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.

PGM-MVS		98.86 31	99.35 27	98.29 34	99.77 1	99.63 29	99.67 5	95.63 45	98.66 119	95.27 53	99.11 28	99.82 42	99.67 4	99.33 24	99.19 21	99.73 58	99.74 74

SMA-MVS		99.38 6	99.60 3	99.12 9	99.76 2	99.62 33	99.39 29	98.23 18	99.52 16	98.03 17	99.45 11	99.98 2	99.64 5	99.58 8	99.30 11	99.68 95	99.76 62
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
CSCG		98.90 30	98.93 53	98.85 24	99.75 3	99.72 12	99.49 21	96.58 42	99.38 25	98.05 16	98.97 37	97.87 77	99.49 18	97.78 127	98.92 40	99.78 34	99.90 6

APDe-MVS		99.49 1	99.64 1	99.32 2	99.74 4	99.74 11	99.75 1	98.34 4	99.56 11	98.72 6	99.57 7	99.97 8	99.53 15	99.65 2	99.25 15	99.84 12	99.77 57
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
ACMMP_NAP		99.05 25	99.45 14	98.58 30	99.73 5	99.60 43	99.64 8	98.28 13	99.23 45	94.57 65	99.35 16	99.97 8	99.55 13	99.63 3	98.66 57	99.70 84	99.74 74

DVP-MVS		99.45 2	99.54 7	99.35 1	99.72 6	99.76 6	99.63 12	98.37 2	99.63 7	99.03 3	98.95 39	99.98 2	99.60 7	99.60 7	99.05 29	99.74 50	99.79 44
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
DVP-MVS++		99.41 4	99.64 1	99.14 7	99.69 7	99.75 9	99.64 8	98.33 6	99.67 4	98.10 13	99.66 4	99.99 1	99.33 30	99.62 5	98.86 45	99.74 50	99.90 6

SED-MVS		99.44 3	99.58 4	99.28 3	99.69 7	99.76 6	99.62 14	98.35 3	99.51 17	99.05 2	99.60 6	99.98 2	99.28 37	99.61 6	98.83 50	99.70 84	99.77 57

HFP-MVS		99.32 8	99.53 9	99.07 13	99.69 7	99.59 45	99.63 12	98.31 9	99.56 11	97.37 26	99.27 19	99.97 8	99.70 3	99.35 22	99.24 17	99.71 76	99.76 62

HPM-MVS++		99.10 21	99.30 30	98.86 23	99.69 7	99.48 64	99.59 16	98.34 4	99.26 42	96.55 36	99.10 30	99.96 12	99.36 28	99.25 27	98.37 75	99.64 116	99.66 107

APD-MVS		99.25 12	99.38 22	99.09 11	99.69 7	99.58 48	99.56 17	98.32 8	98.85 96	97.87 19	98.91 42	99.92 28	99.30 35	99.45 15	99.38 8	99.79 31	99.58 123
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MSP-MVS		99.34 7	99.52 10	99.14 7	99.68 12	99.75 9	99.64 8	98.31 9	99.44 21	98.10 13	99.28 18	99.98 2	99.30 35	99.34 23	99.05 29	99.81 22	99.79 44
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
SR-MVS					99.67 13			98.25 14				99.94 25

X-MVS		98.93 29	99.37 23	98.42 31	99.67 13	99.62 33	99.60 15	98.15 23	99.08 71	93.81 83	98.46 63	99.95 17	99.59 9	99.49 13	99.21 20	99.68 95	99.75 70

MCST-MVS		99.11 20	99.27 32	98.93 21	99.67 13	99.33 90	99.51 20	98.31 9	99.28 38	96.57 35	99.10 30	99.90 33	99.71 2	99.19 31	98.35 76	99.82 16	99.71 91

ACMMPR		99.30 9	99.54 7	99.03 16	99.66 16	99.64 26	99.68 4	98.25 14	99.56 11	97.12 30	99.19 21	99.95 17	99.72 1	99.43 16	99.25 15	99.72 66	99.77 57

SteuartSystems-ACMMP		99.20 15	99.51 11	98.83 26	99.66 16	99.66 21	99.71 3	98.12 27	99.14 62	96.62 33	99.16 23	99.98 2	99.12 49	99.63 3	99.19 21	99.78 34	99.83 28
Skip Steuart: Steuart Systems R&D Blog.
SF-MVS		99.18 16	99.32 28	99.03 16	99.65 18	99.41 76	98.87 54	98.24 17	99.14 62	98.73 5	99.11 28	99.92 28	98.92 61	99.22 28	98.84 49	99.76 41	99.56 129

CNVR-MVS		99.23 14	99.28 31	99.17 5	99.65 18	99.34 87	99.46 24	98.21 19	99.28 38	98.47 8	98.89 44	99.94 25	99.50 16	99.42 17	98.61 60	99.73 58	99.52 135

DPE-MVS		99.39 5	99.55 6	99.20 4	99.63 20	99.71 15	99.66 6	98.33 6	99.29 37	98.40 11	99.64 5	99.98 2	99.31 33	99.56 9	98.96 37	99.85 10	99.70 93
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MP-MVS		99.07 23	99.36 24	98.74 27	99.63 20	99.57 50	99.66 6	98.25 14	99.00 82	95.62 45	98.97 37	99.94 25	99.54 14	99.51 12	98.79 54	99.71 76	99.73 78
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
NCCC		99.05 25	99.08 41	99.02 18	99.62 22	99.38 78	99.43 28	98.21 19	99.36 30	97.66 23	97.79 81	99.90 33	99.45 22	99.17 32	98.43 70	99.77 39	99.51 140

CP-MVS		99.27 10	99.44 17	99.08 12	99.62 22	99.58 48	99.53 18	98.16 21	99.21 49	97.79 20	99.15 24	99.96 12	99.59 9	99.54 11	98.86 45	99.78 34	99.74 74

AdaColmap		99.06 24	98.98 51	99.15 6	99.60 24	99.30 93	99.38 30	98.16 21	99.02 80	98.55 7	98.71 53	99.57 56	99.58 12	99.09 37	97.84 105	99.64 116	99.36 153

CPTT-MVS		99.14 19	99.20 36	99.06 14	99.58 25	99.53 55	99.45 25	97.80 36	99.19 52	98.32 12	98.58 57	99.95 17	99.60 7	99.28 26	98.20 87	99.64 116	99.69 97

TPM-MVS					99.57 26	98.90 116	98.79 58			96.52 37	98.62 56	99.91 31	97.56 113			99.44 167	99.28 156
Ray Leroy Khuboni and Hongjun Xu: Textureless Resilient Propagation Matching in Multiple View Stereosis (TPM-MVS). SATNAC 2025
QAPM		98.62 40	99.04 47	98.13 38	99.57 26	99.48 64	99.17 38	94.78 54	99.57 10	96.16 39	96.73 105	99.80 43	99.33 30	98.79 61	99.29 13	99.75 45	99.64 114

3Dnovator	96.92 7	98.67 37	99.05 44	98.23 37	99.57 26	99.45 68	99.11 42	94.66 57	99.69 3	96.80 32	96.55 114	99.61 53	99.40 25	98.87 57	99.49 3	99.85 10	99.66 107

DeepC-MVS_fast	98.34 1	99.17 17	99.45 14	98.85 24	99.55 29	99.37 81	99.64 8	98.05 31	99.53 14	96.58 34	98.93 40	99.92 28	99.49 18	99.46 14	99.32 10	99.80 30	99.64 114
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
mPP-MVS					99.53 30							99.89 35

