2025
train_agg		97.65 30	98.06 30	97.18 33	98.94 32	98.91 56	98.98 25	97.07 31	96.71 51	90.66 69	97.43 27	99.08 23	98.20 27	97.96 46	97.14 64	99.22 85	99.19 45

CDPH-MVS		96.84 46	97.49 37	96.09 48	98.92 34	98.85 61	98.61 29	95.09 41	96.00 71	87.29 110	95.45 47	97.42 44	97.16 52	97.83 50	97.94 35	99.44 45	98.92 80

CPTT-MVS		97.78 27	97.54 36	98.05 21	98.91 35	99.05 37	99.00 21	96.96 33	97.14 42	95.92 17	95.50 45	98.78 28	98.99 4	97.20 67	96.07 90	98.54 163	99.04 66

3Dnovator+	93.91 7	97.23 36	97.22 41	97.24 32	98.89 36	98.85 61	98.26 38	93.25 57	97.99 16	95.56 22	90.01 100	98.03 41	98.05 34	97.91 47	98.43 11	99.44 45	99.35 24

ACMMP		97.37 34	97.48 38	97.25 31	98.88 37	99.28 25	98.47 34	96.86 34	97.04 46	92.15 51	97.57 24	96.05 60	97.67 40	97.27 65	95.99 95	99.46 34	99.14 53
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
PHI-MVS		97.78 27	98.44 19	97.02 36	98.73 38	99.25 29	98.11 40	95.54 39	96.66 53	92.79 44	98.52 7	99.38 9	97.50 45	97.84 49	98.39 15	99.45 38	99.03 67

OMC-MVS		97.00 40	96.92 49	97.09 34	98.69 39	98.66 74	97.85 47	95.02 42	98.09 14	94.47 28	93.15 63	96.90 47	97.38 47	97.16 70	96.82 75	99.13 102	97.65 146

MAR-MVS		95.50 56	95.60 65	95.39 61	98.67 40	98.18 96	95.89 104	89.81 111	94.55 109	91.97 54	92.99 65	90.21 91	97.30 49	96.79 80	97.49 51	98.72 149	98.99 72
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
TSAR-MVS + ACMM		97.71 29	98.60 13	96.66 40	98.64 41	99.05 37	98.85 26	97.23 27	98.45 4	89.40 91	97.51 25	99.27 14	96.88 61	98.53 15	97.81 43	98.96 125	99.59 8

CNLPA		96.90 43	96.28 57	97.64 28	98.56 42	98.63 79	96.85 68	96.60 36	97.73 20	97.08 6	89.78 102	96.28 56	97.80 39	96.73 83	96.63 77	98.94 127	98.14 129

MVS_0304		97.94 24	98.72 10	97.02 36	98.48 43	99.50 9	99.02 19	94.06 47	98.33 6	94.51 27	98.78 5	97.73 43	96.60 68	98.51 16	98.68 5	99.45 38	99.53 12

EPNet		96.27 53	96.97 47	95.46 59	98.47 44	98.28 90	97.41 53	93.67 50	95.86 76	92.86 43	97.51 25	93.79 71	91.76 145	97.03 74	97.03 67	98.61 159	99.28 30
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MVS_111021_LR		97.16 37	98.01 32	96.16 47	98.47 44	98.98 48	96.94 64	93.89 49	97.64 27	91.44 56	98.89 3	96.41 53	97.20 51	98.02 45	97.29 62	99.04 119	98.85 89

MVS_111021_HR		97.04 39	98.20 27	95.69 55	98.44 46	99.29 24	96.59 80	93.20 58	97.70 23	89.94 83	98.46 9	96.89 48	96.71 65	98.11 42	97.95 34	99.27 74	99.01 70

MSDG		94.82 71	93.73 106	96.09 48	98.34 47	97.43 113	97.06 59	96.05 37	95.84 77	90.56 70	86.30 133	89.10 101	95.55 89	96.13 111	95.61 107	99.00 120	95.73 182

TAPA-MVS	94.18 5	96.38 51	96.49 55	96.25 44	98.26 48	98.66 74	98.00 44	94.96 43	97.17 40	89.48 88	92.91 67	96.35 54	97.53 44	96.59 89	95.90 98	99.28 72	97.82 137
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
DeepC-MVS	94.87 4	96.76 49	96.50 54	97.05 35	98.21 49	99.28 25	98.67 28	97.38 20	97.31 36	90.36 76	89.19 104	93.58 72	98.19 28	98.31 28	98.50 8	99.51 27	99.36 23
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SD-MVS		98.52 8	98.77 9	98.23 15	98.15 50	99.26 27	98.79 27	97.59 15	98.52 3	96.25 15	97.99 16	99.75 6	99.01 3	98.27 33	97.97 32	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 + MP.		98.49 9	98.78 8	98.15 19	98.14 51	99.17 33	99.34 6	97.18 29	98.44 5	95.72 19	97.84 17	99.28 12	98.87 7	99.05 1	98.05 27	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
DPM-MVS		96.86 45	96.82 50	96.91 39	98.08 52	98.20 94	98.52 33	97.20 28	97.24 39	91.42 57	91.84 79	98.45 35	97.25 50	97.07 72	97.40 56	98.95 126	97.55 149

EPNet_dtu		92.45 121	95.02 81	89.46 149	98.02 53	95.47 172	94.79 124	92.62 66	94.97 102	70.11 196	94.76 55	92.61 78	84.07 206	95.94 114	95.56 108	97.15 192	95.82 181
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CANet		96.84 46	97.20 42	96.42 41	97.92 54	99.24 31	98.60 30	93.51 52	97.11 43	93.07 37	91.16 87	97.24 46	96.21 76	98.24 36	98.05 27	99.22 85	99.35 24

LS3D		95.46 59	95.14 77	95.84 53	97.91 55	98.90 58	98.58 31	97.79 5	97.07 45	83.65 125	88.71 107	88.64 104	97.82 37	97.49 59	97.42 54	99.26 77	97.72 145

SPE-MVS-test		97.00 40	97.85 34	96.00 51	97.77 56	99.56 5	96.35 89	91.95 76	97.54 30	92.20 50	96.14 37	96.00 61	98.19 28	98.46 20	97.78 44	99.57 14	99.45 19

