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 32	99.35 27	98.29 36	99.77 1	99.63 28	99.67 5	95.63 47	98.66 118	95.27 52	99.11 28	99.82 43	99.67 4	99.33 24	99.19 21	99.73 56	99.74 72

SMA-MVS		99.38 6	99.60 3	99.12 10	99.76 2	99.62 32	99.39 30	98.23 20	99.52 16	98.03 18	99.45 11	99.98 2	99.64 5	99.58 9	99.30 11	99.68 94	99.76 61
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 31	98.93 54	98.85 26	99.75 3	99.72 9	99.49 22	96.58 44	99.38 24	98.05 17	98.97 37	97.87 77	99.49 20	97.78 126	98.92 39	99.78 32	99.90 6

APDe-MVS		99.49 1	99.64 1	99.32 2	99.74 4	99.74 8	99.75 1	98.34 4	99.56 11	98.72 7	99.57 7	99.97 8	99.53 17	99.65 2	99.25 15	99.84 10	99.77 56

ACMMP_NAP		99.05 26	99.45 14	98.58 32	99.73 5	99.60 43	99.64 8	98.28 13	99.23 46	94.57 63	99.35 15	99.97 8	99.55 14	99.63 3	98.66 56	99.70 82	99.74 72

zzz-MVS		99.31 9	99.44 17	99.16 6	99.73 5	99.65 20	99.63 12	98.26 14	99.27 40	98.01 19	99.27 19	99.97 8	99.60 7	99.59 8	98.58 61	99.71 73	99.73 76

DVP-MVS		99.45 2	99.54 7	99.35 1	99.72 7	99.76 3	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 48	99.79 42
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 8	99.69 8	99.75 6	99.64 8	98.33 6	99.67 4	98.10 14	99.66 4	99.99 1	99.33 32	99.62 5	98.86 44	99.74 48	99.90 6

SED-MVS		99.44 3	99.58 4	99.28 3	99.69 8	99.76 3	99.62 15	98.35 3	99.51 17	99.05 2	99.60 6	99.98 2	99.28 39	99.61 6	98.83 49	99.70 82	99.77 56

HFP-MVS		99.32 8	99.53 9	99.07 14	99.69 8	99.59 45	99.63 12	98.31 9	99.56 11	97.37 27	99.27 19	99.97 8	99.70 3	99.35 22	99.24 17	99.71 73	99.76 61

HPM-MVS++		99.10 22	99.30 30	98.86 25	99.69 8	99.48 62	99.59 17	98.34 4	99.26 43	96.55 38	99.10 31	99.96 13	99.36 30	99.25 28	98.37 74	99.64 115	99.66 106

APD-MVS		99.25 13	99.38 22	99.09 12	99.69 8	99.58 48	99.56 18	98.32 8	98.85 95	97.87 21	98.91 42	99.92 29	99.30 37	99.45 16	99.38 8	99.79 29	99.58 121
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 8	99.68 13	99.75 6	99.64 8	98.31 9	99.44 21	98.10 14	99.28 18	99.98 2	99.30 37	99.34 23	99.05 29	99.81 20	99.79 42
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 14			98.25 15				99.94 26

X-MVS		98.93 30	99.37 23	98.42 33	99.67 14	99.62 32	99.60 16	98.15 25	99.08 71	93.81 82	98.46 62	99.95 18	99.59 10	99.49 14	99.21 20	99.68 94	99.75 68

MCST-MVS		99.11 21	99.27 32	98.93 23	99.67 14	99.33 88	99.51 21	98.31 9	99.28 38	96.57 37	99.10 31	99.90 33	99.71 2	99.19 32	98.35 75	99.82 14	99.71 90

ACMMPR		99.30 10	99.54 7	99.03 17	99.66 17	99.64 25	99.68 4	98.25 15	99.56 11	97.12 31	99.19 22	99.95 18	99.72 1	99.43 17	99.25 15	99.72 63	99.77 56

SteuartSystems-ACMMP		99.20 16	99.51 11	98.83 28	99.66 17	99.66 19	99.71 3	98.12 29	99.14 61	96.62 35	99.16 24	99.98 2	99.12 49	99.63 3	99.19 21	99.78 32	99.83 27
Skip Steuart: Steuart Systems R&D Blog.
xxxxxxxxxxxxxcwj		98.14 53	97.38 108	99.03 17	99.65 19	99.41 73	98.87 56	98.24 18	99.14 61	98.73 5	99.11 28	86.38 168	98.92 61	99.22 29	98.84 47	99.76 39	99.56 127

SF-MVS		99.18 17	99.32 28	99.03 17	99.65 19	99.41 73	98.87 56	98.24 18	99.14 61	98.73 5	99.11 28	99.92 29	98.92 61	99.22 29	98.84 47	99.76 39	99.56 127

CNVR-MVS		99.23 15	99.28 31	99.17 5	99.65 19	99.34 85	99.46 25	98.21 21	99.28 38	98.47 9	98.89 44	99.94 26	99.50 18	99.42 18	98.61 59	99.73 56	99.52 134

DPE-MVS		99.39 5	99.55 6	99.20 4	99.63 22	99.71 12	99.66 6	98.33 6	99.29 37	98.40 12	99.64 5	99.98 2	99.31 35	99.56 10	98.96 36	99.85 8	99.70 92
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MP-MVS		99.07 24	99.36 24	98.74 29	99.63 22	99.57 50	99.66 6	98.25 15	99.00 82	95.62 46	98.97 37	99.94 26	99.54 16	99.51 13	98.79 53	99.71 73	99.73 76
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
NCCC		99.05 26	99.08 42	99.02 20	99.62 24	99.38 76	99.43 29	98.21 21	99.36 29	97.66 24	97.79 80	99.90 33	99.45 25	99.17 33	98.43 69	99.77 37	99.51 138

CP-MVS		99.27 11	99.44 17	99.08 13	99.62 24	99.58 48	99.53 19	98.16 23	99.21 49	97.79 22	99.15 25	99.96 13	99.59 10	99.54 12	98.86 44	99.78 32	99.74 72

AdaColmap		99.06 25	98.98 52	99.15 7	99.60 26	99.30 91	99.38 31	98.16 23	99.02 80	98.55 8	98.71 53	99.57 57	99.58 13	99.09 39	97.84 104	99.64 115	99.36 152

CPTT-MVS		99.14 20	99.20 37	99.06 15	99.58 27	99.53 55	99.45 26	97.80 38	99.19 52	98.32 13	98.58 56	99.95 18	99.60 7	99.28 26	98.20 86	99.64 115	99.69 96

QAPM		98.62 41	99.04 48	98.13 40	99.57 28	99.48 62	99.17 39	94.78 57	99.57 10	96.16 40	96.73 104	99.80 44	99.33 32	98.79 61	99.29 13	99.75 43	99.64 113

3Dnovator	96.92 7	98.67 38	99.05 45	98.23 39	99.57 28	99.45 66	99.11 43	94.66 60	99.69 3	96.80 34	96.55 113	99.61 54	99.40 28	98.87 57	99.49 3	99.85 8	99.66 106

DeepC-MVS_fast	98.34 1	99.17 18	99.45 14	98.85 26	99.55 30	99.37 79	99.64 8	98.05 33	99.53 14	96.58 36	98.93 40	99.92 29	99.49 20	99.46 15	99.32 10	99.80 28	99.64 113
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 31							99.89 35

