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.

ME-MVS		99.62 1	99.80 3	99.41 1	99.64 7	99.95 14	99.91 1	97.15 2	99.93 33	99.83 2	99.61 24	100.00 1	99.94 1	99.86 14	99.29 26	100.00 1	100.00 1

SED-MVS		99.44 2	99.69 22	99.15 2	99.61 14	99.95 14	99.81 8	96.94 9	99.97 9	98.73 4	99.53 31	100.00 1	99.91 5	99.90 8	98.52 60	99.87 32	100.00 1

SF-MVS		99.41 3	99.68 24	99.10 4	99.65 6	99.94 21	99.76 12	96.95 6	99.88 44	98.39 7	99.60 25	100.00 1	99.82 15	99.43 29	98.93 39	99.99 7	100.00 1

APDe-MVS		99.40 4	99.81 2	98.92 9	99.62 9	99.96 7	99.76 12	96.87 17	99.95 26	97.66 9	99.57 29	100.00 1	99.63 30	99.88 11	99.28 27	100.00 1	100.00 1
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
MSLP-MVS++		99.39 5	99.76 10	98.95 7	99.60 18	99.99 1	99.83 6	96.82 19	99.92 38	97.58 12	99.58 28	100.00 1	99.93 2	98.98 36	99.86 8	99.96 15	100.00 1

CNVR-MVS		99.39 5	99.75 13	98.98 5	99.69 1	99.95 14	99.76 12	96.91 12	99.98 3	97.59 11	99.64 21	100.00 1	99.93 2	99.94 2	98.75 53	99.97 14	99.97 97

DVP-MVS		99.38 7	99.57 38	99.15 2	99.62 9	99.94 21	99.72 25	96.99 4	99.98 3	98.85 3	98.21 85	100.00 1	99.88 9	99.88 11	98.96 37	99.85 36	100.00 1
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
MSP-MVS		99.38 7	99.78 6	98.91 12	99.61 14	99.96 7	99.85 4	96.94 9	99.96 20	97.38 15	99.60 25	100.00 1	99.70 22	99.96 1	98.96 37	100.00 1	100.00 1
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
DPE-MVS		99.37 9	99.74 16	98.94 8	99.60 18	99.94 21	99.87 3	96.95 6	99.94 30	97.42 13	99.62 23	100.00 1	99.80 18	99.91 5	98.78 51	99.98 12	100.00 1
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
DVP-MVS++		99.36 10	99.70 21	98.96 6	99.62 9	99.94 21	99.85 4	96.90 16	99.97 9	97.64 10	99.50 35	100.00 1	99.88 9	99.90 8	98.60 55	99.87 32	100.00 1

SMA-MVS		99.34 11	99.79 5	98.81 14	99.69 1	99.94 21	99.75 19	96.91 12	99.98 3	96.76 17	99.37 42	100.00 1	99.90 6	99.88 11	99.46 17	99.84 39	99.92 143
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
APD-MVS		99.33 12	99.85 1	98.73 15	99.61 14	99.92 42	99.77 11	96.91 12	99.93 33	96.31 21	99.59 27	99.95 43	99.84 13	99.73 19	99.84 9	99.95 17	100.00 1
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
NCCC		99.24 13	99.75 13	98.65 16	99.63 8	99.96 7	99.76 12	96.91 12	99.97 9	95.86 25	99.67 12	100.00 1	99.75 19	99.85 15	98.80 49	99.98 12	99.97 97

CNLPA		99.24 13	99.58 35	98.85 13	99.34 34	99.95 14	99.32 39	96.65 29	99.96 20	98.44 6	98.97 57	100.00 1	99.57 32	98.66 45	99.56 15	99.76 89	99.97 97

AdaColmap		99.21 15	99.45 41	98.92 9	99.67 4	99.95 14	99.65 30	96.77 24	99.97 9	97.67 8	100.00 1	99.69 57	99.93 2	99.26 32	97.25 117	99.85 36	100.00 1

HFP-MVS		99.19 16	99.77 9	98.51 19	99.55 22	99.94 21	99.76 12	96.84 18	99.88 44	95.27 29	99.67 12	100.00 1	99.85 12	99.56 24	99.36 21	99.79 70	99.97 97

PLC	98.06 1	99.17 17	99.38 43	98.92 9	99.47 24	99.90 51	99.48 35	96.47 34	99.96 20	98.73 4	99.52 34	100.00 1	99.55 34	98.54 58	97.73 90	99.84 39	99.99 62
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
SD-MVS		99.16 18	99.73 17	98.49 20	97.93 54	99.95 14	99.74 22	96.94 9	99.96 20	96.60 19	99.47 38	100.00 1	99.88 9	99.15 34	99.59 13	99.84 39	100.00 1
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
CP-MVS		99.14 19	99.67 25	98.53 18	99.45 26	99.94 21	99.63 32	96.62 31	99.82 57	95.92 24	99.65 17	100.00 1	99.71 21	99.76 18	98.56 57	99.83 45	100.00 1

ACMMPR		99.12 20	99.76 10	98.36 21	99.45 26	99.94 21	99.75 19	96.70 28	99.93 33	94.65 33	99.65 17	99.96 41	99.84 13	99.51 27	99.35 22	99.79 70	99.96 118

MCST-MVS		99.08 21	99.72 19	98.33 22	99.59 21	99.97 3	99.78 10	96.96 5	99.95 26	93.72 38	99.67 12	100.00 1	99.90 6	99.91 5	98.55 58	100.00 1	100.00 1

CPTT-MVS		99.08 21	99.53 40	98.57 17	99.44 28	99.93 36	99.60 33	95.92 39	99.77 65	97.01 16	99.67 12	100.00 1	99.72 20	99.56 24	97.76 86	99.70 131	99.98 81

DeepC-MVS_fast	98.03 2	99.05 23	99.78 6	98.21 25	99.47 24	99.97 3	99.75 19	96.80 20	99.97 9	93.58 40	98.68 68	99.94 44	99.69 23	99.93 4	99.95 3	99.96 15	99.98 81
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TSAR-MVS + MP.		98.99 24	99.61 32	98.27 23	97.88 55	99.92 42	99.71 27	96.80 20	99.96 20	95.58 27	98.71 67	100.00 1	99.68 25	99.91 5	98.78 51	99.99 7	100.00 1
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
HPM-MVS++		98.98 25	99.62 31	98.22 24	99.62 9	99.94 21	99.74 22	96.95 6	99.87 48	93.76 37	99.49 37	100.00 1	99.39 40	99.73 19	98.35 63	99.89 28	99.96 118

SteuartSystems-ACMMP		98.95 26	99.80 3	97.95 28	99.43 29	99.96 7	99.76 12	96.45 35	99.82 57	93.63 39	99.64 21	100.00 1	98.56 84	99.90 8	99.31 24	99.84 39	100.00 1
Skip Steuart: Steuart Systems R&D Blog.
PHI-MVS		98.85 27	99.67 25	97.89 29	98.63 49	99.93 36	98.95 49	95.20 41	99.84 55	94.94 30	99.74 11	100.00 1	99.69 23	98.40 65	99.75 11	99.93 21	99.99 62

MP-MVS		98.82 28	99.63 29	97.88 30	99.41 30	99.91 50	99.74 22	96.76 25	99.88 44	91.89 51	99.50 35	99.94 44	99.65 28	99.71 22	98.49 61	99.82 49	99.97 97
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
ACMMP_NAP		98.68 29	99.58 35	97.62 31	99.62 9	99.92 42	99.72 25	96.78 23	99.71 70	90.13 80	99.66 16	99.99 34	99.64 29	99.78 17	98.14 71	99.82 49	99.89 157

train_agg		98.62 30	99.76 10	97.28 33	99.03 42	99.93 36	99.65 30	96.37 36	99.98 3	89.24 91	99.53 31	99.83 49	99.59 31	99.85 15	99.19 31	99.80 63	100.00 1

X-MVS		98.62 30	99.75 13	97.29 32	99.50 23	99.94 21	99.71 27	96.55 32	99.85 52	88.58 96	99.65 17	99.98 36	99.67 26	99.60 23	99.26 28	99.77 81	99.97 97

OMC-MVS		98.59 32	99.07 48	98.03 27	99.41 30	99.90 51	99.26 42	94.33 43	99.94 30	96.03 22	96.68 100	99.72 56	99.42 37	98.86 39	98.84 45	99.72 124	99.58 205

DPM-MVS		98.58 33	99.78 6	97.17 35	98.02 53	99.64 84	99.80 9	96.72 27	99.96 20	90.05 82	99.57 29	100.00 1	98.66 81	99.56 24	99.96 2	99.80 63	99.80 182

