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 64	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 87	99.17 95	98.39 118

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	94.64 108	89.18 96	84.44 145	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 94	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 89	99.09 108	98.01 134
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 83			98.92 130	97.55 150
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 72	87.29 111	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 91	98.54 164	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 96	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	97.65 147

MAR-MVS		95.50 56	95.60 65	95.39 61	98.67 40	98.18 97	95.89 105	89.81 112	94.55 110	91.97 54	92.99 65	90.21 91	97.30 49	96.79 80	97.49 51	98.72 150	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	98.96 126	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 78	98.94 128	98.14 130

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 91	97.41 53	93.67 50	95.86 77	92.86 43	97.51 25	93.79 71	91.76 146	97.03 74	97.03 67	98.61 160	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	99.04 120	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	93.73 107	96.09 48	98.34 47	97.43 114	97.06 59	96.05 37	95.84 78	90.56 70	86.30 134	89.10 101	95.55 90	96.13 112	95.61 108	99.00 121	95.73 183

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 99	99.28 72	97.82 138
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 95	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	98.95 127	97.55 150

EPNet_dtu		92.45 122	95.02 81	89.46 150	98.02 53	95.47 173	94.79 125	92.62 66	94.97 103	70.11 197	94.76 55	92.61 78	84.07 207	95.94 115	95.56 109	97.15 193	95.82 182
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	83.65 126	88.71 107	88.64 104	97.82 37	97.49 59	97.42 54	99.26 77	97.72 146

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	94.42 112	92.79 44	88.52 111	93.48 73	95.06 99	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	93.27 114	92.71 123	93.93 95	97.75 58	97.44 113	96.07 98	93.17 59	95.40 87	83.86 124	83.76 149	88.72 103	93.87 119	94.25 156	94.11 151	98.87 135	95.28 189
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	92.50 142	89.09 98	89.85 101	96.65 51	96.90 60	94.97 143	94.89 129	99.08 110	98.38 119
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 98	94.50 131	91.99 74	95.32 91	91.31 59	95.47 46	83.44 143	96.02 81	96.56 90	95.23 119	98.69 153	96.67 175

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	95.89 105	93.10 60	96.16 65	93.12 35	91.99 75	85.27 127	94.66 105	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	95.89 105	93.10 60	96.16 65	93.12 35	91.99 75	85.27 127	94.66 105	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	94.82 124	96.03 38	98.24 10	92.11 52	95.80 42	98.64 33	95.51 91	98.95 7	98.66 6	96.78 196	99.20 44

CHOSEN 280x420		95.46 59	97.01 46	93.66 100	97.28 65	97.98 103	96.40 87	85.39 164	96.10 69	91.07 60	96.53 33	96.34 55	95.61 88	97.65 55	96.95 70	96.21 197	97.49 152

CHOSEN 1792x2688		92.66 119	92.49 129	92.85 110	97.13 66	98.89 59	95.90 103	88.50 130	95.32 91	83.31 127	71.99 203	88.96 102	94.10 116	96.69 85	96.49 80	98.15 177	99.10 54

HyFIR lowres test		92.03 123	91.55 148	92.58 111	97.13 66	98.72 68	94.65 128	86.54 149	93.58 127	82.56 130	67.75 214	90.47 89	95.67 85	95.87 117	95.54 110	98.91 132	98.93 79

OPM-MVS		93.61 107	92.43 133	95.00 68	96.94 68	97.34 115	97.78 48	94.23 46	89.64 175	85.53 118	88.70 108	82.81 147	96.28 75	96.28 104	95.00 128	99.24 79	97.22 160
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	97.54 109	97.99 45	94.54 44	97.81 18	85.88 117	96.73 32	81.28 154	96.99 58	96.29 103	95.21 120	98.76 149	96.73 174

PVSNet_Blended_VisFu		94.77 75	95.54 67	93.87 96	96.48 72	98.97 49	94.33 133	91.84 79	94.93 104	90.37 75	85.04 140	94.99 64	90.87 161	98.12 41	97.30 60	99.30 70	99.45 19

LGP-MVS_train		94.12 92	94.62 84	93.53 101	96.44 73	97.54 109	97.40 54	91.84 79	94.66 107	81.09 139	95.70 44	83.36 144	95.10 98	96.36 101	95.71 106	99.32 65	99.03 67

HQP-MVS		94.43 84	94.57 85	94.27 91	96.41 74	97.23 119	96.89 65	93.98 48	95.94 74	83.68 125	95.01 51	84.46 135	95.58 89	95.47 131	94.85 133	99.07 112	99.00 71

ACMM	92.75 10	94.41 86	93.84 105	95.09 66	96.41 74	96.80 128	94.88 123	93.54 51	96.41 58	90.16 77	92.31 73	83.11 146	96.32 74	96.22 106	94.65 135	99.22 85	97.35 157
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
RPSCF		94.05 94	94.00 100	94.12 93	96.20 76	96.41 142	96.61 79	91.54 87	95.83 79	89.73 85	96.94 31	92.80 76	95.35 95	91.63 195	90.44 197	95.27 209	93.94 200

test2506		94.32 88	93.00 120	95.87 52	96.16 77	99.39 16	96.96 62	92.80 64	95.22 97	94.47 28	91.55 84	70.45 200	95.25 96	98.29 29	97.98 30	99.59 7	98.10 132

ECVR-MVS		94.14 91	92.96 121	95.52 58	96.16 77	99.39 16	96.96 62	92.80 64	95.22 97	92.38 49	81.48 159	80.31 155	95.25 96	98.29 29	97.98 30	99.59 7	98.05 133

test1111		93.94 97	92.78 122	95.29 63	96.14 79	99.42 12	96.79 73	92.85 63	95.08 102	91.39 58	80.69 164	79.86 158	95.00 100	98.28 32	98.00 29	99.58 11	98.11 131