3Dnovator+	96.92 7	98.71 37	99.05 45	98.32 35	99.53 31	99.34 85	99.06 47	94.61 61	99.65 5	97.49 25	96.75 103	99.86 38	99.44 26	98.78 62	99.30 11	99.81 20	99.67 102

MSLP-MVS++		99.15 19	99.24 35	99.04 16	99.52 33	99.49 61	99.09 45	98.07 31	99.37 26	98.47 9	97.79 80	99.89 35	99.50 18	98.93 50	99.45 4	99.61 122	99.76 61

OpenMVS	96.23 11	97.95 60	98.45 68	97.35 55	99.52 33	99.42 71	98.91 55	94.61 61	98.87 92	92.24 108	94.61 139	99.05 63	99.10 51	98.64 72	99.05 29	99.74 48	99.51 138

PLC	97.93 2	99.02 29	98.94 53	99.11 11	99.46 35	99.24 96	99.06 47	97.96 35	99.31 34	99.16 1	97.90 78	99.79 46	99.36 30	98.71 68	98.12 90	99.65 111	99.52 134
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MVS_111021_HR		98.59 42	99.36 24	97.68 49	99.42 36	99.61 37	98.14 89	94.81 56	99.31 34	95.00 57	99.51 9	99.79 46	99.00 58	98.94 49	98.83 49	99.69 85	99.57 126

OMC-MVS		98.84 33	99.01 51	98.65 31	99.39 37	99.23 97	99.22 36	96.70 43	99.40 23	97.77 23	97.89 79	99.80 44	99.21 40	99.02 44	98.65 57	99.57 144	99.07 169

TSAR-MVS + ACMM		98.77 34	99.45 14	97.98 45	99.37 38	99.46 64	99.44 28	98.13 28	99.65 5	92.30 106	98.91 42	99.95 18	99.05 54	99.42 18	98.95 37	99.58 140	99.82 28

MVS_111021_LR		98.67 38	99.41 21	97.81 48	99.37 38	99.53 55	98.51 68	95.52 49	99.27 40	94.85 59	99.56 8	99.69 51	99.04 55	99.36 21	98.88 42	99.60 130	99.58 121

train_agg		98.73 36	99.11 40	98.28 37	99.36 40	99.35 83	99.48 24	97.96 35	98.83 100	93.86 81	98.70 54	99.86 38	99.44 26	99.08 41	98.38 72	99.61 122	99.58 121

ACMMP		98.74 35	99.03 49	98.40 34	99.36 40	99.64 25	99.20 37	97.75 39	98.82 102	95.24 53	98.85 45	99.87 37	99.17 46	98.74 67	97.50 117	99.71 73	99.76 61
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 66	98.04 85	97.32 56	99.35 42	98.91 113	97.65 106	91.68 110	98.00 148	97.01 32	97.72 84	94.83 112	98.85 69	98.44 89	98.86 44	99.41 169	99.52 134
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
abl_6				98.09 41	99.33 43	99.22 98	98.79 61	94.96 55	98.52 127	97.00 33	97.30 90	99.86 38	98.76 71			99.69 85	99.41 147

CDPH-MVS		98.41 44	99.10 41	97.61 51	99.32 44	99.36 80	99.49 22	96.15 46	98.82 102	91.82 111	98.41 63	99.66 52	99.10 51	98.93 50	98.97 35	99.75 43	99.58 121

CNLPA		99.03 28	99.05 45	99.01 21	99.27 45	99.22 98	99.03 49	97.98 34	99.34 31	99.00 4	98.25 69	99.71 50	99.31 35	98.80 60	98.82 51	99.48 159	99.17 162

MSDG		98.27 49	98.29 72	98.24 38	99.20 46	99.22 98	99.20 37	97.82 37	99.37 26	94.43 69	95.90 124	97.31 83	99.12 49	98.76 64	98.35 75	99.67 102	99.14 166

PHI-MVS		99.08 23	99.43 20	98.67 30	99.15 47	99.59 45	99.11 43	97.35 41	99.14 61	97.30 28	99.44 12	99.96 13	99.32 34	98.89 55	99.39 7	99.79 29	99.58 121

PatchMatch-RL		97.77 64	98.25 73	97.21 61	99.11 48	99.25 94	97.06 129	94.09 71	98.72 116	95.14 55	98.47 61	96.29 94	98.43 86	98.65 71	97.44 123	99.45 163	98.94 172

TAPA-MVS	97.53 5	98.41 44	98.84 58	97.91 46	99.08 49	99.33 88	99.15 40	97.13 42	99.34 31	93.20 91	97.75 82	99.19 61	99.20 41	98.66 70	98.13 89	99.66 107	99.48 142
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
EPNet		98.05 56	98.86 56	97.10 63	99.02 50	99.43 70	98.47 70	94.73 58	99.05 77	95.62 46	98.93 40	97.62 81	95.48 166	98.59 80	98.55 62	99.29 178	99.84 23
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EPNet_dtu		96.30 110	98.53 65	93.70 133	98.97 51	98.24 157	97.36 113	94.23 70	98.85 95	79.18 184	99.19 22	98.47 70	94.09 188	97.89 121	98.21 85	98.39 194	98.85 178
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
COLMAP_ROB	96.15 12	97.78 63	98.17 79	97.32 56	98.84 52	99.45 66	99.28 34	95.43 50	99.48 19	91.80 112	94.83 138	98.36 72	98.90 64	98.09 104	97.85 103	99.68 94	99.15 163
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 46	99.46 13	97.04 66	98.82 53	99.33 88	96.28 145	97.47 40	99.58 9	94.70 62	98.99 36	99.85 41	97.24 119	99.55 11	99.34 9	97.73 203	99.56 127

SD-MVS		99.25 13	99.50 12	98.96 22	98.79 54	99.55 53	99.33 33	98.29 12	99.75 1	97.96 20	99.15 25	99.95 18	99.61 6	99.17 33	99.06 28	99.81 20	99.84 23
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 11	99.57 5	98.92 24	98.78 55	99.53 55	99.72 2	98.11 30	99.73 2	97.43 26	99.15 25	99.96 13	99.59 10	99.73 1	99.07 27	99.88 3	99.82 28
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 48	98.53 65	98.05 42	98.76 56	98.77 120	99.13 41	98.07 31	99.10 68	94.27 74	96.70 105	99.84 42	98.70 73	97.90 120	98.11 91	99.40 171	99.28 155

PCF-MVS	97.50 6	98.18 52	98.35 71	97.99 44	98.65 57	99.36 80	98.94 54	98.14 27	98.59 120	93.62 86	96.61 109	99.76 49	99.03 56	97.77 127	97.45 122	99.57 144	98.89 177
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
DeepC-MVS	97.63 4	98.33 47	98.57 63	98.04 43	98.62 58	99.65 20	99.45 26	98.15 25	99.51 17	92.80 99	95.74 128	96.44 92	99.46 24	99.37 20	99.50 2	99.78 32	99.81 33
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CANet		98.46 43	99.16 38	97.64 50	98.48 59	99.64 25	99.35 32	94.71 59	99.53 14	95.17 54	97.63 86	99.59 55	98.38 87	98.88 56	98.99 34	99.74 48	99.86 19

LS3D		97.79 62	98.25 73	97.26 60	98.40 60	99.63 28	99.53 19	98.63 1	99.25 45	88.13 128	96.93 100	94.14 122	99.19 42	99.14 37	99.23 18	99.69 85	99.42 146