PGM-MVS		98.47 34	99.73 17	97.00 37	99.68 3	99.94 21	99.76 12	91.74 49	99.84 55	91.17 67	100.00 1	99.69 57	99.81 16	99.38 30	99.30 25	99.82 49	99.95 130

MGCNet		98.44 35	99.67 25	97.00 37	97.82 57	99.92 42	99.46 36	91.78 48	99.95 26	94.10 35	100.00 1	100.00 1	98.91 67	98.59 52	99.22 29	99.95 17	99.99 62

TSAR-MVS + ACMM		98.30 36	99.64 28	96.74 41	99.08 41	99.94 21	99.67 29	96.73 26	99.97 9	86.30 122	98.30 76	99.99 34	98.78 75	99.73 19	99.57 14	99.88 31	99.98 81

CSCG		98.22 37	98.37 69	98.04 26	99.60 18	99.82 61	99.45 37	93.59 44	99.16 106	96.46 20	98.22 84	95.86 106	99.41 39	96.33 152	99.22 29	99.75 98	99.94 137

3Dnovator+	95.21 7	98.17 38	99.08 47	97.12 36	99.28 37	99.78 72	98.61 56	89.93 63	99.93 33	95.36 28	95.50 110	100.00 1	99.56 33	98.58 53	99.80 10	99.95 17	99.97 97

ACMMP		98.16 39	99.01 49	97.18 34	98.86 44	99.92 42	98.77 54	95.73 40	99.31 102	91.15 68	100.00 1	99.81 51	98.82 73	98.11 86	95.91 158	99.77 81	99.97 97
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
MVS_111021_LR		98.15 40	99.69 22	96.36 46	99.23 39	99.93 36	97.79 68	91.84 47	99.87 48	90.53 76	100.00 1	99.57 62	98.93 66	99.44 28	99.08 34	99.85 36	99.95 130

EPNet		98.11 41	99.63 29	96.34 47	98.44 51	99.88 56	98.55 57	90.25 59	99.93 33	92.60 47	100.00 1	99.73 54	98.41 89	98.87 38	99.02 35	99.82 49	99.97 97
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TSAR-MVS + GP.		98.06 42	99.55 39	96.32 48	94.72 82	99.92 42	99.22 43	89.98 61	99.97 9	94.77 32	99.94 10	100.00 1	99.43 36	98.52 62	98.53 59	99.79 70	100.00 1

3Dnovator	95.01 8	97.98 43	98.89 54	96.92 40	99.36 32	99.76 75	98.72 55	89.98 61	99.98 3	93.99 36	94.60 123	99.43 67	99.50 35	98.55 55	99.91 5	99.99 7	99.98 81

MVS_111021_HR		97.94 44	99.59 33	96.02 50	99.27 38	99.97 3	97.03 96	90.44 56	99.89 42	90.75 71	100.00 1	99.73 54	98.68 80	98.67 44	98.89 42	99.95 17	99.97 97

QAPM		97.90 45	98.89 54	96.74 41	99.35 33	99.80 67	98.84 51	90.20 60	99.94 30	92.85 42	94.17 127	99.78 52	99.42 37	98.71 42	99.87 7	99.79 70	99.98 81

CDPH-MVS		97.88 46	99.59 33	95.89 51	98.90 43	99.95 14	99.40 38	92.86 46	99.86 51	85.33 135	98.62 70	99.45 66	99.06 61	99.29 31	99.94 4	99.81 58	100.00 1

CANet		97.62 47	98.94 52	96.08 49	97.19 60	99.93 36	99.29 41	90.38 57	99.87 48	91.00 69	95.79 109	99.51 63	98.72 79	98.53 59	99.00 36	99.90 26	99.99 62

TAPA-MVS	96.62 5	97.60 48	98.46 68	96.60 44	98.73 47	99.90 51	99.30 40	94.96 42	99.46 88	87.57 107	96.05 107	98.53 79	99.26 51	98.04 91	97.33 113	99.77 81	99.88 163
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
DeepPCF-MVS	97.16 4	97.58 49	99.72 19	95.07 66	98.45 50	99.96 7	93.83 173	95.93 38	100.00 1	90.79 70	98.38 75	99.85 48	95.28 161	99.94 2	99.97 1	96.15 249	99.97 97

SPE-MVS-test		97.51 50	99.18 46	95.56 56	97.16 61	99.96 7	97.39 82	89.82 65	100.00 1	89.88 83	99.16 49	98.38 85	99.23 53	98.85 40	97.93 78	99.87 32	100.00 1

PCF-MVS	97.20 3	97.49 51	98.20 74	96.66 43	97.62 58	99.92 42	98.93 50	96.64 30	98.53 145	88.31 103	94.04 130	99.58 61	98.94 63	97.53 112	97.79 84	99.54 167	99.97 97
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
CS-MVS		97.46 52	98.98 50	95.68 55	96.74 65	99.93 36	97.62 74	89.69 66	99.98 3	91.33 64	98.53 73	97.50 93	98.77 76	98.60 51	98.35 63	99.92 23	100.00 1

MSDG		97.29 53	97.55 90	97.00 37	98.66 48	99.71 79	99.03 47	96.15 37	99.59 77	89.67 89	92.77 144	94.86 109	98.75 77	98.22 76	97.94 76	99.72 124	99.76 187

CHOSEN 280x420		97.16 54	99.58 35	94.35 81	96.95 64	99.97 3	97.19 89	81.55 181	99.92 38	91.75 58	100.00 1	100.00 1	98.84 72	98.55 55	98.65 54	99.79 70	99.97 97

DELS-MVS		97.05 55	98.05 79	95.88 53	97.09 62	99.99 1	98.82 52	90.30 58	98.44 151	91.40 62	92.91 141	96.57 99	97.68 132	98.56 54	99.88 6	100.00 1	100.00 1
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
DeepC-MVS	96.33 6	97.05 55	97.59 89	96.42 45	97.37 59	99.92 42	99.10 45	96.54 33	99.34 101	86.64 119	91.93 154	93.15 120	99.11 59	99.11 35	99.68 12	99.73 116	99.97 97
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
test2506		97.04 57	98.09 78	95.81 54	94.12 87	99.80 67	97.33 85	89.48 70	98.90 126	95.99 23	99.11 52	92.84 122	98.14 108	98.14 82	98.32 67	99.82 49	99.51 210

MAR-MVS		97.03 58	98.00 81	95.89 51	99.32 35	99.74 78	96.76 104	84.89 134	99.97 9	94.86 31	98.29 77	90.58 130	99.67 26	98.02 93	99.50 16	99.82 49	99.92 143
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
MVSTER		97.00 59	98.85 56	94.83 73	92.71 106	97.43 177	99.03 47	85.52 127	99.82 57	92.74 45	99.15 50	99.94 44	99.19 56	98.66 45	96.99 131	99.79 70	99.98 81

EC-MVSNet		96.90 60	99.32 44	94.07 83	91.64 147	99.30 124	98.18 64	85.61 126	99.97 9	89.79 84	99.33 43	99.31 70	99.28 49	98.48 64	98.86 43	99.91 24	100.00 1

baseline1		96.87 61	98.55 62	94.91 68	92.89 105	99.45 99	96.34 112	88.54 83	98.88 129	92.82 43	98.93 59	96.58 98	99.07 60	98.19 78	98.04 73	99.80 63	99.78 184

OpenMVS	94.03 11	96.87 61	98.10 77	95.44 60	99.29 36	99.78 72	98.46 62	89.92 64	99.47 87	85.78 131	91.05 161	98.50 80	99.30 47	98.49 63	99.41 18	99.89 28	99.98 81

PatchMatch-RL		96.84 63	98.03 80	95.47 57	98.84 45	99.81 65	95.61 141	89.20 74	99.65 74	91.28 65	99.39 39	93.46 118	98.18 105	98.05 89	96.28 142	99.69 136	99.55 207

ETV-MVS		96.79 64	99.19 45	94.00 85	91.78 132	99.63 86	97.15 91	88.00 89	99.95 26	88.34 102	99.32 44	98.71 76	98.82 73	98.69 43	98.01 74	99.90 26	100.00 1

IS_MVSNet		96.66 65	98.62 61	94.38 77	92.41 112	99.70 80	97.19 89	87.67 102	99.05 115	91.27 66	95.09 115	98.46 84	97.95 119	98.64 47	99.37 19	99.79 70	100.00 1

PMMVS		96.45 66	98.24 73	94.36 80	92.58 107	99.01 138	97.08 95	87.42 117	99.88 44	90.06 81	99.39 39	94.63 110	99.33 44	97.85 99	96.99 131	99.70 131	99.96 118

LS3D		96.44 67	97.31 97	95.41 61	97.06 63	99.87 57	99.51 34	97.48 1	99.57 78	79.00 159	95.39 111	89.19 136	99.81 16	98.55 55	98.84 45	99.62 156	99.78 184

