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 28	98.29 34	99.77 1	99.63 30	99.67 5	95.63 45	98.66 122	95.27 54	99.11 29	99.82 42	99.67 4	99.33 24	99.19 22	99.73 60	99.74 76

SMA-MVS		99.38 6	99.60 3	99.12 9	99.76 2	99.62 33	99.39 30	98.23 18	99.52 16	98.03 17	99.45 12	99.98 2	99.64 5	99.58 8	99.30 12	99.68 98	99.76 63
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 13	99.49 22	96.58 42	99.38 25	98.05 16	98.97 38	97.87 77	99.49 18	97.78 128	98.92 42	99.78 34	99.90 7

APDe-MVS		99.49 1	99.64 1	99.32 2	99.74 4	99.74 12	99.75 1	98.34 4	99.56 11	98.72 6	99.57 8	99.97 8	99.53 15	99.65 2	99.25 16	99.84 12	99.77 58
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 67	99.35 17	99.97 8	99.55 13	99.63 3	98.66 58	99.70 86	99.74 76

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 8	99.03 3	98.95 40	99.98 2	99.60 7	99.60 7	99.05 30	99.74 52	99.79 45
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 5	98.10 13	99.66 5	99.99 1	99.33 30	99.62 5	98.86 46	99.74 52	99.90 7

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 7	99.98 2	99.28 37	99.61 6	98.83 51	99.70 86	99.77 58

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 20	99.97 8	99.70 3	99.35 22	99.24 18	99.71 78	99.76 63

HPM-MVS++		99.10 21	99.30 31	98.86 23	99.69 7	99.48 64	99.59 16	98.34 4	99.26 42	96.55 36	99.10 31	99.96 12	99.36 28	99.25 27	98.37 76	99.64 119	99.66 110

APD-MVS		99.25 12	99.38 23	99.09 11	99.69 7	99.58 48	99.56 18	98.32 8	98.85 98	97.87 19	98.91 43	99.92 28	99.30 35	99.45 15	99.38 8	99.79 31	99.58 126
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 19	99.98 2	99.30 35	99.34 23	99.05 30	99.81 23	99.79 45
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 24	98.42 31	99.67 13	99.62 33	99.60 15	98.15 23	99.08 72	93.81 85	98.46 65	99.95 17	99.59 9	99.49 13	99.21 21	99.68 98	99.75 71

MCST-MVS		99.11 20	99.27 33	98.93 21	99.67 13	99.33 93	99.51 21	98.31 9	99.28 38	96.57 35	99.10 31	99.90 33	99.71 2	99.19 31	98.35 77	99.82 16	99.71 94

ACMMPR		99.30 9	99.54 7	99.03 16	99.66 16	99.64 27	99.68 4	98.25 14	99.56 11	97.12 30	99.19 22	99.95 17	99.72 1	99.43 16	99.25 16	99.72 68	99.77 58

SteuartSystems-ACMMP		99.20 15	99.51 11	98.83 26	99.66 16	99.66 22	99.71 3	98.12 27	99.14 63	96.62 33	99.16 24	99.98 2	99.12 49	99.63 3	99.19 22	99.78 34	99.83 29
Skip Steuart: Steuart Systems R&D Blog.
SF-MVS		99.18 16	99.32 29	99.03 16	99.65 18	99.41 78	98.87 54	98.24 17	99.14 63	98.73 5	99.11 29	99.92 28	98.92 62	99.22 28	98.84 50	99.76 41	99.56 132

CNVR-MVS		99.23 14	99.28 32	99.17 5	99.65 18	99.34 90	99.46 25	98.21 19	99.28 38	98.47 8	98.89 45	99.94 25	99.50 16	99.42 17	98.61 61	99.73 60	99.52 138

DPE-MVS		99.39 5	99.55 6	99.20 4	99.63 20	99.71 16	99.66 6	98.33 6	99.29 37	98.40 11	99.64 6	99.98 2	99.31 33	99.56 9	98.96 39	99.85 10	99.70 96
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 25	98.74 27	99.63 20	99.57 50	99.66 6	98.25 14	99.00 83	95.62 46	98.97 38	99.94 25	99.54 14	99.51 12	98.79 55	99.71 78	99.73 81
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
NCCC		99.05 25	99.08 42	99.02 18	99.62 22	99.38 80	99.43 29	98.21 19	99.36 30	97.66 23	97.79 83	99.90 33	99.45 22	99.17 32	98.43 71	99.77 39	99.51 143

CP-MVS		99.27 10	99.44 17	99.08 12	99.62 22	99.58 48	99.53 19	98.16 21	99.21 50	97.79 20	99.15 25	99.96 12	99.59 9	99.54 11	98.86 46	99.78 34	99.74 76

AdaColmap		99.06 24	98.98 51	99.15 6	99.60 24	99.30 96	99.38 31	98.16 21	99.02 81	98.55 7	98.71 54	99.57 56	99.58 12	99.09 37	97.84 108	99.64 119	99.36 156

CPTT-MVS		99.14 19	99.20 37	99.06 14	99.58 25	99.53 55	99.45 26	97.80 36	99.19 53	98.32 12	98.58 58	99.95 17	99.60 7	99.28 26	98.20 89	99.64 119	99.69 100

TPM-MVS					99.57 26	98.90 119	98.79 58			96.52 37	98.62 57	99.91 31	97.56 116			99.44 170	99.28 159
Ray Leroy Khuboni and Hongjun Xu: Textureless Resilient Propagation Matching in Multiple View Stereosis (TPM-MVS). SATNAC 2025
QAPM		98.62 41	99.04 48	98.13 38	99.57 26	99.48 64	99.17 38	94.78 55	99.57 10	96.16 40	96.73 106	99.80 43	99.33 30	98.79 61	99.29 14	99.75 46	99.64 117

3Dnovator	96.92 7	98.67 38	99.05 45	98.23 37	99.57 26	99.45 68	99.11 42	94.66 58	99.69 4	96.80 32	96.55 115	99.61 53	99.40 25	98.87 58	99.49 3	99.85 10	99.66 110

DeepC-MVS_fast	98.34 1	99.17 17	99.45 14	98.85 24	99.55 29	99.37 84	99.64 8	98.05 31	99.53 14	96.58 34	98.93 41	99.92 28	99.49 18	99.46 14	99.32 11	99.80 30	99.64 117
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 37	99.05 45	98.32 33	99.53 30	99.34 90	99.06 46	94.61 59	99.65 6	97.49 24	96.75 105	99.86 38	99.44 23	98.78 62	99.30 12	99.81 23	99.67 106

MSLP-MVS++		99.15 18	99.24 35	99.04 15	99.52 32	99.49 63	99.09 44	98.07 29	99.37 27	98.47 8	97.79 83	99.89 35	99.50 16	98.93 50	99.45 4	99.61 127	99.76 63

OpenMVS	96.23 11	97.95 58	98.45 67	97.35 56	99.52 32	99.42 76	98.91 53	94.61 59	98.87 95	92.24 112	94.61 145	99.05 64	99.10 51	98.64 73	99.05 30	99.74 52	99.51 143