UA-Net		93.96 96	95.95 62	91.64 122	96.06 80	98.59 81	95.29 114	90.00 106	91.06 162	82.87 128	90.64 94	98.06 40	86.06 194	98.14 40	98.20 20	99.58 11	96.96 168

UGNet		94.92 68	96.63 52	92.93 109	96.03 81	98.63 79	94.53 130	91.52 88	96.23 63	90.03 80	92.87 68	96.10 59	86.28 193	96.68 86	96.60 79	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	97.69 107	95.85 108	92.09 73	95.74 80	89.12 97	95.14 49	82.62 149	94.77 101	95.73 124	94.67 134	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	91.71 129	92.50 128	90.79 134	95.94 83	98.44 87	87.05 205	91.38 93	93.15 131	92.98 42	84.78 141	85.14 130	78.27 212	92.47 183	94.44 146	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		91.82 127	92.51 127	91.02 128	95.83 84	96.88 124	95.05 118	84.55 178	93.85 122	82.01 132	82.51 155	91.71 80	90.52 168	95.07 141	93.03 173	98.13 178	94.52 191

CANet_DTU		93.92 99	96.57 53	90.83 132	95.63 85	98.39 88	96.99 61	87.38 140	96.26 62	71.97 186	96.31 35	93.02 74	94.53 108	97.38 63	96.83 74	98.49 167	97.79 139

ACMH	90.77 13	91.51 134	91.63 146	91.38 125	95.62 86	96.87 126	91.76 178	89.66 115	91.58 157	78.67 149	86.73 121	78.12 164	93.77 123	94.59 147	94.54 142	98.78 147	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		93.49 110	92.37 136	94.79 78	95.42 88	98.93 53	96.58 81	92.31 68	93.04 132	87.88 107	86.62 124	76.94 173	97.09 56	96.82 77	95.63 107	99.45 38	98.63 102

thres400		93.56 108	92.43 133	94.87 75	95.40 89	98.91 56	96.70 77	92.38 67	92.93 134	88.19 105	86.69 122	77.35 170	97.13 53	96.75 82	95.85 101	99.42 50	98.56 105

thres200		93.62 106	92.54 126	94.88 73	95.36 90	98.93 53	96.75 75	92.31 68	92.84 135	88.28 103	86.99 119	77.81 169	97.13 53	96.82 77	95.92 97	99.45 38	98.49 111

thres100view900		93.55 109	92.47 132	94.81 77	95.33 91	98.74 66	96.78 74	92.30 71	92.63 138	88.29 101	87.21 117	78.01 166	96.78 63	96.38 98	95.92 97	99.38 57	98.40 117

tfpn200view9		93.64 105	92.57 125	94.89 72	95.33 91	98.94 51	96.82 69	92.31 68	92.63 138	88.29 101	87.21 117	78.01 166	97.12 55	96.82 77	95.85 101	99.45 38	98.56 105

IS_MVSNet		95.28 63	96.43 56	93.94 94	95.30 93	99.01 47	95.90 103	91.12 95	94.13 118	87.50 110	91.23 86	94.45 67	94.17 114	98.45 21	98.50 8	99.65 3	99.23 39

CMPMVS	65.18 17	84.76 203	83.10 209	86.69 191	95.29 94	95.05 185	88.37 200	85.51 163	80.27 218	71.31 190	68.37 212	73.85 185	85.25 198	87.72 210	87.75 207	94.38 217	88.70 217
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	89.82 110	96.51 54	90.84 66	93.72 59	86.01 119	97.66 41	95.78 121	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	89.82 110	96.51 54	90.84 66	93.72 59	86.01 119	97.66 41	95.78 121	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	89.72 114	96.48 56	90.11 79	93.64 61	85.86 123	97.36 48	95.69 127	97.92 38	99.53 21	99.49 16

Vis-MVSNet (Re-imp)		94.46 83	96.24 58	92.40 113	95.23 98	98.64 77	95.56 111	90.99 96	94.42 112	85.02 120	90.88 93	94.65 66	88.01 183	98.17 38	98.37 17	99.57 14	98.53 108

CLD-MVS		94.79 73	94.36 90	95.30 62	95.21 99	97.46 112	97.23 57	92.24 72	96.43 57	91.77 55	92.69 69	84.31 136	96.06 79	95.52 129	95.03 125	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	97.97 104	96.07 98	91.94 77	94.86 105	89.98 81	91.60 83	85.87 122	95.64 86	97.07 72	96.90 71	99.52 22	97.06 167

TDRefinement		89.07 166	88.15 175	90.14 143	95.16 100	96.88 124	95.55 112	90.20 104	89.68 174	76.42 162	76.67 177	74.30 183	84.85 201	93.11 173	91.91 191	98.64 159	94.47 192

ACMH+	90.88 12	91.41 135	91.13 151	91.74 121	95.11 102	96.95 123	93.13 150	89.48 119	92.42 144	79.93 144	85.13 139	78.02 165	93.82 122	93.49 168	93.88 157	98.94 128	97.99 135

DCV-MVSNet		94.76 76	95.12 79	94.35 90	95.10 103	95.81 162	96.46 85	89.49 118	96.33 61	90.16 77	92.55 71	90.26 90	95.83 84	95.52 129	96.03 94	99.06 115	99.33 26