DELS-MVS		96.06 54	96.04 61	96.07 50	97.77 56	99.25 29	98.10 41	93.26 55	94.42 111	92.79 44	88.52 111	93.48 73	95.06 98	98.51 16	98.83 1	99.45 38	99.28 30
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
COLMAP_ROB	90.49 14	93.27 113	92.71 122	93.93 94	97.75 58	97.44 112	96.07 97	93.17 59	95.40 86	83.86 123	83.76 148	88.72 103	93.87 118	94.25 155	94.11 150	98.87 134	95.28 188
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
PCF-MVS	93.95 6	95.65 55	95.14 77	96.25 44	97.73 59	98.73 67	97.59 51	97.13 30	92.50 141	89.09 98	89.85 101	96.65 51	96.90 60	94.97 142	94.89 128	99.08 109	98.38 118
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PatchMatch-RL		94.69 77	94.41 88	95.02 67	97.63 60	98.15 97	94.50 130	91.99 74	95.32 90	91.31 59	95.47 46	83.44 143	96.02 80	96.56 90	95.23 118	98.69 152	96.67 174

CS-MVS		96.87 44	97.41 40	96.24 46	97.42 61	99.48 10	97.30 56	91.83 82	97.17 40	93.02 41	94.80 53	94.45 67	98.16 30	98.61 13	97.85 41	99.69 1	99.50 13

PVSNet_BlendedMVS		95.41 61	95.28 73	95.57 56	97.42 61	99.02 45	95.89 104	93.10 60	96.16 64	93.12 35	91.99 75	85.27 127	94.66 104	98.09 43	97.34 58	99.24 79	99.08 57

PVSNet_Blended		95.41 61	95.28 73	95.57 56	97.42 61	99.02 45	95.89 104	93.10 60	96.16 64	93.12 35	91.99 75	85.27 127	94.66 104	98.09 43	97.34 58	99.24 79	99.08 57

DeepPCF-MVS	95.28 2	97.00 40	98.35 22	95.42 60	97.30 64	98.94 51	94.82 123	96.03 38	98.24 10	92.11 52	95.80 42	98.64 33	95.51 90	98.95 7	98.66 6	96.78 195	99.20 44

CHOSEN 280x420		95.46 59	97.01 46	93.66 99	97.28 65	97.98 102	96.40 87	85.39 163	96.10 68	91.07 60	96.53 33	96.34 55	95.61 87	97.65 55	96.95 70	96.21 196	97.49 151

CHOSEN 1792x2688		92.66 118	92.49 128	92.85 109	97.13 66	98.89 59	95.90 102	88.50 129	95.32 90	83.31 126	71.99 202	88.96 102	94.10 115	96.69 85	96.49 79	98.15 176	99.10 54

HyFIR lowres test		92.03 122	91.55 147	92.58 110	97.13 66	98.72 68	94.65 127	86.54 148	93.58 126	82.56 129	67.75 213	90.47 89	95.67 84	95.87 116	95.54 109	98.91 131	98.93 79

OPM-MVS		93.61 106	92.43 132	95.00 68	96.94 68	97.34 114	97.78 48	94.23 46	89.64 174	85.53 117	88.70 108	82.81 146	96.28 75	96.28 104	95.00 127	99.24 79	97.22 159
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
XVS					96.60 69	99.35 18	96.82 69			90.85 63		98.72 30				99.46 34

X-MVStestdata					96.60 69	99.35 18	96.82 69			90.85 63		98.72 30				99.46 34

TSAR-MVS + COLMAP		94.79 73	94.51 86	95.11 65	96.50 71	97.54 108	97.99 45	94.54 44	97.81 18	85.88 116	96.73 32	81.28 153	96.99 58	96.29 103	95.21 119	98.76 148	96.73 173

PVSNet_Blended_VisFu		94.77 75	95.54 67	93.87 95	96.48 72	98.97 49	94.33 132	91.84 79	94.93 103	90.37 75	85.04 139	94.99 64	90.87 160	98.12 41	97.30 60	99.30 70	99.45 19

LGP-MVS_train		94.12 92	94.62 84	93.53 100	96.44 73	97.54 108	97.40 54	91.84 79	94.66 106	81.09 138	95.70 44	83.36 144	95.10 97	96.36 101	95.71 105	99.32 65	99.03 67

HQP-MVS		94.43 84	94.57 85	94.27 90	96.41 74	97.23 118	96.89 65	93.98 48	95.94 73	83.68 124	95.01 51	84.46 135	95.58 88	95.47 130	94.85 132	99.07 111	99.00 71

ACMM	92.75 10	94.41 86	93.84 104	95.09 66	96.41 74	96.80 127	94.88 122	93.54 51	96.41 58	90.16 77	92.31 73	83.11 145	96.32 74	96.22 106	94.65 134	99.22 85	97.35 156
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
RPSCF		94.05 93	94.00 100	94.12 92	96.20 76	96.41 141	96.61 79	91.54 87	95.83 78	89.73 85	96.94 31	92.80 76	95.35 94	91.63 194	90.44 196	95.27 208	93.94 199

test2506		94.32 88	93.00 119	95.87 52	96.16 77	99.39 16	96.96 62	92.80 64	95.22 96	94.47 28	91.55 84	70.45 199	95.25 95	98.29 29	97.98 30	99.59 7	98.10 131

ECVR-MVS		94.14 91	92.96 120	95.52 58	96.16 77	99.39 16	96.96 62	92.80 64	95.22 96	92.38 49	81.48 158	80.31 154	95.25 95	98.29 29	97.98 30	99.59 7	98.05 132

test1111		93.94 96	92.78 121	95.29 63	96.14 79	99.42 12	96.79 73	92.85 63	95.08 101	91.39 58	80.69 163	79.86 157	95.00 99	98.28 32	98.00 29	99.58 11	98.11 130

UA-Net		93.96 95	95.95 62	91.64 121	96.06 80	98.59 81	95.29 113	90.00 105	91.06 161	82.87 127	90.64 94	98.06 40	86.06 193	98.14 40	98.20 20	99.58 11	96.96 167

UGNet		94.92 68	96.63 52	92.93 108	96.03 81	98.63 79	94.53 129	91.52 88	96.23 62	90.03 80	92.87 68	96.10 59	86.28 192	96.68 86	96.60 78	99.16 98	99.32 28
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
ACMP	92.88 9	94.43 84	94.38 89	94.50 85	96.01 82	97.69 106	95.85 107	92.09 73	95.74 79	89.12 97	95.14 49	82.62 148	94.77 100	95.73 123	94.67 133	99.14 101	99.06 62
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
IB-MVS	89.56 15	91.71 128	92.50 127	90.79 133	95.94 83	98.44 87	87.05 204	91.38 92	93.15 130	92.98 42	84.78 140	85.14 130	78.27 211	92.47 182	94.44 145	99.10 106	99.08 57
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
MS-PatchMatch		91.82 126	92.51 126	91.02 127	95.83 84	96.88 123	95.05 117	84.55 177	93.85 121	82.01 131	82.51 154	91.71 80	90.52 167	95.07 140	93.03 172	98.13 177	94.52 190

