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	98.66 123	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	97.78 129	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 46	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 43	96.55 36	99.10 31	99.96 12	99.36 28	99.25 27	98.37 76	99.64 120	99.66 111

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 99	97.87 19	98.91 43	99.92 28	99.30 35	99.45 15	99.38 8	99.79 31	99.58 127
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 73	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 94	99.51 21	98.31 9	99.28 39	96.57 35	99.10 31	99.90 33	99.71 2	99.19 31	98.35 77	99.82 16	99.71 95

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 64	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 64	98.73 5	99.11 29	99.92 28	98.92 62	99.22 28	98.84 50	99.76 41	99.56 133

CNVR-MVS		99.23 14	99.28 32	99.17 5	99.65 18	99.34 91	99.46 25	98.21 19	99.28 39	98.47 8	98.89 45	99.94 25	99.50 16	99.42 17	98.61 61	99.73 60	99.52 139

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 38	98.40 11	99.64 6	99.98 2	99.31 33	99.56 9	98.96 39	99.85 10	99.70 97
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 84	95.62 46	98.97 38	99.94 25	99.54 14	99.51 12	98.79 55	99.71 78	99.73 82
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	99.51 144

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 51	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 97	99.38 31	98.16 21	99.02 82	98.55 7	98.71 54	99.57 56	99.58 12	99.09 37	97.84 109	99.64 120	99.36 157

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 54	98.32 12	98.58 58	99.95 17	99.60 7	99.28 26	98.20 90	99.64 120	99.69 101

TPM-MVS					99.57 26	98.90 120	98.79 58			96.52 37	98.62 57	99.91 31	97.56 117			99.44 171	99.28 160
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	99.64 118

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	99.66 111

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	99.64 118
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 91	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	99.67 107

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	99.61 128	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 96	92.24 112	94.61 146	99.05 64	99.10 51	98.64 73	99.05 30	99.74 52	99.51 144

PLC	97.93 2	99.02 28	98.94 52	99.11 10	99.46 34	99.24 103	99.06 46	97.96 33	99.31 35	99.16 1	97.90 81	99.79 45	99.36 28	98.71 69	98.12 94	99.65 116	99.52 139
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 35	95.00 60	99.51 10	99.79 45	99.00 59	98.94 49	98.83 51	99.69 90	99.57 132

OMC-MVS		98.84 32	99.01 50	98.65 29	99.39 36	99.23 104	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	99.57 150	99.07 175

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	99.58 146	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 41	94.85 62	99.56 9	99.69 50	99.04 56	99.36 20	98.88 45	99.60 136	99.58 127

train_agg		98.73 36	99.11 40	98.28 35	99.36 39	99.35 89	99.48 24	97.96 33	98.83 104	93.86 84	98.70 55	99.86 38	99.44 23	99.08 39	98.38 74	99.61 128	99.58 127

ACMMP		98.74 35	99.03 49	98.40 32	99.36 39	99.64 27	99.20 36	97.75 37	98.82 106	95.24 55	98.85 46	99.87 37	99.17 45	98.74 67	97.50 122	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	98.91 119	97.65 112	91.68 113	98.00 153	97.01 31	97.72 87	94.83 113	98.85 71	98.44 90	98.86 46	99.41 176	99.52 139
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 86	99.49 22	96.15 44	98.82 106	91.82 115	98.41 66	99.66 51	99.10 51	98.93 50	98.97 38	99.75 46	99.58 127

CNLPA		99.03 27	99.05 45	99.01 19	99.27 43	99.22 105	99.03 48	97.98 32	99.34 33	99.00 4	98.25 72	99.71 49	99.31 33	98.80 60	98.82 53	99.48 165	99.17 168

MSDG		98.27 51	98.29 71	98.24 36	99.20 44	99.22 105	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	99.67 107	99.14 172

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 64	97.30 27	99.44 13	99.96 12	99.32 32	98.89 55	99.39 7	99.79 31	99.58 127

PatchMatch-RL		97.77 62	98.25 73	97.21 62	99.11 46	99.25 101	97.06 135	94.09 71	98.72 121	95.14 58	98.47 64	96.29 94	98.43 90	98.65 72	97.44 128	99.45 169	98.94 178

TAPA-MVS	97.53 5	98.41 46	98.84 57	97.91 44	99.08 47	99.33 94	99.15 39	97.13 40	99.34 33	93.20 95	97.75 85	99.19 60	99.20 39	98.66 71	98.13 93	99.66 112	99.48 148
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 79	95.62 46	98.93 41	97.62 81	95.48 172	98.59 81	98.55 63	99.29 185	99.84 25
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EPNet_dtu		96.30 116	98.53 64	93.70 138	98.97 49	98.24 164	97.36 118	94.23 70	98.85 99	79.18 190	99.19 22	98.47 70	94.09 194	97.89 124	98.21 89	98.39 200	98.85 184
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	91.80 116	94.83 145	98.36 72	98.90 65	98.09 106	97.85 108	99.68 98	99.15 169
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 94	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 94	96.28 151	97.47 38	99.58 9	94.70 65	98.99 37	99.85 40	97.24 125	99.55 10	99.34 9	97.73 209	99.56 133

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	98.77 127	99.13 40	98.07 29	99.10 70	94.27 78	96.70 107	99.84 41	98.70 76	97.90 123	98.11 95	99.40 178	99.28 160

PCF-MVS	97.50 6	98.18 54	98.35 70	97.99 42	98.65 56	99.36 86	98.94 52	98.14 25	98.59 125	93.62 90	96.61 111	99.76 48	99.03 57	97.77 130	97.45 127	99.57 150	98.89 183
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	95.74 133	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 91	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 45	88.13 134	96.93 102	94.14 123	99.19 40	99.14 35	99.23 19	99.69 90	99.42 152

CHOSEN 280x420		97.99 57	99.24 35	96.53 85	98.34 61	99.61 38	98.36 79	89.80 149	99.27 41	95.08 59	99.81 1	98.58 68	98.64 81	99.02 43	98.92 42	98.93 194	99.48 148