3Dnovator+	96.92 7	98.71 36	99.05 44	98.32 33	99.53 30	99.34 87	99.06 46	94.61 58	99.65 5	97.49 24	96.75 104	99.86 38	99.44 23	98.78 62	99.30 11	99.81 22	99.67 103

MSLP-MVS++		99.15 18	99.24 34	99.04 15	99.52 32	99.49 63	99.09 44	98.07 29	99.37 27	98.47 8	97.79 81	99.89 35	99.50 16	98.93 49	99.45 4	99.61 124	99.76 62

OpenMVS	96.23 11	97.95 58	98.45 67	97.35 55	99.52 32	99.42 74	98.91 53	94.61 58	98.87 93	92.24 110	94.61 142	99.05 64	99.10 51	98.64 73	99.05 29	99.74 50	99.51 140

PLC	97.93 2	99.02 28	98.94 52	99.11 10	99.46 34	99.24 99	99.06 46	97.96 33	99.31 34	99.16 1	97.90 79	99.79 45	99.36 28	98.71 69	98.12 91	99.65 112	99.52 135
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MVS_111021_HR		98.59 41	99.36 24	97.68 47	99.42 35	99.61 38	98.14 90	94.81 53	99.31 34	95.00 58	99.51 9	99.79 45	99.00 58	98.94 48	98.83 50	99.69 87	99.57 128

OMC-MVS		98.84 32	99.01 50	98.65 29	99.39 36	99.23 100	99.22 35	96.70 41	99.40 24	97.77 21	97.89 80	99.80 43	99.21 38	99.02 43	98.65 58	99.57 146	99.07 171

TSAR-MVS + ACMM		98.77 33	99.45 14	97.98 42	99.37 37	99.46 66	99.44 27	98.13 26	99.65 5	92.30 108	98.91 42	99.95 17	99.05 54	99.42 17	98.95 38	99.58 142	99.82 29

MVS_111021_LR		98.67 37	99.41 21	97.81 45	99.37 37	99.53 55	98.51 67	95.52 47	99.27 40	94.85 60	99.56 8	99.69 50	99.04 55	99.36 20	98.88 43	99.60 132	99.58 123

train_agg		98.73 35	99.11 39	98.28 35	99.36 39	99.35 85	99.48 23	97.96 33	98.83 101	93.86 82	98.70 54	99.86 38	99.44 23	99.08 39	98.38 73	99.61 124	99.58 123

ACMMP		98.74 34	99.03 48	98.40 32	99.36 39	99.64 26	99.20 36	97.75 37	98.82 103	95.24 54	98.85 45	99.87 37	99.17 45	98.74 67	97.50 118	99.71 76	99.76 62
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
MAR-MVS		97.71 64	98.04 85	97.32 56	99.35 41	98.91 115	97.65 108	91.68 110	98.00 149	97.01 31	97.72 85	94.83 113	98.85 70	98.44 90	98.86 45	99.41 172	99.52 135
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
CDPH-MVS		98.41 45	99.10 40	97.61 50	99.32 42	99.36 82	99.49 21	96.15 44	98.82 103	91.82 112	98.41 64	99.66 51	99.10 51	98.93 49	98.97 36	99.75 45	99.58 123

CNLPA		99.03 27	99.05 44	99.01 19	99.27 43	99.22 101	99.03 48	97.98 32	99.34 32	99.00 4	98.25 70	99.71 49	99.31 33	98.80 60	98.82 52	99.48 161	99.17 164

MSDG		98.27 50	98.29 71	98.24 36	99.20 44	99.22 101	99.20 36	97.82 35	99.37 27	94.43 71	95.90 125	97.31 83	99.12 49	98.76 64	98.35 76	99.67 103	99.14 168

PHI-MVS		99.08 22	99.43 19	98.67 28	99.15 45	99.59 45	99.11 42	97.35 39	99.14 62	97.30 27	99.44 12	99.96 12	99.32 32	98.89 54	99.39 7	99.79 31	99.58 123

PatchMatch-RL		97.77 62	98.25 73	97.21 61	99.11 46	99.25 97	97.06 131	94.09 71	98.72 117	95.14 56	98.47 62	96.29 94	98.43 86	98.65 72	97.44 124	99.45 165	98.94 174

TAPA-MVS	97.53 5	98.41 45	98.84 57	97.91 43	99.08 47	99.33 90	99.15 39	97.13 40	99.34 32	93.20 93	97.75 83	99.19 60	99.20 39	98.66 71	98.13 90	99.66 108	99.48 144
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
EPNet		98.05 55	98.86 55	97.10 63	99.02 48	99.43 73	98.47 70	94.73 55	99.05 77	95.62 45	98.93 40	97.62 81	95.48 168	98.59 81	98.55 62	99.29 181	99.84 24
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EPNet_dtu		96.30 112	98.53 64	93.70 134	98.97 49	98.24 160	97.36 114	94.23 70	98.85 96	79.18 186	99.19 21	98.47 70	94.09 190	97.89 122	98.21 86	98.39 196	98.85 180
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
COLMAP_ROB	96.15 12	97.78 61	98.17 79	97.32 56	98.84 50	99.45 68	99.28 33	95.43 48	99.48 19	91.80 113	94.83 141	98.36 72	98.90 64	98.09 105	97.85 104	99.68 95	99.15 165
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
DeepPCF-MVS	97.74 3	98.34 47	99.46 13	97.04 66	98.82 51	99.33 90	96.28 147	97.47 38	99.58 9	94.70 63	98.99 36	99.85 40	97.24 121	99.55 10	99.34 9	97.73 205	99.56 129

SD-MVS		99.25 12	99.50 12	98.96 20	98.79 52	99.55 53	99.33 32	98.29 12	99.75 1	97.96 18	99.15 24	99.95 17	99.61 6	99.17 32	99.06 28	99.81 22	99.84 24
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.		99.27 10	99.57 5	98.92 22	98.78 53	99.53 55	99.72 2	98.11 28	99.73 2	97.43 25	99.15 24	99.96 12	99.59 9	99.73 1	99.07 26	99.88 4	99.82 29
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
DPM-MVS		98.31 49	98.53 64	98.05 39	98.76 54	98.77 123	99.13 40	98.07 29	99.10 68	94.27 76	96.70 106	99.84 41	98.70 73	97.90 121	98.11 92	99.40 174	99.28 156

PCF-MVS	97.50 6	98.18 53	98.35 70	97.99 41	98.65 55	99.36 82	98.94 52	98.14 25	98.59 121	93.62 88	96.61 110	99.76 48	99.03 56	97.77 128	97.45 123	99.57 146	98.89 179
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
DeepC-MVS	97.63 4	98.33 48	98.57 62	98.04 40	98.62 56	99.65 22	99.45 25	98.15 23	99.51 17	92.80 100	95.74 129	96.44 92	99.46 21	99.37 19	99.50 2	99.78 34	99.81 34
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CS-MVS-test		98.58 42	99.42 20	97.60 51	98.52 57	99.91 1	98.60 64	94.60 60	99.37 27	94.62 64	99.40 14	99.16 61	99.39 26	99.36 20	98.85 48	99.90 3	99.92 3

CANet		98.46 44	99.16 37	97.64 49	98.48 58	99.64 26	99.35 31	94.71 56	99.53 14	95.17 55	97.63 87	99.59 54	98.38 87	98.88 56	98.99 35	99.74 50	99.86 20

LS3D		97.79 60	98.25 73	97.26 60	98.40 59	99.63 29	99.53 18	98.63 1	99.25 44	88.13 130	96.93 101	94.14 123	99.19 40	99.14 35	99.23 18	99.69 87	99.42 148