Anonymous202405211			92.18 138		95.04 104	98.20 95	96.14 94	91.79 84	93.93 119		74.60 186	88.38 107	96.48 71	95.17 139	95.82 104	99.00 121	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	94.28 115	89.20 95	86.60 125	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		93.85 101	93.91 101	93.78 98	94.94 106	96.79 131	94.29 134	91.13 94	93.84 123	88.26 104	90.40 96	85.23 129	94.65 107	96.54 92	95.31 116	99.38 57	99.28 30

EPP-MVSNet		95.27 64	96.18 60	94.20 92	94.88 107	98.64 77	94.97 120	90.70 99	95.34 90	89.67 86	91.66 82	93.84 70	95.42 94	97.32 64	97.00 68	99.58 11	99.47 18

FA-MVS(training)		93.94 97	95.16 76	92.53 112	94.87 108	98.57 83	95.42 113	79.49 198	95.37 88	90.98 61	86.54 127	94.26 69	95.44 93	97.80 53	95.19 121	98.97 124	98.38 119

EIA-MVS		95.50 56	96.19 59	94.69 81	94.83 109	98.88 60	95.93 102	91.50 90	94.47 111	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 97	91.41 92	96.16 65	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 97	94.79 110	98.35 89	96.49 84	89.10 123	96.12 68	87.09 113	92.58 70	90.61 88	96.48 71	96.51 96	96.89 72	99.11 105	98.54 107

Anonymous20231211		93.49 110	92.33 137	94.84 76	94.78 112	98.00 102	96.11 95	91.85 78	94.86 105	90.91 62	74.69 185	89.18 99	96.73 64	94.82 144	95.51 111	98.67 154	99.24 38

baseline		94.83 70	95.82 63	93.68 99	94.75 113	97.80 105	96.51 83	88.53 129	97.02 47	89.34 93	92.93 66	92.18 79	94.69 104	95.78 121	96.08 90	98.27 175	98.97 78

EC-MVSNet		96.49 50	97.63 35	95.16 64	94.75 113	98.69 71	97.39 55	88.97 124	96.34 60	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 96	91.84 79	95.35 89	87.92 106	87.86 114	85.54 124	96.45 73	96.71 84	97.04 66	99.26 77	98.67 99

MVSTER		94.89 69	95.07 80	94.68 82	94.71 116	96.68 134	97.00 60	90.57 101	95.18 99	93.05 39	95.21 48	86.41 116	93.72 124	97.59 57	95.88 100	99.00 121	98.50 110

EPMVS		90.88 140	92.12 139	89.44 151	94.71 116	97.24 118	93.55 141	76.81 205	95.89 75	81.77 134	91.49 85	86.47 115	93.87 119	90.21 202	90.07 199	95.92 200	93.49 206

casdiffmvs		94.38 87	94.15 99	94.64 83	94.70 118	98.51 84	96.03 100	91.66 85	95.70 81	89.36 92	86.48 129	85.03 133	96.60 68	97.40 62	97.30 60	99.52 22	98.67 99
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 91	96.36 88	91.56 86	96.77 49	88.89 99	88.97 105	84.23 137	96.01 82	96.05 113	96.41 82	99.05 119	98.79 96
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_AUTHOR		94.09 93	93.86 103	94.36 89	94.60 120	98.31 90	96.29 90	91.51 89	96.39 59	88.49 100	87.35 116	83.32 145	96.16 78	96.17 111	96.64 77	99.10 106	98.82 94

viewmambaseed2359dif		93.92 99	93.38 115	94.54 84	94.55 121	98.15 98	96.41 86	91.47 91	95.10 101	89.58 87	86.64 123	85.10 131	96.17 77	94.08 160	95.77 105	99.09 108	98.84 91

DI_MVS_pp		94.01 95	93.63 109	94.44 86	94.54 122	98.26 93	97.51 52	90.63 100	95.88 76	89.34 93	80.54 166	89.36 96	95.48 92	96.33 102	96.27 86	99.17 95	98.78 97

thisisatest0530		94.54 81	95.47 68	93.46 103	94.51 123	98.65 76	94.66 127	90.72 97	95.69 83	86.90 114	93.80 57	89.44 95	94.74 102	96.98 76	94.86 130	99.19 93	98.85 89

tttt0517		94.52 82	95.44 71	93.44 104	94.51 123	98.68 72	94.61 129	90.72 97	95.61 85	86.84 115	93.78 58	89.26 98	94.74 102	97.02 75	94.86 130	99.20 92	98.87 87

ADS-MVSNet		89.80 155	91.33 150	88.00 172	94.43 125	96.71 133	92.29 166	74.95 215	96.07 70	77.39 154	88.67 109	86.09 118	93.26 131	88.44 208	89.57 202	95.68 203	93.81 203

tpmrst		88.86 170	89.62 162	87.97 173	94.33 126	95.98 152	92.62 158	76.36 208	94.62 109	76.94 158	85.98 135	82.80 148	92.80 136	86.90 214	87.15 210	94.77 214	93.93 201

PMMVS		94.61 78	95.56 66	93.50 102	94.30 127	96.74 132	94.91 122	89.56 117	95.58 86	87.72 108	96.15 36	92.86 75	96.06 79	95.47 131	95.02 126	98.43 172	97.09 163

CostFormer		90.69 141	90.48 159	90.93 130	94.18 128	96.08 150	94.03 136	78.20 201	93.47 128	89.96 82	90.97 92	80.30 156	93.72 124	87.66 212	88.75 204	95.51 206	96.12 179

USDC		90.69 141	90.52 158	90.88 131	94.17 129	96.43 141	95.82 109	86.76 146	93.92 120	76.27 164	86.49 128	74.30 183	93.67 126	95.04 142	93.36 166	98.61 160	94.13 196