CANet_DTU		93.92 98	96.57 53	90.83 131	95.63 85	98.39 88	96.99 61	87.38 139	96.26 61	71.97 185	96.31 35	93.02 74	94.53 107	97.38 63	96.83 74	98.49 166	97.79 138

ACMH	90.77 13	91.51 133	91.63 145	91.38 124	95.62 86	96.87 125	91.76 177	89.66 114	91.58 156	78.67 148	86.73 120	78.12 163	93.77 122	94.59 146	94.54 141	98.78 146	98.98 74
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
TSAR-MVS + GP.		97.45 32	98.36 20	96.39 42	95.56 87	98.93 53	97.74 49	93.31 54	97.61 28	94.24 31	98.44 10	99.19 17	98.03 35	97.60 56	97.41 55	99.44 45	99.33 26

thres600view7		93.49 109	92.37 135	94.79 78	95.42 88	98.93 53	96.58 81	92.31 68	93.04 131	87.88 106	86.62 123	76.94 172	97.09 56	96.82 77	95.63 106	99.45 38	98.63 101

thres400		93.56 107	92.43 132	94.87 75	95.40 89	98.91 56	96.70 77	92.38 67	92.93 133	88.19 104	86.69 121	77.35 169	97.13 53	96.75 82	95.85 100	99.42 50	98.56 104

thres200		93.62 105	92.54 125	94.88 73	95.36 90	98.93 53	96.75 75	92.31 68	92.84 134	88.28 102	86.99 118	77.81 168	97.13 53	96.82 77	95.92 96	99.45 38	98.49 110

thres100view900		93.55 108	92.47 131	94.81 77	95.33 91	98.74 66	96.78 74	92.30 71	92.63 137	88.29 100	87.21 116	78.01 165	96.78 63	96.38 98	95.92 96	99.38 57	98.40 116

tfpn200view9		93.64 104	92.57 124	94.89 72	95.33 91	98.94 51	96.82 69	92.31 68	92.63 137	88.29 100	87.21 116	78.01 165	97.12 55	96.82 77	95.85 100	99.45 38	98.56 104

IS_MVSNet		95.28 63	96.43 56	93.94 93	95.30 93	99.01 47	95.90 102	91.12 94	94.13 117	87.50 109	91.23 86	94.45 67	94.17 113	98.45 21	98.50 8	99.65 3	99.23 39

CMPMVS	65.18 17	84.76 202	83.10 208	86.69 190	95.29 94	95.05 184	88.37 199	85.51 162	80.27 217	71.31 189	68.37 211	73.85 184	85.25 197	87.72 209	87.75 206	94.38 216	88.70 216
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
sasdasda		95.25 65	95.45 69	95.00 68	95.27 95	98.72 68	96.89 65	89.82 109	96.51 54	90.84 66	93.72 59	86.01 119	97.66 41	95.78 120	97.94 35	99.54 19	99.50 13

canonicalmvs		95.25 65	95.45 69	95.00 68	95.27 95	98.72 68	96.89 65	89.82 109	96.51 54	90.84 66	93.72 59	86.01 119	97.66 41	95.78 120	97.94 35	99.54 19	99.50 13

MGCFI-Net		95.12 67	95.39 72	94.79 78	95.24 97	98.68 72	96.80 72	89.72 113	96.48 56	90.11 79	93.64 61	85.86 123	97.36 48	95.69 126	97.92 38	99.53 21	99.49 16

Vis-MVSNet (Re-imp)		94.46 83	96.24 58	92.40 112	95.23 98	98.64 77	95.56 110	90.99 95	94.42 111	85.02 119	90.88 93	94.65 66	88.01 182	98.17 38	98.37 17	99.57 14	98.53 107

CLD-MVS		94.79 73	94.36 90	95.30 62	95.21 99	97.46 111	97.23 57	92.24 72	96.43 57	91.77 55	92.69 69	84.31 136	96.06 78	95.52 128	95.03 124	99.31 68	99.06 62
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
baseline1		94.59 79	94.47 87	94.72 80	95.16 100	97.97 103	96.07 97	91.94 77	94.86 104	89.98 81	91.60 83	85.87 122	95.64 85	97.07 72	96.90 71	99.52 22	97.06 166

TDRefinement		89.07 165	88.15 174	90.14 142	95.16 100	96.88 123	95.55 111	90.20 103	89.68 173	76.42 161	76.67 176	74.30 182	84.85 200	93.11 172	91.91 190	98.64 158	94.47 191

ACMH+	90.88 12	91.41 134	91.13 150	91.74 120	95.11 102	96.95 122	93.13 149	89.48 118	92.42 143	79.93 143	85.13 138	78.02 164	93.82 121	93.49 167	93.88 156	98.94 127	97.99 134

DCV-MVSNet		94.76 76	95.12 79	94.35 89	95.10 103	95.81 161	96.46 85	89.49 117	96.33 60	90.16 77	92.55 71	90.26 90	95.83 83	95.52 128	96.03 93	99.06 114	99.33 26

Anonymous202405211			92.18 137		95.04 104	98.20 94	96.14 93	91.79 84	93.93 118		74.60 185	88.38 107	96.48 71	95.17 138	95.82 103	99.00 120	99.15 51

casdiffmvs_mvg		94.55 80	94.26 92	94.88 73	94.96 105	98.51 84	97.11 58	91.82 83	94.28 114	89.20 95	86.60 124	86.85 112	96.56 70	97.47 60	97.25 63	99.64 4	98.83 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
FC-MVSNet-train		93.85 100	93.91 101	93.78 97	94.94 106	96.79 130	94.29 133	91.13 93	93.84 122	88.26 103	90.40 96	85.23 129	94.65 106	96.54 92	95.31 115	99.38 57	99.28 30

EPP-MVSNet		95.27 64	96.18 60	94.20 91	94.88 107	98.64 77	94.97 119	90.70 98	95.34 89	89.67 86	91.66 82	93.84 70	95.42 93	97.32 64	97.00 68	99.58 11	99.47 18

FA-MVS(training)		93.94 96	95.16 76	92.53 111	94.87 108	98.57 83	95.42 112	79.49 197	95.37 87	90.98 61	86.54 126	94.26 69	95.44 92	97.80 53	95.19 120	98.97 123	98.38 118

EIA-MVS		95.50 56	96.19 59	94.69 81	94.83 109	98.88 60	95.93 101	91.50 89	94.47 110	89.43 89	93.14 64	92.72 77	97.05 57	97.82 52	97.13 65	99.43 48	99.15 51