CHOSEN 280x420		97.99 58	99.24 35	96.53 84	98.34 61	99.61 37	98.36 79	89.80 145	99.27 40	95.08 56	99.81 1	98.58 68	98.64 77	99.02 44	98.92 39	98.93 188	99.48 142

DELS-MVS		98.19 51	98.77 60	97.52 52	98.29 62	99.71 12	99.12 42	94.58 64	98.80 105	95.38 51	96.24 118	98.24 74	97.92 101	99.06 42	99.52 1	99.82 14	99.79 42
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
RPSCF		97.61 69	98.16 80	96.96 74	98.10 63	99.00 106	98.84 59	93.76 78	99.45 20	94.78 61	99.39 14	99.31 59	98.53 84	96.61 162	95.43 172	97.74 201	97.93 195

PVSNet_BlendedMVS		97.51 73	97.71 95	97.28 58	98.06 64	99.61 37	97.31 115	95.02 53	99.08 71	95.51 48	98.05 73	90.11 143	98.07 95	98.91 53	98.40 70	99.72 63	99.78 48

PVSNet_Blended		97.51 73	97.71 95	97.28 58	98.06 64	99.61 37	97.31 115	95.02 53	99.08 71	95.51 48	98.05 73	90.11 143	98.07 95	98.91 53	98.40 70	99.72 63	99.78 48

MVS_0304		98.14 53	99.03 49	97.10 63	98.05 66	99.63 28	99.27 35	94.33 68	99.63 7	93.06 94	97.32 89	99.05 63	98.09 94	98.82 59	98.87 43	99.81 20	99.89 10

CHOSEN 1792x2688		96.41 107	96.99 124	95.74 104	98.01 67	99.72 9	97.70 105	90.78 129	99.13 66	90.03 121	87.35 196	95.36 106	98.33 88	98.59 80	98.91 41	99.59 136	99.87 16

HyFIR lowres test		95.99 117	96.56 133	95.32 109	97.99 68	99.65 20	96.54 138	88.86 154	98.44 130	89.77 124	84.14 206	97.05 87	99.03 56	98.55 82	98.19 87	99.73 56	99.86 19

OPM-MVS		96.22 112	95.85 153	96.65 80	97.75 69	98.54 139	99.00 53	95.53 48	96.88 182	89.88 122	95.95 123	86.46 167	98.07 95	97.65 135	96.63 141	99.67 102	98.83 179
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
tmp_tt				82.25 210	97.73 70	88.71 218	80.18 218	68.65 221	99.15 58	86.98 137	99.47 10	85.31 177	68.35 219	87.51 213	83.81 215	91.64 218

TSAR-MVS + COLMAP		96.79 94	96.55 134	97.06 65	97.70 71	98.46 144	99.07 46	96.23 45	99.38 24	91.32 115	98.80 46	85.61 174	98.69 75	97.64 136	96.92 134	99.37 173	99.06 170

PVSNet_Blended_VisFu		97.41 76	98.49 67	96.15 93	97.49 72	99.76 3	96.02 149	93.75 80	99.26 43	93.38 90	93.73 147	99.35 58	96.47 141	98.96 47	98.46 66	99.77 37	99.90 6

MS-PatchMatch		95.99 117	97.26 116	94.51 118	97.46 73	98.76 123	97.27 117	86.97 173	99.09 69	89.83 123	93.51 151	97.78 78	96.18 147	97.53 140	95.71 169	99.35 174	98.41 185

XVS					97.42 74	99.62 32	98.59 66			93.81 82		99.95 18				99.69 85

X-MVStestdata					97.42 74	99.62 32	98.59 66			93.81 82		99.95 18				99.69 85

LGP-MVS_train		96.23 111	96.89 126	95.46 108	97.32 76	98.77 120	98.81 60	93.60 83	98.58 121	85.52 146	99.08 33	86.67 164	97.83 108	97.87 122	97.51 116	99.69 85	99.73 76

CMPMVS	70.31 18	90.74 197	91.06 205	90.36 189	97.32 76	97.43 194	92.97 193	87.82 169	93.50 212	75.34 200	83.27 208	84.90 180	92.19 203	92.64 207	91.21 211	96.50 214	94.46 212
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
HQP-MVS		96.37 108	96.58 132	96.13 94	97.31 78	98.44 146	98.45 72	95.22 51	98.86 93	88.58 126	98.33 67	87.00 159	97.67 110	97.23 150	96.56 144	99.56 147	99.62 117

ACMM	96.26 9	96.67 102	96.69 131	96.66 79	97.29 79	98.46 144	96.48 141	95.09 52	99.21 49	93.19 92	98.78 48	86.73 163	98.17 89	97.84 124	96.32 150	99.74 48	99.49 141
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
UA-Net		97.13 84	99.14 39	94.78 114	97.21 80	99.38 76	97.56 108	92.04 103	98.48 128	88.03 129	98.39 65	99.91 32	94.03 189	99.33 24	99.23 18	99.81 20	99.25 158

UGNet		97.66 68	99.07 44	96.01 98	97.19 81	99.65 20	97.09 127	93.39 86	99.35 30	94.40 71	98.79 47	99.59 55	94.24 186	98.04 112	98.29 82	99.73 56	99.80 35
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 40	99.36 24	97.85 47	97.16 82	99.46 64	99.03 49	94.59 63	99.09 69	97.19 30	99.73 3	99.95 18	99.39 29	98.95 48	98.69 55	99.75 43	99.65 109

CANet_DTU		96.64 103	99.08 42	93.81 129	97.10 83	99.42 71	98.85 58	90.01 139	99.31 34	79.98 180	99.78 2	99.10 62	97.42 116	98.35 91	98.05 94	99.47 161	99.53 131

IB-MVS	93.96 15	95.02 136	96.44 143	93.36 143	97.05 84	99.28 92	90.43 203	93.39 86	98.02 147	96.02 41	94.92 137	92.07 136	83.52 212	95.38 189	95.82 166	99.72 63	99.59 120
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	96.62 105	96.72 130	96.50 87	96.96 85	98.75 124	97.80 100	94.30 69	98.85 95	93.12 93	98.78 48	86.61 165	97.23 120	97.73 130	96.61 142	99.62 120	99.71 90
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ECVR-MVS		97.27 80	97.09 119	97.48 53	96.95 86	99.79 2	98.48 69	94.42 66	99.17 54	96.28 39	93.54 149	89.39 150	98.89 67	99.03 43	99.09 26	99.88 3	99.61 119

ECVR-MVS11		97.09 86	96.83 129	97.39 54	96.92 87	99.81 1	98.44 73	94.45 65	99.17 54	95.85 44	92.10 162	88.97 151	98.78 70	99.02 44	99.11 25	99.88 3	99.63 115

ACMH	95.42 14	95.27 133	95.96 149	94.45 120	96.83 88	98.78 119	94.72 176	91.67 111	98.95 85	86.82 139	96.42 115	83.67 185	97.00 123	97.48 142	96.68 139	99.69 85	99.76 61
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CLD-MVS		96.74 97	96.51 137	97.01 71	96.71 89	98.62 133	98.73 62	94.38 67	98.94 87	94.46 68	97.33 88	87.03 158	98.07 95	97.20 152	96.87 135	99.72 63	99.54 130
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
TDRefinement		93.04 172	93.57 189	92.41 152	96.58 90	98.77 120	97.78 102	91.96 106	98.12 144	80.84 173	89.13 182	79.87 208	87.78 208	96.44 167	94.50 194	99.54 153	98.15 190