EIA-MVS		96.34 68	98.55 62	93.76 90	91.93 124	99.66 82	97.14 92	88.33 87	99.51 82	85.98 127	98.82 63	96.08 104	99.33 44	98.38 68	97.40 107	99.81 58	100.00 1

EPP-MVSNet		96.29 69	98.34 70	93.90 86	91.77 134	99.38 107	95.45 146	87.25 122	99.38 97	91.36 63	94.86 121	98.49 82	97.83 125	98.01 94	98.23 69	99.75 98	99.99 62

UGNet		96.05 70	98.55 62	93.13 108	94.64 83	99.65 83	94.70 161	87.78 92	99.40 96	89.69 88	98.25 80	99.25 72	92.12 202	96.50 144	97.08 126	99.84 39	99.72 195
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
COLMAP_ROB	93.56 12	96.03 71	96.83 111	95.11 65	97.87 56	99.52 90	98.81 53	91.40 52	99.42 92	84.97 138	90.46 166	96.82 97	98.05 113	96.46 148	96.19 145	99.54 167	98.92 225
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
PVSNet_BlendedMVS		96.01 72	96.48 120	95.46 58	96.47 67	99.89 54	95.64 138	91.23 53	99.75 67	91.59 60	96.80 97	82.44 170	98.05 113	98.53 59	97.92 79	99.80 63	100.00 1

PVSNet_Blended		96.01 72	96.48 120	95.46 58	96.47 67	99.89 54	95.64 138	91.23 53	99.75 67	91.59 60	96.80 97	82.44 170	98.05 113	98.53 59	97.92 79	99.80 63	100.00 1

thisisatest0530		95.89 74	98.32 71	93.06 115	91.76 135	99.75 76	94.94 154	87.60 107	99.91 40	86.66 118	98.28 78	99.98 36	97.72 128	97.10 127	93.24 194	99.65 148	99.95 130

tttt0517		95.88 75	98.31 72	93.04 116	91.75 137	99.75 76	94.90 155	87.60 107	99.91 40	86.63 120	98.28 78	99.98 36	97.72 128	97.10 127	93.24 194	99.65 148	99.95 130

thres100view900		95.86 76	96.62 114	94.97 67	93.10 95	99.83 59	97.76 69	89.15 75	98.62 141	90.69 72	99.00 54	84.86 157	99.30 47	97.57 110	96.48 137	99.81 58	100.00 1

RPSCF		95.86 76	96.94 109	94.61 75	96.52 66	98.67 154	98.54 58	88.43 85	99.56 79	90.51 78	99.39 39	98.70 77	97.72 128	93.77 201	92.00 211	95.93 250	96.50 244

DCV-MVSNet		95.85 78	97.53 91	93.89 87	93.20 94	97.01 183	97.14 92	84.77 135	99.16 106	90.38 79	98.96 58	93.73 115	98.23 104	96.57 143	97.37 108	99.64 152	99.93 139

baseline		95.85 78	98.13 76	93.20 106	92.29 115	99.58 88	97.49 76	84.33 144	99.44 89	87.28 112	97.00 96	94.04 114	97.93 120	98.36 70	98.47 62	99.87 32	99.99 62

sasdasda		95.80 80	97.02 102	94.37 78	92.96 101	99.47 95	97.49 76	84.58 137	99.44 89	92.05 49	98.54 71	86.65 144	99.37 41	96.18 155	98.93 39	99.77 81	99.92 143

canonicalmvs		95.80 80	97.02 102	94.37 78	92.96 101	99.47 95	97.49 76	84.58 137	99.44 89	92.05 49	98.54 71	86.65 144	99.37 41	96.18 155	98.93 39	99.77 81	99.92 143

tfpn200view9		95.78 82	96.54 117	94.89 70	93.10 95	99.82 61	97.67 70	88.85 78	98.62 141	90.69 72	99.00 54	84.86 157	99.28 49	97.41 120	96.10 148	99.76 89	99.99 62

thres200		95.77 83	96.55 116	94.86 71	93.09 97	99.82 61	97.63 73	88.85 78	98.49 146	90.66 74	98.99 56	84.86 157	99.20 54	97.41 120	96.28 142	99.76 89	100.00 1

MVS_Test		95.74 84	98.18 75	92.90 120	92.16 116	99.49 94	97.36 83	84.30 145	99.79 62	84.94 139	96.65 101	93.63 117	98.85 71	98.61 50	99.10 33	99.81 58	100.00 1

thres400		95.72 85	96.48 120	94.84 72	93.00 100	99.83 59	97.55 75	88.93 76	98.49 146	90.61 75	98.86 60	84.63 161	99.20 54	97.45 114	96.10 148	99.77 81	99.99 62

MGCFI-Net		95.71 86	96.97 108	94.25 82	92.90 104	99.44 102	97.35 84	84.44 142	99.42 92	91.70 59	98.51 74	86.56 147	99.33 44	96.09 160	98.83 47	99.77 81	99.92 143

thres600view7		95.64 87	96.38 124	94.79 74	92.96 101	99.82 61	97.48 81	88.85 78	98.38 152	90.52 77	98.84 62	84.61 162	99.15 57	97.41 120	95.60 163	99.76 89	99.99 62

Vis-MVSNet (Re-imp)		95.60 88	98.52 67	92.19 130	92.37 113	99.56 89	96.37 110	87.41 118	98.95 121	84.77 142	94.88 120	98.48 83	92.44 199	98.63 49	99.37 19	99.76 89	99.77 186

FMVSNet3		95.59 89	97.51 93	93.34 100	89.48 171	96.57 191	97.67 70	84.17 147	99.48 84	89.76 85	95.09 115	94.35 111	99.14 58	98.37 69	98.86 43	99.82 49	99.89 157

ECVR-MVS		95.46 90	95.58 144	95.31 63	94.12 87	99.80 67	97.33 85	89.48 70	98.90 126	92.99 41	87.97 180	86.41 149	98.14 108	98.14 82	98.32 67	99.82 49	99.52 209

E2		95.42 91	96.83 111	93.78 89	91.73 139	99.38 107	96.39 109	87.87 90	98.79 132	88.36 101	95.90 108	88.17 138	98.59 83	97.72 102	97.85 81	99.75 98	99.98 81

PVSNet_Blended_VisFu		95.37 92	97.44 95	92.95 117	95.20 75	99.80 67	92.68 182	88.41 86	99.12 109	87.64 106	88.31 179	99.10 73	94.07 177	98.27 73	97.51 100	99.73 116	100.00 1

DI_MVS_pp		95.29 93	97.02 102	93.28 102	91.76 135	99.52 90	97.84 67	85.67 125	99.08 113	87.29 111	87.76 183	97.46 94	97.31 136	97.83 100	97.48 101	99.83 45	100.00 1

ET-MVSNet_ETH3D		95.20 94	97.82 86	92.15 131	80.77 237	98.13 165	97.65 72	86.93 123	99.72 69	88.56 99	99.29 47	97.01 96	99.24 52	94.58 188	95.98 155	99.75 98	99.99 62

TSAR-MVS + COLMAP		95.20 94	95.03 153	95.41 61	96.17 69	98.69 153	99.11 44	93.40 45	99.97 9	84.89 140	98.23 82	75.01 207	99.34 43	97.27 125	96.37 141	99.58 160	99.64 203

GBi-Net		95.19 96	96.99 106	93.09 110	89.11 172	96.47 193	96.90 97	84.17 147	99.48 84	89.76 85	95.09 115	94.35 111	98.87 68	96.50 144	97.21 118	99.74 103	99.81 179

test1		95.19 96	96.99 106	93.09 110	89.11 172	96.47 193	96.90 97	84.17 147	99.48 84	89.76 85	95.09 115	94.35 111	98.87 68	96.50 144	97.21 118	99.74 103	99.81 179

test1111		95.15 98	95.18 150	95.12 64	94.07 89	99.80 67	97.20 88	89.53 69	98.80 131	92.22 48	85.44 194	86.24 151	97.89 121	98.12 84	98.34 66	99.80 63	99.51 210

test0.0.03 1		95.15 98	97.87 85	91.99 132	91.69 141	98.82 149	93.04 179	83.60 152	99.65 74	88.80 94	94.15 128	97.67 91	94.97 163	96.62 141	98.16 70	99.83 45	100.00 1

baseline2		95.13 100	98.55 62	91.15 138	90.29 167	99.00 139	94.49 165	82.00 175	99.68 72	84.82 141	96.47 102	99.30 71	95.71 155	98.24 75	97.14 124	99.57 162	100.00 1

viewcassd2359sk11		95.10 101	96.36 125	93.63 91	91.68 144	99.37 111	96.09 121	87.78 92	98.72 134	88.01 104	94.74 122	86.41 149	98.47 87	97.69 104	97.61 96	99.73 116	99.98 81

casdiffmvs_mvg		95.10 101	96.45 123	93.53 93	92.05 119	99.42 104	97.25 87	87.66 103	97.17 181	86.09 123	91.79 156	91.27 124	98.31 99	98.06 88	97.42 106	99.81 58	100.00 1
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
EPNet_dtu		95.10 101	98.81 58	90.78 140	98.38 52	98.47 156	96.54 106	89.36 72	99.78 64	65.65 219	99.31 45	98.24 87	94.79 166	98.28 72	99.35 22	99.93 21	98.27 229
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Anonymous20231211		94.96 104	94.99 154	94.91 68	93.01 99	99.44 102	96.85 101	88.49 84	98.78 133	92.61 46	83.94 201	90.25 132	98.94 63	95.87 166	96.77 133	99.58 160	99.89 157