PLC	97.93 2	99.02 28	98.94 52	99.11 10	99.46 34	99.24 102	99.06 46	97.96 33	99.31 34	99.16 1	97.90 81	99.79 45	99.36 28	98.71 69	98.12 93	99.65 115	99.52 138
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 25	97.68 48	99.42 35	99.61 38	98.14 91	94.81 54	99.31 34	95.00 60	99.51 10	99.79 45	99.00 59	98.94 49	98.83 51	99.69 90	99.57 131

OMC-MVS		98.84 32	99.01 50	98.65 29	99.39 36	99.23 103	99.22 35	96.70 41	99.40 24	97.77 21	97.89 82	99.80 43	99.21 38	99.02 43	98.65 59	99.57 149	99.07 174

TSAR-MVS + ACMM		98.77 34	99.45 14	97.98 43	99.37 37	99.46 66	99.44 28	98.13 26	99.65 6	92.30 110	98.91 43	99.95 17	99.05 55	99.42 17	98.95 40	99.58 145	99.82 30

MVS_111021_LR		98.67 38	99.41 22	97.81 46	99.37 37	99.53 55	98.51 67	95.52 48	99.27 40	94.85 62	99.56 9	99.69 50	99.04 56	99.36 20	98.88 45	99.60 135	99.58 126

train_agg		98.73 36	99.11 40	98.28 35	99.36 39	99.35 88	99.48 24	97.96 33	98.83 103	93.86 84	98.70 55	99.86 38	99.44 23	99.08 39	98.38 74	99.61 127	99.58 126

ACMMP		98.74 35	99.03 49	98.40 32	99.36 39	99.64 27	99.20 36	97.75 37	98.82 105	95.24 55	98.85 46	99.87 37	99.17 45	98.74 67	97.50 121	99.71 78	99.76 63
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 57	99.35 41	98.91 118	97.65 111	91.68 112	98.00 152	97.01 31	97.72 87	94.83 113	98.85 71	98.44 90	98.86 46	99.41 175	99.52 138
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 46	99.10 41	97.61 51	99.32 42	99.36 85	99.49 22	96.15 44	98.82 105	91.82 114	98.41 66	99.66 51	99.10 51	98.93 50	98.97 38	99.75 46	99.58 126

CNLPA		99.03 27	99.05 45	99.01 19	99.27 43	99.22 104	99.03 48	97.98 32	99.34 32	99.00 4	98.25 72	99.71 49	99.31 33	98.80 60	98.82 53	99.48 164	99.17 167

MSDG		98.27 51	98.29 71	98.24 36	99.20 44	99.22 104	99.20 36	97.82 35	99.37 27	94.43 73	95.90 128	97.31 83	99.12 49	98.76 64	98.35 77	99.67 106	99.14 171

PHI-MVS		99.08 22	99.43 20	98.67 28	99.15 45	99.59 45	99.11 42	97.35 39	99.14 63	97.30 27	99.44 13	99.96 12	99.32 32	98.89 55	99.39 7	99.79 31	99.58 126

PatchMatch-RL		97.77 62	98.25 73	97.21 62	99.11 46	99.25 100	97.06 134	94.09 71	98.72 120	95.14 58	98.47 64	96.29 94	98.43 89	98.65 72	97.44 127	99.45 168	98.94 177

TAPA-MVS	97.53 5	98.41 46	98.84 57	97.91 44	99.08 47	99.33 93	99.15 39	97.13 40	99.34 32	93.20 95	97.75 85	99.19 60	99.20 39	98.66 71	98.13 92	99.66 111	99.48 147
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
EPNet		98.05 55	98.86 55	97.10 64	99.02 48	99.43 74	98.47 70	94.73 56	99.05 78	95.62 46	98.93 41	97.62 81	95.48 171	98.59 81	98.55 63	99.29 184	99.84 25
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EPNet_dtu		96.30 115	98.53 64	93.70 137	98.97 49	98.24 163	97.36 117	94.23 70	98.85 98	79.18 189	99.19 22	98.47 70	94.09 193	97.89 123	98.21 88	98.39 199	98.85 183
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 57	98.84 50	99.45 68	99.28 34	95.43 49	99.48 19	91.80 115	94.83 144	98.36 72	98.90 65	98.09 106	97.85 107	99.68 98	99.15 168
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MVS_0304		98.81 33	99.44 17	98.08 39	98.83 51	99.75 9	99.58 17	95.53 46	99.76 1	96.48 38	99.70 4	98.64 66	98.21 93	99.00 46	99.33 10	99.82 16	99.90 7

DeepPCF-MVS	97.74 3	98.34 48	99.46 13	97.04 66	98.82 52	99.33 93	96.28 150	97.47 38	99.58 9	94.70 65	98.99 37	99.85 40	97.24 124	99.55 10	99.34 9	97.73 208	99.56 132

SD-MVS		99.25 12	99.50 12	98.96 20	98.79 53	99.55 53	99.33 33	98.29 12	99.75 2	97.96 18	99.15 25	99.95 17	99.61 6	99.17 32	99.06 29	99.81 23	99.84 25
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 54	99.53 55	99.72 2	98.11 28	99.73 3	97.43 25	99.15 25	99.96 12	99.59 9	99.73 1	99.07 27	99.88 4	99.82 30
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 50	98.53 64	98.05 40	98.76 55	98.77 126	99.13 40	98.07 29	99.10 69	94.27 78	96.70 107	99.84 41	98.70 76	97.90 122	98.11 94	99.40 177	99.28 159

PCF-MVS	97.50 6	98.18 54	98.35 70	97.99 42	98.65 56	99.36 85	98.94 52	98.14 25	98.59 124	93.62 90	96.61 111	99.76 48	99.03 57	97.77 129	97.45 126	99.57 149	98.89 182
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
DeepC-MVS	97.63 4	98.33 49	98.57 62	98.04 41	98.62 57	99.65 23	99.45 26	98.15 23	99.51 17	92.80 102	95.74 132	96.44 92	99.46 21	99.37 19	99.50 2	99.78 34	99.81 35
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SPE-MVS-test		98.58 43	99.42 21	97.60 52	98.52 58	99.91 1	98.60 64	94.60 61	99.37 27	94.62 66	99.40 15	99.16 61	99.39 26	99.36 20	98.85 49	99.90 3	99.92 3

CANet		98.46 45	99.16 38	97.64 50	98.48 59	99.64 27	99.35 32	94.71 57	99.53 14	95.17 56	97.63 89	99.59 54	98.38 90	98.88 57	98.99 37	99.74 52	99.86 21

LS3D		97.79 60	98.25 73	97.26 61	98.40 60	99.63 30	99.53 19	98.63 1	99.25 44	88.13 133	96.93 102	94.14 123	99.19 40	99.14 35	99.23 19	99.69 90	99.42 151

CHOSEN 280x420		97.99 57	99.24 35	96.53 85	98.34 61	99.61 38	98.36 79	89.80 148	99.27 40	95.08 59	99.81 1	98.58 68	98.64 80	99.02 43	98.92 42	98.93 193	99.48 147

DELS-MVS		98.19 53	98.77 59	97.52 53	98.29 62	99.71 16	99.12 41	94.58 63	98.80 108	95.38 53	96.24 120	98.24 74	97.92 105	99.06 40	99.52 1	99.82 16	99.79 45
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 44	99.32 29	97.68 48	98.28 63	99.89 2	98.71 61	94.53 64	99.41 23	95.43 50	99.05 36	98.66 65	99.19 40	99.21 29	99.07 27	99.93 1	99.94 1