ETV-MVS		96.31 52	97.47 39	94.96 71	94.79 110	98.78 64	96.08 96	91.41 91	96.16 64	90.50 71	95.76 43	96.20 57	97.39 46	98.42 24	97.82 42	99.57 14	99.18 48

MVS_Test		94.82 71	95.66 64	93.84 96	94.79 110	98.35 89	96.49 84	89.10 122	96.12 67	87.09 112	92.58 70	90.61 88	96.48 71	96.51 96	96.89 72	99.11 105	98.54 106

Anonymous20231211		93.49 109	92.33 136	94.84 76	94.78 112	98.00 101	96.11 94	91.85 78	94.86 104	90.91 62	74.69 184	89.18 99	96.73 64	94.82 143	95.51 110	98.67 153	99.24 38

baseline		94.83 70	95.82 63	93.68 98	94.75 113	97.80 104	96.51 83	88.53 128	97.02 47	89.34 93	92.93 66	92.18 79	94.69 103	95.78 120	96.08 89	98.27 174	98.97 78

EC-MVSNet		96.49 50	97.63 35	95.16 64	94.75 113	98.69 71	97.39 55	88.97 123	96.34 59	92.02 53	96.04 39	96.46 52	98.21 26	98.41 25	97.96 33	99.61 6	99.55 10

viewmanbaseed2359cas		94.31 89	94.25 93	94.38 88	94.72 115	98.59 81	96.09 95	91.84 79	95.35 88	87.92 105	87.86 114	85.54 124	96.45 73	96.71 84	97.04 66	99.26 77	98.67 98

MVSTER		94.89 69	95.07 80	94.68 82	94.71 116	96.68 133	97.00 60	90.57 100	95.18 98	93.05 39	95.21 48	86.41 116	93.72 123	97.59 57	95.88 99	99.00 120	98.50 109

EPMVS		90.88 139	92.12 138	89.44 150	94.71 116	97.24 117	93.55 140	76.81 204	95.89 74	81.77 133	91.49 85	86.47 115	93.87 118	90.21 201	90.07 198	95.92 199	93.49 205

casdiffmvs		94.38 87	94.15 99	94.64 83	94.70 118	98.51 84	96.03 99	91.66 85	95.70 80	89.36 92	86.48 128	85.03 133	96.60 68	97.40 62	97.30 60	99.52 22	98.67 98
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
diffmvs		94.31 89	94.21 94	94.42 87	94.64 119	98.28 90	96.36 88	91.56 86	96.77 49	88.89 99	88.97 105	84.23 137	96.01 81	96.05 112	96.41 81	99.05 118	98.79 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
viewmambaseed2359dif		93.92 98	93.38 114	94.54 84	94.55 120	98.15 97	96.41 86	91.47 90	95.10 100	89.58 87	86.64 122	85.10 131	96.17 77	94.08 159	95.77 104	99.09 107	98.84 91

DI_MVS_pp		94.01 94	93.63 108	94.44 86	94.54 121	98.26 92	97.51 52	90.63 99	95.88 75	89.34 93	80.54 165	89.36 96	95.48 91	96.33 102	96.27 85	99.17 95	98.78 96

thisisatest0530		94.54 81	95.47 68	93.46 102	94.51 122	98.65 76	94.66 126	90.72 96	95.69 82	86.90 113	93.80 57	89.44 95	94.74 101	96.98 76	94.86 129	99.19 93	98.85 89

tttt0517		94.52 82	95.44 71	93.44 103	94.51 122	98.68 72	94.61 128	90.72 96	95.61 84	86.84 114	93.78 58	89.26 98	94.74 101	97.02 75	94.86 129	99.20 92	98.87 87

ADS-MVSNet		89.80 154	91.33 149	88.00 171	94.43 124	96.71 132	92.29 165	74.95 214	96.07 69	77.39 153	88.67 109	86.09 118	93.26 130	88.44 207	89.57 201	95.68 202	93.81 202

tpmrst		88.86 169	89.62 161	87.97 172	94.33 125	95.98 151	92.62 157	76.36 207	94.62 108	76.94 157	85.98 134	82.80 147	92.80 135	86.90 213	87.15 209	94.77 213	93.93 200

PMMVS		94.61 78	95.56 66	93.50 101	94.30 126	96.74 131	94.91 121	89.56 116	95.58 85	87.72 107	96.15 36	92.86 75	96.06 78	95.47 130	95.02 125	98.43 171	97.09 162

CostFormer		90.69 140	90.48 158	90.93 129	94.18 127	96.08 149	94.03 135	78.20 200	93.47 127	89.96 82	90.97 92	80.30 155	93.72 123	87.66 211	88.75 203	95.51 205	96.12 178

USDC		90.69 140	90.52 157	90.88 130	94.17 128	96.43 140	95.82 108	86.76 145	93.92 119	76.27 163	86.49 127	74.30 182	93.67 125	95.04 141	93.36 165	98.61 159	94.13 195

Vis-MVSNet		92.77 116	95.00 82	90.16 140	94.10 129	98.79 63	94.76 125	88.26 130	92.37 146	79.95 142	88.19 113	91.58 81	84.38 203	97.59 57	97.58 50	99.52 22	98.91 83
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
Effi-MVS+		92.93 115	93.86 103	91.86 117	94.07 130	98.09 100	95.59 109	85.98 155	94.27 115	79.54 146	91.12 90	81.81 150	96.71 65	96.67 87	96.06 91	99.27 74	98.98 74

GeoE		92.52 120	92.64 123	92.39 113	93.96 131	97.76 105	96.01 100	85.60 160	93.23 129	83.94 122	81.56 157	84.80 134	95.63 86	96.22 106	95.83 102	99.19 93	99.07 61

IterMVS-LS		92.56 119	93.18 116	91.84 118	93.90 132	94.97 186	94.99 118	86.20 152	94.18 116	82.68 128	85.81 135	87.36 111	94.43 108	95.31 134	96.02 94	98.87 134	98.60 103
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
dps		90.11 152	89.37 165	90.98 128	93.89 133	96.21 146	93.49 142	77.61 202	91.95 152	92.74 46	88.85 106	78.77 162	92.37 138	87.71 210	87.71 207	95.80 201	94.38 193

tpm cat1		88.90 167	87.78 183	90.22 139	93.88 134	95.39 175	93.79 138	78.11 201	92.55 140	89.43 89	81.31 159	79.84 158	91.40 148	84.95 214	86.34 212	94.68 215	94.09 196