UA-Net		94.95 105	98.66 60	90.63 142	94.60 85	98.94 145	96.03 122	85.28 129	98.01 165	78.92 160	97.42 94	99.96 41	89.09 226	98.95 37	98.80 49	99.82 49	98.57 227

CANet_DTU		94.90 106	98.98 50	90.13 150	94.74 81	99.81 65	98.53 59	82.23 174	99.97 9	66.76 216	100.00 1	98.50 80	98.74 78	97.52 113	97.19 123	99.76 89	99.88 163

viewdifsd2359ckpt09		94.88 107	96.22 127	93.31 101	91.61 149	99.38 107	96.37 110	87.74 94	98.82 130	85.85 128	93.69 134	86.65 144	98.61 82	97.57 110	97.44 104	99.72 124	100.00 1

viewdifsd2359ckpt07		94.83 108	96.18 132	93.25 103	91.96 123	99.31 122	97.10 94	87.65 104	98.66 139	85.26 136	91.50 158	88.11 139	97.77 127	98.16 79	97.69 92	99.74 103	99.84 174

viewdifsd2359ckpt13		94.69 109	96.20 130	92.93 119	91.67 146	99.42 104	95.73 135	87.71 96	98.67 137	84.46 143	94.31 125	86.03 153	98.27 103	97.60 107	97.35 111	99.73 116	99.99 62

E3new		94.68 110	95.67 142	93.52 95	91.63 148	99.36 113	95.96 125	87.69 100	97.81 170	87.65 105	93.38 137	84.22 166	98.48 86	97.44 115	97.52 98	99.71 127	99.96 118

E3		94.68 110	95.70 139	93.49 96	91.68 144	99.37 111	95.98 124	87.70 97	97.97 166	87.46 108	93.38 137	84.35 164	98.42 88	97.43 116	97.47 102	99.71 127	99.96 118

viewmanbaseed2359cas		94.61 112	95.93 136	93.07 114	91.90 126	99.38 107	96.32 113	87.84 91	98.33 156	84.29 144	92.71 145	85.68 155	98.33 98	97.68 105	97.74 89	99.74 103	99.99 62

FC-MVSNet-train		94.61 112	96.27 126	92.68 127	92.35 114	97.14 181	93.45 177	87.73 95	98.93 122	87.31 110	96.42 103	89.35 134	95.67 156	96.06 164	96.01 154	99.56 164	99.98 81

diffmvs		94.60 114	95.63 143	93.41 98	91.98 122	99.30 124	96.86 100	87.62 106	99.30 103	86.07 126	94.12 129	81.63 180	98.16 106	97.43 116	97.60 97	99.76 89	100.00 1
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
casdiffmvs		94.54 115	95.56 145	93.36 99	91.84 128	99.46 98	95.92 126	87.54 111	98.45 149	86.57 121	90.51 165	84.72 160	98.49 85	97.97 95	97.80 83	99.77 81	100.00 1
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
CLD-MVS		94.53 116	94.45 168	94.61 75	93.85 91	98.36 158	98.12 65	89.68 67	99.35 100	89.62 90	95.19 113	77.08 197	96.66 146	95.51 171	95.67 161	99.74 103	100.00 1
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
viewmambaseed2359dif		94.51 117	95.35 148	93.53 93	91.78 132	99.34 115	96.78 103	87.58 110	98.29 157	86.97 116	92.34 147	84.00 167	98.35 95	96.15 158	97.31 116	99.74 103	100.00 1

FMVSNet2		94.48 118	95.95 134	92.77 125	89.11 172	96.47 193	96.90 97	83.38 155	99.11 110	88.64 95	87.50 188	92.26 123	98.87 68	97.91 97	98.60 55	99.74 103	99.81 179

HQP-MVS		94.48 118	95.39 147	93.42 97	95.10 76	98.35 159	98.19 63	91.41 51	99.77 65	79.79 156	99.30 46	77.08 197	96.25 149	96.93 129	96.28 142	99.76 89	99.99 62

FA-MVS(training)		94.33 120	97.52 92	90.60 144	92.42 111	99.77 74	96.13 120	68.75 234	99.05 115	88.49 100	91.95 152	99.48 64	98.12 111	98.39 66	94.02 186	99.68 138	99.98 81

MDTV_nov1_ep13		94.32 121	98.77 59	89.14 160	91.70 140	99.52 90	95.21 149	72.09 232	99.80 60	78.91 161	96.32 104	99.62 59	97.71 131	98.39 66	97.71 91	99.22 217	100.00 1

CDS-MVSNet		94.32 121	97.00 105	91.19 137	89.82 170	98.71 152	95.51 143	85.14 133	96.85 187	82.33 151	92.48 146	96.40 102	94.71 167	96.86 132	97.76 86	99.63 154	99.92 143
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
dps		94.29 123	97.33 96	90.75 141	92.02 120	99.21 129	94.31 167	66.97 240	99.50 83	95.61 26	96.22 106	98.64 78	96.08 151	93.71 203	94.03 185	99.52 171	99.98 81

ACMM	94.44 10	94.26 124	94.62 164	93.84 88	94.86 80	97.73 172	93.48 176	90.76 55	99.27 104	87.46 108	99.04 53	76.60 199	96.76 144	96.37 151	93.76 189	99.74 103	99.55 207
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
diffmvs_AUTHOR		94.21 125	95.08 151	93.18 107	91.86 127	99.26 128	96.42 107	87.48 112	99.02 118	85.45 134	92.20 149	80.25 190	98.14 108	97.16 126	97.69 92	99.73 116	100.00 1

ACMP	94.49 9	94.19 126	94.74 162	93.56 92	94.25 86	98.32 161	96.02 123	89.35 73	98.90 126	87.28 112	99.14 51	76.41 202	94.94 164	96.07 163	94.35 182	99.49 178	99.99 62
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
E5new		94.12 127	94.89 156	93.22 104	91.52 151	99.34 115	95.92 126	87.70 97	97.17 181	86.08 124	91.24 159	82.32 172	98.41 89	96.85 133	97.36 109	99.68 138	99.96 118

E5		94.12 127	94.89 156	93.22 104	91.52 151	99.34 115	95.92 126	87.70 97	97.17 181	86.08 124	91.24 159	82.32 172	98.41 89	96.85 133	97.36 109	99.68 138	99.96 118

EPMVS		94.08 129	98.54 66	88.87 161	92.51 109	99.47 95	94.18 169	66.53 241	99.68 72	82.40 150	95.24 112	99.40 68	97.86 122	98.12 84	97.99 75	99.75 98	99.88 163

E6new		93.99 130	94.76 159	93.09 110	91.51 153	99.33 120	95.80 131	87.45 115	97.13 184	85.80 129	90.97 162	81.86 177	98.30 100	96.74 137	97.32 114	99.67 142	99.95 130

E6		93.99 130	94.76 159	93.09 110	91.51 153	99.33 120	95.80 131	87.45 115	97.13 184	85.80 129	90.97 162	81.86 177	98.30 100	96.74 137	97.32 114	99.67 142	99.95 130

E4		93.99 130	94.72 163	93.13 108	91.53 150	99.34 115	95.92 126	87.59 109	97.20 179	85.67 132	90.19 167	82.18 174	98.41 89	96.83 135	97.34 112	99.68 138	99.96 118

viewmacassd2359aftdt		93.75 133	94.76 159	92.58 128	91.75 137	99.34 115	95.82 130	87.64 105	97.11 186	82.51 149	89.66 170	83.19 168	98.02 116	96.61 142	97.45 103	99.71 127	99.97 97

test-LLR		93.71 134	97.23 98	89.60 154	91.69 141	99.10 135	94.68 163	83.60 152	99.36 98	71.94 193	93.82 132	96.51 100	95.96 153	97.42 118	94.37 179	99.74 103	99.99 62