RPSCF		97.61 67	98.16 80	96.96 74	98.10 64	99.00 111	98.84 56	93.76 79	99.45 20	94.78 64	99.39 16	99.31 58	98.53 87	96.61 168	95.43 177	97.74 206	97.93 200

PVSNet_BlendedMVS		97.51 71	97.71 98	97.28 59	98.06 65	99.61 38	97.31 119	95.02 52	99.08 72	95.51 48	98.05 76	90.11 148	98.07 100	98.91 53	98.40 72	99.72 68	99.78 51

PVSNet_Blended		97.51 71	97.71 98	97.28 59	98.06 65	99.61 38	97.31 119	95.02 52	99.08 72	95.51 48	98.05 76	90.11 148	98.07 100	98.91 53	98.40 72	99.72 68	99.78 51

CHOSEN 1792x2688		96.41 112	96.99 128	95.74 106	98.01 67	99.72 13	97.70 109	90.78 133	99.13 67	90.03 126	87.35 202	95.36 106	98.33 91	98.59 81	98.91 44	99.59 141	99.87 18

HyFIR lowres test		95.99 123	96.56 138	95.32 112	97.99 68	99.65 23	96.54 143	88.86 157	98.44 133	89.77 129	84.14 212	97.05 87	99.03 57	98.55 83	98.19 90	99.73 60	99.86 21

OPM-MVS		96.22 117	95.85 159	96.65 80	97.75 69	98.54 145	99.00 51	95.53 46	96.88 186	89.88 127	95.95 126	86.46 172	98.07 100	97.65 138	96.63 144	99.67 106	98.83 184
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
tmp_tt				82.25 214	97.73 70	88.71 223	80.18 223	68.65 226	99.15 60	86.98 142	99.47 11	85.31 181	68.35 224	87.51 219	83.81 221	91.64 223

TSAR-MVS + COLMAP		96.79 98	96.55 139	97.06 65	97.70 71	98.46 150	99.07 45	96.23 43	99.38 25	91.32 119	98.80 47	85.61 178	98.69 78	97.64 139	96.92 137	99.37 179	99.06 175

PVSNet_Blended_VisFu		97.41 74	98.49 66	96.15 95	97.49 72	99.76 6	96.02 154	93.75 81	99.26 42	93.38 94	93.73 153	99.35 57	96.47 146	98.96 47	98.46 67	99.77 39	99.90 7

MS-PatchMatch		95.99 123	97.26 119	94.51 121	97.46 73	98.76 129	97.27 121	86.97 177	99.09 70	89.83 128	93.51 157	97.78 78	96.18 152	97.53 144	95.71 174	99.35 180	98.41 190

XVS					97.42 74	99.62 33	98.59 65			93.81 85		99.95 17				99.69 90

X-MVStestdata					97.42 74	99.62 33	98.59 65			93.81 85		99.95 17				99.69 90

LGP-MVS_train		96.23 116	96.89 130	95.46 111	97.32 76	98.77 126	98.81 57	93.60 84	98.58 125	85.52 151	99.08 33	86.67 169	97.83 112	97.87 124	97.51 120	99.69 90	99.73 81

CMPMVS	70.31 18	90.74 202	91.06 210	90.36 193	97.32 76	97.43 199	92.97 198	87.82 173	93.50 217	75.34 205	83.27 214	84.90 184	92.19 208	92.64 212	91.21 216	96.50 219	94.46 217
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
HQP-MVS		96.37 113	96.58 137	96.13 97	97.31 78	98.44 152	98.45 71	95.22 50	98.86 96	88.58 131	98.33 70	87.00 164	97.67 114	97.23 155	96.56 148	99.56 152	99.62 121

ACMM	96.26 9	96.67 106	96.69 135	96.66 79	97.29 79	98.46 150	96.48 146	95.09 51	99.21 50	93.19 96	98.78 49	86.73 168	98.17 94	97.84 126	96.32 155	99.74 52	99.49 146
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
UA-Net		97.13 86	99.14 39	94.78 117	97.21 80	99.38 80	97.56 112	92.04 105	98.48 131	88.03 134	98.39 68	99.91 31	94.03 194	99.33 24	99.23 19	99.81 23	99.25 163

UGNet		97.66 66	99.07 44	96.01 101	97.19 81	99.65 23	97.09 132	93.39 87	99.35 31	94.40 75	98.79 48	99.59 54	94.24 191	98.04 114	98.29 85	99.73 60	99.80 37
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 25	97.85 45	97.16 82	99.46 66	99.03 48	94.59 62	99.09 70	97.19 29	99.73 3	99.95 17	99.39 26	98.95 48	98.69 57	99.75 46	99.65 113

CANet_DTU		96.64 107	99.08 42	93.81 133	97.10 83	99.42 76	98.85 55	90.01 142	99.31 34	79.98 185	99.78 2	99.10 63	97.42 121	98.35 93	98.05 97	99.47 166	99.53 135

IB-MVS	93.96 15	95.02 141	96.44 149	93.36 147	97.05 84	99.28 97	90.43 208	93.39 87	98.02 151	96.02 41	94.92 143	92.07 138	83.52 217	95.38 194	95.82 171	99.72 68	99.59 125
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	96.62 109	96.72 134	96.50 88	96.96 85	98.75 130	97.80 104	94.30 69	98.85 98	93.12 97	98.78 49	86.61 170	97.23 125	97.73 132	96.61 145	99.62 125	99.71 94
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test2506		97.16 84	96.68 136	97.73 47	96.95 86	99.79 4	98.48 68	94.42 66	99.17 55	97.74 22	99.15 25	80.93 206	98.89 68	99.03 41	99.09 25	99.88 4	99.62 121

ECVR-MVS		97.27 79	97.09 123	97.48 54	96.95 86	99.79 4	98.48 68	94.42 66	99.17 55	96.28 39	93.54 155	89.39 154	98.89 68	99.03 41	99.09 25	99.88 4	99.61 124

test1111		97.09 88	96.83 133	97.39 55	96.92 88	99.81 3	98.44 72	94.45 65	99.17 55	95.85 44	92.10 169	88.97 155	98.78 73	99.02 43	99.11 24	99.88 4	99.63 119

ACMH	95.42 14	95.27 138	95.96 155	94.45 123	96.83 89	98.78 125	94.72 181	91.67 113	98.95 86	86.82 144	96.42 117	83.67 189	97.00 128	97.48 146	96.68 142	99.69 90	99.76 63
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CLD-MVS		96.74 101	96.51 142	97.01 71	96.71 90	98.62 139	98.73 59	94.38 68	98.94 88	94.46 72	97.33 92	87.03 163	98.07 100	97.20 157	96.87 138	99.72 68	99.54 134
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
TDRefinement		93.04 177	93.57 194	92.41 156	96.58 91	98.77 126	97.78 106	91.96 108	98.12 148	80.84 178	89.13 189	79.87 214	87.78 213	96.44 173	94.50 199	99.54 158	98.15 195

Anonymous202405211			97.40 111		96.45 92	99.54 54	98.08 97	93.79 78	98.24 144		93.55 154	94.41 119	98.88 70	98.04 114	98.24 87	99.75 46	99.76 63

DCV-MVSNet		97.56 69	98.36 69	96.62 84	96.44 93	98.36 159	98.37 77	91.73 111	99.11 68	94.80 63	98.36 69	96.28 95	98.60 83	98.12 103	98.44 69	99.76 41	99.87 18