Anonymous202405211			97.40 107		96.45 91	99.54 54	98.08 94	93.79 77	98.24 140		93.55 148	94.41 118	98.88 68	98.04 112	98.24 84	99.75 43	99.76 61

DCV-MVSNet		97.56 71	98.36 70	96.62 83	96.44 92	98.36 153	98.37 77	91.73 109	99.11 67	94.80 60	98.36 66	96.28 95	98.60 80	98.12 101	98.44 67	99.76 39	99.87 16

ACMH+	95.51 13	95.40 129	96.00 147	94.70 115	96.33 93	98.79 117	96.79 133	91.32 119	98.77 111	87.18 136	95.60 132	85.46 175	96.97 124	97.15 153	96.59 143	99.59 136	99.65 109

Anonymous20231211		97.10 85	97.06 122	97.14 62	96.32 94	99.52 58	98.16 88	93.76 78	98.84 99	95.98 42	90.92 168	94.58 117	98.90 64	97.72 131	98.10 92	99.71 73	99.75 68

thres100view900		96.72 98	96.47 140	97.00 72	96.31 95	99.52 58	98.28 83	94.01 72	97.35 169	94.52 64	95.90 124	86.93 160	99.09 53	98.07 107	97.87 102	99.81 20	99.63 115

tfpn200view9		96.75 96	96.51 137	97.03 67	96.31 95	99.67 15	98.41 74	93.99 74	97.35 169	94.52 64	95.90 124	86.93 160	99.14 48	98.26 94	97.80 106	99.82 14	99.70 92

thres200		96.76 95	96.53 135	97.03 67	96.31 95	99.67 15	98.37 77	93.99 74	97.68 164	94.49 67	95.83 127	86.77 162	99.18 44	98.26 94	97.82 105	99.82 14	99.66 106

thres600view7		96.69 100	96.43 144	97.00 72	96.28 98	99.67 15	98.41 74	93.99 74	97.85 158	94.29 73	95.96 122	85.91 172	99.19 42	98.26 94	97.63 111	99.82 14	99.73 76

thres400		96.71 99	96.45 142	97.02 69	96.28 98	99.63 28	98.41 74	94.00 73	97.82 159	94.42 70	95.74 128	86.26 169	99.18 44	98.20 98	97.79 107	99.81 20	99.70 92

baseline1		97.58 70	98.05 84	97.02 69	96.21 100	99.45 66	97.71 104	93.71 82	98.47 129	95.75 45	98.78 48	93.20 132	98.91 63	98.52 84	98.44 67	99.81 20	99.53 131

canonicalmvs		97.31 78	97.81 94	96.72 76	96.20 101	99.45 66	98.21 86	91.60 112	99.22 47	95.39 50	98.48 60	90.95 140	99.16 47	97.66 133	99.05 29	99.76 39	99.90 6

test_part1		95.56 125	95.38 157	95.78 101	96.07 102	98.16 160	97.57 107	90.78 129	97.43 168	93.04 95	89.12 183	89.41 149	97.93 100	96.38 170	97.38 126	99.29 178	99.78 48

IS_MVSNet		97.86 61	98.86 56	96.68 77	96.02 103	99.72 9	98.35 80	93.37 88	98.75 115	94.01 75	96.88 102	98.40 71	98.48 85	99.09 39	99.42 5	99.83 13	99.80 35

USDC		94.26 152	94.83 164	93.59 135	96.02 103	98.44 146	97.84 98	88.65 158	98.86 93	82.73 165	94.02 144	80.56 201	96.76 130	97.28 149	96.15 157	99.55 149	98.50 183

FC-MVSNet-train		97.04 87	97.91 91	96.03 97	96.00 105	98.41 149	96.53 140	93.42 85	99.04 79	93.02 96	98.03 75	94.32 120	97.47 115	97.93 118	97.77 108	99.75 43	99.88 14

Vis-MVSNet (Re-imp)		97.40 77	98.89 55	95.66 106	95.99 106	99.62 32	97.82 99	93.22 91	98.82 102	91.40 114	96.94 99	98.56 69	95.70 158	99.14 37	99.41 6	99.79 29	99.75 68

MVSTER		97.16 83	97.71 95	96.52 85	95.97 107	98.48 142	98.63 65	92.10 102	98.68 117	95.96 43	99.23 21	91.79 137	96.87 127	98.76 64	97.37 127	99.57 144	99.68 101

baseline		97.45 75	98.70 62	95.99 99	95.89 108	99.36 80	98.29 82	91.37 118	99.21 49	92.99 97	98.40 64	96.87 89	97.96 99	98.60 78	98.60 60	99.42 168	99.86 19

TinyColmap		94.00 156	94.35 173	93.60 134	95.89 108	98.26 155	97.49 110	88.82 155	98.56 123	83.21 159	91.28 167	80.48 203	96.68 133	97.34 146	96.26 153	99.53 155	98.24 189

EPMVS		95.05 135	96.86 128	92.94 149	95.84 110	98.96 111	96.68 134	79.87 201	99.05 77	90.15 119	97.12 96	95.99 101	97.49 114	95.17 193	94.75 191	97.59 205	96.96 205

PMMVS		97.52 72	98.39 69	96.51 86	95.82 111	98.73 127	97.80 100	93.05 96	98.76 112	94.39 72	99.07 34	97.03 88	98.55 82	98.31 93	97.61 112	99.43 166	99.21 161

diffmvs		96.83 93	97.33 111	96.25 91	95.76 112	99.34 85	98.06 95	93.22 91	99.43 22	92.30 106	96.90 101	89.83 148	98.55 82	98.00 115	98.14 88	99.64 115	99.70 92

MVS_Test		97.30 79	98.54 64	95.87 100	95.74 113	99.28 92	98.19 87	91.40 117	99.18 53	91.59 113	98.17 71	96.18 97	98.63 78	98.61 75	98.55 62	99.66 107	99.78 48

EIA-MVS		97.70 67	98.78 59	96.44 89	95.72 114	99.65 20	98.14 89	93.72 81	98.30 136	92.31 105	98.63 55	97.90 76	98.97 59	98.92 52	98.30 81	99.78 32	99.80 35

casdiffmvs		96.93 91	97.43 106	96.34 90	95.70 115	99.50 60	97.75 103	93.22 91	98.98 84	92.64 100	94.97 135	91.71 138	98.93 60	98.62 74	98.52 65	99.82 14	99.72 87

tpmrst		93.86 161	95.88 151	91.50 171	95.69 116	98.62 133	95.64 155	79.41 204	98.80 105	83.76 155	95.63 131	96.13 98	97.25 118	92.92 205	92.31 204	97.27 208	96.74 206

ADS-MVSNet		94.65 144	97.04 123	91.88 167	95.68 117	98.99 108	95.89 150	79.03 208	99.15 58	85.81 144	96.96 98	98.21 75	97.10 121	94.48 201	94.24 195	97.74 201	97.21 201

EPP-MVSNet		97.75 65	98.71 61	96.63 82	95.68 117	99.56 51	97.51 109	93.10 95	99.22 47	94.99 58	97.18 95	97.30 84	98.65 76	98.83 58	98.93 38	99.84 10	99.92 2