CHOSEN 280x420		97.99 57	99.24 34	96.53 84	98.34 60	99.61 38	98.36 79	89.80 145	99.27 40	95.08 57	99.81 1	98.58 68	98.64 77	99.02 43	98.92 40	98.93 190	99.48 144

DELS-MVS		98.19 52	98.77 59	97.52 52	98.29 61	99.71 15	99.12 41	94.58 62	98.80 106	95.38 52	96.24 119	98.24 74	97.92 102	99.06 40	99.52 1	99.82 16	99.79 44
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
CS-MVS		98.56 43	99.32 28	97.68 47	98.28 62	99.89 2	98.71 61	94.53 63	99.41 23	95.43 49	99.05 35	98.66 66	99.19 40	99.21 29	99.07 26	99.93 1	99.94 1

RPSCF		97.61 67	98.16 80	96.96 74	98.10 63	99.00 108	98.84 56	93.76 79	99.45 20	94.78 62	99.39 15	99.31 58	98.53 84	96.61 165	95.43 174	97.74 203	97.93 197

PVSNet_BlendedMVS		97.51 71	97.71 97	97.28 58	98.06 64	99.61 38	97.31 116	95.02 51	99.08 71	95.51 47	98.05 74	90.11 145	98.07 97	98.91 52	98.40 71	99.72 66	99.78 50

PVSNet_Blended		97.51 71	97.71 97	97.28 58	98.06 64	99.61 38	97.31 116	95.02 51	99.08 71	95.51 47	98.05 74	90.11 145	98.07 97	98.91 52	98.40 71	99.72 66	99.78 50

MVS_0304		98.14 54	99.03 48	97.10 63	98.05 66	99.63 29	99.27 34	94.33 68	99.63 7	93.06 96	97.32 91	99.05 64	98.09 96	98.82 59	98.87 44	99.81 22	99.89 11

CHOSEN 1792x2688		96.41 109	96.99 125	95.74 103	98.01 67	99.72 12	97.70 106	90.78 130	99.13 66	90.03 123	87.35 199	95.36 106	98.33 88	98.59 81	98.91 42	99.59 138	99.87 17

HyFIR lowres test		95.99 120	96.56 135	95.32 109	97.99 68	99.65 22	96.54 140	88.86 154	98.44 130	89.77 126	84.14 209	97.05 87	99.03 56	98.55 83	98.19 88	99.73 58	99.86 20

OPM-MVS		96.22 114	95.85 156	96.65 80	97.75 69	98.54 142	99.00 51	95.53 46	96.88 183	89.88 124	95.95 124	86.46 169	98.07 97	97.65 137	96.63 141	99.67 103	98.83 181
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
tmp_tt				82.25 211	97.73 70	88.71 220	80.18 220	68.65 223	99.15 59	86.98 139	99.47 10	85.31 178	68.35 221	87.51 216	83.81 218	91.64 220

TSAR-MVS + COLMAP		96.79 95	96.55 136	97.06 65	97.70 71	98.46 147	99.07 45	96.23 43	99.38 25	91.32 116	98.80 46	85.61 175	98.69 75	97.64 138	96.92 134	99.37 176	99.06 172

PVSNet_Blended_VisFu		97.41 74	98.49 66	96.15 93	97.49 72	99.76 6	96.02 151	93.75 81	99.26 42	93.38 92	93.73 150	99.35 57	96.47 143	98.96 46	98.46 66	99.77 39	99.90 6

MS-PatchMatch		95.99 120	97.26 117	94.51 118	97.46 73	98.76 126	97.27 118	86.97 174	99.09 69	89.83 125	93.51 154	97.78 78	96.18 149	97.53 142	95.71 171	99.35 177	98.41 187

XVS					97.42 74	99.62 33	98.59 65			93.81 83		99.95 17				99.69 87

X-MVStestdata					97.42 74	99.62 33	98.59 65			93.81 83		99.95 17				99.69 87

LGP-MVS_train		96.23 113	96.89 127	95.46 108	97.32 76	98.77 123	98.81 57	93.60 84	98.58 122	85.52 148	99.08 32	86.67 166	97.83 109	97.87 123	97.51 117	99.69 87	99.73 78

CMPMVS	70.31 18	90.74 199	91.06 207	90.36 190	97.32 76	97.43 196	92.97 195	87.82 170	93.50 214	75.34 202	83.27 211	84.90 181	92.19 205	92.64 209	91.21 213	96.50 216	94.46 214
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
HQP-MVS		96.37 110	96.58 134	96.13 94	97.31 78	98.44 149	98.45 71	95.22 49	98.86 94	88.58 128	98.33 68	87.00 161	97.67 111	97.23 152	96.56 145	99.56 149	99.62 118

ACMM	96.26 9	96.67 103	96.69 132	96.66 79	97.29 79	98.46 147	96.48 143	95.09 50	99.21 49	93.19 94	98.78 48	86.73 165	98.17 90	97.84 125	96.32 152	99.74 50	99.49 143
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
UA-Net		97.13 85	99.14 38	94.78 114	97.21 80	99.38 78	97.56 109	92.04 103	98.48 128	88.03 131	98.39 66	99.91 31	94.03 191	99.33 24	99.23 18	99.81 22	99.25 160

UGNet		97.66 66	99.07 43	96.01 98	97.19 81	99.65 22	97.09 129	93.39 87	99.35 31	94.40 73	98.79 47	99.59 54	94.24 188	98.04 113	98.29 83	99.73 58	99.80 36
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
TSAR-MVS + GP.		98.66 39	99.36 24	97.85 44	97.16 82	99.46 66	99.03 48	94.59 61	99.09 69	97.19 29	99.73 3	99.95 17	99.39 26	98.95 47	98.69 56	99.75 45	99.65 110

CANet_DTU		96.64 104	99.08 41	93.81 130	97.10 83	99.42 74	98.85 55	90.01 139	99.31 34	79.98 182	99.78 2	99.10 63	97.42 118	98.35 92	98.05 95	99.47 163	99.53 132

IB-MVS	93.96 15	95.02 138	96.44 146	93.36 144	97.05 84	99.28 94	90.43 205	93.39 87	98.02 148	96.02 40	94.92 140	92.07 137	83.52 214	95.38 191	95.82 168	99.72 66	99.59 122
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
ACMP	96.25 10	96.62 106	96.72 131	96.50 87	96.96 85	98.75 127	97.80 101	94.30 69	98.85 96	93.12 95	98.78 48	86.61 167	97.23 122	97.73 131	96.61 142	99.62 122	99.71 91
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test2506		97.16 83	96.68 133	97.73 46	96.95 86	99.79 4	98.48 68	94.42 65	99.17 54	97.74 22	99.15 24	80.93 203	98.89 67	99.03 41	99.09 24	99.88 4	99.62 118

ECVR-MVS		97.27 79	97.09 120	97.48 53	96.95 86	99.79 4	98.48 68	94.42 65	99.17 54	96.28 38	93.54 152	89.39 151	98.89 67	99.03 41	99.09 24	99.88 4	99.61 121

test1111		97.09 87	96.83 130	97.39 54	96.92 88	99.81 3	98.44 72	94.45 64	99.17 54	95.85 43	92.10 166	88.97 152	98.78 71	99.02 43	99.11 23	99.88 4	99.63 116