Vis-MVSNet		92.77 117	95.00 82	90.16 141	94.10 130	98.79 63	94.76 126	88.26 131	92.37 147	79.95 143	88.19 113	91.58 81	84.38 204	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+		92.93 116	93.86 103	91.86 118	94.07 131	98.09 101	95.59 110	85.98 156	94.27 116	79.54 147	91.12 90	81.81 151	96.71 65	96.67 87	96.06 92	99.27 74	98.98 74

GeoE		92.52 121	92.64 124	92.39 114	93.96 132	97.76 106	96.01 101	85.60 161	93.23 130	83.94 123	81.56 158	84.80 134	95.63 87	96.22 106	95.83 103	99.19 93	99.07 61

IterMVS-LS		92.56 120	93.18 117	91.84 119	93.90 133	94.97 187	94.99 119	86.20 153	94.18 117	82.68 129	85.81 136	87.36 111	94.43 109	95.31 135	96.02 95	98.87 135	98.60 104
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
dps		90.11 153	89.37 166	90.98 129	93.89 134	96.21 147	93.49 143	77.61 203	91.95 153	92.74 46	88.85 106	78.77 163	92.37 139	87.71 211	87.71 208	95.80 202	94.38 194

tpm cat1		88.90 168	87.78 184	90.22 140	93.88 135	95.39 176	93.79 139	78.11 202	92.55 141	89.43 89	81.31 160	79.84 159	91.40 149	84.95 215	86.34 213	94.68 216	94.09 197

PatchmatchNet		90.56 143	92.49 129	88.31 163	93.83 136	96.86 127	92.42 162	76.50 207	95.96 73	78.31 150	91.96 77	89.66 94	93.48 128	90.04 204	89.20 203	95.32 207	93.73 204
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
TinyColmap		89.42 158	88.58 170	90.40 138	93.80 137	95.45 174	93.96 138	86.54 149	92.24 150	76.49 161	80.83 162	70.44 201	93.37 129	94.45 151	93.30 169	98.26 176	93.37 207

SCA		90.92 139	93.04 119	88.45 160	93.72 138	97.33 116	92.77 154	76.08 210	96.02 71	78.26 151	91.96 77	90.86 85	93.99 118	90.98 199	90.04 200	95.88 201	94.06 199

RPMNet		90.19 150	92.03 142	88.05 169	93.46 139	95.95 155	93.41 144	74.59 216	92.40 145	75.91 166	84.22 146	86.41 116	92.49 137	94.42 152	93.85 159	98.44 170	96.96 168

gg-mvs-nofinetune		86.17 197	88.57 171	83.36 204	93.44 140	98.15 98	96.58 81	72.05 219	74.12 223	49.23 227	64.81 218	90.85 86	89.90 176	97.83 50	96.84 73	98.97 124	97.41 155

MDTV_nov1_ep13		91.57 132	93.18 117	89.70 147	93.39 141	96.97 122	93.53 142	80.91 195	95.70 81	81.86 133	92.40 72	89.93 92	93.25 132	91.97 192	90.80 195	95.25 210	94.46 193

CR-MVSNet		90.16 151	91.96 143	88.06 168	93.32 142	95.95 155	93.36 146	75.99 211	92.40 145	75.19 172	83.18 151	85.37 126	92.05 141	95.21 137	94.56 140	98.47 169	97.08 165

test-LLR		91.62 131	93.56 112	89.35 153	93.31 143	96.57 137	92.02 174	87.06 144	92.34 148	75.05 175	90.20 97	88.64 104	90.93 157	96.19 109	94.07 152	97.75 187	96.90 171

test0.0.03 1		91.97 124	93.91 101	89.72 146	93.31 143	96.40 143	91.34 183	87.06 144	93.86 121	81.67 135	91.15 89	89.16 100	86.02 195	95.08 140	95.09 122	98.91 132	96.64 177

CVMVSNet		89.77 156	91.66 145	87.56 182	93.21 145	95.45 174	91.94 177	89.22 121	89.62 176	69.34 203	83.99 148	85.90 121	84.81 202	94.30 155	95.28 117	96.85 195	97.09 163

PatchT		89.13 165	91.71 144	86.11 196	92.92 146	95.59 169	83.64 213	75.09 214	91.87 154	75.19 172	82.63 154	85.06 132	92.05 141	95.21 137	94.56 140	97.76 186	97.08 165

Fast-Effi-MVS+		91.87 125	92.08 140	91.62 124	92.91 147	97.21 120	94.93 121	84.60 176	93.61 126	81.49 137	83.50 150	78.95 161	96.62 67	96.55 91	96.22 88	99.16 98	98.51 109

IterMVS-SCA-FT		90.24 148	92.48 131	87.63 179	92.85 148	94.30 203	93.79 139	81.47 194	92.66 137	69.95 198	84.66 143	88.38 107	89.99 174	95.39 134	94.34 147	97.74 189	97.63 148

baseline2		93.01 115	94.17 97	91.64 122	92.83 149	97.49 111	93.40 145	87.53 138	93.67 125	86.07 116	91.83 80	86.58 113	91.36 150	96.38 98	95.06 124	98.67 154	98.20 128

IterMVS		90.20 149	92.43 133	87.61 180	92.82 150	94.31 202	94.11 135	81.54 192	92.97 133	69.90 199	84.71 142	88.16 110	89.96 175	95.25 136	94.17 150	97.31 191	97.46 153
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CDS-MVSNet		92.77 117	93.60 110	91.80 120	92.63 151	96.80 128	95.24 115	89.14 122	90.30 172	84.58 121	86.76 120	90.65 87	90.42 169	95.89 116	96.49 80	98.79 146	98.32 124
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
tpm		87.95 178	89.44 165	86.21 195	92.53 152	94.62 197	91.40 181	76.36 208	91.46 158	69.80 201	87.43 115	75.14 178	91.55 148	89.85 206	90.60 196	95.61 204	96.96 168