DROMVSNet		98.22 50	99.44 17	96.79 75	95.62 119	99.56 51	99.01 51	92.22 99	99.17 54	94.51 66	99.41 13	99.62 53	99.49 20	99.16 35	99.26 14	99.91 2	99.94 1

ETV-MVS		98.05 56	99.25 34	96.65 80	95.61 120	99.61 37	98.26 85	93.52 84	98.90 91	93.74 85	99.32 16	99.20 60	98.90 64	99.21 31	98.72 54	99.87 7	99.79 42

DI_MVS_plusplus_trai		96.90 92	97.49 101	96.21 92	95.61 120	99.40 75	98.72 63	92.11 101	99.14 61	92.98 98	93.08 159	95.14 108	98.13 93	98.05 111	97.91 100	99.74 48	99.73 76

CS-MVS		97.98 59	99.26 33	96.48 88	95.60 122	99.67 15	98.46 71	93.16 94	99.37 26	92.22 109	98.49 59	98.95 65	99.55 14	99.27 27	99.17 23	99.88 3	99.92 2

thisisatest0530		97.23 81	98.25 73	96.05 95	95.60 122	99.59 45	96.96 131	93.23 89	99.17 54	92.60 102	98.75 51	96.19 96	98.17 89	98.19 99	96.10 158	99.72 63	99.77 56

tttt0517		97.23 81	98.24 76	96.04 96	95.60 122	99.60 43	96.94 132	93.23 89	99.15 58	92.56 103	98.74 52	96.12 99	98.17 89	98.21 97	96.10 158	99.73 56	99.78 48

SCA		94.95 137	97.44 105	92.04 159	95.55 125	99.16 101	96.26 146	79.30 205	99.02 80	85.73 145	98.18 70	97.13 86	97.69 109	96.03 182	94.91 186	97.69 204	97.65 197

dps		94.63 145	95.31 160	93.84 128	95.53 126	98.71 128	96.54 138	80.12 200	97.81 161	97.21 29	96.98 97	92.37 133	96.34 144	92.46 208	91.77 208	97.26 209	97.08 203

PatchmatchNet		94.70 142	97.08 121	91.92 164	95.53 126	98.85 115	95.77 152	79.54 203	98.95 85	85.98 142	98.52 57	96.45 90	97.39 117	95.32 190	94.09 196	97.32 207	97.38 200
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test-LLR		95.50 127	97.32 112	93.37 142	95.49 128	98.74 125	96.44 143	90.82 127	98.18 141	82.75 163	96.60 110	94.67 115	95.54 164	98.09 104	96.00 160	99.20 182	98.93 173

test0.0.03 1		96.69 100	98.12 82	95.01 112	95.49 128	98.99 108	95.86 151	90.82 127	98.38 132	92.54 104	96.66 107	97.33 82	95.75 156	97.75 129	98.34 77	99.60 130	99.40 150

CS-MVS-test		98.09 55	99.32 28	96.67 78	95.48 130	99.61 37	99.01 51	92.22 99	99.32 33	93.89 80	99.30 17	98.77 66	99.49 20	99.16 35	99.16 24	99.92 1	99.91 5

CostFormer		94.25 153	94.88 163	93.51 139	95.43 131	98.34 154	96.21 147	80.64 198	97.94 153	94.01 75	98.30 68	86.20 171	97.52 112	92.71 206	92.69 202	97.23 210	98.02 193

MDTV_nov1_ep13		95.57 124	97.48 102	93.35 144	95.43 131	98.97 110	97.19 122	83.72 194	98.92 90	87.91 131	97.75 82	96.12 99	97.88 105	96.84 161	95.64 170	97.96 199	98.10 191

tpm cat1		94.06 154	94.90 162	93.06 147	95.42 133	98.52 141	96.64 136	80.67 197	97.82 159	92.63 101	93.39 153	95.00 110	96.06 151	91.36 211	91.58 210	96.98 211	96.66 208

Vis-MVSNet		96.16 114	98.22 77	93.75 130	95.33 134	99.70 14	97.27 117	90.85 126	98.30 136	85.51 147	95.72 130	96.45 90	93.69 195	98.70 69	99.00 33	99.84 10	99.69 96
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CVMVSNet		95.33 132	97.09 119	93.27 145	95.23 135	98.39 151	95.49 158	92.58 98	97.71 163	83.00 162	94.44 142	93.28 130	93.92 192	97.79 125	98.54 64	99.41 169	99.45 144

IterMVS-LS		96.12 115	97.48 102	94.53 117	95.19 136	97.56 188	97.15 123	89.19 152	99.08 71	88.23 127	94.97 135	94.73 114	97.84 107	97.86 123	98.26 83	99.60 130	99.88 14
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Effi-MVS+		95.81 120	97.31 115	94.06 125	95.09 137	99.35 83	97.24 119	88.22 163	98.54 124	85.38 148	98.52 57	88.68 152	98.70 73	98.32 92	97.93 97	99.74 48	99.84 23

testgi		95.67 123	97.48 102	93.56 136	95.07 138	99.00 106	95.33 162	88.47 160	98.80 105	86.90 138	97.30 90	92.33 134	95.97 153	97.66 133	97.91 100	99.60 130	99.38 151

GeoE		95.98 119	97.24 117	94.51 118	95.02 139	99.38 76	98.02 96	87.86 168	98.37 133	87.86 132	92.99 161	93.54 127	98.56 81	98.61 75	97.92 98	99.73 56	99.85 22

RPMNet		94.66 143	97.16 118	91.75 168	94.98 140	98.59 136	97.00 130	78.37 212	97.98 149	83.78 153	96.27 117	94.09 125	96.91 126	97.36 145	96.73 137	99.48 159	99.09 168

CR-MVSNet		94.57 149	97.34 110	91.33 175	94.90 141	98.59 136	97.15 123	79.14 206	97.98 149	80.42 176	96.59 112	93.50 129	96.85 128	98.10 102	97.49 118	99.50 158	99.15 163

gg-mvs-nofinetune		90.85 196	94.14 175	87.02 202	94.89 142	99.25 94	98.64 64	76.29 216	88.24 217	57.50 221	79.93 212	95.45 105	95.18 175	98.77 63	98.07 93	99.62 120	99.24 159

IterMVS-SCA-FT		94.89 139	97.87 92	91.42 172	94.86 143	97.70 174	97.24 119	84.88 188	98.93 88	75.74 196	94.26 143	98.25 73	96.69 132	98.52 84	97.68 110	99.10 186	99.73 76

IterMVS		94.81 141	97.71 95	91.42 172	94.83 144	97.63 181	97.38 112	85.08 185	98.93 88	75.67 197	94.02 144	97.64 79	96.66 135	98.45 87	97.60 113	98.90 189	99.72 87
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PatchT		93.96 158	97.36 109	90.00 191	94.76 145	98.65 131	90.11 206	78.57 211	97.96 152	80.42 176	96.07 120	94.10 124	96.85 128	98.10 102	97.49 118	99.26 180	99.15 163

baseline2		96.36 109	97.82 93	94.65 116	94.60 146	99.09 104	96.45 142	89.63 147	98.36 134	91.29 116	97.60 87	94.13 123	96.37 142	98.45 87	97.70 109	99.54 153	99.41 147