ACMH	95.42 14	95.27 135	95.96 152	94.45 120	96.83 89	98.78 122	94.72 178	91.67 111	98.95 85	86.82 141	96.42 116	83.67 186	97.00 125	97.48 144	96.68 139	99.69 87	99.76 62
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CLD-MVS		96.74 98	96.51 139	97.01 71	96.71 90	98.62 136	98.73 59	94.38 67	98.94 87	94.46 70	97.33 90	87.03 160	98.07 97	97.20 154	96.87 135	99.72 66	99.54 131
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
TDRefinement		93.04 174	93.57 191	92.41 153	96.58 91	98.77 123	97.78 103	91.96 106	98.12 145	80.84 175	89.13 186	79.87 211	87.78 210	96.44 170	94.50 196	99.54 155	98.15 192

Anonymous202405211			97.40 109		96.45 92	99.54 54	98.08 95	93.79 78	98.24 141		93.55 151	94.41 119	98.88 69	98.04 113	98.24 85	99.75 45	99.76 62

DCV-MVSNet		97.56 69	98.36 69	96.62 83	96.44 93	98.36 156	98.37 77	91.73 109	99.11 67	94.80 61	98.36 67	96.28 95	98.60 80	98.12 102	98.44 68	99.76 41	99.87 17

ACMH+	95.51 13	95.40 131	96.00 150	94.70 115	96.33 94	98.79 120	96.79 135	91.32 120	98.77 112	87.18 138	95.60 134	85.46 176	96.97 126	97.15 155	96.59 143	99.59 138	99.65 110

Anonymous20231211		97.10 86	97.06 123	97.14 62	96.32 95	99.52 58	98.16 89	93.76 79	98.84 100	95.98 41	90.92 172	94.58 118	98.90 64	97.72 132	98.10 93	99.71 76	99.75 70

thres100view900		96.72 99	96.47 143	97.00 72	96.31 96	99.52 58	98.28 83	94.01 72	97.35 170	94.52 66	95.90 125	86.93 162	99.09 53	98.07 108	97.87 103	99.81 22	99.63 116

tfpn200view9		96.75 97	96.51 139	97.03 67	96.31 96	99.67 18	98.41 74	93.99 74	97.35 170	94.52 66	95.90 125	86.93 162	99.14 48	98.26 95	97.80 107	99.82 16	99.70 93

thres200		96.76 96	96.53 137	97.03 67	96.31 96	99.67 18	98.37 77	93.99 74	97.68 165	94.49 69	95.83 128	86.77 164	99.18 43	98.26 95	97.82 106	99.82 16	99.66 107

thres600view7		96.69 101	96.43 147	97.00 72	96.28 99	99.67 18	98.41 74	93.99 74	97.85 159	94.29 75	95.96 123	85.91 173	99.19 40	98.26 95	97.63 112	99.82 16	99.73 78

thres400		96.71 100	96.45 145	97.02 69	96.28 99	99.63 29	98.41 74	94.00 73	97.82 160	94.42 72	95.74 129	86.26 170	99.18 43	98.20 99	97.79 108	99.81 22	99.70 93

baseline1		97.58 68	98.05 84	97.02 69	96.21 101	99.45 68	97.71 105	93.71 83	98.47 129	95.75 44	98.78 48	93.20 133	98.91 62	98.52 85	98.44 68	99.81 22	99.53 132

sasdasda		97.31 76	97.81 95	96.72 76	96.20 102	99.45 68	98.21 86	91.60 112	99.22 46	95.39 50	98.48 60	90.95 141	99.16 46	97.66 134	99.05 29	99.76 41	99.90 6

canonicalmvs		97.31 76	97.81 95	96.72 76	96.20 102	99.45 68	98.21 86	91.60 112	99.22 46	95.39 50	98.48 60	90.95 141	99.16 46	97.66 134	99.05 29	99.76 41	99.90 6

IS_MVSNet		97.86 59	98.86 55	96.68 78	96.02 104	99.72 12	98.35 80	93.37 89	98.75 116	94.01 77	96.88 103	98.40 71	98.48 85	99.09 37	99.42 5	99.83 15	99.80 36

USDC		94.26 154	94.83 166	93.59 136	96.02 104	98.44 149	97.84 99	88.65 158	98.86 94	82.73 167	94.02 147	80.56 204	96.76 132	97.28 151	96.15 159	99.55 151	98.50 185

FC-MVSNet-train		97.04 88	97.91 92	96.03 97	96.00 106	98.41 152	96.53 142	93.42 86	99.04 79	93.02 97	98.03 76	94.32 121	97.47 117	97.93 119	97.77 109	99.75 45	99.88 15

Vis-MVSNet (Re-imp)		97.40 75	98.89 54	95.66 105	95.99 107	99.62 33	97.82 100	93.22 93	98.82 103	91.40 115	96.94 100	98.56 69	95.70 160	99.14 35	99.41 6	99.79 31	99.75 70

MVSTER		97.16 83	97.71 97	96.52 85	95.97 108	98.48 145	98.63 63	92.10 102	98.68 118	95.96 42	99.23 20	91.79 138	96.87 129	98.76 64	97.37 127	99.57 146	99.68 102

baseline		97.45 73	98.70 61	95.99 99	95.89 109	99.36 82	98.29 82	91.37 119	99.21 49	92.99 98	98.40 65	96.87 89	97.96 101	98.60 79	98.60 61	99.42 171	99.86 20

TinyColmap		94.00 158	94.35 175	93.60 135	95.89 109	98.26 158	97.49 111	88.82 155	98.56 124	83.21 161	91.28 171	80.48 206	96.68 135	97.34 148	96.26 155	99.53 157	98.24 191

FA-MVS(training)		96.52 108	98.29 71	94.45 120	95.88 111	99.52 58	97.66 107	81.47 197	98.94 87	93.79 86	95.54 136	99.11 62	98.29 89	98.89 54	96.49 147	99.63 121	99.52 135

EPMVS		95.05 137	96.86 129	92.94 150	95.84 112	98.96 113	96.68 136	79.87 203	99.05 77	90.15 121	97.12 97	95.99 101	97.49 116	95.17 195	94.75 193	97.59 207	96.96 207

casdiffmvs_mvg		97.27 79	97.97 90	96.46 88	95.83 113	99.51 61	98.42 73	93.32 90	98.34 135	92.38 106	95.64 132	95.35 107	98.91 62	98.73 68	98.45 67	99.86 9	99.80 36
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
PMMVS		97.52 70	98.39 68	96.51 86	95.82 114	98.73 130	97.80 101	93.05 97	98.76 113	94.39 74	99.07 33	97.03 88	98.55 82	98.31 94	97.61 113	99.43 169	99.21 163

diffmvs		96.83 94	97.33 112	96.25 91	95.76 115	99.34 87	98.06 96	93.22 93	99.43 22	92.30 108	96.90 102	89.83 150	98.55 82	98.00 116	98.14 89	99.64 116	99.70 93
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
MVS_Test		97.30 78	98.54 63	95.87 100	95.74 116	99.28 94	98.19 88	91.40 118	99.18 53	91.59 114	98.17 72	96.18 97	98.63 78	98.61 76	98.55 62	99.66 108	99.78 50

EIA-MVS		97.70 65	98.78 58	96.44 89	95.72 117	99.65 22	98.14 90	93.72 82	98.30 137	92.31 107	98.63 55	97.90 76	98.97 59	98.92 51	98.30 82	99.78 34	99.80 36

casdiffmvs		96.93 92	97.43 108	96.34 90	95.70 118	99.50 62	97.75 104	93.22 93	98.98 84	92.64 101	94.97 138	91.71 139	98.93 60	98.62 75	98.52 65	99.82 16	99.72 88
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
tpmrst		93.86 163	95.88 154	91.50 172	95.69 119	98.62 136	95.64 157	79.41 206	98.80 106	83.76 157	95.63 133	96.13 98	97.25 120	92.92 207	92.31 206	97.27 210	96.74 208