DELS-MVS		98.19 53	98.77 59	97.52 53	98.29 62	99.71 16	99.12 41	94.58 63	98.80 109	95.38 53	96.24 120	98.24 74	97.92 106	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	99.00 112	98.84 56	93.76 79	99.45 20	94.78 64	99.39 16	99.31 58	98.53 88	96.61 169	95.43 178	97.74 207	97.93 201

PVSNet_BlendedMVS		97.51 71	97.71 98	97.28 59	98.06 65	99.61 38	97.31 120	95.02 52	99.08 73	95.51 48	98.05 76	90.11 148	98.07 101	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	97.31 120	95.02 52	99.08 73	95.51 48	98.05 76	90.11 148	98.07 101	98.91 53	98.40 72	99.72 68	99.78 51

CHOSEN 1792x2688		96.41 113	96.99 129	95.74 107	98.01 67	99.72 13	97.70 110	90.78 134	99.13 68	90.03 127	87.35 203	95.36 106	98.33 92	98.59 81	98.91 44	99.59 142	99.87 18

HyFIR lowres test		95.99 124	96.56 139	95.32 113	97.99 68	99.65 23	96.54 144	88.86 158	98.44 134	89.77 130	84.14 213	97.05 87	99.03 57	98.55 83	98.19 91	99.73 60	99.86 21

OPM-MVS		96.22 118	95.85 160	96.65 80	97.75 69	98.54 146	99.00 51	95.53 46	96.88 187	89.88 128	95.95 126	86.46 173	98.07 101	97.65 139	96.63 145	99.67 107	98.83 185
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
tmp_tt				82.25 215	97.73 70	88.71 224	80.18 224	68.65 227	99.15 61	86.98 143	99.47 11	85.31 182	68.35 225	87.51 220	83.81 222	91.64 224

TSAR-MVS + COLMAP		96.79 98	96.55 140	97.06 65	97.70 71	98.46 151	99.07 45	96.23 43	99.38 25	91.32 120	98.80 47	85.61 179	98.69 78	97.64 140	96.92 138	99.37 180	99.06 176

PVSNet_Blended_VisFu		97.41 74	98.49 66	96.15 96	97.49 72	99.76 6	96.02 155	93.75 81	99.26 43	93.38 94	93.73 154	99.35 57	96.47 147	98.96 47	98.46 67	99.77 39	99.90 7

MS-PatchMatch		95.99 124	97.26 119	94.51 122	97.46 73	98.76 130	97.27 122	86.97 178	99.09 71	89.83 129	93.51 158	97.78 78	96.18 153	97.53 145	95.71 175	99.35 181	98.41 191

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		96.23 117	96.89 131	95.46 112	97.32 76	98.77 127	98.81 57	93.60 84	98.58 126	85.52 152	99.08 33	86.67 170	97.83 113	97.87 125	97.51 121	99.69 90	99.73 82

CMPMVS	70.31 18	90.74 203	91.06 211	90.36 194	97.32 76	97.43 200	92.97 199	87.82 174	93.50 218	75.34 206	83.27 215	84.90 185	92.19 209	92.64 213	91.21 217	96.50 220	94.46 218
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
HQP-MVS		96.37 114	96.58 138	96.13 98	97.31 78	98.44 153	98.45 71	95.22 50	98.86 97	88.58 132	98.33 70	87.00 165	97.67 115	97.23 156	96.56 149	99.56 153	99.62 122

ACMM	96.26 9	96.67 107	96.69 136	96.66 79	97.29 79	98.46 151	96.48 147	95.09 51	99.21 51	93.19 96	98.78 49	86.73 169	98.17 95	97.84 127	96.32 156	99.74 52	99.49 147
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
UA-Net		97.13 86	99.14 39	94.78 118	97.21 80	99.38 80	97.56 113	92.04 106	98.48 132	88.03 135	98.39 68	99.91 31	94.03 195	99.33 24	99.23 19	99.81 23	99.25 164

UGNet		97.66 66	99.07 44	96.01 102	97.19 81	99.65 23	97.09 133	93.39 87	99.35 32	94.40 75	98.79 48	99.59 54	94.24 192	98.04 114	98.29 86	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 71	97.19 29	99.73 3	99.95 17	99.39 26	98.95 48	98.69 57	99.75 46	99.65 114

CANet_DTU		96.64 108	99.08 42	93.81 134	97.10 83	99.42 76	98.85 55	90.01 143	99.31 35	79.98 186	99.78 2	99.10 63	97.42 122	98.35 93	98.05 98	99.47 167	99.53 136

IB-MVS	93.96 15	95.02 142	96.44 150	93.36 148	97.05 84	99.28 98	90.43 209	93.39 87	98.02 152	96.02 41	94.92 144	92.07 138	83.52 218	95.38 195	95.82 172	99.72 68	99.59 126
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	96.62 110	96.72 135	96.50 88	96.96 85	98.75 131	97.80 105	94.30 69	98.85 99	93.12 97	98.78 49	86.61 171	97.23 126	97.73 133	96.61 146	99.62 126	99.71 95
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test2506		97.16 84	96.68 137	97.73 47	96.95 86	99.79 4	98.48 68	94.42 66	99.17 56	97.74 22	99.15 25	80.93 207	98.89 68	99.03 41	99.09 25	99.88 4	99.62 122

ECVR-MVS		97.27 79	97.09 124	97.48 54	96.95 86	99.79 4	98.48 68	94.42 66	99.17 56	96.28 39	93.54 156	89.39 154	98.89 68	99.03 41	99.09 25	99.88 4	99.61 125

test1111		97.09 88	96.83 134	97.39 55	96.92 88	99.81 3	98.44 72	94.45 65	99.17 56	95.85 44	92.10 170	88.97 156	98.78 73	99.02 43	99.11 24	99.88 4	99.63 120