Effi-MVS+-dtu		91.78 128	93.59 111	89.68 149	92.44 153	97.11 121	94.40 132	84.94 172	92.43 143	75.48 168	91.09 91	83.75 141	93.55 127	96.61 88	95.47 112	97.24 192	98.67 99

testgi		89.42 158	91.50 149	87.00 189	92.40 154	95.59 169	89.15 199	85.27 168	92.78 136	72.42 184	91.75 81	76.00 176	84.09 206	94.38 153	93.82 161	98.65 158	96.15 178

Fast-Effi-MVS+-dtu		91.19 136	93.64 108	88.33 162	92.19 155	96.46 140	93.99 137	81.52 193	92.59 140	71.82 187	92.17 74	85.54 124	91.68 147	95.73 124	94.64 136	98.80 144	98.34 121

FC-MVSNet-test		91.63 130	93.82 106	89.08 154	92.02 156	96.40 143	93.26 148	87.26 141	93.72 124	77.26 155	88.61 110	89.86 93	85.50 197	95.72 126	95.02 126	99.16 98	97.44 154

GA-MVS		89.28 161	90.75 157	87.57 181	91.77 157	96.48 139	92.29 166	87.58 137	90.61 169	65.77 208	84.48 144	76.84 174	89.46 177	95.84 118	93.68 162	98.52 165	97.34 158

dmvs_re		91.84 126	91.60 147	92.12 117	91.60 158	97.26 117	95.14 117	91.96 75	91.02 163	80.98 140	86.56 126	77.96 168	93.84 121	94.71 145	95.08 123	99.22 85	98.62 103

UniMVSNet_ETH3D		88.47 172	86.00 202	91.35 126	91.55 159	96.29 145	92.53 159	88.81 125	85.58 205	82.33 131	67.63 215	66.87 215	94.04 117	91.49 196	95.24 118	98.84 138	98.92 80

TAMVS		90.54 145	90.87 156	90.16 141	91.48 160	96.61 136	93.26 148	86.08 154	87.71 191	81.66 136	83.11 153	84.04 138	90.42 169	94.54 148	94.60 137	98.04 182	95.48 187

tfpnnormal		88.50 171	87.01 193	90.23 139	91.36 161	95.78 164	92.74 155	90.09 105	83.65 210	76.33 163	71.46 206	69.58 206	91.84 144	95.54 128	94.02 154	99.06 115	99.03 67

GBi-Net		93.81 102	94.18 95	93.38 105	91.34 162	95.86 158	96.22 91	88.68 126	95.23 94	90.40 72	86.39 130	91.16 82	94.40 111	96.52 93	96.30 83	99.21 89	97.79 139

test1		93.81 102	94.18 95	93.38 105	91.34 162	95.86 158	96.22 91	88.68 126	95.23 94	90.40 72	86.39 130	91.16 82	94.40 111	96.52 93	96.30 83	99.21 89	97.79 139

FMVSNet2		93.30 113	93.36 116	93.22 108	91.34 162	95.86 158	96.22 91	88.24 132	95.15 100	89.92 84	81.64 157	89.36 96	94.40 111	96.77 81	96.98 69	99.21 89	97.79 139

FMVSNet3		93.79 104	94.17 97	93.35 107	91.21 165	95.99 151	96.62 78	88.68 126	95.23 94	90.40 72	86.39 130	91.16 82	94.11 115	95.96 114	96.67 76	99.07 112	97.79 139

TransMVSNet (Re)		87.73 184	86.79 195	88.83 156	90.76 166	94.40 200	91.33 184	89.62 116	84.73 207	75.41 170	72.73 199	71.41 196	86.80 189	94.53 149	93.93 156	99.06 115	95.83 181

LTVRE_ROB	87.32 16	87.55 185	88.25 174	86.73 190	90.66 167	95.80 163	93.05 151	84.77 173	83.35 211	60.32 220	83.12 152	67.39 213	93.32 130	94.36 154	94.86 130	98.28 174	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		86.68 193	87.24 189	86.02 197	90.58 168	96.26 146	91.08 187	81.59 191	84.96 206	69.80 201	71.35 207	75.08 180	84.23 205	94.24 157	93.35 167	98.82 139	95.46 188

TESTMET0.1,1		91.07 137	93.56 112	88.17 164	90.43 169	96.57 137	92.02 174	82.83 187	92.34 148	75.05 175	90.20 97	88.64 104	90.93 157	96.19 109	94.07 152	97.75 187	96.90 171

pm-mvs1		89.19 164	89.02 167	89.38 152	90.40 170	95.74 165	92.05 172	88.10 134	86.13 201	77.70 152	73.72 194	79.44 160	88.97 180	95.81 120	94.51 144	99.08 110	97.78 144

NR-MVSNet		89.34 160	88.66 169	90.13 144	90.40 170	95.61 167	93.04 152	89.91 107	91.22 160	78.96 148	77.72 174	68.90 209	89.16 179	94.24 157	93.95 155	99.32 65	98.99 72

FMVSNet1		91.54 133	90.93 154	92.26 115	90.35 172	95.27 180	95.22 116	87.16 143	91.37 159	87.62 109	75.45 180	83.84 140	94.43 109	96.52 93	96.30 83	98.82 139	97.74 145

test-mter		90.95 138	93.54 114	87.93 174	90.28 173	96.80 128	91.44 180	82.68 188	92.15 152	74.37 179	89.57 103	88.23 109	90.88 160	96.37 100	94.31 148	97.93 184	97.37 156

pmmvs4		90.55 144	89.91 161	91.30 127	90.26 174	94.95 188	92.73 156	87.94 135	93.44 129	85.35 119	82.28 156	76.09 175	93.02 135	93.56 166	92.26 189	98.51 166	96.77 173