ACMH+	95.51 13	95.40 134	96.00 153	94.70 118	96.33 94	98.79 123	96.79 138	91.32 123	98.77 115	87.18 141	95.60 137	85.46 179	96.97 129	97.15 158	96.59 146	99.59 141	99.65 113

Anonymous20231211		97.10 87	97.06 126	97.14 63	96.32 95	99.52 58	98.16 89	93.76 79	98.84 102	95.98 42	90.92 175	94.58 118	98.90 65	97.72 133	98.10 95	99.71 78	99.75 71

thres100view900		96.72 102	96.47 146	97.00 72	96.31 96	99.52 58	98.28 83	94.01 72	97.35 173	94.52 68	95.90 128	86.93 165	99.09 53	98.07 109	97.87 105	99.81 23	99.63 119

tfpn200view9		96.75 100	96.51 142	97.03 67	96.31 96	99.67 19	98.41 74	93.99 74	97.35 173	94.52 68	95.90 128	86.93 165	99.14 48	98.26 96	97.80 110	99.82 16	99.70 96

thres200		96.76 99	96.53 140	97.03 67	96.31 96	99.67 19	98.37 77	93.99 74	97.68 168	94.49 71	95.83 131	86.77 167	99.18 43	98.26 96	97.82 109	99.82 16	99.66 110

thres600view7		96.69 104	96.43 150	97.00 72	96.28 99	99.67 19	98.41 74	93.99 74	97.85 162	94.29 77	95.96 125	85.91 176	99.19 40	98.26 96	97.63 115	99.82 16	99.73 81

thres400		96.71 103	96.45 148	97.02 69	96.28 99	99.63 30	98.41 74	94.00 73	97.82 163	94.42 74	95.74 132	86.26 173	99.18 43	98.20 100	97.79 111	99.81 23	99.70 96

baseline1		97.58 68	98.05 84	97.02 69	96.21 101	99.45 68	97.71 108	93.71 83	98.47 132	95.75 45	98.78 49	93.20 133	98.91 63	98.52 85	98.44 69	99.81 23	99.53 135

sasdasda		97.31 76	97.81 95	96.72 76	96.20 102	99.45 68	98.21 86	91.60 114	99.22 47	95.39 51	98.48 61	90.95 143	99.16 46	97.66 135	99.05 30	99.76 41	99.90 7

canonicalmvs		97.31 76	97.81 95	96.72 76	96.20 102	99.45 68	98.21 86	91.60 114	99.22 47	95.39 51	98.48 61	90.95 143	99.16 46	97.66 135	99.05 30	99.76 41	99.90 7

MGCFI-Net		97.26 81	97.79 97	96.64 82	96.17 104	99.43 74	98.14 91	91.52 119	99.23 45	95.16 57	98.48 61	90.87 145	99.07 54	97.59 141	99.02 35	99.76 41	99.91 6

IS_MVSNet		97.86 59	98.86 55	96.68 78	96.02 105	99.72 13	98.35 80	93.37 90	98.75 119	94.01 79	96.88 104	98.40 71	98.48 88	99.09 37	99.42 5	99.83 15	99.80 37

USDC		94.26 157	94.83 169	93.59 139	96.02 105	98.44 152	97.84 101	88.65 161	98.86 96	82.73 170	94.02 150	80.56 207	96.76 135	97.28 154	96.15 162	99.55 154	98.50 188

FC-MVSNet-train		97.04 89	97.91 92	96.03 100	96.00 107	98.41 155	96.53 145	93.42 86	99.04 80	93.02 98	98.03 78	94.32 121	97.47 120	97.93 120	97.77 112	99.75 46	99.88 16

Vis-MVSNet (Re-imp)		97.40 75	98.89 54	95.66 108	95.99 108	99.62 33	97.82 102	93.22 95	98.82 105	91.40 117	96.94 101	98.56 69	95.70 163	99.14 35	99.41 6	99.79 31	99.75 71

MVSTER		97.16 84	97.71 98	96.52 86	95.97 109	98.48 148	98.63 63	92.10 104	98.68 121	95.96 43	99.23 21	91.79 139	96.87 132	98.76 64	97.37 130	99.57 149	99.68 105

baseline		97.45 73	98.70 61	95.99 102	95.89 110	99.36 85	98.29 82	91.37 122	99.21 50	92.99 99	98.40 67	96.87 89	97.96 104	98.60 79	98.60 62	99.42 174	99.86 21

TinyColmap		94.00 161	94.35 178	93.60 138	95.89 110	98.26 161	97.49 114	88.82 158	98.56 127	83.21 164	91.28 174	80.48 209	96.68 138	97.34 151	96.26 158	99.53 160	98.24 194

FA-MVS(training)		96.52 111	98.29 71	94.45 123	95.88 112	99.52 58	97.66 110	81.47 200	98.94 88	93.79 88	95.54 139	99.11 62	98.29 92	98.89 55	96.49 150	99.63 124	99.52 138

EPMVS		95.05 140	96.86 132	92.94 153	95.84 113	98.96 116	96.68 139	79.87 206	99.05 78	90.15 124	97.12 98	95.99 101	97.49 119	95.17 198	94.75 196	97.59 210	96.96 210

casdiffmvs_mvg		97.27 79	97.97 90	96.46 89	95.83 114	99.51 61	98.42 73	93.32 91	98.34 138	92.38 108	95.64 135	95.35 107	98.91 63	98.73 68	98.45 68	99.86 9	99.80 37
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 87	95.82 115	98.73 133	97.80 104	93.05 99	98.76 116	94.39 76	99.07 34	97.03 88	98.55 85	98.31 95	97.61 116	99.43 172	99.21 166

diffmvs		96.83 96	97.33 114	96.25 93	95.76 116	99.34 90	98.06 98	93.22 95	99.43 22	92.30 110	96.90 103	89.83 153	98.55 85	98.00 117	98.14 91	99.64 119	99.70 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
MVS_Test		97.30 78	98.54 63	95.87 103	95.74 117	99.28 97	98.19 88	91.40 121	99.18 54	91.59 116	98.17 74	96.18 97	98.63 81	98.61 76	98.55 63	99.66 111	99.78 51

EIA-MVS		97.70 65	98.78 58	96.44 90	95.72 118	99.65 23	98.14 91	93.72 82	98.30 140	92.31 109	98.63 56	97.90 76	98.97 60	98.92 52	98.30 83	99.78 34	99.80 37

viewmanbaseed2359cas		96.92 94	97.60 102	96.14 96	95.71 119	99.44 73	97.82 102	93.39 87	98.93 90	91.34 118	96.10 122	92.27 136	98.82 72	98.40 92	98.30 83	99.75 46	99.75 71

casdiffmvs		96.93 93	97.43 110	96.34 92	95.70 120	99.50 62	97.75 107	93.22 95	98.98 85	92.64 103	94.97 141	91.71 140	98.93 61	98.62 75	98.52 66	99.82 16	99.72 91
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		93.86 166	95.88 157	91.50 175	95.69 121	98.62 139	95.64 160	79.41 209	98.80 108	83.76 160	95.63 136	96.13 98	97.25 123	92.92 210	92.31 209	97.27 213	96.74 211