ACMH	95.42 14	95.27 139	95.96 156	94.45 124	96.83 89	98.78 126	94.72 182	91.67 114	98.95 87	86.82 145	96.42 117	83.67 190	97.00 129	97.48 147	96.68 143	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	96.51 143	97.01 71	96.71 90	98.62 140	98.73 59	94.38 68	98.94 89	94.46 72	97.33 92	87.03 164	98.07 101	97.20 158	96.87 139	99.72 68	99.54 135
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
TDRefinement		93.04 178	93.57 195	92.41 157	96.58 91	98.77 127	97.78 107	91.96 109	98.12 149	80.84 179	89.13 190	79.87 215	87.78 214	96.44 174	94.50 200	99.54 159	98.15 196

Anonymous202405211			97.40 111		96.45 92	99.54 54	98.08 97	93.79 78	98.24 145		93.55 155	94.41 119	98.88 70	98.04 114	98.24 88	99.75 46	99.76 63

DCV-MVSNet		97.56 69	98.36 69	96.62 84	96.44 93	98.36 160	98.37 77	91.73 112	99.11 69	94.80 63	98.36 69	96.28 95	98.60 84	98.12 103	98.44 69	99.76 41	99.87 18

ACMH+	95.51 13	95.40 135	96.00 154	94.70 119	96.33 94	98.79 124	96.79 139	91.32 124	98.77 116	87.18 142	95.60 138	85.46 180	96.97 130	97.15 159	96.59 147	99.59 142	99.65 114

Anonymous20231211		97.10 87	97.06 127	97.14 63	96.32 95	99.52 58	98.16 89	93.76 79	98.84 103	95.98 42	90.92 176	94.58 118	98.90 65	97.72 134	98.10 96	99.71 78	99.75 71

thres100view900		96.72 102	96.47 147	97.00 72	96.31 96	99.52 58	98.28 83	94.01 72	97.35 174	94.52 68	95.90 128	86.93 166	99.09 53	98.07 109	97.87 106	99.81 23	99.63 120

tfpn200view9		96.75 100	96.51 143	97.03 67	96.31 96	99.67 19	98.41 74	93.99 74	97.35 174	94.52 68	95.90 128	86.93 166	99.14 48	98.26 96	97.80 111	99.82 16	99.70 97

thres200		96.76 99	96.53 141	97.03 67	96.31 96	99.67 19	98.37 77	93.99 74	97.68 169	94.49 71	95.83 132	86.77 168	99.18 43	98.26 96	97.82 110	99.82 16	99.66 111

thres600view7		96.69 104	96.43 151	97.00 72	96.28 99	99.67 19	98.41 74	93.99 74	97.85 163	94.29 77	95.96 125	85.91 177	99.19 40	98.26 96	97.63 116	99.82 16	99.73 82

thres400		96.71 103	96.45 149	97.02 69	96.28 99	99.63 30	98.41 74	94.00 73	97.82 164	94.42 74	95.74 133	86.26 174	99.18 43	98.20 100	97.79 112	99.81 23	99.70 97

baseline1		97.58 68	98.05 84	97.02 69	96.21 101	99.45 68	97.71 109	93.71 83	98.47 133	95.75 45	98.78 49	93.20 133	98.91 63	98.52 85	98.44 69	99.81 23	99.53 136

sasdasda		97.31 76	97.81 95	96.72 76	96.20 102	99.45 68	98.21 86	91.60 115	99.22 48	95.39 51	98.48 61	90.95 143	99.16 46	97.66 136	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	91.60 115	99.22 48	95.39 51	98.48 61	90.95 143	99.16 46	97.66 136	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	91.52 120	99.23 46	95.16 57	98.48 61	90.87 145	99.07 54	97.59 142	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	98.75 120	94.01 79	96.88 104	98.40 71	98.48 89	99.09 37	99.42 5	99.83 15	99.80 37

USDC		94.26 158	94.83 170	93.59 140	96.02 105	98.44 153	97.84 102	88.65 162	98.86 97	82.73 171	94.02 151	80.56 208	96.76 136	97.28 155	96.15 163	99.55 155	98.50 189

FC-MVSNet-train		97.04 89	97.91 92	96.03 101	96.00 107	98.41 156	96.53 146	93.42 86	99.04 81	93.02 98	98.03 78	94.32 121	97.47 121	97.93 121	97.77 113	99.75 46	99.88 16

Vis-MVSNet (Re-imp)		97.40 75	98.89 54	95.66 109	95.99 108	99.62 33	97.82 103	93.22 95	98.82 106	91.40 118	96.94 101	98.56 69	95.70 164	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	98.48 149	98.63 63	92.10 105	98.68 122	95.96 43	99.23 21	91.79 139	96.87 133	98.76 64	97.37 131	99.57 150	99.68 106

baseline		97.45 73	98.70 61	95.99 103	95.89 110	99.36 86	98.29 82	91.37 123	99.21 51	92.99 99	98.40 67	96.87 89	97.96 105	98.60 79	98.60 62	99.42 175	99.86 21

TinyColmap		94.00 162	94.35 179	93.60 139	95.89 110	98.26 162	97.49 115	88.82 159	98.56 128	83.21 165	91.28 175	80.48 210	96.68 139	97.34 152	96.26 159	99.53 161	98.24 195

FA-MVS(training)		96.52 112	98.29 71	94.45 124	95.88 112	99.52 58	97.66 111	81.47 201	98.94 89	93.79 88	95.54 140	99.11 62	98.29 93	98.89 55	96.49 151	99.63 125	99.52 139