CHOSEN 1792x2688		93.69 135	94.89 156	92.28 129	96.17 69	99.84 58	95.69 137	83.17 158	98.54 144	82.04 152	77.58 233	91.15 126	96.90 139	98.36 70	98.82 48	99.73 116	99.98 81

viewdifsd2359ckpt11		93.64 136	94.30 171	92.88 122	91.82 130	98.82 149	94.88 156	87.46 113	99.08 113	86.98 115	92.20 149	80.79 181	97.85 123	93.32 211	96.13 146	98.30 232	99.75 189

viewmsd2359difaftdt		93.64 136	94.29 172	92.89 121	91.82 130	98.82 149	94.88 156	87.46 113	99.04 117	87.03 114	92.20 149	80.78 182	97.85 123	93.31 212	96.13 146	98.30 232	99.75 189

LGP-MVS_train		93.60 138	95.05 152	91.90 133	94.90 79	98.29 162	97.93 66	88.06 88	99.14 108	74.83 178	99.26 48	76.50 200	96.07 152	96.31 153	95.90 160	99.59 158	99.97 97

SCA		93.53 139	98.90 53	87.27 181	92.01 121	99.30 124	93.43 178	65.72 245	99.80 60	75.20 177	97.66 92	99.74 53	97.44 134	98.21 77	97.62 95	99.84 39	100.00 1

FMVSNet5		93.53 139	96.09 133	90.56 145	86.74 187	92.84 237	92.64 183	77.50 209	99.41 95	88.97 93	98.02 88	97.81 89	98.00 117	94.85 183	95.43 165	99.50 177	94.25 249

OPM-MVS		93.50 141	93.00 185	94.07 83	95.82 72	98.26 163	98.49 61	91.62 50	94.69 208	81.93 153	92.82 143	76.18 204	96.82 141	96.12 159	94.57 173	99.74 103	98.39 228
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
CostFormer		93.50 141	96.50 119	90.00 151	91.69 141	98.65 155	93.88 172	67.64 238	98.97 119	89.16 92	97.79 90	88.92 137	97.97 118	95.14 180	96.06 150	99.63 154	100.00 1

IterMVS-LS		93.50 141	96.22 127	90.33 148	90.93 158	95.50 221	94.83 159	80.54 185	98.92 123	79.11 158	90.64 164	93.70 116	96.79 142	96.93 129	97.85 81	99.78 77	99.99 62
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PatchmatchNet		93.48 144	98.84 57	87.22 182	91.93 124	99.39 106	92.55 184	66.06 243	99.71 70	75.61 173	98.24 81	99.59 60	97.35 135	97.87 98	97.64 94	99.83 45	99.43 213
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MS-PatchMatch		93.46 145	95.91 137	90.61 143	95.48 73	99.31 122	95.62 140	77.23 211	99.42 92	81.88 154	88.92 176	96.06 105	93.80 179	96.45 150	93.11 199	99.65 148	98.10 234

0.3-1-1-0.015		93.45 146	93.88 175	92.95 117	85.17 203	95.96 206	96.24 118	87.68 101	97.58 173	91.83 52	98.67 69	80.39 184	98.94 63	88.61 235	96.06 150	97.85 236	99.90 152

0.4-1-1-0.2		93.35 147	93.81 176	92.80 123	85.14 205	95.96 206	96.25 116	87.39 119	97.58 173	91.79 56	98.23 82	80.39 184	98.39 93	88.57 236	96.06 150	97.85 236	99.91 150

dmvs_re		93.34 148	94.59 165	91.88 134	87.97 183	99.14 134	95.29 148	88.61 81	98.09 162	82.71 148	97.34 95	78.96 191	96.98 137	94.62 186	93.98 187	99.73 116	99.98 81

0.4-1-1-0.1		93.31 149	93.77 177	92.77 125	85.13 206	95.94 209	96.21 119	87.29 120	97.58 173	91.79 56	98.11 87	80.39 184	98.36 94	88.54 237	95.98 155	97.82 239	99.89 157

tpm cat1		93.29 150	96.53 118	89.50 156	91.84 128	99.18 132	94.70 161	67.70 237	98.38 152	86.67 117	89.16 173	99.38 69	96.66 146	94.33 189	95.30 166	99.43 195	100.00 1

casdiffseed414692147		93.14 151	93.44 180	92.79 124	91.46 155	99.20 130	95.06 152	87.27 121	96.60 190	85.16 137	87.25 189	77.77 194	98.09 112	96.80 136	96.57 135	99.67 142	99.90 152

Effi-MVS+-dtu		93.13 152	97.13 100	88.47 170	88.86 178	99.19 131	96.79 102	79.08 198	99.64 76	70.01 203	97.51 93	89.38 133	96.53 148	97.60 107	96.55 136	99.57 162	100.00 1

HyFIR lowres test		93.13 152	94.48 167	91.56 135	96.12 71	99.68 81	93.52 175	79.98 189	97.24 178	81.73 155	72.66 242	95.74 107	98.29 102	98.27 73	97.79 84	99.70 131	100.00 1

Vis-MVSNet		93.08 154	96.76 113	88.78 165	91.14 157	99.63 86	94.85 158	83.34 156	97.19 180	74.78 179	91.92 155	93.15 120	88.81 229	97.59 109	98.35 63	99.78 77	99.49 212
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
Effi-MVS+		93.06 155	95.94 135	89.70 153	90.82 159	99.45 99	95.71 136	78.94 199	98.72 134	74.71 180	97.92 89	80.73 183	98.35 95	97.72 102	97.05 129	99.70 131	100.00 1

ADS-MVSNet		92.91 156	97.97 82	87.01 184	92.07 118	99.27 127	92.70 181	65.39 248	99.85 52	75.40 174	94.93 119	98.26 86	96.86 140	96.09 160	97.52 98	99.65 148	99.84 174

GeoE		92.88 157	95.20 149	90.18 149	90.59 163	99.18 132	96.31 114	78.36 204	97.52 177	78.53 163	87.11 190	88.01 140	97.63 133	97.79 101	96.76 134	99.66 146	100.00 1

TESTMET0.1,1		92.87 158	97.23 98	87.79 177	86.96 186	99.10 135	94.68 163	77.46 210	99.36 98	71.94 193	93.82 132	96.51 100	95.96 153	97.42 118	94.37 179	99.74 103	99.99 62

FC-MVSNet-test		92.78 159	96.19 131	88.80 164	88.00 182	97.54 174	93.60 174	82.36 173	98.16 158	79.71 157	91.55 157	95.41 108	89.65 221	96.09 160	95.23 167	99.49 178	99.31 216

Fast-Effi-MVS+-dtu		92.73 160	97.62 88	87.02 183	88.91 176	98.83 148	95.79 133	73.98 226	99.89 42	68.62 208	97.73 91	93.30 119	95.21 162	97.67 106	95.96 157	99.59 158	100.00 1

IB-MVS	90.59 15	92.70 161	95.70 139	89.21 159	94.62 84	99.45 99	83.77 238	88.92 77	99.53 80	92.82 43	98.86 60	86.08 152	75.24 251	92.81 219	93.17 197	99.89 28	100.00 1
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
test-mter		92.67 162	97.13 100	87.47 180	86.72 188	99.07 137	94.28 168	76.90 212	99.21 105	71.53 197	93.63 135	96.32 103	95.67 156	97.32 123	94.36 181	99.74 103	99.99 62

RPMNet		92.64 163	97.88 84	86.53 189	90.79 160	98.95 143	95.13 150	64.44 252	99.09 111	72.36 189	93.58 136	99.01 74	96.74 145	98.05 89	96.45 139	99.71 127	100.00 1

FMVSNet1		92.55 164	93.66 179	91.26 136	87.91 184	96.12 200	94.75 160	81.69 180	97.67 171	85.63 133	80.56 218	87.88 142	98.15 107	96.50 144	97.21 118	99.41 200	99.71 198

tpmrst		92.52 165	97.45 94	86.77 187	92.15 117	99.36 113	92.53 185	65.95 244	99.53 80	72.50 187	92.22 148	99.83 49	97.81 126	95.18 179	96.05 153	99.69 136	100.00 1

testgi		92.47 166	95.68 141	88.73 166	90.68 161	98.35 159	91.67 192	79.50 194	98.96 120	77.12 169	95.17 114	85.84 154	93.95 178	95.75 169	96.47 138	99.45 190	99.21 219

TAMVS		92.43 167	94.21 173	90.35 147	88.68 179	98.85 147	94.15 170	81.53 182	95.58 198	83.61 146	87.05 191	86.45 148	94.71 167	96.27 154	95.91 158	99.42 198	99.38 215