CDS-MVSNet		96.59 106	98.02 87	94.92 113	94.45 147	98.96 111	97.46 111	91.75 108	97.86 157	90.07 120	96.02 121	97.25 85	96.21 145	98.04 112	98.38 72	99.60 130	99.65 109
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
tpm		92.38 188	94.79 165	89.56 195	94.30 148	97.50 191	94.24 188	78.97 209	97.72 162	74.93 201	97.97 77	82.91 190	96.60 137	93.65 204	94.81 190	98.33 195	98.98 171

Fast-Effi-MVS+		95.38 130	96.52 136	94.05 126	94.15 149	99.14 103	97.24 119	86.79 174	98.53 125	87.62 134	94.51 140	87.06 157	98.76 71	98.60 78	98.04 95	99.72 63	99.77 56

Effi-MVS+-dtu		95.74 122	98.04 85	93.06 147	93.92 150	99.16 101	97.90 97	88.16 165	99.07 76	82.02 168	98.02 76	94.32 120	96.74 131	98.53 83	97.56 114	99.61 122	99.62 117

UniMVSNet_ETH3D		93.15 169	92.33 202	94.11 124	93.91 151	98.61 135	94.81 173	90.98 124	97.06 178	87.51 135	82.27 210	76.33 216	97.87 106	94.79 199	97.47 121	99.56 147	99.81 33

Fast-Effi-MVS+-dtu		95.38 130	98.20 78	92.09 158	93.91 151	98.87 114	97.35 114	85.01 187	99.08 71	81.09 172	98.10 72	96.36 93	95.62 161	98.43 90	97.03 131	99.55 149	99.50 140

TAMVS		95.53 126	96.50 139	94.39 121	93.86 153	99.03 105	96.67 135	89.55 149	97.33 171	90.64 118	93.02 160	91.58 139	96.21 145	97.72 131	97.43 124	99.43 166	99.36 152

GBi-Net		96.98 89	98.00 88	95.78 101	93.81 154	97.98 163	98.09 91	91.32 119	98.80 105	93.92 77	97.21 92	95.94 102	97.89 102	98.07 107	98.34 77	99.68 94	99.67 102

test1		96.98 89	98.00 88	95.78 101	93.81 154	97.98 163	98.09 91	91.32 119	98.80 105	93.92 77	97.21 92	95.94 102	97.89 102	98.07 107	98.34 77	99.68 94	99.67 102

FMVSNet2		96.64 103	97.50 100	95.63 107	93.81 154	97.98 163	98.09 91	90.87 125	98.99 83	93.48 88	93.17 156	95.25 107	97.89 102	98.63 73	98.80 52	99.68 94	99.67 102

MVS-HIRNet		92.51 182	95.97 148	88.48 199	93.73 157	98.37 152	90.33 204	75.36 218	98.32 135	77.78 190	89.15 181	94.87 111	95.14 176	97.62 137	96.39 148	98.51 191	97.11 202

GA-MVS		93.93 159	96.31 146	91.16 179	93.61 158	98.79 117	95.39 161	90.69 133	98.25 139	73.28 205	96.15 119	88.42 153	94.39 184	97.76 128	95.35 174	99.58 140	99.45 144

FC-MVSNet-test		96.07 116	97.94 90	93.89 127	93.60 159	98.67 130	96.62 137	90.30 138	98.76 112	88.62 125	95.57 133	97.63 80	94.48 182	97.97 116	97.48 120	99.71 73	99.52 134

FMVSNet3		97.02 88	98.12 82	95.73 105	93.59 160	97.98 163	98.34 81	91.32 119	98.80 105	93.92 77	97.21 92	95.94 102	97.63 111	98.61 75	98.62 58	99.61 122	99.65 109

FMVSNet1		95.77 121	96.41 145	95.03 111	93.42 161	97.86 170	97.11 126	89.89 142	98.53 125	92.00 110	89.17 180	93.23 131	98.15 92	98.07 107	98.34 77	99.61 122	99.69 96

tfpnnormal		93.85 162	94.12 177	93.54 138	93.22 162	98.24 157	95.45 159	91.96 106	94.61 208	83.91 151	90.74 170	81.75 198	97.04 122	97.49 141	96.16 156	99.68 94	99.84 23

TransMVSNet (Re)		93.45 165	94.08 178	92.72 151	92.83 163	97.62 184	94.94 167	91.54 115	95.65 205	83.06 161	88.93 184	83.53 186	94.25 185	97.41 143	97.03 131	99.67 102	98.40 188

LTVRE_ROB	93.20 16	92.84 174	94.92 161	90.43 188	92.83 163	98.63 132	97.08 128	87.87 167	97.91 154	68.42 214	93.54 149	79.46 210	96.62 136	97.55 139	97.40 125	99.74 48	99.92 2
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
TESTMET0.1,1		94.95 137	97.32 112	92.20 156	92.62 165	98.74 125	96.44 143	86.67 176	98.18 141	82.75 163	96.60 110	94.67 115	95.54 164	98.09 104	96.00 160	99.20 182	98.93 173

pm-mvs1		94.27 151	95.57 155	92.75 150	92.58 166	98.13 161	94.87 171	90.71 132	96.70 188	83.78 153	89.94 176	89.85 147	94.96 179	97.58 138	97.07 130	99.61 122	99.72 87

NR-MVSNet		94.01 155	94.51 170	93.44 140	92.56 167	97.77 171	95.67 153	91.57 113	97.17 175	85.84 143	93.13 157	80.53 202	95.29 172	97.01 157	96.17 155	99.69 85	99.75 68

EG-PatchMatch MVS		92.45 183	93.92 184	90.72 185	92.56 167	98.43 148	94.88 170	84.54 190	97.18 174	79.55 182	86.12 203	83.23 189	93.15 199	97.22 151	96.00 160	99.67 102	99.27 157

pmnet_mix02		92.44 184	94.68 167	89.83 194	92.46 169	97.65 180	89.92 208	90.49 135	98.76 112	73.05 207	91.78 163	90.08 145	94.86 180	94.53 200	91.94 207	98.21 197	98.01 194

test-mter		94.86 140	97.32 112	92.00 161	92.41 170	98.82 116	96.18 148	86.35 180	98.05 146	82.28 166	96.48 114	94.39 119	95.46 168	98.17 100	96.20 154	99.32 176	99.13 167

our_test_3					92.30 171	97.58 186	90.09 207

pmmvs4		95.09 134	95.90 150	94.14 123	92.29 172	97.70 174	95.45 159	90.31 136	98.60 119	90.70 117	93.25 154	89.90 146	96.67 134	97.13 154	95.42 173	99.44 165	99.28 155

FMVSNet5		95.42 128	96.47 140	94.20 122	92.26 173	95.99 209	95.66 154	87.15 172	97.87 156	93.46 89	96.68 106	93.79 126	97.52 112	97.10 156	97.21 129	99.11 185	96.62 209

UniMVSNet (Re)		94.58 148	95.34 158	93.71 132	92.25 174	98.08 162	94.97 166	91.29 123	97.03 180	87.94 130	93.97 146	86.25 170	96.07 150	96.27 176	95.97 163	99.72 63	99.79 42