EPMVS		95.05 141	96.86 133	92.94 154	95.84 113	98.96 117	96.68 140	79.87 207	99.05 79	90.15 125	97.12 98	95.99 101	97.49 120	95.17 199	94.75 197	97.59 211	96.96 211

casdiffmvs_mvg		97.27 79	97.97 90	96.46 89	95.83 114	99.51 61	98.42 73	93.32 91	98.34 139	92.38 108	95.64 136	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	98.73 134	97.80 105	93.05 100	98.76 117	94.39 76	99.07 34	97.03 88	98.55 86	98.31 95	97.61 117	99.43 173	99.21 167

diffmvs		96.83 96	97.33 114	96.25 93	95.76 116	99.34 91	98.06 98	93.22 95	99.43 22	92.30 110	96.90 103	89.83 153	98.55 86	98.00 118	98.14 92	99.64 120	99.70 97
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	95.87 104	95.74 117	99.28 98	98.19 88	91.40 122	99.18 55	91.59 117	98.17 74	96.18 97	98.63 82	98.61 76	98.55 63	99.66 112	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	98.30 141	92.31 109	98.63 56	97.90 76	98.97 60	98.92 52	98.30 83	99.78 34	99.80 37

diffmvs_AUTHOR		96.68 106	97.10 123	96.19 95	95.71 119	99.37 84	97.91 100	93.19 98	99.36 30	91.97 114	95.90 128	89.02 155	98.67 79	98.01 117	98.30 83	99.68 98	99.74 76

viewmanbaseed2359cas		96.92 94	97.60 102	96.14 97	95.71 119	99.44 73	97.82 103	93.39 87	98.93 91	91.34 119	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	95.70 121	99.50 62	97.75 108	93.22 95	98.98 86	92.64 103	94.97 142	91.71 140	98.93 61	98.62 75	98.52 66	99.82 16	99.72 92
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
tpmrst		93.86 167	95.88 158	91.50 176	95.69 122	98.62 140	95.64 161	79.41 210	98.80 109	83.76 161	95.63 137	96.13 98	97.25 124	92.92 211	92.31 210	97.27 214	96.74 212

ADS-MVSNet		94.65 150	97.04 128	91.88 172	95.68 123	98.99 114	95.89 156	79.03 214	99.15 61	85.81 150	96.96 100	98.21 75	97.10 127	94.48 207	94.24 201	97.74 207	97.21 207

EPP-MVSNet		97.75 63	98.71 60	96.63 83	95.68 123	99.56 51	97.51 114	93.10 99	99.22 48	94.99 61	97.18 97	97.30 84	98.65 80	98.83 59	98.93 41	99.84 12	99.92 3

viewmambaseed2359dif		96.82 97	97.19 121	96.39 91	95.64 125	99.38 80	98.15 90	93.24 92	98.78 115	92.85 101	95.93 127	91.24 142	98.75 75	97.41 148	97.86 107	99.70 86	99.74 76

EC-MVSNet		98.22 52	99.44 17	96.79 75	95.62 126	99.56 51	99.01 50	92.22 103	99.17 56	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	95.61 127	99.61 38	98.26 85	93.52 85	98.90 95	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	95.61 127	99.40 79	98.72 60	92.11 104	99.14 64	92.98 100	93.08 166	95.14 109	98.13 99	98.05 113	97.91 104	99.74 52	99.73 82

thisisatest0530		97.23 82	98.25 73	96.05 99	95.60 129	99.59 45	96.96 137	93.23 93	99.17 56	92.60 105	98.75 52	96.19 96	98.17 95	98.19 101	96.10 164	99.72 68	99.77 58

tttt0517		97.23 82	98.24 76	96.04 100	95.60 129	99.60 43	96.94 138	93.23 93	99.15 61	92.56 106	98.74 53	96.12 99	98.17 95	98.21 99	96.10 164	99.73 60	99.78 51

SCA		94.95 143	97.44 109	92.04 164	95.55 131	99.16 107	96.26 152	79.30 211	99.02 82	85.73 151	98.18 73	97.13 86	97.69 114	96.03 188	94.91 192	97.69 210	97.65 203

dps		94.63 151	95.31 166	93.84 133	95.53 132	98.71 135	96.54 144	80.12 206	97.81 166	97.21 28	96.98 99	92.37 134	96.34 150	92.46 214	91.77 214	97.26 215	97.08 209

PatchmatchNet		94.70 148	97.08 126	91.92 169	95.53 132	98.85 122	95.77 158	79.54 209	98.95 87	85.98 148	98.52 59	96.45 90	97.39 123	95.32 196	94.09 202	97.32 213	97.38 206
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test-LLR		95.50 133	97.32 115	93.37 147	95.49 134	98.74 132	96.44 149	90.82 132	98.18 146	82.75 169	96.60 112	94.67 116	95.54 170	98.09 106	96.00 166	99.20 188	98.93 179

test0.0.03 1		96.69 104	98.12 82	95.01 116	95.49 134	98.99 114	95.86 157	90.82 132	98.38 136	92.54 107	96.66 109	97.33 82	95.75 162	97.75 132	98.34 79	99.60 136	99.40 155

CostFormer		94.25 159	94.88 169	93.51 144	95.43 136	98.34 161	96.21 153	80.64 204	97.94 158	94.01 79	98.30 71	86.20 176	97.52 118	92.71 212	92.69 208	97.23 216	98.02 199

MDTV_nov1_ep13		95.57 131	97.48 106	93.35 149	95.43 136	98.97 116	97.19 128	83.72 199	98.92 94	87.91 137	97.75 85	96.12 99	97.88 110	96.84 168	95.64 176	97.96 205	98.10 197

tpm cat1		94.06 160	94.90 168	93.06 152	95.42 138	98.52 148	96.64 142	80.67 203	97.82 164	92.63 104	93.39 160	95.00 111	96.06 157	91.36 218	91.58 216	96.98 217	96.66 214

Vis-MVSNet		96.16 120	98.22 77	93.75 135	95.33 139	99.70 18	97.27 122	90.85 131	98.30 141	85.51 153	95.72 135	96.45 90	93.69 201	98.70 70	99.00 36	99.84 12	99.69 101
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CVMVSNet		95.33 138	97.09 124	93.27 150	95.23 140	98.39 158	95.49 164	92.58 102	97.71 168	83.00 168	94.44 149	93.28 131	93.92 198	97.79 128	98.54 65	99.41 176	99.45 150

IterMVS-LS		96.12 121	97.48 106	94.53 121	95.19 141	97.56 194	97.15 129	89.19 156	99.08 73	88.23 133	94.97 142	94.73 115	97.84 112	97.86 126	98.26 87	99.60 136	99.88 16
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Effi-MVS+		95.81 127	97.31 118	94.06 130	95.09 142	99.35 89	97.24 125	88.22 167	98.54 129	85.38 154	98.52 59	88.68 157	98.70 76	98.32 94	97.93 101	99.74 52	99.84 25

testgi		95.67 130	97.48 106	93.56 141	95.07 143	99.00 112	95.33 168	88.47 164	98.80 109	86.90 144	97.30 93	92.33 135	95.97 159	97.66 136	97.91 104	99.60 136	99.38 156