ADS-MVSNet		94.65 149	97.04 127	91.88 171	95.68 122	98.99 113	95.89 155	79.03 213	99.15 60	85.81 149	96.96 100	98.21 75	97.10 126	94.48 206	94.24 200	97.74 206	97.21 206

EPP-MVSNet		97.75 63	98.71 60	96.63 83	95.68 122	99.56 51	97.51 113	93.10 98	99.22 47	94.99 61	97.18 97	97.30 84	98.65 79	98.83 59	98.93 41	99.84 12	99.92 3

viewmambaseed2359dif		96.82 97	97.19 121	96.39 91	95.64 124	99.38 80	98.15 90	93.24 92	98.78 114	92.85 101	95.93 127	91.24 142	98.75 75	97.41 147	97.86 106	99.70 86	99.74 76

EC-MVSNet		98.22 52	99.44 17	96.79 75	95.62 125	99.56 51	99.01 50	92.22 102	99.17 55	94.51 70	99.41 14	99.62 52	99.49 18	99.16 34	99.26 15	99.91 2	99.94 1

ETV-MVS		98.05 55	99.25 34	96.65 80	95.61 126	99.61 38	98.26 85	93.52 85	98.90 94	93.74 89	99.32 18	99.20 59	98.90 65	99.21 29	98.72 56	99.87 8	99.79 45

DI_MVS_pp		96.90 95	97.49 105	96.21 94	95.61 126	99.40 79	98.72 60	92.11 103	99.14 63	92.98 100	93.08 165	95.14 109	98.13 98	98.05 113	97.91 103	99.74 52	99.73 81

thisisatest0530		97.23 82	98.25 73	96.05 98	95.60 128	99.59 45	96.96 136	93.23 93	99.17 55	92.60 105	98.75 52	96.19 96	98.17 94	98.19 101	96.10 163	99.72 68	99.77 58

tttt0517		97.23 82	98.24 76	96.04 99	95.60 128	99.60 43	96.94 137	93.23 93	99.15 60	92.56 106	98.74 53	96.12 99	98.17 94	98.21 99	96.10 163	99.73 60	99.78 51

SCA		94.95 142	97.44 109	92.04 163	95.55 130	99.16 106	96.26 151	79.30 210	99.02 81	85.73 150	98.18 73	97.13 86	97.69 113	96.03 187	94.91 191	97.69 209	97.65 202

dps		94.63 150	95.31 165	93.84 132	95.53 131	98.71 134	96.54 143	80.12 205	97.81 165	97.21 28	96.98 99	92.37 134	96.34 149	92.46 213	91.77 213	97.26 214	97.08 208

PatchmatchNet		94.70 147	97.08 125	91.92 168	95.53 131	98.85 121	95.77 157	79.54 208	98.95 86	85.98 147	98.52 59	96.45 90	97.39 122	95.32 195	94.09 201	97.32 212	97.38 205
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test-LLR		95.50 132	97.32 115	93.37 146	95.49 133	98.74 131	96.44 148	90.82 131	98.18 145	82.75 168	96.60 112	94.67 116	95.54 169	98.09 106	96.00 165	99.20 187	98.93 178

test0.0.03 1		96.69 104	98.12 82	95.01 115	95.49 133	98.99 113	95.86 156	90.82 131	98.38 135	92.54 107	96.66 109	97.33 82	95.75 161	97.75 131	98.34 79	99.60 135	99.40 154

CostFormer		94.25 158	94.88 168	93.51 143	95.43 135	98.34 160	96.21 152	80.64 203	97.94 157	94.01 79	98.30 71	86.20 175	97.52 117	92.71 211	92.69 207	97.23 215	98.02 198

MDTV_nov1_ep13		95.57 130	97.48 106	93.35 148	95.43 135	98.97 115	97.19 127	83.72 198	98.92 93	87.91 136	97.75 85	96.12 99	97.88 109	96.84 167	95.64 175	97.96 204	98.10 196

tpm cat1		94.06 159	94.90 167	93.06 151	95.42 137	98.52 147	96.64 141	80.67 202	97.82 163	92.63 104	93.39 159	95.00 111	96.06 156	91.36 217	91.58 215	96.98 216	96.66 213

Vis-MVSNet		96.16 119	98.22 77	93.75 134	95.33 138	99.70 18	97.27 121	90.85 130	98.30 140	85.51 152	95.72 134	96.45 90	93.69 200	98.70 70	99.00 36	99.84 12	99.69 100
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CVMVSNet		95.33 137	97.09 123	93.27 149	95.23 139	98.39 157	95.49 163	92.58 101	97.71 167	83.00 167	94.44 148	93.28 131	93.92 197	97.79 127	98.54 65	99.41 175	99.45 149

IterMVS-LS		96.12 120	97.48 106	94.53 120	95.19 140	97.56 193	97.15 128	89.19 155	99.08 72	88.23 132	94.97 141	94.73 115	97.84 111	97.86 125	98.26 86	99.60 135	99.88 16
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Effi-MVS+		95.81 126	97.31 118	94.06 129	95.09 141	99.35 88	97.24 124	88.22 166	98.54 128	85.38 153	98.52 59	88.68 156	98.70 76	98.32 94	97.93 100	99.74 52	99.84 25

testgi		95.67 129	97.48 106	93.56 140	95.07 142	99.00 111	95.33 167	88.47 163	98.80 108	86.90 143	97.30 93	92.33 135	95.97 158	97.66 135	97.91 103	99.60 135	99.38 155

GeoE		95.98 125	97.24 120	94.51 121	95.02 143	99.38 80	98.02 99	87.86 172	98.37 136	87.86 137	92.99 167	93.54 128	98.56 84	98.61 76	97.92 101	99.73 60	99.85 24

RPMNet		94.66 148	97.16 122	91.75 172	94.98 144	98.59 142	97.00 135	78.37 217	97.98 153	83.78 158	96.27 119	94.09 126	96.91 131	97.36 150	96.73 140	99.48 164	99.09 173

CR-MVSNet		94.57 154	97.34 113	91.33 179	94.90 145	98.59 142	97.15 128	79.14 211	97.98 153	80.42 181	96.59 114	93.50 130	96.85 133	98.10 104	97.49 122	99.50 163	99.15 168

gg-mvs-nofinetune		90.85 201	94.14 180	87.02 206	94.89 146	99.25 100	98.64 62	76.29 221	88.24 222	57.50 226	79.93 218	95.45 105	95.18 180	98.77 63	98.07 96	99.62 125	99.24 164

IterMVS-SCA-FT		94.89 144	97.87 93	91.42 176	94.86 147	97.70 179	97.24 124	84.88 192	98.93 90	75.74 201	94.26 149	98.25 73	96.69 137	98.52 85	97.68 114	99.10 191	99.73 81

IterMVS		94.81 146	97.71 98	91.42 176	94.83 148	97.63 186	97.38 116	85.08 189	98.93 90	75.67 202	94.02 150	97.64 79	96.66 140	98.45 88	97.60 117	98.90 194	99.72 91
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PatchT		93.96 163	97.36 112	90.00 195	94.76 149	98.65 137	90.11 211	78.57 216	97.96 156	80.42 181	96.07 123	94.10 125	96.85 133	98.10 104	97.49 122	99.26 185	99.15 168

baseline2		96.36 114	97.82 94	94.65 119	94.60 150	99.09 109	96.45 147	89.63 150	98.36 137	91.29 120	97.60 90	94.13 124	96.37 147	98.45 88	97.70 113	99.54 158	99.41 152