CR-MVSNet		92.32 168	97.97 82	85.74 198	90.63 162	98.95 143	95.46 144	65.50 246	99.09 111	67.51 212	94.20 126	98.18 88	95.59 159	98.16 79	97.20 121	99.74 103	100.00 1

CVMVSNet		92.13 169	95.40 146	88.32 173	91.29 156	97.29 179	91.85 189	86.42 124	96.71 189	71.84 195	89.56 171	91.18 125	88.98 228	96.17 157	97.76 86	99.51 175	99.14 221

Fast-Effi-MVS+		92.11 170	94.33 169	89.52 155	89.06 175	99.00 139	95.13 150	76.72 214	98.59 143	78.21 165	89.99 168	77.35 196	98.34 97	97.97 95	97.44 104	99.67 142	99.96 118

ACMH+	92.61 13	91.80 171	93.03 183	90.37 146	93.03 98	98.17 164	94.00 171	84.13 150	98.12 160	77.39 167	91.95 152	74.62 211	94.36 174	94.62 186	93.82 188	99.32 209	99.87 168

IterMVS-SCA-FT		91.75 172	96.87 110	85.78 196	90.34 165	95.93 210	95.06 152	73.85 227	98.91 124	61.01 232	89.21 172	98.87 75	94.66 170	98.09 87	97.12 125	99.76 89	99.99 62

IterMVS		91.65 173	96.62 114	85.85 195	90.27 168	95.80 212	95.32 147	74.15 223	98.91 124	60.95 233	88.79 178	97.76 90	94.69 169	98.04 91	97.07 127	99.73 116	100.00 1
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ACMH	92.34 14	91.59 174	93.02 184	89.92 152	93.97 90	97.98 169	90.10 213	84.70 136	98.46 148	76.80 170	93.38 137	71.94 223	94.39 172	95.34 175	94.04 184	99.54 167	100.00 1
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs4		91.41 175	93.05 182	89.49 157	85.85 196	96.52 192	91.70 191	82.49 170	98.14 159	83.17 147	87.57 185	81.76 179	94.39 172	95.47 172	92.62 205	99.33 207	99.29 217

blend_shiyan4		91.06 176	91.01 197	91.13 139	85.43 198	91.84 240	96.41 108	82.84 166	97.61 172	91.83 52	98.80 64	80.39 184	92.83 190	85.75 241	82.95 242	97.42 240	99.73 191

PatchT		91.06 176	97.66 87	83.36 225	90.32 166	98.96 142	82.30 243	64.72 251	98.45 149	67.51 212	93.28 140	97.60 92	95.59 159	98.16 79	97.20 121	99.70 131	100.00 1

usedtu_dtu_shiyan1		91.03 178	93.73 178	87.88 176	80.10 239	96.73 187	93.00 180	84.24 146	97.91 168	77.39 167	84.98 195	87.83 143	93.08 186	95.84 167	93.18 196	99.46 186	99.63 204

MIMVSNet		91.01 179	96.22 127	84.93 207	85.24 201	98.09 166	90.40 208	64.96 250	97.55 176	72.65 185	96.23 105	90.81 128	96.79 142	96.69 139	97.06 128	99.52 171	97.09 241

UniMVSNet_NR-MVSNet		90.50 180	92.31 188	88.38 171	85.04 210	96.34 196	90.94 195	85.32 128	95.87 197	75.69 171	87.68 184	78.49 192	93.78 180	93.21 214	94.60 172	99.53 170	99.97 97

UniMVSNet (Re)		90.41 181	91.96 190	88.59 169	85.71 197	96.73 187	90.82 198	84.11 151	95.23 204	78.54 162	88.91 177	76.41 202	92.84 189	93.40 210	93.05 200	99.55 166	100.00 1

GA-MVS		90.38 182	94.59 165	85.46 202	88.30 181	98.44 157	92.18 186	83.30 157	97.89 169	58.05 242	92.86 142	84.25 165	91.27 212	96.65 140	92.61 206	99.66 146	99.43 213

USDC		90.36 183	91.68 191	88.82 163	92.58 107	98.02 167	96.27 115	79.83 190	98.37 154	70.61 202	89.05 174	67.50 240	94.17 175	95.77 168	94.43 177	99.46 186	98.62 226

thisisatest0515		90.28 184	94.32 170	85.57 201	85.23 202	97.23 180	85.44 234	83.09 159	96.80 188	72.41 188	89.82 169	90.87 127	87.93 234	95.27 178	90.39 228	99.33 207	99.88 163

TinyColmap		89.94 185	90.88 198	88.84 162	92.43 110	97.91 170	95.59 142	80.10 188	98.12 160	71.33 199	84.56 197	67.46 241	94.15 176	95.57 170	94.27 183	99.43 195	98.26 230

pm-mvs1		89.68 186	92.00 189	86.96 185	86.23 192	96.62 190	90.36 209	83.05 160	93.97 216	72.15 192	81.77 213	82.10 175	90.69 218	95.38 174	94.50 175	99.29 213	99.65 201

tpm		89.60 187	94.93 155	83.39 223	89.94 169	97.11 182	90.09 214	65.28 249	98.67 137	60.03 237	96.79 99	84.38 163	95.66 158	91.90 223	95.65 162	99.32 209	99.98 81

NR-MVSNet		89.52 188	90.71 199	88.14 175	86.19 193	96.20 198	92.07 187	84.58 137	95.54 199	75.27 176	87.52 186	67.96 238	91.24 213	94.33 189	93.45 192	99.49 178	99.97 97

DU-MVS		89.49 189	90.60 200	88.19 174	84.71 214	96.20 198	90.94 195	84.58 137	95.54 199	75.69 171	87.52 186	68.74 237	93.78 180	91.10 228	95.13 169	99.47 184	99.97 97

usedtu_blend_shiyan5		89.34 190	89.98 205	88.60 168	70.40 248	91.71 243	96.25 116	82.93 162	90.83 238	91.83 52	98.80 64	80.39 184	92.83 190	85.63 242	82.75 243	97.39 241	99.73 191

Baseline_NR-MVSNet		89.13 191	89.53 214	88.66 167	84.71 214	94.43 229	91.79 190	84.49 141	95.54 199	78.28 164	78.52 230	72.46 222	93.29 184	91.10 228	94.82 171	99.42 198	99.86 171

tfpnnormal		89.09 192	89.71 208	88.38 171	87.37 185	96.78 186	91.46 193	85.20 131	90.33 244	72.35 190	83.45 206	69.30 235	94.45 171	95.29 176	92.86 202	99.44 194	99.93 139

FE-MVSNET3		88.92 193	89.98 205	87.69 178	70.40 248	91.71 243	90.75 200	82.93 162	90.83 238	91.83 52	98.80 64	80.39 184	92.83 190	85.63 242	82.75 243	97.39 241	99.72 195

TranMVSNet+NR-MVSNet		88.88 194	89.90 207	87.69 178	84.06 226	95.68 213	91.88 188	85.23 130	95.16 205	72.54 186	83.06 209	70.14 232	92.93 188	90.81 231	94.53 174	99.48 182	99.89 157

WR-MVS_H		88.47 195	90.55 201	86.04 191	85.13 206	96.07 202	89.86 220	79.80 191	94.37 213	72.32 191	83.12 208	74.44 215	89.60 222	93.52 207	92.40 207	99.51 175	99.96 118

SixPastTwentyTwo		88.35 196	91.51 193	84.66 209	85.39 200	96.96 184	86.57 230	79.62 193	96.57 191	63.73 226	87.86 182	75.18 206	93.43 183	94.03 193	90.37 229	99.24 216	99.58 205

TransMVSNet (Re)		88.33 197	89.55 213	86.91 186	86.65 189	95.56 218	90.48 206	84.44 142	92.02 236	71.07 201	80.13 220	72.48 221	89.41 223	95.05 182	94.44 176	99.39 202	97.14 240

MVS-HIRNet		88.27 198	94.05 174	81.51 231	88.90 177	98.93 146	83.38 240	60.52 259	98.06 163	63.78 225	80.67 217	90.36 131	92.94 187	97.29 124	96.41 140	99.56 164	96.66 243

WR-MVS		88.23 199	90.15 203	86.00 193	84.39 221	95.64 214	89.96 217	81.80 177	94.46 211	71.60 196	82.10 211	74.36 216	88.76 230	92.48 220	92.20 209	99.46 186	99.83 177

CP-MVSNet		88.09 200	89.57 211	86.36 190	84.63 217	95.46 223	89.48 222	80.53 186	93.42 223	71.26 200	81.25 215	69.90 233	92.78 193	93.30 213	93.69 190	99.47 184	99.96 118

pmnet_mix02		88.07 201	92.32 187	83.10 226	86.14 194	96.23 197	81.90 246	83.05 160	98.04 164	57.59 245	84.93 196	82.02 176	90.87 217	93.54 206	91.53 220	99.06 225	99.97 97