GeoE		95.98 126	97.24 120	94.51 122	95.02 144	99.38 80	98.02 99	87.86 173	98.37 137	87.86 138	92.99 168	93.54 128	98.56 85	98.61 76	97.92 102	99.73 60	99.85 24

RPMNet		94.66 149	97.16 122	91.75 173	94.98 145	98.59 143	97.00 136	78.37 218	97.98 154	83.78 159	96.27 119	94.09 126	96.91 132	97.36 151	96.73 141	99.48 165	99.09 174

CR-MVSNet		94.57 155	97.34 113	91.33 180	94.90 146	98.59 143	97.15 129	79.14 212	97.98 154	80.42 182	96.59 114	93.50 130	96.85 134	98.10 104	97.49 123	99.50 164	99.15 169

gg-mvs-nofinetune		90.85 202	94.14 181	87.02 207	94.89 147	99.25 101	98.64 62	76.29 222	88.24 223	57.50 227	79.93 219	95.45 105	95.18 181	98.77 63	98.07 97	99.62 126	99.24 165

IterMVS-SCA-FT		94.89 145	97.87 93	91.42 177	94.86 148	97.70 180	97.24 125	84.88 193	98.93 91	75.74 202	94.26 150	98.25 73	96.69 138	98.52 85	97.68 115	99.10 192	99.73 82

IterMVS		94.81 147	97.71 98	91.42 177	94.83 149	97.63 187	97.38 117	85.08 190	98.93 91	75.67 203	94.02 151	97.64 79	96.66 141	98.45 88	97.60 118	98.90 195	99.72 92
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PatchT		93.96 164	97.36 112	90.00 196	94.76 150	98.65 138	90.11 212	78.57 217	97.96 157	80.42 182	96.07 123	94.10 125	96.85 134	98.10 104	97.49 123	99.26 186	99.15 169

baseline2		96.36 115	97.82 94	94.65 120	94.60 151	99.09 110	96.45 148	89.63 151	98.36 138	91.29 121	97.60 90	94.13 124	96.37 148	98.45 88	97.70 114	99.54 159	99.41 153

CDS-MVSNet		96.59 111	98.02 87	94.92 117	94.45 152	98.96 117	97.46 116	91.75 111	97.86 162	90.07 126	96.02 124	97.25 85	96.21 151	98.04 114	98.38 74	99.60 136	99.65 114
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
tpm		92.38 194	94.79 171	89.56 200	94.30 153	97.50 197	94.24 194	78.97 215	97.72 167	74.93 207	97.97 80	82.91 196	96.60 143	93.65 210	94.81 196	98.33 201	98.98 177

Fast-Effi-MVS+		95.38 136	96.52 142	94.05 131	94.15 154	99.14 109	97.24 125	86.79 179	98.53 130	87.62 140	94.51 147	87.06 163	98.76 74	98.60 79	98.04 99	99.72 68	99.77 58

Effi-MVS+-dtu		95.74 129	98.04 85	93.06 152	93.92 155	99.16 107	97.90 101	88.16 169	99.07 78	82.02 174	98.02 79	94.32 121	96.74 137	98.53 84	97.56 119	99.61 128	99.62 122

UniMVSNet_ETH3D		93.15 175	92.33 208	94.11 129	93.91 156	98.61 142	94.81 179	90.98 129	97.06 183	87.51 141	82.27 217	76.33 223	97.87 111	94.79 205	97.47 126	99.56 153	99.81 35

Fast-Effi-MVS+-dtu		95.38 136	98.20 78	92.09 163	93.91 156	98.87 121	97.35 119	85.01 192	99.08 73	81.09 178	98.10 75	96.36 93	95.62 167	98.43 91	97.03 135	99.55 155	99.50 146

TAMVS		95.53 132	96.50 145	94.39 126	93.86 158	99.03 111	96.67 141	89.55 153	97.33 176	90.64 123	93.02 167	91.58 141	96.21 151	97.72 134	97.43 129	99.43 173	99.36 157

GBi-Net		96.98 91	98.00 88	95.78 105	93.81 159	97.98 169	98.09 94	91.32 124	98.80 109	93.92 81	97.21 94	95.94 102	97.89 107	98.07 109	98.34 79	99.68 98	99.67 107

test1		96.98 91	98.00 88	95.78 105	93.81 159	97.98 169	98.09 94	91.32 124	98.80 109	93.92 81	97.21 94	95.94 102	97.89 107	98.07 109	98.34 79	99.68 98	99.67 107

FMVSNet2		96.64 108	97.50 104	95.63 110	93.81 159	97.98 169	98.09 94	90.87 130	98.99 85	93.48 92	93.17 163	95.25 108	97.89 107	98.63 74	98.80 54	99.68 98	99.67 107

MVS-HIRNet		92.51 188	95.97 155	88.48 204	93.73 162	98.37 159	90.33 210	75.36 224	98.32 140	77.78 196	89.15 189	94.87 112	95.14 182	97.62 141	96.39 154	98.51 197	97.11 208

GA-MVS		93.93 165	96.31 153	91.16 184	93.61 163	98.79 124	95.39 167	90.69 137	98.25 144	73.28 211	96.15 121	88.42 158	94.39 190	97.76 131	95.35 180	99.58 146	99.45 150

FC-MVSNet-test		96.07 122	97.94 91	93.89 132	93.60 164	98.67 137	96.62 143	90.30 142	98.76 117	88.62 131	95.57 139	97.63 80	94.48 188	97.97 119	97.48 125	99.71 78	99.52 139