CDS-MVSNet		96.59 110	98.02 87	94.92 116	94.45 151	98.96 116	97.46 115	91.75 110	97.86 161	90.07 125	96.02 124	97.25 85	96.21 150	98.04 114	98.38 74	99.60 135	99.65 113
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
tpm		92.38 193	94.79 170	89.56 199	94.30 152	97.50 196	94.24 193	78.97 214	97.72 166	74.93 206	97.97 80	82.91 195	96.60 142	93.65 209	94.81 195	98.33 200	98.98 176

Fast-Effi-MVS+		95.38 135	96.52 141	94.05 130	94.15 153	99.14 108	97.24 124	86.79 178	98.53 129	87.62 139	94.51 146	87.06 162	98.76 74	98.60 79	98.04 98	99.72 68	99.77 58

Effi-MVS+-dtu		95.74 128	98.04 85	93.06 151	93.92 154	99.16 106	97.90 100	88.16 168	99.07 77	82.02 173	98.02 79	94.32 121	96.74 136	98.53 84	97.56 118	99.61 127	99.62 121

UniMVSNet_ETH3D		93.15 174	92.33 207	94.11 128	93.91 155	98.61 141	94.81 178	90.98 128	97.06 182	87.51 140	82.27 216	76.33 222	97.87 110	94.79 204	97.47 125	99.56 152	99.81 35

Fast-Effi-MVS+-dtu		95.38 135	98.20 78	92.09 162	93.91 155	98.87 120	97.35 118	85.01 191	99.08 72	81.09 177	98.10 75	96.36 93	95.62 166	98.43 91	97.03 134	99.55 154	99.50 145

TAMVS		95.53 131	96.50 144	94.39 125	93.86 157	99.03 110	96.67 140	89.55 152	97.33 175	90.64 122	93.02 166	91.58 141	96.21 150	97.72 133	97.43 128	99.43 172	99.36 156

GBi-Net		96.98 91	98.00 88	95.78 104	93.81 158	97.98 168	98.09 94	91.32 123	98.80 108	93.92 81	97.21 94	95.94 102	97.89 106	98.07 109	98.34 79	99.68 98	99.67 106

test1		96.98 91	98.00 88	95.78 104	93.81 158	97.98 168	98.09 94	91.32 123	98.80 108	93.92 81	97.21 94	95.94 102	97.89 106	98.07 109	98.34 79	99.68 98	99.67 106

FMVSNet2		96.64 107	97.50 104	95.63 109	93.81 158	97.98 168	98.09 94	90.87 129	98.99 84	93.48 92	93.17 162	95.25 108	97.89 106	98.63 74	98.80 54	99.68 98	99.67 106

MVS-HIRNet		92.51 187	95.97 154	88.48 203	93.73 161	98.37 158	90.33 209	75.36 223	98.32 139	77.78 195	89.15 188	94.87 112	95.14 181	97.62 140	96.39 153	98.51 196	97.11 207

GA-MVS		93.93 164	96.31 152	91.16 183	93.61 162	98.79 123	95.39 166	90.69 136	98.25 143	73.28 210	96.15 121	88.42 157	94.39 189	97.76 130	95.35 179	99.58 145	99.45 149

FC-MVSNet-test		96.07 121	97.94 91	93.89 131	93.60 163	98.67 136	96.62 142	90.30 141	98.76 116	88.62 130	95.57 138	97.63 80	94.48 187	97.97 118	97.48 124	99.71 78	99.52 138

FMVSNet3		97.02 90	98.12 82	95.73 107	93.59 164	97.98 168	98.34 81	91.32 123	98.80 108	93.92 81	97.21 94	95.94 102	97.63 115	98.61 76	98.62 60	99.61 127	99.65 113

dmvs_re		96.02 122	96.49 145	95.47 110	93.49 165	99.26 99	97.25 123	93.82 77	97.51 170	90.43 123	97.52 91	87.93 158	98.12 99	96.86 165	96.59 146	99.73 60	99.76 63

FMVSNet1		95.77 127	96.41 151	95.03 114	93.42 166	97.86 175	97.11 131	89.89 145	98.53 129	92.00 113	89.17 187	93.23 132	98.15 97	98.07 109	98.34 79	99.61 127	99.69 100

tfpnnormal		93.85 167	94.12 182	93.54 142	93.22 167	98.24 163	95.45 164	91.96 108	94.61 213	83.91 156	90.74 177	81.75 203	97.04 127	97.49 145	96.16 161	99.68 98	99.84 25

TransMVSNet (Re)		93.45 170	94.08 183	92.72 155	92.83 168	97.62 189	94.94 172	91.54 118	95.65 210	83.06 166	88.93 190	83.53 190	94.25 190	97.41 147	97.03 134	99.67 106	98.40 193

LTVRE_ROB	93.20 16	92.84 179	94.92 166	90.43 192	92.83 168	98.63 138	97.08 133	87.87 171	97.91 158	68.42 219	93.54 155	79.46 216	96.62 141	97.55 143	97.40 129	99.74 52	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		94.95 142	97.32 115	92.20 160	92.62 170	98.74 131	96.44 148	86.67 180	98.18 145	82.75 168	96.60 112	94.67 116	95.54 169	98.09 106	96.00 165	99.20 187	98.93 178

pm-mvs1		94.27 156	95.57 161	92.75 154	92.58 171	98.13 166	94.87 176	90.71 135	96.70 192	83.78 158	89.94 183	89.85 152	94.96 184	97.58 142	97.07 133	99.61 127	99.72 91

NR-MVSNet		94.01 160	94.51 175	93.44 144	92.56 172	97.77 176	95.67 158	91.57 116	97.17 179	85.84 148	93.13 163	80.53 208	95.29 177	97.01 162	96.17 160	99.69 90	99.75 71

EG-PatchMatch MVS		92.45 188	93.92 189	90.72 189	92.56 172	98.43 154	94.88 175	84.54 194	97.18 178	79.55 187	86.12 209	83.23 193	93.15 204	97.22 156	96.00 165	99.67 106	99.27 162

pmnet_mix02		92.44 189	94.68 172	89.83 198	92.46 174	97.65 185	89.92 213	90.49 138	98.76 116	73.05 212	91.78 170	90.08 150	94.86 185	94.53 205	91.94 212	98.21 202	98.01 199

test-mter		94.86 145	97.32 115	92.00 165	92.41 175	98.82 122	96.18 153	86.35 184	98.05 150	82.28 171	96.48 116	94.39 120	95.46 173	98.17 102	96.20 159	99.32 182	99.13 172

our_test_3					92.30 176	97.58 191	90.09 212

pmmvs4		95.09 139	95.90 156	94.14 127	92.29 177	97.70 179	95.45 164	90.31 139	98.60 123	90.70 121	93.25 160	89.90 151	96.67 139	97.13 159	95.42 178	99.44 170	99.28 159

FMVSNet5		95.42 133	96.47 146	94.20 126	92.26 178	95.99 214	95.66 159	87.15 176	97.87 160	93.46 93	96.68 108	93.79 127	97.52 117	97.10 161	97.21 132	99.11 190	96.62 214