ADS-MVSNet		94.65 146	97.04 124	91.88 168	95.68 120	98.99 110	95.89 152	79.03 210	99.15 59	85.81 146	96.96 99	98.21 75	97.10 123	94.48 203	94.24 197	97.74 203	97.21 203

EPP-MVSNet		97.75 63	98.71 60	96.63 82	95.68 120	99.56 51	97.51 110	93.10 96	99.22 46	94.99 59	97.18 96	97.30 84	98.65 76	98.83 58	98.93 39	99.84 12	99.92 3

EC-MVSNet		98.22 51	99.44 17	96.79 75	95.62 122	99.56 51	99.01 50	92.22 100	99.17 54	94.51 68	99.41 13	99.62 52	99.49 18	99.16 34	99.26 14	99.91 2	99.94 1

ETV-MVS		98.05 55	99.25 33	96.65 80	95.61 123	99.61 38	98.26 85	93.52 85	98.90 92	93.74 87	99.32 17	99.20 59	98.90 64	99.21 29	98.72 55	99.87 8	99.79 44

DI_MVS_plusplus_trai		96.90 93	97.49 103	96.21 92	95.61 123	99.40 77	98.72 60	92.11 101	99.14 62	92.98 99	93.08 162	95.14 109	98.13 94	98.05 112	97.91 101	99.74 50	99.73 78

thisisatest0530		97.23 81	98.25 73	96.05 95	95.60 125	99.59 45	96.96 133	93.23 91	99.17 54	92.60 103	98.75 51	96.19 96	98.17 90	98.19 100	96.10 160	99.72 66	99.77 57

tttt0517		97.23 81	98.24 76	96.04 96	95.60 125	99.60 43	96.94 134	93.23 91	99.15 59	92.56 104	98.74 52	96.12 99	98.17 90	98.21 98	96.10 160	99.73 58	99.78 50

SCA		94.95 139	97.44 107	92.04 160	95.55 127	99.16 103	96.26 148	79.30 207	99.02 80	85.73 147	98.18 71	97.13 86	97.69 110	96.03 184	94.91 188	97.69 206	97.65 199

dps		94.63 147	95.31 162	93.84 129	95.53 128	98.71 131	96.54 140	80.12 202	97.81 162	97.21 28	96.98 98	92.37 134	96.34 146	92.46 210	91.77 210	97.26 211	97.08 205

PatchmatchNet		94.70 144	97.08 122	91.92 165	95.53 128	98.85 118	95.77 154	79.54 205	98.95 85	85.98 144	98.52 58	96.45 90	97.39 119	95.32 192	94.09 198	97.32 209	97.38 202
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test-LLR		95.50 129	97.32 113	93.37 143	95.49 130	98.74 128	96.44 145	90.82 128	98.18 142	82.75 165	96.60 111	94.67 116	95.54 166	98.09 105	96.00 162	99.20 184	98.93 175

test0.0.03 1		96.69 101	98.12 82	95.01 112	95.49 130	98.99 110	95.86 153	90.82 128	98.38 132	92.54 105	96.66 108	97.33 82	95.75 158	97.75 130	98.34 78	99.60 132	99.40 151

CostFormer		94.25 155	94.88 165	93.51 140	95.43 132	98.34 157	96.21 149	80.64 200	97.94 154	94.01 77	98.30 69	86.20 172	97.52 114	92.71 208	92.69 204	97.23 212	98.02 195

MDTV_nov1_ep13		95.57 127	97.48 104	93.35 145	95.43 132	98.97 112	97.19 124	83.72 195	98.92 91	87.91 133	97.75 83	96.12 99	97.88 106	96.84 164	95.64 172	97.96 201	98.10 193

tpm cat1		94.06 156	94.90 164	93.06 148	95.42 134	98.52 144	96.64 138	80.67 199	97.82 160	92.63 102	93.39 156	95.00 111	96.06 153	91.36 214	91.58 212	96.98 213	96.66 210

Vis-MVSNet		96.16 116	98.22 77	93.75 131	95.33 135	99.70 17	97.27 118	90.85 127	98.30 137	85.51 149	95.72 131	96.45 90	93.69 197	98.70 70	99.00 34	99.84 12	99.69 97
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CVMVSNet		95.33 134	97.09 120	93.27 146	95.23 136	98.39 154	95.49 160	92.58 99	97.71 164	83.00 164	94.44 145	93.28 131	93.92 194	97.79 126	98.54 64	99.41 172	99.45 146

IterMVS-LS		96.12 117	97.48 104	94.53 117	95.19 137	97.56 190	97.15 125	89.19 152	99.08 71	88.23 129	94.97 138	94.73 115	97.84 108	97.86 124	98.26 84	99.60 132	99.88 15
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Effi-MVS+		95.81 123	97.31 116	94.06 126	95.09 138	99.35 85	97.24 121	88.22 163	98.54 125	85.38 150	98.52 58	88.68 153	98.70 73	98.32 93	97.93 98	99.74 50	99.84 24

testgi		95.67 126	97.48 104	93.56 137	95.07 139	99.00 108	95.33 164	88.47 160	98.80 106	86.90 140	97.30 92	92.33 135	95.97 155	97.66 134	97.91 101	99.60 132	99.38 152

GeoE		95.98 122	97.24 118	94.51 118	95.02 140	99.38 78	98.02 97	87.86 169	98.37 133	87.86 134	92.99 164	93.54 128	98.56 81	98.61 76	97.92 99	99.73 58	99.85 23

RPMNet		94.66 145	97.16 119	91.75 169	94.98 141	98.59 139	97.00 132	78.37 214	97.98 150	83.78 155	96.27 118	94.09 126	96.91 128	97.36 147	96.73 137	99.48 161	99.09 170

CR-MVSNet		94.57 151	97.34 111	91.33 176	94.90 142	98.59 139	97.15 125	79.14 208	97.98 150	80.42 178	96.59 113	93.50 130	96.85 130	98.10 103	97.49 119	99.50 160	99.15 165

gg-mvs-nofinetune		90.85 198	94.14 177	87.02 203	94.89 143	99.25 97	98.64 62	76.29 218	88.24 219	57.50 223	79.93 215	95.45 105	95.18 177	98.77 63	98.07 94	99.62 122	99.24 161

IterMVS-SCA-FT		94.89 141	97.87 93	91.42 173	94.86 144	97.70 176	97.24 121	84.88 189	98.93 89	75.74 198	94.26 146	98.25 73	96.69 134	98.52 85	97.68 111	99.10 188	99.73 78

IterMVS		94.81 143	97.71 97	91.42 173	94.83 145	97.63 183	97.38 113	85.08 186	98.93 89	75.67 199	94.02 147	97.64 79	96.66 137	98.45 88	97.60 114	98.90 191	99.72 88
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PatchT		93.96 160	97.36 110	90.00 192	94.76 146	98.65 134	90.11 208	78.57 213	97.96 153	80.42 178	96.07 121	94.10 125	96.85 130	98.10 103	97.49 119	99.26 182	99.15 165

baseline2		96.36 111	97.82 94	94.65 116	94.60 147	99.09 106	96.45 144	89.63 147	98.36 134	91.29 117	97.60 88	94.13 124	96.37 144	98.45 88	97.70 110	99.54 155	99.41 149