FMVSNet3		97.02 90	98.12 82	95.73 108	93.59 165	97.98 169	98.34 81	91.32 124	98.80 109	93.92 81	97.21 94	95.94 102	97.63 116	98.61 76	98.62 60	99.61 128	99.65 114

dmvs_re		96.02 123	96.49 146	95.47 111	93.49 166	99.26 100	97.25 124	93.82 77	97.51 171	90.43 124	97.52 91	87.93 159	98.12 100	96.86 166	96.59 147	99.73 60	99.76 63

FMVSNet1		95.77 128	96.41 152	95.03 115	93.42 167	97.86 176	97.11 132	89.89 146	98.53 130	92.00 113	89.17 188	93.23 132	98.15 98	98.07 109	98.34 79	99.61 128	99.69 101

tfpnnormal		93.85 168	94.12 183	93.54 143	93.22 168	98.24 164	95.45 165	91.96 109	94.61 214	83.91 157	90.74 178	81.75 204	97.04 128	97.49 146	96.16 162	99.68 98	99.84 25

TransMVSNet (Re)		93.45 171	94.08 184	92.72 156	92.83 169	97.62 190	94.94 173	91.54 119	95.65 211	83.06 167	88.93 191	83.53 191	94.25 191	97.41 148	97.03 135	99.67 107	98.40 194

LTVRE_ROB	93.20 16	92.84 180	94.92 167	90.43 193	92.83 169	98.63 139	97.08 134	87.87 172	97.91 159	68.42 220	93.54 156	79.46 217	96.62 142	97.55 144	97.40 130	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		94.95 143	97.32 115	92.20 161	92.62 171	98.74 132	96.44 149	86.67 181	98.18 146	82.75 169	96.60 112	94.67 116	95.54 170	98.09 106	96.00 166	99.20 188	98.93 179

pm-mvs1		94.27 157	95.57 162	92.75 155	92.58 172	98.13 167	94.87 177	90.71 136	96.70 193	83.78 159	89.94 184	89.85 152	94.96 185	97.58 143	97.07 134	99.61 128	99.72 92

NR-MVSNet		94.01 161	94.51 176	93.44 145	92.56 173	97.77 177	95.67 159	91.57 117	97.17 180	85.84 149	93.13 164	80.53 209	95.29 178	97.01 163	96.17 161	99.69 90	99.75 71

EG-PatchMatch MVS		92.45 189	93.92 190	90.72 190	92.56 173	98.43 155	94.88 176	84.54 195	97.18 179	79.55 188	86.12 210	83.23 194	93.15 205	97.22 157	96.00 166	99.67 107	99.27 163

pmnet_mix02		92.44 190	94.68 173	89.83 199	92.46 175	97.65 186	89.92 214	90.49 139	98.76 117	73.05 213	91.78 171	90.08 150	94.86 186	94.53 206	91.94 213	98.21 203	98.01 200

test-mter		94.86 146	97.32 115	92.00 166	92.41 176	98.82 123	96.18 154	86.35 185	98.05 151	82.28 172	96.48 116	94.39 120	95.46 174	98.17 102	96.20 160	99.32 183	99.13 173

our_test_3					92.30 177	97.58 192	90.09 213

pmmvs4		95.09 140	95.90 157	94.14 128	92.29 178	97.70 180	95.45 165	90.31 140	98.60 124	90.70 122	93.25 161	89.90 151	96.67 140	97.13 160	95.42 179	99.44 171	99.28 160

FMVSNet5		95.42 134	96.47 147	94.20 127	92.26 179	95.99 215	95.66 160	87.15 177	97.87 161	93.46 93	96.68 108	93.79 127	97.52 118	97.10 162	97.21 133	99.11 191	96.62 215

UniMVSNet (Re)		94.58 154	95.34 164	93.71 137	92.25 180	98.08 168	94.97 172	91.29 128	97.03 185	87.94 136	93.97 153	86.25 175	96.07 156	96.27 182	95.97 169	99.72 68	99.79 45

SixPastTwentyTwo		93.44 172	95.32 165	91.24 182	92.11 181	98.40 157	92.77 200	88.64 163	98.09 150	77.83 195	93.51 158	85.74 178	96.52 146	96.91 165	94.89 195	99.59 142	99.73 82

v8		92.87 179	93.87 192	91.72 175	92.05 182	97.50 197	94.79 180	88.20 168	96.85 189	80.11 185	90.01 183	82.86 198	95.48 172	95.15 200	94.90 193	99.66 112	99.80 37

thisisatest0515		94.61 152	96.89 131	91.95 168	92.00 183	98.47 150	92.01 204	90.73 135	98.18 146	83.96 156	94.51 147	95.13 110	93.38 202	97.38 150	94.74 198	99.61 128	99.79 45

WR-MVS_H		93.54 170	94.67 174	92.22 159	91.95 184	97.91 174	94.58 188	88.75 160	96.64 194	83.88 158	90.66 180	85.13 183	94.40 189	96.54 173	95.91 171	99.73 60	99.89 13

V42		93.05 177	93.90 191	92.04 164	91.91 185	97.66 184	94.91 174	89.91 145	96.85 189	80.58 181	89.66 185	83.43 193	95.37 176	95.03 203	94.90 193	99.59 142	99.78 51

EU-MVSNet		92.80 182	94.76 172	90.51 191	91.88 186	96.74 212	92.48 202	88.69 161	96.21 200	79.00 191	91.51 172	87.82 160	91.83 210	95.87 192	96.27 157	99.21 187	98.92 182

N_pmnet		92.21 198	94.60 175	89.42 201	91.88 186	97.38 203	89.15 216	89.74 150	97.89 160	73.75 209	87.94 200	92.23 137	93.85 199	96.10 186	93.20 207	98.15 204	97.43 205

UniMVSNet_NR-MVSNet		94.59 153	95.47 163	93.55 142	91.85 188	97.89 175	95.03 170	92.00 107	97.33 176	86.12 146	93.19 162	87.29 162	96.60 143	96.12 185	96.70 142	99.72 68	99.80 37

pmmvs6		91.90 200	92.53 207	91.17 183	91.81 189	97.63 187	93.23 197	88.37 166	93.43 219	80.61 180	77.32 221	87.47 161	94.12 193	96.58 171	95.72 174	98.88 196	99.53 136

v10		92.79 183	94.06 185	91.31 181	91.78 190	97.29 206	94.87 177	86.10 186	96.97 186	79.82 187	88.16 197	84.56 187	95.63 166	96.33 180	95.31 181	99.65 116	99.80 37