MVS-HIRNet		85.36 201	86.89 194	83.57 203	90.13 175	94.51 198	83.57 214	72.61 218	88.27 187	71.22 191	68.97 210	81.81 151	88.91 181	93.08 174	91.94 190	94.97 213	89.64 216

thisisatest0515		90.12 152	92.06 141	87.85 175	90.03 176	96.17 148	87.83 202	87.45 139	91.71 156	77.15 156	85.40 138	84.01 139	85.74 196	95.41 133	93.30 169	98.88 134	98.43 113

SixPastTwentyTwo		88.37 173	89.47 164	87.08 187	90.01 177	95.93 157	87.41 203	85.32 165	90.26 173	70.26 195	86.34 133	71.95 193	90.93 157	92.89 178	91.72 192	98.55 163	97.22 160

UniMVSNet (Re)		90.03 154	89.61 163	90.51 137	89.97 178	96.12 149	92.32 164	89.26 120	90.99 164	80.95 141	78.25 173	75.08 180	91.14 153	93.78 161	93.87 158	99.41 51	99.21 43

pmnet_mix02		86.12 198	87.12 192	84.96 200	89.82 179	94.12 204	84.88 211	86.63 148	91.78 155	65.60 209	80.76 163	76.98 172	86.61 191	87.29 213	84.80 216	96.21 197	94.09 197

our_test_3					89.78 180	93.84 206	85.59 208

UniMVSNet_NR-MVSNet		90.35 147	89.96 160	90.80 133	89.66 181	95.83 161	92.48 160	90.53 102	90.96 165	79.57 145	79.33 170	77.14 171	93.21 133	92.91 177	94.50 145	99.37 59	99.05 64

v8		88.21 176	87.94 181	88.51 159	89.62 182	95.01 186	92.31 165	84.99 170	88.94 178	74.70 177	75.03 182	73.51 187	90.67 165	92.11 188	92.74 181	98.80 144	98.24 126

WR-MVS_H		87.93 179	87.85 182	88.03 171	89.62 182	95.58 171	90.47 192	85.55 162	87.20 196	76.83 159	74.42 189	72.67 191	86.37 192	93.22 172	93.04 172	99.33 63	98.83 92

pmmvs5		87.83 183	88.09 176	87.51 184	89.59 184	95.48 172	89.75 197	84.73 174	86.07 203	71.44 189	80.57 165	70.09 204	90.74 164	94.47 150	92.87 177	98.82 139	97.10 162

gm-plane-assit		83.26 207	85.29 204	80.89 207	89.52 185	89.89 218	70.26 224	78.24 200	77.11 221	58.01 224	74.16 191	66.90 214	90.63 167	97.20 67	96.05 93	98.66 157	95.68 184

v10		88.00 177	87.96 179	88.05 169	89.44 186	94.68 194	92.36 163	83.35 183	89.37 177	72.96 183	73.98 192	72.79 190	91.35 151	93.59 163	92.88 176	98.81 142	98.42 115

V42		88.31 174	87.95 180	88.73 157	89.44 186	95.34 177	92.23 168	87.21 142	88.83 180	74.49 178	74.89 184	73.43 188	90.41 171	92.08 190	92.77 180	98.60 162	98.33 122

v148		87.51 186	86.79 195	88.36 161	89.39 188	95.21 182	89.84 196	88.20 133	87.61 193	77.56 153	73.38 197	70.32 203	86.80 189	90.70 200	92.31 187	98.37 173	97.98 137

CP-MVSNet		87.89 182	87.27 188	88.62 158	89.30 189	95.06 184	90.60 191	85.78 158	87.43 195	75.98 165	74.60 186	68.14 212	90.76 162	93.07 175	93.60 163	99.30 70	98.98 74

v1144		87.92 181	87.79 183	88.07 166	89.27 190	95.15 183	92.17 169	85.62 160	88.52 184	71.52 188	73.80 193	72.40 192	91.06 155	93.54 167	92.80 178	98.81 142	98.33 122

DU-MVS		89.67 157	88.84 168	90.63 136	89.26 191	95.61 167	92.48 160	89.91 107	91.22 160	79.57 145	77.72 174	71.18 197	93.21 133	92.53 181	94.57 139	99.35 62	99.05 64

WR-MVS		87.93 179	88.09 176	87.75 176	89.26 191	95.28 178	90.81 189	86.69 147	88.90 179	75.29 171	74.31 190	73.72 186	85.19 200	92.26 184	93.32 168	99.27 74	98.81 95

Baseline_NR-MVSNet		89.27 162	88.01 178	90.73 135	89.26 191	93.71 207	92.71 157	89.78 113	90.73 166	81.28 138	73.53 195	72.85 189	92.30 140	92.53 181	93.84 160	99.07 112	98.88 85

N_pmnet		84.80 202	85.10 206	84.45 201	89.25 194	92.86 210	84.04 212	86.21 151	88.78 181	66.73 207	72.41 202	74.87 182	85.21 199	88.32 209	86.45 211	95.30 208	92.04 210

v2v482		88.25 175	87.71 185	88.88 155	89.23 195	95.28 178	92.10 170	87.89 136	88.69 183	73.31 182	75.32 181	71.64 194	91.89 143	92.10 189	92.92 175	98.86 137	97.99 135