PatchmatchNet		90.56 142	92.49 128	88.31 162	93.83 135	96.86 126	92.42 161	76.50 206	95.96 72	78.31 149	91.96 77	89.66 94	93.48 127	90.04 203	89.20 202	95.32 206	93.73 203
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
TinyColmap		89.42 157	88.58 169	90.40 137	93.80 136	95.45 173	93.96 137	86.54 148	92.24 149	76.49 160	80.83 161	70.44 200	93.37 128	94.45 150	93.30 168	98.26 175	93.37 206

SCA		90.92 138	93.04 118	88.45 159	93.72 137	97.33 115	92.77 153	76.08 209	96.02 70	78.26 150	91.96 77	90.86 85	93.99 117	90.98 198	90.04 199	95.88 200	94.06 198

RPMNet		90.19 149	92.03 141	88.05 168	93.46 138	95.95 154	93.41 143	74.59 215	92.40 144	75.91 165	84.22 145	86.41 116	92.49 136	94.42 151	93.85 158	98.44 169	96.96 167

gg-mvs-nofinetune		86.17 196	88.57 170	83.36 203	93.44 139	98.15 97	96.58 81	72.05 218	74.12 222	49.23 226	64.81 217	90.85 86	89.90 175	97.83 50	96.84 73	98.97 123	97.41 154

MDTV_nov1_ep13		91.57 131	93.18 116	89.70 146	93.39 140	96.97 121	93.53 141	80.91 194	95.70 80	81.86 132	92.40 72	89.93 92	93.25 131	91.97 191	90.80 194	95.25 209	94.46 192

CR-MVSNet		90.16 150	91.96 142	88.06 167	93.32 141	95.95 154	93.36 145	75.99 210	92.40 144	75.19 171	83.18 150	85.37 126	92.05 140	95.21 136	94.56 139	98.47 168	97.08 164

test-LLR		91.62 130	93.56 111	89.35 152	93.31 142	96.57 136	92.02 173	87.06 143	92.34 147	75.05 174	90.20 97	88.64 104	90.93 156	96.19 109	94.07 151	97.75 186	96.90 170

test0.0.03 1		91.97 123	93.91 101	89.72 145	93.31 142	96.40 142	91.34 182	87.06 143	93.86 120	81.67 134	91.15 89	89.16 100	86.02 194	95.08 139	95.09 121	98.91 131	96.64 176

CVMVSNet		89.77 155	91.66 144	87.56 181	93.21 144	95.45 173	91.94 176	89.22 120	89.62 175	69.34 202	83.99 147	85.90 121	84.81 201	94.30 154	95.28 116	96.85 194	97.09 162

PatchT		89.13 164	91.71 143	86.11 195	92.92 145	95.59 168	83.64 212	75.09 213	91.87 153	75.19 171	82.63 153	85.06 132	92.05 140	95.21 136	94.56 139	97.76 185	97.08 164

Fast-Effi-MVS+		91.87 124	92.08 139	91.62 123	92.91 146	97.21 119	94.93 120	84.60 175	93.61 125	81.49 136	83.50 149	78.95 160	96.62 67	96.55 91	96.22 87	99.16 98	98.51 108

IterMVS-SCA-FT		90.24 147	92.48 130	87.63 178	92.85 147	94.30 202	93.79 138	81.47 193	92.66 136	69.95 197	84.66 142	88.38 107	89.99 173	95.39 133	94.34 146	97.74 188	97.63 147

baseline2		93.01 114	94.17 97	91.64 121	92.83 148	97.49 110	93.40 144	87.53 137	93.67 124	86.07 115	91.83 80	86.58 113	91.36 149	96.38 98	95.06 123	98.67 153	98.20 127

IterMVS		90.20 148	92.43 132	87.61 179	92.82 149	94.31 201	94.11 134	81.54 191	92.97 132	69.90 198	84.71 141	88.16 110	89.96 174	95.25 135	94.17 149	97.31 190	97.46 152
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CDS-MVSNet		92.77 116	93.60 109	91.80 119	92.63 150	96.80 127	95.24 114	89.14 121	90.30 171	84.58 120	86.76 119	90.65 87	90.42 168	95.89 115	96.49 79	98.79 145	98.32 123
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
tpm		87.95 177	89.44 164	86.21 194	92.53 151	94.62 196	91.40 180	76.36 207	91.46 157	69.80 200	87.43 115	75.14 177	91.55 147	89.85 205	90.60 195	95.61 203	96.96 167

Effi-MVS+-dtu		91.78 127	93.59 110	89.68 148	92.44 152	97.11 120	94.40 131	84.94 171	92.43 142	75.48 167	91.09 91	83.75 141	93.55 126	96.61 88	95.47 111	97.24 191	98.67 98

testgi		89.42 157	91.50 148	87.00 188	92.40 153	95.59 168	89.15 198	85.27 167	92.78 135	72.42 183	91.75 81	76.00 175	84.09 205	94.38 152	93.82 160	98.65 157	96.15 177

Fast-Effi-MVS+-dtu		91.19 135	93.64 107	88.33 161	92.19 154	96.46 139	93.99 136	81.52 192	92.59 139	71.82 186	92.17 74	85.54 124	91.68 146	95.73 123	94.64 135	98.80 143	98.34 120

FC-MVSNet-test		91.63 129	93.82 105	89.08 153	92.02 155	96.40 142	93.26 147	87.26 140	93.72 123	77.26 154	88.61 110	89.86 93	85.50 196	95.72 125	95.02 125	99.16 98	97.44 153

GA-MVS		89.28 160	90.75 156	87.57 180	91.77 156	96.48 138	92.29 165	87.58 136	90.61 168	65.77 207	84.48 143	76.84 173	89.46 176	95.84 117	93.68 161	98.52 164	97.34 157

dmvs_re		91.84 125	91.60 146	92.12 116	91.60 157	97.26 116	95.14 116	91.96 75	91.02 162	80.98 139	86.56 125	77.96 167	93.84 120	94.71 144	95.08 122	99.22 85	98.62 102

UniMVSNet_ETH3D		88.47 171	86.00 201	91.35 125	91.55 158	96.29 144	92.53 158	88.81 124	85.58 204	82.33 130	67.63 214	66.87 214	94.04 116	91.49 195	95.24 117	98.84 137	98.92 80

TAMVS		90.54 144	90.87 155	90.16 140	91.48 159	96.61 135	93.26 147	86.08 153	87.71 190	81.66 135	83.11 152	84.04 138	90.42 168	94.54 147	94.60 136	98.04 181	95.48 186

tfpnnormal		88.50 170	87.01 192	90.23 138	91.36 160	95.78 163	92.74 154	90.09 104	83.65 209	76.33 162	71.46 205	69.58 205	91.84 143	95.54 127	94.02 153	99.06 114	99.03 67