MIMVSNet		94.49 156	97.59 103	90.87 189	91.74 191	98.70 136	94.68 184	78.73 216	97.98 154	83.71 162	97.71 88	94.81 114	96.96 131	97.97 119	97.92 102	99.40 178	98.04 198

v1144		92.81 181	94.03 186	91.40 179	91.68 192	97.60 191	94.73 181	88.40 165	96.71 192	78.48 193	88.14 198	84.46 188	95.45 175	96.31 181	95.22 184	99.65 116	99.76 63

DU-MVS		93.98 163	94.44 178	93.44 145	91.66 193	97.77 177	95.03 170	91.57 117	97.17 180	86.12 146	93.13 164	81.13 206	96.60 143	95.10 201	97.01 137	99.67 107	99.80 37

Baseline_NR-MVSNet		93.87 166	93.98 188	93.75 135	91.66 193	97.02 207	95.53 163	91.52 120	97.16 182	87.77 139	87.93 201	83.69 189	96.35 149	95.10 201	97.23 132	99.68 98	99.73 82

CP-MVSNet		93.25 174	94.00 187	92.38 158	91.65 195	97.56 194	94.38 191	89.20 155	96.05 205	83.16 166	89.51 186	81.97 202	96.16 155	96.43 175	96.56 149	99.71 78	99.89 13

v148		92.36 196	92.88 203	91.75 173	91.63 196	97.66 184	92.64 201	90.55 138	96.09 203	83.34 164	88.19 196	80.00 212	92.74 206	93.98 209	94.58 199	99.58 146	99.69 101

PS-CasMVS		92.72 185	93.36 199	91.98 167	91.62 197	97.52 196	94.13 195	88.98 157	95.94 208	81.51 177	87.35 203	79.95 214	95.91 160	96.37 177	96.49 151	99.70 86	99.89 13

v2v482		92.77 184	93.52 198	91.90 171	91.59 198	97.63 187	94.57 189	90.31 140	96.80 191	79.22 189	88.74 193	81.55 205	96.04 158	95.26 197	94.97 191	99.66 112	99.69 101

v1192		92.43 192	93.61 194	91.05 185	91.53 199	97.43 200	94.61 187	87.99 171	96.60 195	76.72 198	87.11 205	82.74 199	95.85 161	96.35 179	95.30 182	99.60 136	99.74 76

WR-MVS		93.43 173	94.48 177	92.21 160	91.52 200	97.69 182	94.66 186	89.98 144	96.86 188	83.43 163	90.12 182	85.03 184	93.94 197	96.02 189	95.82 172	99.71 78	99.82 30

v144192		92.38 194	93.55 197	91.00 186	91.44 201	97.47 199	94.27 192	87.41 176	96.52 197	78.03 194	87.50 202	82.65 200	95.32 177	95.82 193	95.15 186	99.55 155	99.78 51

pmmvs5		92.71 187	94.27 180	90.90 188	91.42 202	97.74 179	93.23 197	86.66 182	95.99 207	78.96 192	91.45 173	83.44 192	95.55 169	97.30 154	95.05 189	99.58 146	98.93 179

v1921920		92.36 196	93.57 195	90.94 187	91.39 203	97.39 202	94.70 183	87.63 175	96.60 195	76.63 199	86.98 206	82.89 197	95.75 162	96.26 183	95.14 187	99.55 155	99.73 82

gm-plane-assit		89.44 209	92.82 206	85.49 211	91.37 204	95.34 218	79.55 226	82.12 200	91.68 222	64.79 224	87.98 199	80.26 211	95.66 165	98.51 87	97.56 119	99.45 169	98.41 191

v1240		91.99 199	93.33 200	90.44 192	91.29 205	97.30 205	94.25 193	86.79 179	96.43 198	75.49 205	86.34 209	81.85 203	95.29 178	96.42 176	95.22 184	99.52 162	99.73 82

PEN-MVS		92.72 185	93.20 201	92.15 162	91.29 205	97.31 204	94.67 185	89.81 147	96.19 201	81.83 175	88.58 194	79.06 218	95.61 168	95.21 198	96.27 157	99.72 68	99.82 30

TranMVSNet+NR-MVSNet		93.67 169	94.14 181	93.13 151	91.28 207	97.58 192	95.60 162	91.97 108	97.06 183	84.05 155	90.64 181	82.22 201	96.17 154	94.94 204	96.78 140	99.69 90	99.78 51

anonymousdsp		93.12 176	95.86 159	89.93 198	91.09 208	98.25 163	95.12 169	85.08 190	97.44 173	73.30 210	90.89 177	90.78 146	95.25 180	97.91 122	95.96 170	99.71 78	99.82 30

MDTV_nov1_ep13_2view		92.44 190	95.66 161	88.68 202	91.05 209	97.92 173	92.17 203	79.64 208	98.83 104	76.20 200	91.45 173	93.51 129	95.04 183	95.68 194	93.70 205	97.96 205	98.53 188

DTE-MVSNet		92.42 193	92.85 204	91.91 170	90.87 210	96.97 208	94.53 190	89.81 147	95.86 210	81.59 176	88.83 192	77.88 221	95.01 184	94.34 208	96.35 155	99.64 120	99.73 82

v7n		91.61 201	92.95 202	90.04 195	90.56 211	97.69 182	93.74 196	85.59 188	95.89 209	76.95 197	86.60 208	78.60 220	93.76 200	97.01 163	94.99 190	99.65 116	99.87 18

test20.03		90.65 205	93.71 193	87.09 206	90.44 212	96.24 213	89.74 215	85.46 189	95.59 212	72.99 214	90.68 179	85.33 181	84.41 217	95.94 191	95.10 188	99.52 162	97.06 210

FPMVS		83.82 215	84.61 218	82.90 214	90.39 213	90.71 223	90.85 208	84.10 198	95.47 213	65.15 222	83.44 214	74.46 224	75.48 220	81.63 222	79.42 224	91.42 225	87.14 224