CDS-MVSNet		96.59 107	98.02 87	94.92 113	94.45 148	98.96 113	97.46 112	91.75 108	97.86 158	90.07 122	96.02 122	97.25 85	96.21 147	98.04 113	98.38 73	99.60 132	99.65 110
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
tpm		92.38 190	94.79 167	89.56 196	94.30 149	97.50 193	94.24 190	78.97 211	97.72 163	74.93 203	97.97 78	82.91 192	96.60 139	93.65 206	94.81 192	98.33 197	98.98 173

Fast-Effi-MVS+		95.38 132	96.52 138	94.05 127	94.15 150	99.14 105	97.24 121	86.79 175	98.53 126	87.62 136	94.51 143	87.06 159	98.76 72	98.60 79	98.04 96	99.72 66	99.77 57

Effi-MVS+-dtu		95.74 125	98.04 85	93.06 148	93.92 151	99.16 103	97.90 98	88.16 165	99.07 76	82.02 170	98.02 77	94.32 121	96.74 133	98.53 84	97.56 115	99.61 124	99.62 118

UniMVSNet_ETH3D		93.15 171	92.33 204	94.11 125	93.91 152	98.61 138	94.81 175	90.98 125	97.06 179	87.51 137	82.27 213	76.33 219	97.87 107	94.79 201	97.47 122	99.56 149	99.81 34

Fast-Effi-MVS+-dtu		95.38 132	98.20 78	92.09 159	93.91 152	98.87 117	97.35 115	85.01 188	99.08 71	81.09 174	98.10 73	96.36 93	95.62 163	98.43 91	97.03 131	99.55 151	99.50 142

TAMVS		95.53 128	96.50 141	94.39 122	93.86 154	99.03 107	96.67 137	89.55 149	97.33 172	90.64 119	93.02 163	91.58 140	96.21 147	97.72 132	97.43 125	99.43 169	99.36 153

GBi-Net		96.98 90	98.00 88	95.78 101	93.81 155	97.98 165	98.09 92	91.32 120	98.80 106	93.92 79	97.21 93	95.94 102	97.89 103	98.07 108	98.34 78	99.68 95	99.67 103

test1		96.98 90	98.00 88	95.78 101	93.81 155	97.98 165	98.09 92	91.32 120	98.80 106	93.92 79	97.21 93	95.94 102	97.89 103	98.07 108	98.34 78	99.68 95	99.67 103

FMVSNet2		96.64 104	97.50 102	95.63 106	93.81 155	97.98 165	98.09 92	90.87 126	98.99 83	93.48 90	93.17 159	95.25 108	97.89 103	98.63 74	98.80 53	99.68 95	99.67 103

MVS-HIRNet		92.51 184	95.97 151	88.48 200	93.73 158	98.37 155	90.33 206	75.36 220	98.32 136	77.78 192	89.15 185	94.87 112	95.14 178	97.62 139	96.39 150	98.51 193	97.11 204

GA-MVS		93.93 161	96.31 149	91.16 180	93.61 159	98.79 120	95.39 163	90.69 133	98.25 140	73.28 207	96.15 120	88.42 154	94.39 186	97.76 129	95.35 176	99.58 142	99.45 146

FC-MVSNet-test		96.07 118	97.94 91	93.89 128	93.60 160	98.67 133	96.62 139	90.30 138	98.76 113	88.62 127	95.57 135	97.63 80	94.48 184	97.97 117	97.48 121	99.71 76	99.52 135

FMVSNet3		97.02 89	98.12 82	95.73 104	93.59 161	97.98 165	98.34 81	91.32 120	98.80 106	93.92 79	97.21 93	95.94 102	97.63 112	98.61 76	98.62 59	99.61 124	99.65 110

dmvs_re		96.02 119	96.49 142	95.47 107	93.49 162	99.26 96	97.25 120	93.82 77	97.51 167	90.43 120	97.52 89	87.93 155	98.12 95	96.86 162	96.59 143	99.73 58	99.76 62

FMVSNet1		95.77 124	96.41 148	95.03 111	93.42 163	97.86 172	97.11 128	89.89 142	98.53 126	92.00 111	89.17 184	93.23 132	98.15 93	98.07 108	98.34 78	99.61 124	99.69 97

tfpnnormal		93.85 164	94.12 179	93.54 139	93.22 164	98.24 160	95.45 161	91.96 106	94.61 210	83.91 153	90.74 174	81.75 200	97.04 124	97.49 143	96.16 158	99.68 95	99.84 24

TransMVSNet (Re)		93.45 167	94.08 180	92.72 152	92.83 165	97.62 186	94.94 169	91.54 116	95.65 207	83.06 163	88.93 187	83.53 187	94.25 187	97.41 145	97.03 131	99.67 103	98.40 190

LTVRE_ROB	93.20 16	92.84 176	94.92 163	90.43 189	92.83 165	98.63 135	97.08 130	87.87 168	97.91 155	68.42 216	93.54 152	79.46 213	96.62 138	97.55 141	97.40 126	99.74 50	99.92 3
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
TESTMET0.1,1		94.95 139	97.32 113	92.20 157	92.62 167	98.74 128	96.44 145	86.67 177	98.18 142	82.75 165	96.60 111	94.67 116	95.54 166	98.09 105	96.00 162	99.20 184	98.93 175

pm-mvs1		94.27 153	95.57 158	92.75 151	92.58 168	98.13 163	94.87 173	90.71 132	96.70 189	83.78 155	89.94 180	89.85 149	94.96 181	97.58 140	97.07 130	99.61 124	99.72 88

NR-MVSNet		94.01 157	94.51 172	93.44 141	92.56 169	97.77 173	95.67 155	91.57 114	97.17 176	85.84 145	93.13 160	80.53 205	95.29 174	97.01 159	96.17 157	99.69 87	99.75 70

EG-PatchMatch MVS		92.45 185	93.92 186	90.72 186	92.56 169	98.43 151	94.88 172	84.54 191	97.18 175	79.55 184	86.12 206	83.23 190	93.15 201	97.22 153	96.00 162	99.67 103	99.27 159

pmnet_mix02		92.44 186	94.68 169	89.83 195	92.46 171	97.65 182	89.92 210	90.49 135	98.76 113	73.05 209	91.78 167	90.08 147	94.86 182	94.53 202	91.94 209	98.21 199	98.01 196

test-mter		94.86 142	97.32 113	92.00 162	92.41 172	98.82 119	96.18 150	86.35 181	98.05 147	82.28 168	96.48 115	94.39 120	95.46 170	98.17 101	96.20 156	99.32 179	99.13 169

our_test_3					92.30 173	97.58 188	90.09 209

pmmvs4		95.09 136	95.90 153	94.14 124	92.29 174	97.70 176	95.45 161	90.31 136	98.60 120	90.70 118	93.25 157	89.90 148	96.67 136	97.13 156	95.42 175	99.44 167	99.28 156

FMVSNet5		95.42 130	96.47 143	94.20 123	92.26 175	95.99 211	95.66 156	87.15 173	97.87 157	93.46 91	96.68 107	93.79 127	97.52 114	97.10 158	97.21 129	99.11 187	96.62 211

UniMVSNet (Re)		94.58 150	95.34 160	93.71 133	92.25 176	98.08 164	94.97 168	91.29 124	97.03 181	87.94 132	93.97 149	86.25 171	96.07 152	96.27 178	95.97 165	99.72 66	99.79 44