UniMVSNet (Re)		94.58 153	95.34 163	93.71 136	92.25 179	98.08 167	94.97 171	91.29 127	97.03 184	87.94 135	93.97 152	86.25 174	96.07 155	96.27 181	95.97 168	99.72 68	99.79 45

SixPastTwentyTwo		93.44 171	95.32 164	91.24 181	92.11 180	98.40 156	92.77 199	88.64 162	98.09 149	77.83 194	93.51 157	85.74 177	96.52 145	96.91 164	94.89 194	99.59 141	99.73 81

v8		92.87 178	93.87 191	91.72 174	92.05 181	97.50 196	94.79 179	88.20 167	96.85 188	80.11 184	90.01 182	82.86 197	95.48 171	95.15 199	94.90 192	99.66 111	99.80 37

thisisatest0515		94.61 151	96.89 130	91.95 167	92.00 182	98.47 149	92.01 203	90.73 134	98.18 145	83.96 155	94.51 146	95.13 110	93.38 201	97.38 149	94.74 197	99.61 127	99.79 45

WR-MVS_H		93.54 169	94.67 173	92.22 158	91.95 183	97.91 173	94.58 187	88.75 159	96.64 193	83.88 157	90.66 179	85.13 182	94.40 188	96.54 172	95.91 170	99.73 60	99.89 13

V42		93.05 176	93.90 190	92.04 163	91.91 184	97.66 183	94.91 173	89.91 144	96.85 188	80.58 180	89.66 184	83.43 192	95.37 175	95.03 202	94.90 192	99.59 141	99.78 51

EU-MVSNet		92.80 181	94.76 171	90.51 190	91.88 185	96.74 211	92.48 201	88.69 160	96.21 199	79.00 190	91.51 171	87.82 159	91.83 209	95.87 191	96.27 156	99.21 186	98.92 181

N_pmnet		92.21 197	94.60 174	89.42 200	91.88 185	97.38 202	89.15 215	89.74 149	97.89 159	73.75 208	87.94 199	92.23 137	93.85 198	96.10 185	93.20 206	98.15 203	97.43 204

UniMVSNet_NR-MVSNet		94.59 152	95.47 162	93.55 141	91.85 187	97.89 174	95.03 169	92.00 106	97.33 175	86.12 145	93.19 161	87.29 161	96.60 142	96.12 184	96.70 141	99.72 68	99.80 37

pmmvs6		91.90 199	92.53 206	91.17 182	91.81 188	97.63 186	93.23 196	88.37 165	93.43 218	80.61 179	77.32 220	87.47 160	94.12 192	96.58 170	95.72 173	98.88 195	99.53 135

v10		92.79 182	94.06 184	91.31 180	91.78 189	97.29 205	94.87 176	86.10 185	96.97 185	79.82 186	88.16 196	84.56 186	95.63 165	96.33 179	95.31 180	99.65 115	99.80 37

MIMVSNet		94.49 155	97.59 103	90.87 188	91.74 190	98.70 135	94.68 183	78.73 215	97.98 153	83.71 161	97.71 88	94.81 114	96.96 130	97.97 118	97.92 101	99.40 177	98.04 197

v1144		92.81 180	94.03 185	91.40 178	91.68 191	97.60 190	94.73 180	88.40 164	96.71 191	78.48 192	88.14 197	84.46 187	95.45 174	96.31 180	95.22 183	99.65 115	99.76 63

DU-MVS		93.98 162	94.44 177	93.44 144	91.66 192	97.77 176	95.03 169	91.57 116	97.17 179	86.12 145	93.13 163	81.13 205	96.60 142	95.10 200	97.01 136	99.67 106	99.80 37

Baseline_NR-MVSNet		93.87 165	93.98 187	93.75 134	91.66 192	97.02 206	95.53 162	91.52 119	97.16 181	87.77 138	87.93 200	83.69 188	96.35 148	95.10 200	97.23 131	99.68 98	99.73 81

CP-MVSNet		93.25 173	94.00 186	92.38 157	91.65 194	97.56 193	94.38 190	89.20 154	96.05 204	83.16 165	89.51 185	81.97 201	96.16 154	96.43 174	96.56 148	99.71 78	99.89 13

v148		92.36 195	92.88 202	91.75 172	91.63 195	97.66 183	92.64 200	90.55 137	96.09 202	83.34 163	88.19 195	80.00 211	92.74 205	93.98 208	94.58 198	99.58 145	99.69 100

PS-CasMVS		92.72 184	93.36 198	91.98 166	91.62 196	97.52 195	94.13 194	88.98 156	95.94 207	81.51 176	87.35 202	79.95 213	95.91 159	96.37 176	96.49 150	99.70 86	99.89 13

v2v482		92.77 183	93.52 197	91.90 170	91.59 197	97.63 186	94.57 188	90.31 139	96.80 190	79.22 188	88.74 192	81.55 204	96.04 157	95.26 196	94.97 190	99.66 111	99.69 100

v1192		92.43 191	93.61 193	91.05 184	91.53 198	97.43 199	94.61 186	87.99 170	96.60 194	76.72 197	87.11 204	82.74 198	95.85 160	96.35 178	95.30 181	99.60 135	99.74 76

WR-MVS		93.43 172	94.48 176	92.21 159	91.52 199	97.69 181	94.66 185	89.98 143	96.86 187	83.43 162	90.12 181	85.03 183	93.94 196	96.02 188	95.82 171	99.71 78	99.82 30

v144192		92.38 193	93.55 196	91.00 185	91.44 200	97.47 198	94.27 191	87.41 175	96.52 196	78.03 193	87.50 201	82.65 199	95.32 176	95.82 192	95.15 185	99.55 154	99.78 51

pmmvs5		92.71 186	94.27 179	90.90 187	91.42 201	97.74 178	93.23 196	86.66 181	95.99 206	78.96 191	91.45 172	83.44 191	95.55 168	97.30 153	95.05 188	99.58 145	98.93 178

v1921920		92.36 195	93.57 194	90.94 186	91.39 202	97.39 201	94.70 182	87.63 174	96.60 194	76.63 198	86.98 205	82.89 196	95.75 161	96.26 182	95.14 186	99.55 154	99.73 81

gm-plane-assit		89.44 208	92.82 205	85.49 210	91.37 203	95.34 217	79.55 225	82.12 199	91.68 221	64.79 223	87.98 198	80.26 210	95.66 164	98.51 87	97.56 118	99.45 168	98.41 190

v1240		91.99 198	93.33 199	90.44 191	91.29 204	97.30 204	94.25 192	86.79 178	96.43 197	75.49 204	86.34 208	81.85 202	95.29 177	96.42 175	95.22 183	99.52 161	99.73 81

PEN-MVS		92.72 184	93.20 200	92.15 161	91.29 204	97.31 203	94.67 184	89.81 146	96.19 200	81.83 174	88.58 193	79.06 217	95.61 167	95.21 197	96.27 156	99.72 68	99.82 30

TranMVSNet+NR-MVSNet		93.67 168	94.14 180	93.13 150	91.28 206	97.58 191	95.60 161	91.97 107	97.06 182	84.05 154	90.64 180	82.22 200	96.17 153	94.94 203	96.78 139	99.69 90	99.78 51

anonymousdsp		93.12 175	95.86 158	89.93 197	91.09 207	98.25 162	95.12 168	85.08 189	97.44 172	73.30 209	90.89 176	90.78 146	95.25 179	97.91 121	95.96 169	99.71 78	99.82 30