PS-CasMVS		87.33 189	86.68 198	88.10 165	89.22 196	94.93 189	90.35 194	85.70 159	86.44 200	74.01 180	73.43 196	66.59 218	90.04 173	92.92 176	93.52 164	99.28 72	98.91 83

TranMVSNet+NR-MVSNet		89.23 163	88.48 172	90.11 145	89.07 197	95.25 181	92.91 153	90.43 103	90.31 171	77.10 157	76.62 178	71.57 195	91.83 145	92.12 187	94.59 138	99.32 65	98.92 80

v1192		87.51 186	87.31 187	87.74 177	89.04 198	94.87 192	92.07 171	85.03 169	88.49 185	70.32 194	72.65 200	70.35 202	91.21 152	93.59 163	92.80 178	98.78 147	98.42 115

v144192		87.40 188	87.20 190	87.64 178	88.89 199	94.88 191	91.65 179	84.70 175	87.80 190	71.17 192	73.20 198	70.91 198	90.75 163	92.69 179	92.49 184	98.71 151	98.43 113

PEN-MVS		87.22 191	86.50 200	88.07 166	88.88 200	94.44 199	90.99 188	86.21 151	86.53 199	73.66 181	74.97 183	66.56 219	89.42 178	91.20 198	93.48 165	99.24 79	98.31 125

v1921920		87.31 190	87.13 191	87.52 183	88.87 201	94.72 193	91.96 176	84.59 177	88.28 186	69.86 200	72.50 201	70.03 205	91.10 154	93.33 170	92.61 183	98.71 151	98.44 112

pmmvs6		85.98 199	84.89 207	87.25 186	88.83 202	94.35 201	89.36 198	85.30 167	78.51 220	75.44 169	62.71 220	75.41 177	87.65 185	93.58 165	92.40 186	96.89 194	97.29 159

v1240		86.89 192	86.75 197	87.06 188	88.75 203	94.65 196	91.30 185	84.05 179	87.49 194	68.94 204	71.96 204	68.86 210	90.65 166	93.33 170	92.72 182	98.67 154	98.24 126

anonymousdsp		88.90 168	91.00 153	86.44 193	88.74 204	95.97 153	90.40 193	82.86 186	88.77 182	67.33 206	81.18 161	81.44 153	90.22 172	96.23 105	94.27 149	99.12 104	99.16 50

EU-MVSNet		85.62 200	87.65 186	83.24 205	88.54 205	92.77 211	87.12 204	85.32 165	86.71 197	64.54 211	78.52 172	75.11 179	78.35 211	92.25 185	92.28 188	95.58 205	95.93 180

DTE-MVSNet		86.67 194	86.09 201	87.35 185	88.45 206	94.08 205	90.65 190	86.05 155	86.13 201	72.19 185	74.58 188	66.77 217	87.61 186	90.31 201	93.12 171	99.13 102	97.62 149

FMVSNet5		90.36 146	90.93 154	89.70 147	87.99 207	92.25 212	92.03 173	83.51 182	92.20 151	84.13 122	85.59 137	86.48 114	92.43 138	94.61 146	94.52 143	98.13 178	90.85 213

v7n		86.43 195	86.52 199	86.33 194	87.91 208	94.93 189	90.15 195	83.05 184	86.57 198	70.21 196	71.48 205	66.78 216	87.72 184	94.19 159	92.96 174	98.92 130	98.76 98

test20.03		82.92 208	85.52 203	79.90 210	87.75 209	91.84 213	82.80 215	82.99 185	82.65 215	60.32 220	78.90 171	70.50 199	67.10 219	92.05 191	90.89 194	98.44 170	91.80 211

MDTV_nov1_ep13_2view		86.30 196	88.27 173	84.01 202	87.71 210	94.67 195	88.08 201	76.78 206	90.59 170	68.66 205	80.46 167	80.12 157	87.58 187	89.95 205	88.20 206	95.25 210	93.90 202

Anonymous20231206		83.84 206	85.19 205	82.26 206	87.38 211	92.87 209	85.49 209	83.65 181	86.07 203	63.44 215	68.42 211	69.01 208	75.45 215	93.34 169	92.44 185	98.12 180	94.20 195

FPMVS		75.84 214	74.59 218	77.29 214	86.92 212	83.89 223	85.01 210	80.05 197	82.91 213	60.61 219	65.25 217	60.41 223	63.86 220	75.60 221	73.60 223	87.29 224	80.47 221

MIMVSNet		88.99 167	91.07 152	86.57 192	86.78 213	95.62 166	91.20 186	75.40 213	90.65 168	76.57 160	84.05 147	82.44 150	91.01 156	95.84 118	95.38 114	98.48 168	93.50 205

tmp_tt				66.88 217	86.07 214	73.86 226	68.22 225	33.38 227	96.88 48	80.67 142	88.23 112	78.82 162	49.78 224	82.68 218	77.47 221	83.19 226

WB-MVS		69.22 216	76.91 217	60.24 220	85.80 215	79.37 224	56.86 229	84.96 171	81.50 216	18.16 232	76.85 176	61.07 221	34.23 227	82.46 219	81.81 218	81.43 227	75.31 225

PM-MVS		84.72 204	84.47 208	85.03 199	84.67 216	91.57 214	86.27 207	82.31 190	87.65 192	70.62 193	76.54 179	56.41 227	88.75 182	92.59 180	89.85 201	97.54 190	96.66 176

pmmvs-eth3d		84.33 205	82.94 210	85.96 198	84.16 217	90.94 215	86.55 206	83.79 180	84.25 208	75.85 167	70.64 208	56.43 226	87.44 188	92.20 186	90.41 198	97.97 183	95.68 184

new-patchmatchnet		78.49 213	78.19 216	78.84 212	84.13 218	90.06 217	77.11 222	80.39 196	79.57 219	59.64 223	66.01 216	55.65 228	75.62 214	84.55 216	80.70 219	96.14 199	90.77 214

new_pmnet		81.53 209	82.68 211	80.20 208	83.47 219	89.47 219	82.21 217	78.36 199	87.86 189	60.14 222	67.90 213	69.43 207	82.03 209	89.22 207	87.47 209	94.99 212	87.39 218