UniMVSNet_ETH3D		88.05 202	87.01 235	89.27 158	88.53 180	97.49 175	90.35 210	83.48 154	94.57 209	77.87 166	70.08 246	61.75 252	96.22 150	90.17 232	95.21 168	99.16 221	99.82 178

anonymousdsp		87.98 203	92.38 186	82.85 227	83.68 230	96.79 185	90.78 199	74.06 225	95.29 203	57.91 244	83.33 207	83.12 169	91.15 215	95.96 165	92.37 208	99.52 171	99.76 187

LTVRE_ROB	88.65 16	87.87 204	91.11 196	84.10 220	86.64 190	97.47 176	94.40 166	78.41 203	96.13 195	52.02 253	87.95 181	65.92 246	93.59 182	95.29 176	95.09 170	99.52 171	99.95 130
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
V42		87.84 205	89.42 216	85.99 194	85.16 204	96.01 204	90.52 205	81.78 179	94.43 212	67.59 210	81.32 214	71.87 224	91.48 210	91.25 227	91.16 224	99.43 195	99.92 143

TDRefinement		87.79 206	88.76 223	86.66 188	93.54 92	98.02 167	95.76 134	85.18 132	96.57 191	67.90 209	80.51 219	66.51 245	78.37 247	93.20 215	89.73 230	99.22 217	96.75 242

MDTV_nov1_ep13_2view		87.75 207	93.32 181	81.26 233	83.74 229	96.64 189	85.66 233	66.20 242	98.36 155	61.61 230	84.34 199	87.95 141	91.12 216	94.01 194	92.66 204	99.22 217	99.27 218

v8		87.54 208	89.33 217	85.45 203	85.41 199	95.50 221	90.32 211	78.94 199	94.35 214	66.93 215	81.90 212	70.99 229	91.62 208	91.49 226	91.22 223	99.48 182	99.87 168

v1144		87.49 209	89.64 209	84.97 206	84.73 213	95.84 211	90.17 212	79.30 195	93.96 217	64.65 223	78.83 227	73.38 220	91.51 209	93.77 201	91.77 215	99.45 190	99.93 139

v2v482		87.46 210	88.90 221	85.78 196	84.58 218	95.95 208	89.90 219	82.43 172	94.19 215	65.65 219	79.80 222	69.12 236	92.67 194	91.88 224	91.46 221	99.45 190	99.93 139

v10		87.40 211	89.62 210	84.80 208	84.93 211	95.07 227	90.44 207	75.63 219	94.51 210	66.52 217	78.87 226	73.47 219	91.86 206	93.69 204	91.87 214	99.45 190	99.86 171

pmmvs5		87.33 212	90.01 204	84.20 218	84.31 223	96.04 203	87.63 228	76.59 215	93.17 228	65.35 222	84.30 200	71.68 225	91.91 205	95.41 173	91.37 222	99.39 202	98.13 232

N_pmnet		87.31 213	91.51 193	82.41 230	85.13 206	95.57 217	80.59 248	81.79 178	96.20 193	58.52 241	78.62 228	85.66 156	89.36 224	94.64 185	92.14 210	99.08 224	97.72 238

PS-CasMVS		87.24 214	88.52 226	85.73 199	84.58 218	95.35 225	89.03 225	80.17 187	93.11 229	68.86 207	77.71 232	66.89 242	92.30 200	93.13 216	93.50 191	99.46 186	99.96 118

EU-MVSNet		87.20 215	90.47 202	83.38 224	85.11 209	93.85 234	86.10 232	79.76 192	93.30 227	65.39 221	84.41 198	78.43 193	85.04 242	92.20 222	93.03 201	98.86 227	98.05 235

PEN-MVS		87.20 215	88.22 227	86.01 192	84.01 228	94.93 228	90.00 216	81.52 184	93.46 222	69.29 205	79.69 223	65.51 247	91.72 207	91.01 230	93.12 198	99.49 178	99.84 174

EG-PatchMatch MVS		86.96 217	89.56 212	83.93 221	86.29 191	97.61 173	90.75 200	73.31 230	95.43 202	66.08 218	75.88 239	71.31 226	87.55 236	94.79 184	92.74 203	99.61 157	99.13 222

v1192		86.93 218	89.01 219	84.50 214	84.46 220	95.51 220	89.93 218	78.65 202	93.75 218	62.29 228	77.19 234	70.88 230	92.28 201	93.84 198	91.96 212	99.38 204	99.90 152

v1921920		86.81 219	88.93 220	84.33 217	84.23 224	95.41 224	90.09 214	78.10 205	93.74 219	62.17 229	76.98 236	71.14 227	92.05 203	93.69 204	91.69 218	99.32 209	99.88 163

v144192		86.80 220	88.90 221	84.35 215	84.33 222	95.56 218	89.34 223	77.74 207	93.60 220	64.03 224	77.82 231	70.76 231	91.28 211	92.91 218	91.74 217	99.37 205	99.90 152

DTE-MVSNet		86.70 221	87.66 231	85.58 200	83.30 231	94.29 230	89.74 221	81.53 182	92.77 232	68.93 206	80.13 220	64.00 250	90.62 219	89.45 233	93.34 193	99.32 209	99.67 199

gg-mvs-nofinetune		86.69 222	91.30 195	81.30 232	90.42 164	99.64 84	98.50 60	61.68 257	79.23 255	40.35 260	66.58 248	97.14 95	96.92 138	98.64 47	97.94 76	99.91 24	99.97 97

v148		86.63 223	87.79 229	85.28 204	84.65 216	95.97 205	86.46 231	82.84 166	92.91 231	71.52 198	78.99 225	66.74 244	86.83 238	89.28 234	90.69 226	99.41 200	99.94 137

gbinet_0.2-2-1-0.02		86.42 224	87.47 232	85.19 205	71.78 245	91.76 241	90.97 194	82.60 169	90.87 237	75.35 175	85.62 193	76.07 205	93.09 185	85.42 248	82.55 249	97.37 246	99.98 81

v1240		86.24 225	88.56 225	83.54 222	84.05 227	95.21 226	89.27 224	76.76 213	93.42 223	60.68 236	75.99 238	69.80 234	91.21 214	93.83 200	91.76 216	99.29 213	99.91 150

wanda-best-256-512		85.94 226	87.03 233	84.66 209	70.40 248	91.71 243	90.75 200	82.93 162	90.83 238	73.88 182	83.78 202	74.80 208	92.62 195	85.63 242	82.75 243	97.39 241	99.73 191

FE-blended-shiyan7		85.94 226	87.03 233	84.66 209	70.40 248	91.71 243	90.75 200	82.93 162	90.83 238	73.88 182	83.78 202	74.80 208	92.62 195	85.63 242	82.75 243	97.39 241	99.73 191

blended_shiyan8		85.87 228	86.93 238	84.64 212	70.41 247	91.71 243	90.90 197	82.61 168	90.54 243	74.01 181	83.77 204	74.58 212	92.53 198	85.57 247	82.67 248	97.37 246	99.66 200

blended_shiyan6		85.86 229	86.98 236	84.56 213	70.38 252	91.69 248	90.72 204	82.45 171	90.79 242	73.86 184	83.58 205	74.80 208	92.57 197	85.60 246	82.69 247	97.38 245	99.72 195

pmmvs6		85.75 230	86.97 237	84.34 216	84.88 212	95.59 216	87.41 229	79.19 197	87.81 250	67.56 211	63.05 252	77.76 195	89.15 225	93.45 209	91.90 213	97.83 238	99.21 219

v7n		85.39 231	87.70 230	82.70 228	82.77 233	95.64 214	88.27 227	74.83 221	92.30 234	62.58 227	76.37 237	64.80 249	88.38 232	94.29 191	90.61 227	99.34 206	99.87 168

gm-plane-assit		84.93 232	91.61 192	77.14 242	84.14 225	91.29 249	66.18 260	69.70 233	85.22 254	47.95 257	78.58 229	89.24 135	94.90 165	98.82 41	98.12 72	99.99 7	100.00 1

CMPMVS	65.66 17	84.62 233	85.02 240	84.15 219	95.40 74	97.79 171	88.35 226	79.22 196	89.66 247	60.71 235	72.20 243	73.94 217	87.32 237	86.73 239	84.55 241	93.90 252	90.31 253
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test_method		84.44 234	89.04 218	79.08 236	81.15 236	92.82 238	82.06 245	61.92 255	96.17 194	59.38 239	74.47 241	67.52 239	91.96 204	96.92 131	95.53 164	97.98 235	99.85 173