GBi-Net		93.81 101	94.18 95	93.38 104	91.34 161	95.86 157	96.22 90	88.68 125	95.23 93	90.40 72	86.39 129	91.16 82	94.40 110	96.52 93	96.30 82	99.21 89	97.79 138

test1		93.81 101	94.18 95	93.38 104	91.34 161	95.86 157	96.22 90	88.68 125	95.23 93	90.40 72	86.39 129	91.16 82	94.40 110	96.52 93	96.30 82	99.21 89	97.79 138

FMVSNet2		93.30 112	93.36 115	93.22 107	91.34 161	95.86 157	96.22 90	88.24 131	95.15 99	89.92 84	81.64 156	89.36 96	94.40 110	96.77 81	96.98 69	99.21 89	97.79 138

FMVSNet3		93.79 103	94.17 97	93.35 106	91.21 164	95.99 150	96.62 78	88.68 125	95.23 93	90.40 72	86.39 129	91.16 82	94.11 114	95.96 113	96.67 76	99.07 111	97.79 138

TransMVSNet (Re)		87.73 183	86.79 194	88.83 155	90.76 165	94.40 199	91.33 183	89.62 115	84.73 206	75.41 169	72.73 198	71.41 195	86.80 188	94.53 148	93.93 155	99.06 114	95.83 180

LTVRE_ROB	87.32 16	87.55 184	88.25 173	86.73 189	90.66 166	95.80 162	93.05 150	84.77 172	83.35 210	60.32 219	83.12 151	67.39 212	93.32 129	94.36 153	94.86 129	98.28 173	98.87 87
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
EG-PatchMatch MVS		86.68 192	87.24 188	86.02 196	90.58 167	96.26 145	91.08 186	81.59 190	84.96 205	69.80 200	71.35 206	75.08 179	84.23 204	94.24 156	93.35 166	98.82 138	95.46 187

TESTMET0.1,1		91.07 136	93.56 111	88.17 163	90.43 168	96.57 136	92.02 173	82.83 186	92.34 147	75.05 174	90.20 97	88.64 104	90.93 156	96.19 109	94.07 151	97.75 186	96.90 170

pm-mvs1		89.19 163	89.02 166	89.38 151	90.40 169	95.74 164	92.05 171	88.10 133	86.13 200	77.70 151	73.72 193	79.44 159	88.97 179	95.81 119	94.51 143	99.08 109	97.78 143

NR-MVSNet		89.34 159	88.66 168	90.13 143	90.40 169	95.61 166	93.04 151	89.91 106	91.22 159	78.96 147	77.72 173	68.90 208	89.16 178	94.24 156	93.95 154	99.32 65	98.99 72

FMVSNet1		91.54 132	90.93 153	92.26 114	90.35 171	95.27 179	95.22 115	87.16 142	91.37 158	87.62 108	75.45 179	83.84 140	94.43 108	96.52 93	96.30 82	98.82 138	97.74 144

test-mter		90.95 137	93.54 113	87.93 173	90.28 172	96.80 127	91.44 179	82.68 187	92.15 151	74.37 178	89.57 103	88.23 109	90.88 159	96.37 100	94.31 147	97.93 183	97.37 155

pmmvs4		90.55 143	89.91 160	91.30 126	90.26 173	94.95 187	92.73 155	87.94 134	93.44 128	85.35 118	82.28 155	76.09 174	93.02 134	93.56 165	92.26 188	98.51 165	96.77 172

MVS-HIRNet		85.36 200	86.89 193	83.57 202	90.13 174	94.51 197	83.57 213	72.61 217	88.27 186	71.22 190	68.97 209	81.81 150	88.91 180	93.08 173	91.94 189	94.97 212	89.64 215

thisisatest0515		90.12 151	92.06 140	87.85 174	90.03 175	96.17 147	87.83 201	87.45 138	91.71 155	77.15 155	85.40 137	84.01 139	85.74 195	95.41 132	93.30 168	98.88 133	98.43 112

SixPastTwentyTwo		88.37 172	89.47 163	87.08 186	90.01 176	95.93 156	87.41 202	85.32 164	90.26 172	70.26 194	86.34 132	71.95 192	90.93 156	92.89 177	91.72 191	98.55 162	97.22 159

UniMVSNet (Re)		90.03 153	89.61 162	90.51 136	89.97 177	96.12 148	92.32 163	89.26 119	90.99 163	80.95 140	78.25 172	75.08 179	91.14 152	93.78 160	93.87 157	99.41 51	99.21 43

pmnet_mix02		86.12 197	87.12 191	84.96 199	89.82 178	94.12 203	84.88 210	86.63 147	91.78 154	65.60 208	80.76 162	76.98 171	86.61 190	87.29 212	84.80 215	96.21 196	94.09 196

our_test_3					89.78 179	93.84 205	85.59 207

UniMVSNet_NR-MVSNet		90.35 146	89.96 159	90.80 132	89.66 180	95.83 160	92.48 159	90.53 101	90.96 164	79.57 144	79.33 169	77.14 170	93.21 132	92.91 176	94.50 144	99.37 59	99.05 64

v8		88.21 175	87.94 180	88.51 158	89.62 181	95.01 185	92.31 164	84.99 169	88.94 177	74.70 176	75.03 181	73.51 186	90.67 164	92.11 187	92.74 180	98.80 143	98.24 125

WR-MVS_H		87.93 178	87.85 181	88.03 170	89.62 181	95.58 170	90.47 191	85.55 161	87.20 195	76.83 158	74.42 188	72.67 190	86.37 191	93.22 171	93.04 171	99.33 63	98.83 92

pmmvs5		87.83 182	88.09 175	87.51 183	89.59 183	95.48 171	89.75 196	84.73 173	86.07 202	71.44 188	80.57 164	70.09 203	90.74 163	94.47 149	92.87 176	98.82 138	97.10 161

gm-plane-assit		83.26 206	85.29 203	80.89 206	89.52 184	89.89 217	70.26 223	78.24 199	77.11 220	58.01 223	74.16 190	66.90 213	90.63 166	97.20 67	96.05 92	98.66 156	95.68 183

v10		88.00 176	87.96 178	88.05 168	89.44 185	94.68 193	92.36 162	83.35 182	89.37 176	72.96 182	73.98 191	72.79 189	91.35 150	93.59 162	92.88 175	98.81 141	98.42 114

V42		88.31 173	87.95 179	88.73 156	89.44 185	95.34 176	92.23 167	87.21 141	88.83 179	74.49 177	74.89 183	73.43 187	90.41 170	92.08 189	92.77 179	98.60 161	98.33 121

v148		87.51 185	86.79 194	88.36 160	89.39 187	95.21 181	89.84 195	88.20 132	87.61 192	77.56 152	73.38 196	70.32 202	86.80 188	90.70 199	92.31 186	98.37 172	97.98 136