MDTV_nov1_ep13_2view		92.44 189	95.66 160	88.68 201	91.05 208	97.92 172	92.17 202	79.64 207	98.83 103	76.20 199	91.45 172	93.51 129	95.04 182	95.68 193	93.70 204	97.96 204	98.53 187

DTE-MVSNet		92.42 192	92.85 203	91.91 169	90.87 209	96.97 207	94.53 189	89.81 146	95.86 209	81.59 175	88.83 191	77.88 220	95.01 183	94.34 207	96.35 154	99.64 119	99.73 81

v7n		91.61 200	92.95 201	90.04 194	90.56 210	97.69 181	93.74 195	85.59 187	95.89 208	76.95 196	86.60 207	78.60 219	93.76 199	97.01 162	94.99 189	99.65 115	99.87 18

test20.03		90.65 204	93.71 192	87.09 205	90.44 211	96.24 212	89.74 214	85.46 188	95.59 211	72.99 213	90.68 178	85.33 180	84.41 216	95.94 190	95.10 187	99.52 161	97.06 209

FPMVS		83.82 214	84.61 217	82.90 213	90.39 212	90.71 222	90.85 207	84.10 197	95.47 212	65.15 221	83.44 213	74.46 223	75.48 219	81.63 221	79.42 223	91.42 224	87.14 223

Anonymous20231206		90.70 203	93.93 188	86.92 207	90.21 213	96.79 209	90.30 210	86.61 182	96.05 204	69.25 217	88.46 194	84.86 185	85.86 215	97.11 160	96.47 152	99.30 183	97.80 201

new_pmnet		90.45 205	92.84 204	87.66 204	88.96 214	96.16 213	88.71 216	84.66 193	97.56 169	71.91 216	85.60 210	86.58 171	93.28 202	96.07 186	93.54 205	98.46 197	94.39 218

WB-MVS		81.36 216	89.93 213	71.35 219	88.65 215	87.85 225	71.46 227	88.12 169	96.23 198	32.21 231	92.61 168	83.00 194	56.27 226	91.92 216	89.43 217	91.39 225	88.49 222

ET-MVSNet_ETH3D		96.17 118	96.99 128	95.21 113	88.53 216	98.54 145	98.28 83	92.61 100	98.85 98	93.60 91	99.06 35	90.39 147	98.63 81	95.98 189	96.68 142	99.61 127	99.41 152

PM-MVS		89.55 207	90.30 212	88.67 202	87.06 217	95.60 215	90.88 206	84.51 195	96.14 201	75.75 200	86.89 206	63.47 228	94.64 186	96.85 166	93.89 202	99.17 189	99.29 158

pmmvs-eth3d		89.81 206	89.65 214	90.00 195	86.94 218	95.38 216	91.08 204	86.39 183	94.57 214	82.27 172	83.03 215	64.94 225	93.96 195	96.57 171	93.82 203	99.35 180	99.24 164

new-patchmatchnet		86.12 213	87.30 216	84.74 211	86.92 219	95.19 219	83.57 222	84.42 196	92.67 219	65.66 220	80.32 217	64.72 226	89.41 211	92.33 215	89.21 218	98.43 198	96.69 212

pmmvs3		88.19 210	91.27 209	84.60 212	85.60 220	93.66 220	85.68 220	81.13 201	92.36 220	63.66 225	89.51 185	77.10 221	93.22 203	96.37 176	92.40 208	98.30 201	97.46 203

Gipuma		81.40 215	81.78 218	80.96 216	83.21 221	85.61 227	79.73 224	76.25 222	97.33 175	64.21 224	55.32 224	55.55 229	86.04 214	92.43 214	92.20 211	96.32 220	93.99 219
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MDA-MVSNet-bldmvs		87.84 211	89.22 215	86.23 208	81.74 222	96.77 210	83.74 221	89.57 151	94.50 215	72.83 214	96.64 110	64.47 227	92.71 206	81.43 222	92.28 210	96.81 217	98.47 189

MIMVSNet1		88.61 209	90.68 211	86.19 209	81.56 223	95.30 218	87.78 217	85.98 186	94.19 216	72.30 215	78.84 219	78.90 218	90.06 210	96.59 169	95.47 176	99.46 167	95.49 216

PMVS	72.60 17	76.39 218	77.66 221	74.92 217	81.04 224	69.37 231	68.47 228	80.54 204	85.39 223	65.07 222	73.52 221	72.91 224	65.67 225	80.35 223	76.81 224	88.71 226	85.25 226
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
ambc			80.99 219		80.04 225	90.84 221	90.91 205		96.09 202	74.18 207	62.81 223	30.59 234	82.44 218	96.25 183	91.77 213	95.91 221	98.56 186

PMMVS2		77.26 217	79.47 220	74.70 218	76.00 226	88.37 224	74.22 226	76.34 220	78.31 224	54.13 227	69.96 222	52.50 230	70.14 223	84.83 220	88.71 219	97.35 211	93.58 220

test_method		87.27 212	91.58 208	82.25 214	75.65 227	87.52 226	86.81 219	72.60 224	97.51 170	73.20 211	85.07 211	79.97 212	88.69 212	97.31 152	95.24 182	96.53 218	98.41 190

EMVS		68.12 221	68.11 223	68.14 221	75.51 228	71.76 229	55.38 231	77.20 219	77.78 225	37.79 230	53.59 225	43.61 231	74.72 220	67.05 226	76.70 225	88.27 228	86.24 224

E-PMN		68.30 220	68.43 222	68.15 220	74.70 229	71.56 230	55.64 230	77.24 218	77.48 226	39.46 229	51.95 227	41.68 232	73.28 221	70.65 225	79.51 222	88.61 227	86.20 225

MVE	67.97 19	65.53 222	67.43 224	63.31 222	59.33 230	74.20 228	53.09 232	70.43 225	66.27 227	43.13 228	45.98 228	30.62 233	70.65 222	79.34 224	86.30 220	83.25 229	89.33 221
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		31.24 223	40.15 225	20.86 224	12.61 231	17.99 232	25.16 233	13.30 227	48.42 228	24.82 232	53.07 226	30.13 235	28.47 227	42.73 227	37.65 226	20.79 230	51.04 227

test123		26.75 224	34.25 226	18.01 225	7.93 232	17.18 233	24.85 234	12.36 228	44.83 229	16.52 233	41.80 229	18.10 236	28.29 228	33.08 228	34.79 227	18.10 231	49.95 228

GG-mvs-BLEND		69.11 219	98.13 81	35.26 223	3.49 233	98.20 165	94.89 174	2.38 229	98.42 134	5.82 234	96.37 118	98.60 67	5.97 229	98.75 66	97.98 99	99.01 192	98.61 185

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

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

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

RE-MVS-def										69.05 218

9.14												99.79 45

MTAPA										98.09 15		99.97 8

MTMP										98.46 10		99.96 12

Patchmatch-RL test							66.86 229

NP-MVS									98.57 126

Patchmtry						98.59 142	97.15 128	79.14 211		80.42 181

DeepMVS_CX						96.85 208	87.43 218	89.27 153	98.30 140	75.55 203	95.05 140	79.47 215	92.62 207	89.48 218		95.18 222	95.96 215

High-resolution multi-view benchmark