Anonymous20231206		84.28 235	89.53 214	78.17 239	82.31 235	94.16 232	82.57 242	76.51 216	93.38 226	52.98 250	79.47 224	73.74 218	75.45 250	95.07 181	94.41 178	99.18 220	96.46 245

new_pmnet		84.12 236	87.89 228	79.72 235	80.43 238	94.14 233	80.26 249	74.14 224	96.01 196	56.30 249	74.94 240	76.45 201	88.59 231	93.11 217	89.31 232	98.59 231	91.27 252

test20.03		83.86 237	88.73 224	78.16 240	82.60 234	93.00 235	81.61 247	74.68 222	92.36 233	57.50 246	83.01 210	74.48 214	73.30 252	92.40 221	91.14 225	99.29 213	94.75 248

pmmvs-eth3d		82.92 238	83.31 243	82.47 229	76.97 242	91.76 241	83.79 237	76.10 217	90.33 244	69.95 204	71.04 245	48.09 258	89.02 227	93.85 197	89.14 233	99.02 226	98.96 224

PM-MVS		82.79 239	84.51 241	80.77 234	77.22 241	92.13 239	83.61 239	73.31 230	93.50 221	61.06 231	77.15 235	46.52 261	90.55 220	94.14 192	89.05 236	98.85 228	99.12 223

pmmvs3		80.91 240	85.62 239	75.42 244	75.01 244	89.09 253	75.31 254	68.70 235	86.99 252	46.74 259	81.18 216	62.91 251	87.95 233	93.84 198	89.06 235	98.80 230	96.23 246

MIMVSNet1		80.64 241	83.97 242	76.76 243	68.91 254	91.15 251	78.32 253	75.47 220	89.58 248	56.64 248	65.10 249	65.17 248	82.14 243	93.51 208	91.64 219	99.10 223	91.66 251

MDA-MVSNet-bldmvs		80.30 242	82.83 244	77.34 241	69.16 253	94.29 230	72.16 255	81.97 176	90.14 246	57.32 247	94.01 131	47.97 259	86.81 239	68.74 256	86.82 238	96.63 248	97.86 236

FE-MVSNET2		79.98 243	80.91 246	78.89 237	67.11 256	92.85 236	83.34 241	77.59 208	88.33 249	59.81 238	55.71 254	48.82 257	86.33 240	93.94 195	89.34 231	99.14 222	97.39 239

new-patchmatchnet		78.17 244	80.82 248	75.07 245	76.93 243	91.20 250	71.90 256	73.32 229	86.59 253	48.91 254	67.11 247	47.85 260	81.19 244	88.18 238	87.02 237	98.19 234	97.79 237

FE-MVSNET		77.93 245	80.91 246	74.45 246	61.41 258	89.15 252	78.53 252	75.91 218	87.12 251	52.74 251	63.25 251	50.07 256	79.29 246	91.87 225	89.12 234	98.81 229	95.76 247

usedtu_dtu_shiyan2		74.26 246	75.54 249	72.77 248	60.18 261	86.34 254	79.24 251	68.68 236	77.80 256	57.94 243	47.93 257	58.22 254	76.77 248	80.13 251	80.11 252	93.82 253	98.26 230

FPMVS		73.80 247	74.62 250	72.84 247	83.09 232	84.44 256	83.89 236	73.64 228	92.20 235	48.50 255	72.19 244	59.51 253	63.16 254	69.13 255	66.26 259	84.74 258	78.59 260

WB-MVS		71.64 248	82.10 245	59.45 252	79.66 240	78.44 259	55.66 264	78.80 201	93.01 230	19.20 266	86.36 192	71.05 228	39.18 261	85.26 249	81.08 250	84.19 259	79.49 259

Gipuma		71.02 249	72.60 253	69.19 249	71.31 246	75.11 260	66.36 259	61.65 258	94.93 206	47.29 258	38.74 259	38.52 262	75.52 249	86.09 240	85.92 240	93.01 254	88.87 255
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
GG-mvs-BLEND		69.85 250	99.39 42	35.39 258	3.67 266	99.94 21	99.10 45	1.69 263	99.85 52	3.19 268	98.13 86	99.46 65	4.92 262	99.23 33	99.14 32	99.80 63	100.00 1

PMMVS2		65.18 251	68.25 254	61.59 250	61.37 259	79.72 258	59.18 263	61.80 256	64.72 259	37.33 261	53.82 255	35.59 263	54.46 259	73.94 254	80.52 251	95.40 251	89.43 254

PMVS	60.14 18	62.67 252	64.05 255	61.06 251	68.32 255	53.27 266	52.23 265	67.63 239	75.07 258	48.30 256	58.27 253	57.43 255	49.99 260	67.20 257	62.42 260	79.87 262	74.68 261
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
testmvs		61.76 253	72.90 252	48.76 255	21.21 264	68.61 261	66.11 261	37.38 261	94.83 207	33.06 262	64.31 250	29.72 264	86.08 241	74.44 253	78.71 253	48.74 264	99.65 201

E-PMN		55.33 254	55.79 257	54.81 254	59.81 262	57.23 264	38.83 266	63.59 253	64.06 261	24.66 264	35.33 261	26.40 266	58.69 256	55.41 259	70.54 256	83.26 260	81.56 258

EMVS		55.14 255	55.29 258	54.97 253	60.87 260	57.52 263	38.58 267	63.57 254	64.54 260	23.36 265	36.96 260	27.99 265	60.69 255	51.17 260	66.61 258	82.73 261	82.25 257

MVE	58.81 19	52.07 256	55.15 259	48.48 256	42.45 263	62.35 262	36.41 268	54.70 260	49.88 262	27.65 263	29.98 262	18.08 267	54.87 258	65.93 258	77.26 254	74.79 263	82.59 256
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test123		48.14 257	58.11 256	36.51 257	8.71 265	56.81 265	59.55 262	24.08 262	77.50 257	14.41 267	49.20 256	11.94 269	80.98 245	41.62 261	69.81 257	31.32 265	99.90 152

uanet_test		0.00 258	0.00 260	0.00 259	0.00 267	0.00 267	0.00 269	0.00 264	0.00 263	0.00 269	0.00 263	0.00 270	0.00 263	0.00 262	0.00 261	0.00 266	0.00 262

sosnet-low-res		0.00 258	0.00 260	0.00 259	0.00 267	0.00 267	0.00 269	0.00 264	0.00 263	0.00 269	0.00 263	0.00 270	0.00 263	0.00 262	0.00 261	0.00 266	0.00 262

sosnet		0.00 258	0.00 260	0.00 259	0.00 267	0.00 267	0.00 269	0.00 264	0.00 263	0.00 269	0.00 263	0.00 270	0.00 263	0.00 262	0.00 261	0.00 266	0.00 262

TestfortrainingZip							99.91 1	97.15 2		99.89 1						100.00 1

TPM-MVS					99.67 4	99.96 7	99.82 7			94.63 34	99.65 17	100.00 1	99.90 6			99.99 7	99.80 182
Ray Leroy Khuboni and Hongjun Xu: Textureless Resilient Propagation Matching in Multiple View Stereosis (TPM-MVS). SATNAC 2025
RE-MVS-def										52.74 251

9.14												100.00 1

SR-MVS					99.61 14			96.80 20				100.00 1

Anonymous202405211			95.78 138		93.26 93	99.52 90	96.70 105	88.55 82	97.93 167		88.99 175	90.68 129	98.99 62	96.46 148	97.02 130	99.64 152	99.89 157

our_test_3					85.89 195	96.09 201	82.15 244

ambc			74.33 251		66.84 257	84.26 257	84.17 235		93.39 225	58.99 240	45.93 258	18.06 268	70.61 253	93.94 195	86.62 239	92.61 256	98.13 232

MTAPA										96.61 18		100.00 1

MTMP										97.42 13		100.00 1

Patchmatch-RL test							68.01 258

tmp_tt				78.81 238	98.80 46	85.73 255	70.08 257	77.87 206	98.68 136	83.71 145	99.53 31	74.55 213	54.97 257	78.28 252	72.43 255	87.45 257

XVS					95.09 77	99.94 21	97.49 76			88.58 96		99.98 36				99.78 77

X-MVStestdata					95.09 77	99.94 21	97.49 76			88.58 96		99.98 36				99.78 77

mPP-MVS					99.23 39							99.87 47

NP-MVS									99.79 62

Patchmtry						99.00 139	95.46 144	65.50 246		67.51 212

DeepMVS_CX						97.31 178	79.48 250	89.65 68	98.66 139	60.89 234	94.40 124	66.89 242	87.65 235	81.69 250		92.76 255	94.24 250

High-resolution multi-view benchmark