ET-MVSNet_ETH3D		93.34 112	94.33 91	92.18 116	83.26 220	97.66 108	96.72 76	89.89 109	95.62 84	87.17 112	96.00 40	83.69 142	96.99 58	93.78 161	95.34 115	99.06 115	98.18 129

pmmvs3		79.16 212	80.12 214	78.05 213	79.36 221	86.59 221	78.13 221	73.87 217	76.42 222	57.51 225	70.59 209	57.02 225	84.66 203	90.10 203	88.32 205	94.75 215	91.77 212

PMVS	63.12 18	67.27 218	66.39 221	68.30 216	77.98 222	60.24 229	59.53 228	76.82 204	66.65 224	60.74 218	54.39 222	59.82 224	51.24 223	73.92 224	70.52 224	83.48 225	79.17 223
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MDA-MVSNet-bldmvs		80.11 210	80.24 213	79.94 209	77.01 223	93.21 208	78.86 220	85.94 157	82.71 214	60.86 217	79.71 169	51.77 229	83.71 208	75.60 221	86.37 212	93.28 218	92.35 208

ambc			73.83 219		76.23 224	85.13 222	82.27 216		84.16 209	65.58 210	52.82 223	23.31 234	73.55 216	91.41 197	85.26 215	92.97 219	94.70 190

Gipuma		68.35 217	66.71 220	70.27 215	74.16 225	68.78 227	63.93 227	71.77 220	83.34 212	54.57 226	34.37 225	31.88 231	68.69 218	83.30 217	85.53 214	88.48 222	79.78 222
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MIMVSNet1		80.03 211	80.93 212	78.97 211	72.46 226	90.73 216	80.81 218	82.44 189	80.39 217	63.64 213	57.57 221	64.93 220	76.37 213	91.66 194	91.55 193	98.07 181	89.70 215

PMMVS2		64.36 220	65.94 222	62.52 219	67.37 227	77.44 225	64.39 226	69.32 224	61.47 225	34.59 228	46.09 224	41.03 230	48.02 226	74.56 223	78.23 220	91.43 220	82.76 220

EMVS		49.98 222	46.76 225	53.74 222	64.96 228	51.29 231	37.81 231	69.35 223	51.83 226	22.69 231	29.57 227	25.06 232	57.28 221	44.81 227	56.11 226	70.32 229	68.64 227

E-PMN		50.67 221	47.85 224	53.96 221	64.13 229	50.98 232	38.06 230	69.51 222	51.40 227	24.60 230	29.46 228	24.39 233	56.07 222	48.17 226	59.70 225	71.40 228	70.84 226

test_method		72.96 215	78.68 215	66.28 218	50.17 230	64.90 228	75.45 223	50.90 226	87.89 188	62.54 216	62.98 219	68.34 211	70.45 217	91.90 193	82.41 217	88.19 223	92.35 208

MVE	50.86 19	49.54 223	51.43 223	47.33 223	44.14 231	59.20 230	36.45 232	60.59 225	41.47 228	31.14 229	29.58 226	17.06 235	48.52 225	62.22 225	74.63 222	63.12 230	75.87 224
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		12.09 224	16.94 226	6.42 225	3.15 232	6.08 233	9.51 234	3.84 228	21.46 229	5.31 233	27.49 229	6.76 236	10.89 228	17.06 228	15.01 227	5.84 231	24.75 228

GG-mvs-BLEND		66.17 219	94.91 83	32.63 224	1.32 233	96.64 135	91.40 181	0.85 230	94.39 114	2.20 234	90.15 99	95.70 62	2.27 230	96.39 97	95.44 113	97.78 185	95.68 184

test123		9.58 225	13.53 227	4.97 226	1.31 234	5.47 234	8.32 235	2.95 229	18.14 230	2.03 235	20.82 230	2.34 237	10.60 229	10.00 229	14.16 228	4.60 232	23.77 229

uanet_test		0.00 226	0.00 228	0.00 227	0.00 235	0.00 235	0.00 236	0.00 231	0.00 231	0.00 236	0.00 231	0.00 238	0.00 231	0.00 230	0.00 229	0.00 233	0.00 230

sosnet-low-res		0.00 226	0.00 228	0.00 227	0.00 235	0.00 235	0.00 236	0.00 231	0.00 231	0.00 236	0.00 231	0.00 238	0.00 231	0.00 230	0.00 229	0.00 233	0.00 230

sosnet		0.00 226	0.00 228	0.00 227	0.00 235	0.00 235	0.00 236	0.00 231	0.00 231	0.00 236	0.00 231	0.00 238	0.00 231	0.00 230	0.00 229	0.00 233	0.00 230

RE-MVS-def										63.50 214

9.14												99.28 12

MTAPA										96.83 10		99.12 21

MTMP										97.18 5		98.83 26

Patchmatch-RL test							34.61 233

NP-MVS									95.32 91

Patchmtry						95.96 154	93.36 146	75.99 211		75.19 172

DeepMVS_CX						86.86 220	79.50 219	70.43 221	90.73 166	63.66 212	80.36 168	60.83 222	79.68 210	76.23 220		89.46 221	86.53 219

High-resolution multi-view benchmark