Anonymous20231206		90.70 204	93.93 189	86.92 208	90.21 214	96.79 210	90.30 211	86.61 183	96.05 205	69.25 218	88.46 195	84.86 186	85.86 216	97.11 161	96.47 153	99.30 184	97.80 202

new_pmnet		90.45 206	92.84 205	87.66 205	88.96 215	96.16 214	88.71 217	84.66 194	97.56 170	71.91 217	85.60 211	86.58 172	93.28 203	96.07 187	93.54 206	98.46 198	94.39 219

WB-MVS		81.36 217	89.93 214	71.35 220	88.65 216	87.85 226	71.46 228	88.12 170	96.23 199	32.21 232	92.61 169	83.00 195	56.27 227	91.92 217	89.43 218	91.39 226	88.49 223

ET-MVSNet_ETH3D		96.17 119	96.99 129	95.21 114	88.53 217	98.54 146	98.28 83	92.61 101	98.85 99	93.60 91	99.06 35	90.39 147	98.63 82	95.98 190	96.68 143	99.61 128	99.41 153

PM-MVS		89.55 208	90.30 213	88.67 203	87.06 218	95.60 216	90.88 207	84.51 196	96.14 202	75.75 201	86.89 207	63.47 229	94.64 187	96.85 167	93.89 203	99.17 190	99.29 159

pmmvs-eth3d		89.81 207	89.65 215	90.00 196	86.94 219	95.38 217	91.08 205	86.39 184	94.57 215	82.27 173	83.03 216	64.94 226	93.96 196	96.57 172	93.82 204	99.35 181	99.24 165

new-patchmatchnet		86.12 214	87.30 217	84.74 212	86.92 220	95.19 220	83.57 223	84.42 197	92.67 220	65.66 221	80.32 218	64.72 227	89.41 212	92.33 216	89.21 219	98.43 199	96.69 213

pmmvs3		88.19 211	91.27 210	84.60 213	85.60 221	93.66 221	85.68 221	81.13 202	92.36 221	63.66 226	89.51 186	77.10 222	93.22 204	96.37 177	92.40 209	98.30 202	97.46 204

Gipuma		81.40 216	81.78 219	80.96 217	83.21 222	85.61 228	79.73 225	76.25 223	97.33 176	64.21 225	55.32 225	55.55 230	86.04 215	92.43 215	92.20 212	96.32 221	93.99 220
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MDA-MVSNet-bldmvs		87.84 212	89.22 216	86.23 209	81.74 223	96.77 211	83.74 222	89.57 152	94.50 216	72.83 215	96.64 110	64.47 228	92.71 207	81.43 223	92.28 211	96.81 218	98.47 190

MIMVSNet1		88.61 210	90.68 212	86.19 210	81.56 224	95.30 219	87.78 218	85.98 187	94.19 217	72.30 216	78.84 220	78.90 219	90.06 211	96.59 170	95.47 177	99.46 168	95.49 217

PMVS	72.60 17	76.39 219	77.66 222	74.92 218	81.04 225	69.37 232	68.47 229	80.54 205	85.39 224	65.07 223	73.52 222	72.91 225	65.67 226	80.35 224	76.81 225	88.71 227	85.25 227
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
ambc			80.99 220		80.04 226	90.84 222	90.91 206		96.09 203	74.18 208	62.81 224	30.59 235	82.44 219	96.25 184	91.77 214	95.91 222	98.56 187

PMMVS2		77.26 218	79.47 221	74.70 219	76.00 227	88.37 225	74.22 227	76.34 221	78.31 225	54.13 228	69.96 223	52.50 231	70.14 224	84.83 221	88.71 220	97.35 212	93.58 221

test_method		87.27 213	91.58 209	82.25 215	75.65 228	87.52 227	86.81 220	72.60 225	97.51 171	73.20 212	85.07 212	79.97 213	88.69 213	97.31 153	95.24 183	96.53 219	98.41 191

EMVS		68.12 222	68.11 224	68.14 222	75.51 229	71.76 230	55.38 232	77.20 220	77.78 226	37.79 231	53.59 226	43.61 232	74.72 221	67.05 227	76.70 226	88.27 229	86.24 225

E-PMN		68.30 221	68.43 223	68.15 221	74.70 230	71.56 231	55.64 231	77.24 219	77.48 227	39.46 230	51.95 228	41.68 233	73.28 222	70.65 226	79.51 223	88.61 228	86.20 226

MVE	67.97 19	65.53 223	67.43 225	63.31 223	59.33 231	74.20 229	53.09 233	70.43 226	66.27 228	43.13 229	45.98 229	30.62 234	70.65 223	79.34 225	86.30 221	83.25 230	89.33 222
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		31.24 224	40.15 226	20.86 225	12.61 232	17.99 233	25.16 234	13.30 228	48.42 229	24.82 233	53.07 227	30.13 236	28.47 228	42.73 228	37.65 227	20.79 231	51.04 228

test123		26.75 225	34.25 227	18.01 226	7.93 233	17.18 234	24.85 235	12.36 229	44.83 230	16.52 234	41.80 230	18.10 237	28.29 229	33.08 229	34.79 228	18.10 232	49.95 229

GG-mvs-BLEND		69.11 220	98.13 81	35.26 224	3.49 234	98.20 166	94.89 175	2.38 230	98.42 135	5.82 235	96.37 118	98.60 67	5.97 230	98.75 66	97.98 100	99.01 193	98.61 186

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

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

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

RE-MVS-def										69.05 219

9.14												99.79 45

MTAPA										98.09 15		99.97 8

MTMP										98.46 10		99.96 12

Patchmatch-RL test							66.86 230

NP-MVS									98.57 127

Patchmtry						98.59 143	97.15 129	79.14 212		80.42 182

DeepMVS_CX						96.85 209	87.43 219	89.27 154	98.30 141	75.55 204	95.05 141	79.47 216	92.62 208	89.48 219		95.18 223	95.96 216

High-resolution multi-view benchmark