SixPastTwentyTwo		93.44 166	95.32 159	91.24 177	92.11 175	98.40 150	92.77 194	88.64 159	98.09 145	77.83 189	93.51 151	85.74 173	96.52 140	96.91 159	94.89 189	99.59 136	99.73 76

v8		92.87 173	93.87 186	91.72 170	92.05 176	97.50 191	94.79 174	88.20 164	96.85 184	80.11 179	90.01 175	82.86 192	95.48 166	95.15 194	94.90 187	99.66 107	99.80 35

thisisatest0515		94.61 146	96.89 126	91.95 163	92.00 177	98.47 143	92.01 198	90.73 131	98.18 141	83.96 150	94.51 140	95.13 109	93.38 196	97.38 144	94.74 192	99.61 122	99.79 42

WR-MVS_H		93.54 164	94.67 168	92.22 154	91.95 178	97.91 168	94.58 182	88.75 156	96.64 189	83.88 152	90.66 172	85.13 178	94.40 183	96.54 166	95.91 165	99.73 56	99.89 10

V42		93.05 171	93.90 185	92.04 159	91.91 179	97.66 178	94.91 168	89.91 141	96.85 184	80.58 175	89.66 177	83.43 188	95.37 170	95.03 197	94.90 187	99.59 136	99.78 48

EU-MVSNet		92.80 176	94.76 166	90.51 186	91.88 180	96.74 206	92.48 196	88.69 157	96.21 194	79.00 185	91.51 164	87.82 154	91.83 204	95.87 186	96.27 151	99.21 181	98.92 176

N_pmnet		92.21 192	94.60 169	89.42 196	91.88 180	97.38 197	89.15 210	89.74 146	97.89 155	73.75 203	87.94 193	92.23 135	93.85 193	96.10 180	93.20 201	98.15 198	97.43 199

UniMVSNet_NR-MVSNet		94.59 147	95.47 156	93.55 137	91.85 182	97.89 169	95.03 164	92.00 104	97.33 171	86.12 140	93.19 155	87.29 156	96.60 137	96.12 179	96.70 138	99.72 63	99.80 35

pmmvs6		91.90 194	92.53 201	91.17 178	91.81 183	97.63 181	93.23 191	88.37 162	93.43 213	80.61 174	77.32 214	87.47 155	94.12 187	96.58 164	95.72 168	98.88 190	99.53 131

v10		92.79 177	94.06 179	91.31 176	91.78 184	97.29 200	94.87 171	86.10 181	96.97 181	79.82 181	88.16 190	84.56 182	95.63 160	96.33 174	95.31 175	99.65 111	99.80 35

MIMVSNet		94.49 150	97.59 99	90.87 184	91.74 185	98.70 129	94.68 178	78.73 210	97.98 149	83.71 156	97.71 85	94.81 113	96.96 125	97.97 116	97.92 98	99.40 171	98.04 192

v1144		92.81 175	94.03 180	91.40 174	91.68 186	97.60 185	94.73 175	88.40 161	96.71 187	78.48 187	88.14 191	84.46 183	95.45 169	96.31 175	95.22 178	99.65 111	99.76 61

DU-MVS		93.98 157	94.44 172	93.44 140	91.66 187	97.77 171	95.03 164	91.57 113	97.17 175	86.12 140	93.13 157	81.13 200	96.60 137	95.10 195	97.01 133	99.67 102	99.80 35

Baseline_NR-MVSNet		93.87 160	93.98 182	93.75 130	91.66 187	97.02 201	95.53 157	91.52 116	97.16 177	87.77 133	87.93 194	83.69 184	96.35 143	95.10 195	97.23 128	99.68 94	99.73 76

CP-MVSNet		93.25 168	94.00 181	92.38 153	91.65 189	97.56 188	94.38 185	89.20 151	96.05 199	83.16 160	89.51 178	81.97 196	96.16 149	96.43 168	96.56 144	99.71 73	99.89 10

v148		92.36 190	92.88 197	91.75 168	91.63 190	97.66 178	92.64 195	90.55 134	96.09 197	83.34 158	88.19 189	80.00 205	92.74 200	93.98 203	94.58 193	99.58 140	99.69 96

PS-CasMVS		92.72 179	93.36 193	91.98 162	91.62 191	97.52 190	94.13 189	88.98 153	95.94 202	81.51 171	87.35 196	79.95 207	95.91 154	96.37 171	96.49 146	99.70 82	99.89 10

v2v482		92.77 178	93.52 192	91.90 166	91.59 192	97.63 181	94.57 183	90.31 136	96.80 186	79.22 183	88.74 186	81.55 199	96.04 152	95.26 191	94.97 185	99.66 107	99.69 96

v1192		92.43 186	93.61 188	91.05 180	91.53 193	97.43 194	94.61 181	87.99 166	96.60 190	76.72 192	87.11 198	82.74 193	95.85 155	96.35 173	95.30 176	99.60 130	99.74 72

WR-MVS		93.43 167	94.48 171	92.21 155	91.52 194	97.69 176	94.66 180	89.98 140	96.86 183	83.43 157	90.12 174	85.03 179	93.94 191	96.02 183	95.82 166	99.71 73	99.82 28

v144192		92.38 188	93.55 191	91.00 181	91.44 195	97.47 193	94.27 186	87.41 171	96.52 192	78.03 188	87.50 195	82.65 194	95.32 171	95.82 187	95.15 180	99.55 149	99.78 48

pmmvs5		92.71 181	94.27 174	90.90 183	91.42 196	97.74 173	93.23 191	86.66 177	95.99 201	78.96 186	91.45 165	83.44 187	95.55 163	97.30 148	95.05 183	99.58 140	98.93 173

v1921920		92.36 190	93.57 189	90.94 182	91.39 197	97.39 196	94.70 177	87.63 170	96.60 190	76.63 193	86.98 199	82.89 191	95.75 156	96.26 177	95.14 181	99.55 149	99.73 76

gm-plane-assit		89.44 203	92.82 200	85.49 206	91.37 198	95.34 212	79.55 220	82.12 195	91.68 216	64.79 218	87.98 192	80.26 204	95.66 159	98.51 86	97.56 114	99.45 163	98.41 185

v1240		91.99 193	93.33 194	90.44 187	91.29 199	97.30 199	94.25 187	86.79 174	96.43 193	75.49 199	86.34 202	81.85 197	95.29 172	96.42 169	95.22 178	99.52 156	99.73 76

PEN-MVS		92.72 179	93.20 195	92.15 157	91.29 199	97.31 198	94.67 179	89.81 143	96.19 195	81.83 169	88.58 187	79.06 211	95.61 162	95.21 192	96.27 151	99.72 63	99.82 28

TranMVSNet+NR-MVSNet		93.67 163	94.14 175	93.13 146	91.28 201	97.58 186	95.60 156	91.97 105	97.06 178	84.05 149	90.64 173	82.22 195	96.17 148	94.94 198	96.78 136	99.69 85	99.78 48

anonymousdsp		93.12 170	95.86 152	89.93 193	91.09 202	98.25 156	95.12 163	85.08 185	97.44 167	73.30 204	90.89 169	90.78 141	95.25 174	97.91 119	95.96 164	99.71 73	99.82 28

MDTV_nov1_ep13_2view		92.44 184	95.66 154	88.68 197	91.05 203	97.92 167	92.17 197	79.64 202	98.83 100	76.20 194	91.45 165	93.51 128	95.04 177	95.68 188	93.70 199	97.96 199	98.53 182