SixPastTwentyTwo		93.44 168	95.32 161	91.24 178	92.11 177	98.40 153	92.77 196	88.64 159	98.09 146	77.83 191	93.51 154	85.74 174	96.52 142	96.91 161	94.89 191	99.59 138	99.73 78

v8		92.87 175	93.87 188	91.72 171	92.05 178	97.50 193	94.79 176	88.20 164	96.85 185	80.11 181	90.01 179	82.86 194	95.48 168	95.15 196	94.90 189	99.66 108	99.80 36

thisisatest0515		94.61 148	96.89 127	91.95 164	92.00 179	98.47 146	92.01 200	90.73 131	98.18 142	83.96 152	94.51 143	95.13 110	93.38 198	97.38 146	94.74 194	99.61 124	99.79 44

WR-MVS_H		93.54 166	94.67 170	92.22 155	91.95 180	97.91 170	94.58 184	88.75 156	96.64 190	83.88 154	90.66 176	85.13 179	94.40 185	96.54 169	95.91 167	99.73 58	99.89 11

V42		93.05 173	93.90 187	92.04 160	91.91 181	97.66 180	94.91 170	89.91 141	96.85 185	80.58 177	89.66 181	83.43 189	95.37 172	95.03 199	94.90 189	99.59 138	99.78 50

EU-MVSNet		92.80 178	94.76 168	90.51 187	91.88 182	96.74 208	92.48 198	88.69 157	96.21 196	79.00 187	91.51 168	87.82 156	91.83 206	95.87 188	96.27 153	99.21 183	98.92 178

N_pmnet		92.21 194	94.60 171	89.42 197	91.88 182	97.38 199	89.15 212	89.74 146	97.89 156	73.75 205	87.94 196	92.23 136	93.85 195	96.10 182	93.20 203	98.15 200	97.43 201

UniMVSNet_NR-MVSNet		94.59 149	95.47 159	93.55 138	91.85 184	97.89 171	95.03 166	92.00 104	97.33 172	86.12 142	93.19 158	87.29 158	96.60 139	96.12 181	96.70 138	99.72 66	99.80 36

pmmvs6		91.90 196	92.53 203	91.17 179	91.81 185	97.63 183	93.23 193	88.37 162	93.43 215	80.61 176	77.32 217	87.47 157	94.12 189	96.58 167	95.72 170	98.88 192	99.53 132

v10		92.79 179	94.06 181	91.31 177	91.78 186	97.29 202	94.87 173	86.10 182	96.97 182	79.82 183	88.16 193	84.56 183	95.63 162	96.33 176	95.31 177	99.65 112	99.80 36

MIMVSNet		94.49 152	97.59 101	90.87 185	91.74 187	98.70 132	94.68 180	78.73 212	97.98 150	83.71 158	97.71 86	94.81 114	96.96 127	97.97 117	97.92 99	99.40 174	98.04 194

v1144		92.81 177	94.03 182	91.40 175	91.68 188	97.60 187	94.73 177	88.40 161	96.71 188	78.48 189	88.14 194	84.46 184	95.45 171	96.31 177	95.22 180	99.65 112	99.76 62

DU-MVS		93.98 159	94.44 174	93.44 141	91.66 189	97.77 173	95.03 166	91.57 114	97.17 176	86.12 142	93.13 160	81.13 202	96.60 139	95.10 197	97.01 133	99.67 103	99.80 36

Baseline_NR-MVSNet		93.87 162	93.98 184	93.75 131	91.66 189	97.02 203	95.53 159	91.52 117	97.16 178	87.77 135	87.93 197	83.69 185	96.35 145	95.10 197	97.23 128	99.68 95	99.73 78

CP-MVSNet		93.25 170	94.00 183	92.38 154	91.65 191	97.56 190	94.38 187	89.20 151	96.05 201	83.16 162	89.51 182	81.97 198	96.16 151	96.43 171	96.56 145	99.71 76	99.89 11

v148		92.36 192	92.88 199	91.75 169	91.63 192	97.66 180	92.64 197	90.55 134	96.09 199	83.34 160	88.19 192	80.00 208	92.74 202	93.98 205	94.58 195	99.58 142	99.69 97

PS-CasMVS		92.72 181	93.36 195	91.98 163	91.62 193	97.52 192	94.13 191	88.98 153	95.94 204	81.51 173	87.35 199	79.95 210	95.91 156	96.37 173	96.49 147	99.70 84	99.89 11

v2v482		92.77 180	93.52 194	91.90 167	91.59 194	97.63 183	94.57 185	90.31 136	96.80 187	79.22 185	88.74 189	81.55 201	96.04 154	95.26 193	94.97 187	99.66 108	99.69 97

v1192		92.43 188	93.61 190	91.05 181	91.53 195	97.43 196	94.61 183	87.99 167	96.60 191	76.72 194	87.11 201	82.74 195	95.85 157	96.35 175	95.30 178	99.60 132	99.74 74

WR-MVS		93.43 169	94.48 173	92.21 156	91.52 196	97.69 178	94.66 182	89.98 140	96.86 184	83.43 159	90.12 178	85.03 180	93.94 193	96.02 185	95.82 168	99.71 76	99.82 29

v144192		92.38 190	93.55 193	91.00 182	91.44 197	97.47 195	94.27 188	87.41 172	96.52 193	78.03 190	87.50 198	82.65 196	95.32 173	95.82 189	95.15 182	99.55 151	99.78 50

pmmvs5		92.71 183	94.27 176	90.90 184	91.42 198	97.74 175	93.23 193	86.66 178	95.99 203	78.96 188	91.45 169	83.44 188	95.55 165	97.30 150	95.05 185	99.58 142	98.93 175

v1921920		92.36 192	93.57 191	90.94 183	91.39 199	97.39 198	94.70 179	87.63 171	96.60 191	76.63 195	86.98 202	82.89 193	95.75 158	96.26 179	95.14 183	99.55 151	99.73 78

gm-plane-assit		89.44 205	92.82 202	85.49 207	91.37 200	95.34 214	79.55 222	82.12 196	91.68 218	64.79 220	87.98 195	80.26 207	95.66 161	98.51 87	97.56 115	99.45 165	98.41 187

v1240		91.99 195	93.33 196	90.44 188	91.29 201	97.30 201	94.25 189	86.79 175	96.43 194	75.49 201	86.34 205	81.85 199	95.29 174	96.42 172	95.22 180	99.52 158	99.73 78

PEN-MVS		92.72 181	93.20 197	92.15 158	91.29 201	97.31 200	94.67 181	89.81 143	96.19 197	81.83 171	88.58 190	79.06 214	95.61 164	95.21 194	96.27 153	99.72 66	99.82 29

TranMVSNet+NR-MVSNet		93.67 165	94.14 177	93.13 147	91.28 203	97.58 188	95.60 158	91.97 105	97.06 179	84.05 151	90.64 177	82.22 197	96.17 150	94.94 200	96.78 136	99.69 87	99.78 50

anonymousdsp		93.12 172	95.86 155	89.93 194	91.09 204	98.25 159	95.12 165	85.08 186	97.44 169	73.30 206	90.89 173	90.78 143	95.25 176	97.91 120	95.96 166	99.71 76	99.82 29

MDTV_nov1_ep13_2view		92.44 186	95.66 157	88.68 198	91.05 205	97.92 169	92.17 199	79.64 204	98.83 101	76.20 196	91.45 169	93.51 129	95.04 179	95.68 190	93.70 201	97.96 201	98.53 184