CP-MVSNet		87.89 181	87.27 187	88.62 157	89.30 188	95.06 183	90.60 190	85.78 157	87.43 194	75.98 164	74.60 185	68.14 211	90.76 161	93.07 174	93.60 162	99.30 70	98.98 74

v1144		87.92 180	87.79 182	88.07 165	89.27 189	95.15 182	92.17 168	85.62 159	88.52 183	71.52 187	73.80 192	72.40 191	91.06 154	93.54 166	92.80 177	98.81 141	98.33 121

DU-MVS		89.67 156	88.84 167	90.63 135	89.26 190	95.61 166	92.48 159	89.91 106	91.22 159	79.57 144	77.72 173	71.18 196	93.21 132	92.53 180	94.57 138	99.35 62	99.05 64

WR-MVS		87.93 178	88.09 175	87.75 175	89.26 190	95.28 177	90.81 188	86.69 146	88.90 178	75.29 170	74.31 189	73.72 185	85.19 199	92.26 183	93.32 167	99.27 74	98.81 94

Baseline_NR-MVSNet		89.27 161	88.01 177	90.73 134	89.26 190	93.71 206	92.71 156	89.78 112	90.73 165	81.28 137	73.53 194	72.85 188	92.30 139	92.53 180	93.84 159	99.07 111	98.88 85

N_pmnet		84.80 201	85.10 205	84.45 200	89.25 193	92.86 209	84.04 211	86.21 150	88.78 180	66.73 206	72.41 201	74.87 181	85.21 198	88.32 208	86.45 210	95.30 207	92.04 209

v2v482		88.25 174	87.71 184	88.88 154	89.23 194	95.28 177	92.10 169	87.89 135	88.69 182	73.31 181	75.32 180	71.64 193	91.89 142	92.10 188	92.92 174	98.86 136	97.99 134

PS-CasMVS		87.33 188	86.68 197	88.10 164	89.22 195	94.93 188	90.35 193	85.70 158	86.44 199	74.01 179	73.43 195	66.59 217	90.04 172	92.92 175	93.52 163	99.28 72	98.91 83

TranMVSNet+NR-MVSNet		89.23 162	88.48 171	90.11 144	89.07 196	95.25 180	92.91 152	90.43 102	90.31 170	77.10 156	76.62 177	71.57 194	91.83 144	92.12 186	94.59 137	99.32 65	98.92 80

v1192		87.51 185	87.31 186	87.74 176	89.04 197	94.87 191	92.07 170	85.03 168	88.49 184	70.32 193	72.65 199	70.35 201	91.21 151	93.59 162	92.80 177	98.78 146	98.42 114

v144192		87.40 187	87.20 189	87.64 177	88.89 198	94.88 190	91.65 178	84.70 174	87.80 189	71.17 191	73.20 197	70.91 197	90.75 162	92.69 178	92.49 183	98.71 150	98.43 112

PEN-MVS		87.22 190	86.50 199	88.07 165	88.88 199	94.44 198	90.99 187	86.21 150	86.53 198	73.66 180	74.97 182	66.56 218	89.42 177	91.20 197	93.48 164	99.24 79	98.31 124

v1921920		87.31 189	87.13 190	87.52 182	88.87 200	94.72 192	91.96 175	84.59 176	88.28 185	69.86 199	72.50 200	70.03 204	91.10 153	93.33 169	92.61 182	98.71 150	98.44 111

pmmvs6		85.98 198	84.89 206	87.25 185	88.83 201	94.35 200	89.36 197	85.30 166	78.51 219	75.44 168	62.71 219	75.41 176	87.65 184	93.58 164	92.40 185	96.89 193	97.29 158

v1240		86.89 191	86.75 196	87.06 187	88.75 202	94.65 195	91.30 184	84.05 178	87.49 193	68.94 203	71.96 203	68.86 209	90.65 165	93.33 169	92.72 181	98.67 153	98.24 125

anonymousdsp		88.90 167	91.00 152	86.44 192	88.74 203	95.97 152	90.40 192	82.86 185	88.77 181	67.33 205	81.18 160	81.44 152	90.22 171	96.23 105	94.27 148	99.12 104	99.16 50

EU-MVSNet		85.62 199	87.65 185	83.24 204	88.54 204	92.77 210	87.12 203	85.32 164	86.71 196	64.54 210	78.52 171	75.11 178	78.35 210	92.25 184	92.28 187	95.58 204	95.93 179

DTE-MVSNet		86.67 193	86.09 200	87.35 184	88.45 205	94.08 204	90.65 189	86.05 154	86.13 200	72.19 184	74.58 187	66.77 216	87.61 185	90.31 200	93.12 170	99.13 102	97.62 148

FMVSNet5		90.36 145	90.93 153	89.70 146	87.99 206	92.25 211	92.03 172	83.51 181	92.20 150	84.13 121	85.59 136	86.48 114	92.43 137	94.61 145	94.52 142	98.13 177	90.85 212

v7n		86.43 194	86.52 198	86.33 193	87.91 207	94.93 188	90.15 194	83.05 183	86.57 197	70.21 195	71.48 204	66.78 215	87.72 183	94.19 158	92.96 173	98.92 129	98.76 97

test20.03		82.92 207	85.52 202	79.90 209	87.75 208	91.84 212	82.80 214	82.99 184	82.65 214	60.32 219	78.90 170	70.50 198	67.10 218	92.05 190	90.89 193	98.44 169	91.80 210

MDTV_nov1_ep13_2view		86.30 195	88.27 172	84.01 201	87.71 209	94.67 194	88.08 200	76.78 205	90.59 169	68.66 204	80.46 166	80.12 156	87.58 186	89.95 204	88.20 205	95.25 209	93.90 201

Anonymous20231206		83.84 205	85.19 204	82.26 205	87.38 210	92.87 208	85.49 208	83.65 180	86.07 202	63.44 214	68.42 210	69.01 207	75.45 214	93.34 168	92.44 184	98.12 179	94.20 194

FPMVS		75.84 213	74.59 217	77.29 213	86.92 211	83.89 222	85.01 209	80.05 196	82.91 212	60.61 218	65.25 216	60.41 222	63.86 219	75.60 220	73.60 222	87.29 223	80.47 220

MIMVSNet		88.99 166	91.07 151	86.57 191	86.78 212	95.62 165	91.20 185	75.40 212	90.65 167	76.57 159	84.05 146	82.44 149	91.01 155	95.84 117	95.38 113	98.48 167	93.50 204

tmp_tt				66.88 216	86.07 213	73.86 225	68.22 224	33.38 226	96.88 48	80.67 141	88.23 112	78.82 161	49.78 223	82.68 217	77.47 220	83.19 225