DTE-MVSNet		92.42 187	92.85 198	91.91 165	90.87 204	96.97 202	94.53 184	89.81 143	95.86 204	81.59 170	88.83 185	77.88 214	95.01 178	94.34 202	96.35 149	99.64 115	99.73 76

v7n		91.61 195	92.95 196	90.04 190	90.56 205	97.69 176	93.74 190	85.59 183	95.89 203	76.95 191	86.60 201	78.60 213	93.76 194	97.01 157	94.99 184	99.65 111	99.87 16

test20.03		90.65 199	93.71 187	87.09 201	90.44 206	96.24 207	89.74 209	85.46 184	95.59 206	72.99 208	90.68 171	85.33 176	84.41 211	95.94 185	95.10 182	99.52 156	97.06 204

FPMVS		83.82 209	84.61 211	82.90 209	90.39 207	90.71 217	90.85 202	84.10 193	95.47 207	65.15 216	83.44 207	74.46 217	75.48 214	81.63 215	79.42 217	91.42 219	87.14 217

Anonymous20231206		90.70 198	93.93 183	86.92 203	90.21 208	96.79 204	90.30 205	86.61 178	96.05 199	69.25 212	88.46 188	84.86 181	85.86 210	97.11 155	96.47 147	99.30 177	97.80 196

new_pmnet		90.45 200	92.84 199	87.66 200	88.96 209	96.16 208	88.71 211	84.66 189	97.56 165	71.91 211	85.60 204	86.58 166	93.28 197	96.07 181	93.54 200	98.46 192	94.39 213

ET-MVSNet_ETH3D		96.17 113	96.99 124	95.21 110	88.53 210	98.54 139	98.28 83	92.61 97	98.85 95	93.60 87	99.06 35	90.39 142	98.63 78	95.98 184	96.68 139	99.61 122	99.41 147

PM-MVS		89.55 202	90.30 207	88.67 198	87.06 211	95.60 210	90.88 201	84.51 191	96.14 196	75.75 195	86.89 200	63.47 222	94.64 181	96.85 160	93.89 197	99.17 184	99.29 154

pmmvs-eth3d		89.81 201	89.65 208	90.00 191	86.94 212	95.38 211	91.08 199	86.39 179	94.57 209	82.27 167	83.03 209	64.94 219	93.96 190	96.57 165	93.82 198	99.35 174	99.24 159

new-patchmatchnet		86.12 208	87.30 210	84.74 207	86.92 213	95.19 214	83.57 217	84.42 192	92.67 214	65.66 215	80.32 211	64.72 220	89.41 206	92.33 210	89.21 212	98.43 193	96.69 207

pmmvs3		88.19 205	91.27 204	84.60 208	85.60 214	93.66 215	85.68 215	81.13 196	92.36 215	63.66 220	89.51 178	77.10 215	93.22 198	96.37 171	92.40 203	98.30 196	97.46 198

Gipuma		81.40 210	81.78 212	80.96 212	83.21 215	85.61 221	79.73 219	76.25 217	97.33 171	64.21 219	55.32 218	55.55 223	86.04 209	92.43 209	92.20 206	96.32 215	93.99 214
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MDA-MVSNet-bldmvs		87.84 206	89.22 209	86.23 204	81.74 216	96.77 205	83.74 216	89.57 148	94.50 210	72.83 209	96.64 108	64.47 221	92.71 201	81.43 216	92.28 205	96.81 212	98.47 184

MIMVSNet1		88.61 204	90.68 206	86.19 205	81.56 217	95.30 213	87.78 212	85.98 182	94.19 211	72.30 210	78.84 213	78.90 212	90.06 205	96.59 163	95.47 171	99.46 162	95.49 211

PMVS	72.60 17	76.39 212	77.66 215	74.92 213	81.04 218	69.37 225	68.47 222	80.54 199	85.39 218	65.07 217	73.52 215	72.91 218	65.67 220	80.35 217	76.81 218	88.71 220	85.25 220
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
ambc			80.99 213		80.04 219	90.84 216	90.91 200		96.09 197	74.18 202	62.81 217	30.59 228	82.44 213	96.25 178	91.77 208	95.91 216	98.56 181

PMMVS2		77.26 211	79.47 214	74.70 214	76.00 220	88.37 219	74.22 221	76.34 215	78.31 219	54.13 222	69.96 216	52.50 224	70.14 218	84.83 214	88.71 213	97.35 206	93.58 215

test_method		87.27 207	91.58 203	82.25 210	75.65 221	87.52 220	86.81 214	72.60 219	97.51 166	73.20 206	85.07 205	79.97 206	88.69 207	97.31 147	95.24 177	96.53 213	98.41 185

EMVS		68.12 215	68.11 217	68.14 216	75.51 222	71.76 223	55.38 225	77.20 214	77.78 220	37.79 225	53.59 219	43.61 225	74.72 215	67.05 220	76.70 219	88.27 222	86.24 218

E-PMN		68.30 214	68.43 216	68.15 215	74.70 223	71.56 224	55.64 224	77.24 213	77.48 221	39.46 224	51.95 221	41.68 226	73.28 216	70.65 219	79.51 216	88.61 221	86.20 219

MVE	67.97 19	65.53 216	67.43 218	63.31 217	59.33 224	74.20 222	53.09 226	70.43 220	66.27 222	43.13 223	45.98 222	30.62 227	70.65 217	79.34 218	86.30 214	83.25 223	89.33 216
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		31.24 217	40.15 219	20.86 219	12.61 225	17.99 226	25.16 227	13.30 222	48.42 223	24.82 226	53.07 220	30.13 229	28.47 221	42.73 221	37.65 220	20.79 224	51.04 221

test123		26.75 218	34.25 220	18.01 220	7.93 226	17.18 227	24.85 228	12.36 223	44.83 224	16.52 227	41.80 223	18.10 230	28.29 222	33.08 222	34.79 221	18.10 225	49.95 222

GG-mvs-BLEND		69.11 213	98.13 81	35.26 218	3.49 227	98.20 159	94.89 169	2.38 224	98.42 131	5.82 228	96.37 116	98.60 67	5.97 223	98.75 66	97.98 96	99.01 187	98.61 180

uanet_test		0.00 219	0.00 221	0.00 221	0.00 228	0.00 228	0.00 229	0.00 225	0.00 225	0.00 229	0.00 224	0.00 231	0.00 224	0.00 223	0.00 222	0.00 226	0.00 223

sosnet-low-res		0.00 219	0.00 221	0.00 221	0.00 228	0.00 228	0.00 229	0.00 225	0.00 225	0.00 229	0.00 224	0.00 231	0.00 224	0.00 223	0.00 222	0.00 226	0.00 223

sosnet		0.00 219	0.00 221	0.00 221	0.00 228	0.00 228	0.00 229	0.00 225	0.00 225	0.00 229	0.00 224	0.00 231	0.00 224	0.00 223	0.00 222	0.00 226	0.00 223

RE-MVS-def										69.05 213

9.14												99.79 46

MTAPA										98.09 16		99.97 8

MTMP										98.46 11		99.96 13

Patchmatch-RL test							66.86 223

NP-MVS									98.57 122

Patchmtry						98.59 136	97.15 123	79.14 206		80.42 176

DeepMVS_CX						96.85 203	87.43 213	89.27 150	98.30 136	75.55 198	95.05 134	79.47 209	92.62 202	89.48 212		95.18 217	95.96 210

High-resolution multi-view benchmark