DTE-MVSNet		92.42 189	92.85 200	91.91 166	90.87 206	96.97 204	94.53 186	89.81 143	95.86 206	81.59 172	88.83 188	77.88 217	95.01 180	94.34 204	96.35 151	99.64 116	99.73 78

v7n		91.61 197	92.95 198	90.04 191	90.56 207	97.69 178	93.74 192	85.59 184	95.89 205	76.95 193	86.60 204	78.60 216	93.76 196	97.01 159	94.99 186	99.65 112	99.87 17

test20.03		90.65 201	93.71 189	87.09 202	90.44 208	96.24 209	89.74 211	85.46 185	95.59 208	72.99 210	90.68 175	85.33 177	84.41 213	95.94 187	95.10 184	99.52 158	97.06 206

FPMVS		83.82 211	84.61 214	82.90 210	90.39 209	90.71 219	90.85 204	84.10 194	95.47 209	65.15 218	83.44 210	74.46 220	75.48 216	81.63 218	79.42 220	91.42 221	87.14 220

Anonymous20231206		90.70 200	93.93 185	86.92 204	90.21 210	96.79 206	90.30 207	86.61 179	96.05 201	69.25 214	88.46 191	84.86 182	85.86 212	97.11 157	96.47 149	99.30 180	97.80 198

new_pmnet		90.45 202	92.84 201	87.66 201	88.96 211	96.16 210	88.71 213	84.66 190	97.56 166	71.91 213	85.60 207	86.58 168	93.28 199	96.07 183	93.54 202	98.46 194	94.39 215

WB-MVS		81.36 213	89.93 210	71.35 216	88.65 212	87.85 222	71.46 224	88.12 166	96.23 195	32.21 228	92.61 165	83.00 191	56.27 223	91.92 213	89.43 214	91.39 222	88.49 219

ET-MVSNet_ETH3D		96.17 115	96.99 125	95.21 110	88.53 213	98.54 142	98.28 83	92.61 98	98.85 96	93.60 89	99.06 34	90.39 144	98.63 78	95.98 186	96.68 139	99.61 124	99.41 149

PM-MVS		89.55 204	90.30 209	88.67 199	87.06 214	95.60 212	90.88 203	84.51 192	96.14 198	75.75 197	86.89 203	63.47 225	94.64 183	96.85 163	93.89 199	99.17 186	99.29 155

pmmvs-eth3d		89.81 203	89.65 211	90.00 192	86.94 215	95.38 213	91.08 201	86.39 180	94.57 211	82.27 169	83.03 212	64.94 222	93.96 192	96.57 168	93.82 200	99.35 177	99.24 161

new-patchmatchnet		86.12 210	87.30 213	84.74 208	86.92 216	95.19 216	83.57 219	84.42 193	92.67 216	65.66 217	80.32 214	64.72 223	89.41 208	92.33 212	89.21 215	98.43 195	96.69 209

pmmvs3		88.19 207	91.27 206	84.60 209	85.60 217	93.66 217	85.68 217	81.13 198	92.36 217	63.66 222	89.51 182	77.10 218	93.22 200	96.37 173	92.40 205	98.30 198	97.46 200

Gipuma		81.40 212	81.78 215	80.96 213	83.21 218	85.61 224	79.73 221	76.25 219	97.33 172	64.21 221	55.32 221	55.55 226	86.04 211	92.43 211	92.20 208	96.32 217	93.99 216
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MDA-MVSNet-bldmvs		87.84 208	89.22 212	86.23 205	81.74 219	96.77 207	83.74 218	89.57 148	94.50 212	72.83 211	96.64 109	64.47 224	92.71 203	81.43 219	92.28 207	96.81 214	98.47 186

MIMVSNet1		88.61 206	90.68 208	86.19 206	81.56 220	95.30 215	87.78 214	85.98 183	94.19 213	72.30 212	78.84 216	78.90 215	90.06 207	96.59 166	95.47 173	99.46 164	95.49 213

PMVS	72.60 17	76.39 215	77.66 218	74.92 214	81.04 221	69.37 228	68.47 225	80.54 201	85.39 220	65.07 219	73.52 218	72.91 221	65.67 222	80.35 220	76.81 221	88.71 223	85.25 223
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
ambc			80.99 216		80.04 222	90.84 218	90.91 202		96.09 199	74.18 204	62.81 220	30.59 231	82.44 215	96.25 180	91.77 210	95.91 218	98.56 183

PMMVS2		77.26 214	79.47 217	74.70 215	76.00 223	88.37 221	74.22 223	76.34 217	78.31 221	54.13 224	69.96 219	52.50 227	70.14 220	84.83 217	88.71 216	97.35 208	93.58 217

test_method		87.27 209	91.58 205	82.25 211	75.65 224	87.52 223	86.81 216	72.60 221	97.51 167	73.20 208	85.07 208	79.97 209	88.69 209	97.31 149	95.24 179	96.53 215	98.41 187

EMVS		68.12 218	68.11 220	68.14 218	75.51 225	71.76 226	55.38 228	77.20 216	77.78 222	37.79 227	53.59 222	43.61 228	74.72 217	67.05 223	76.70 222	88.27 225	86.24 221

E-PMN		68.30 217	68.43 219	68.15 217	74.70 226	71.56 227	55.64 227	77.24 215	77.48 223	39.46 226	51.95 224	41.68 229	73.28 218	70.65 222	79.51 219	88.61 224	86.20 222

MVE	67.97 19	65.53 219	67.43 221	63.31 219	59.33 227	74.20 225	53.09 229	70.43 222	66.27 224	43.13 225	45.98 225	30.62 230	70.65 219	79.34 221	86.30 217	83.25 226	89.33 218
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		31.24 220	40.15 222	20.86 221	12.61 228	17.99 229	25.16 230	13.30 224	48.42 225	24.82 229	53.07 223	30.13 232	28.47 224	42.73 224	37.65 223	20.79 227	51.04 224

test123		26.75 221	34.25 223	18.01 222	7.93 229	17.18 230	24.85 231	12.36 225	44.83 226	16.52 230	41.80 226	18.10 233	28.29 225	33.08 225	34.79 224	18.10 228	49.95 225

GG-mvs-BLEND		69.11 216	98.13 81	35.26 220	3.49 230	98.20 162	94.89 171	2.38 226	98.42 131	5.82 231	96.37 117	98.60 67	5.97 226	98.75 66	97.98 97	99.01 189	98.61 182

uanet_test		0.00 222	0.00 224	0.00 223	0.00 231	0.00 231	0.00 232	0.00 227	0.00 227	0.00 232	0.00 227	0.00 234	0.00 227	0.00 226	0.00 225	0.00 229	0.00 226

sosnet-low-res		0.00 222	0.00 224	0.00 223	0.00 231	0.00 231	0.00 232	0.00 227	0.00 227	0.00 232	0.00 227	0.00 234	0.00 227	0.00 226	0.00 225	0.00 229	0.00 226

sosnet		0.00 222	0.00 224	0.00 223	0.00 231	0.00 231	0.00 232	0.00 227	0.00 227	0.00 232	0.00 227	0.00 234	0.00 227	0.00 226	0.00 225	0.00 229	0.00 226

RE-MVS-def										69.05 215

9.14												99.79 45

MTAPA										98.09 15		99.97 8

MTMP										98.46 10		99.96 12

Patchmatch-RL test							66.86 226

NP-MVS									98.57 123

Patchmtry						98.59 139	97.15 125	79.14 208		80.42 178

DeepMVS_CX						96.85 205	87.43 215	89.27 150	98.30 137	75.55 200	95.05 137	79.47 212	92.62 204	89.48 215		95.18 219	95.96 212

High-resolution multi-view benchmark