WB-MVS		69.22 215	76.91 216	60.24 219	85.80 214	79.37 223	56.86 228	84.96 170	81.50 215	18.16 231	76.85 175	61.07 220	34.23 226	82.46 218	81.81 217	81.43 226	75.31 224

PM-MVS		84.72 203	84.47 207	85.03 198	84.67 215	91.57 213	86.27 206	82.31 189	87.65 191	70.62 192	76.54 178	56.41 226	88.75 181	92.59 179	89.85 200	97.54 189	96.66 175

pmmvs-eth3d		84.33 204	82.94 209	85.96 197	84.16 216	90.94 214	86.55 205	83.79 179	84.25 207	75.85 166	70.64 207	56.43 225	87.44 187	92.20 185	90.41 197	97.97 182	95.68 183

new-patchmatchnet		78.49 212	78.19 215	78.84 211	84.13 217	90.06 216	77.11 221	80.39 195	79.57 218	59.64 222	66.01 215	55.65 227	75.62 213	84.55 215	80.70 218	96.14 198	90.77 213

new_pmnet		81.53 208	82.68 210	80.20 207	83.47 218	89.47 218	82.21 216	78.36 198	87.86 188	60.14 221	67.90 212	69.43 206	82.03 208	89.22 206	87.47 208	94.99 211	87.39 217

ET-MVSNet_ETH3D		93.34 111	94.33 91	92.18 115	83.26 219	97.66 107	96.72 76	89.89 108	95.62 83	87.17 111	96.00 40	83.69 142	96.99 58	93.78 160	95.34 114	99.06 114	98.18 128

pmmvs3		79.16 211	80.12 213	78.05 212	79.36 220	86.59 220	78.13 220	73.87 216	76.42 221	57.51 224	70.59 208	57.02 224	84.66 202	90.10 202	88.32 204	94.75 214	91.77 211

PMVS	63.12 18	67.27 217	66.39 220	68.30 215	77.98 221	60.24 228	59.53 227	76.82 203	66.65 223	60.74 217	54.39 221	59.82 223	51.24 222	73.92 223	70.52 223	83.48 224	79.17 222
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MDA-MVSNet-bldmvs		80.11 209	80.24 212	79.94 208	77.01 222	93.21 207	78.86 219	85.94 156	82.71 213	60.86 216	79.71 168	51.77 228	83.71 207	75.60 220	86.37 211	93.28 217	92.35 207

ambc			73.83 218		76.23 223	85.13 221	82.27 215		84.16 208	65.58 209	52.82 222	23.31 233	73.55 215	91.41 196	85.26 214	92.97 218	94.70 189

Gipuma		68.35 216	66.71 219	70.27 214	74.16 224	68.78 226	63.93 226	71.77 219	83.34 211	54.57 225	34.37 224	31.88 230	68.69 217	83.30 216	85.53 213	88.48 221	79.78 221
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MIMVSNet1		80.03 210	80.93 211	78.97 210	72.46 225	90.73 215	80.81 217	82.44 188	80.39 216	63.64 212	57.57 220	64.93 219	76.37 212	91.66 193	91.55 192	98.07 180	89.70 214

PMMVS2		64.36 219	65.94 221	62.52 218	67.37 226	77.44 224	64.39 225	69.32 223	61.47 224	34.59 227	46.09 223	41.03 229	48.02 225	74.56 222	78.23 219	91.43 219	82.76 219

EMVS		49.98 221	46.76 224	53.74 221	64.96 227	51.29 230	37.81 230	69.35 222	51.83 225	22.69 230	29.57 226	25.06 231	57.28 220	44.81 226	56.11 225	70.32 228	68.64 226

E-PMN		50.67 220	47.85 223	53.96 220	64.13 228	50.98 231	38.06 229	69.51 221	51.40 226	24.60 229	29.46 227	24.39 232	56.07 221	48.17 225	59.70 224	71.40 227	70.84 225

test_method		72.96 214	78.68 214	66.28 217	50.17 229	64.90 227	75.45 222	50.90 225	87.89 187	62.54 215	62.98 218	68.34 210	70.45 216	91.90 192	82.41 216	88.19 222	92.35 207

MVE	50.86 19	49.54 222	51.43 222	47.33 222	44.14 230	59.20 229	36.45 231	60.59 224	41.47 227	31.14 228	29.58 225	17.06 234	48.52 224	62.22 224	74.63 221	63.12 229	75.87 223
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		12.09 223	16.94 225	6.42 224	3.15 231	6.08 232	9.51 233	3.84 227	21.46 228	5.31 232	27.49 228	6.76 235	10.89 227	17.06 227	15.01 226	5.84 230	24.75 227

GG-mvs-BLEND		66.17 218	94.91 83	32.63 223	1.32 232	96.64 134	91.40 180	0.85 229	94.39 113	2.20 233	90.15 99	95.70 62	2.27 229	96.39 97	95.44 112	97.78 184	95.68 183

test123		9.58 224	13.53 226	4.97 225	1.31 233	5.47 233	8.32 234	2.95 228	18.14 229	2.03 234	20.82 229	2.34 236	10.60 228	10.00 228	14.16 227	4.60 231	23.77 228

uanet_test		0.00 225	0.00 227	0.00 226	0.00 234	0.00 234	0.00 235	0.00 230	0.00 230	0.00 235	0.00 230	0.00 237	0.00 230	0.00 229	0.00 228	0.00 232	0.00 229

sosnet-low-res		0.00 225	0.00 227	0.00 226	0.00 234	0.00 234	0.00 235	0.00 230	0.00 230	0.00 235	0.00 230	0.00 237	0.00 230	0.00 229	0.00 228	0.00 232	0.00 229

sosnet		0.00 225	0.00 227	0.00 226	0.00 234	0.00 234	0.00 235	0.00 230	0.00 230	0.00 235	0.00 230	0.00 237	0.00 230	0.00 229	0.00 228	0.00 232	0.00 229

RE-MVS-def										63.50 213

9.14												99.28 12

MTAPA										96.83 10		99.12 21

MTMP										97.18 5		98.83 26

Patchmatch-RL test							34.61 232

NP-MVS									95.32 90

Patchmtry						95.96 153	93.36 145	75.99 210		75.19 171

DeepMVS_CX						86.86 219	79.50 218	70.43 220	90.73 165	63.66 211	80.36 167	60.83 221	79.68 209	76.23 219		89.46 220	86.53 218

High-resolution multi-view benchmark