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.

aaEdge-Enhanced		99.62 1	99.80 3	99.41 1	99.64 7	99.95 15	99.91 1	97.15 2	99.93 35	99.83 2	99.61 24	100.00 1	99.94 1	99.86 14	99.29 27	100.00 1	100.00 1

MED-MVS		99.53 2	99.72 19	99.31 2	99.64 7	99.96 7	99.75 19	96.92 12	99.98 3	99.83 2	99.61 24	100.00 1	99.91 5	99.65 23	98.83 48	99.79 71	100.00 1

SED-MVS		99.44 3	99.69 23	99.15 3	99.61 15	99.95 15	99.81 8	96.94 9	99.97 10	98.73 5	99.53 32	100.00 1	99.91 5	99.90 8	98.52 62	99.87 32	100.00 1

SF-MVS		99.41 4	99.68 25	99.10 5	99.65 6	99.94 22	99.76 12	96.95 6	99.88 46	98.39 8	99.60 26	100.00 1	99.82 16	99.43 30	98.93 40	99.99 7	100.00 1

APDe-MVS		99.40 5	99.81 2	98.92 10	99.62 10	99.96 7	99.76 12	96.87 18	99.95 27	97.66 10	99.57 30	100.00 1	99.63 32	99.88 11	99.28 28	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 6	99.76 10	98.95 8	99.60 19	99.99 1	99.83 6	96.82 20	99.92 40	97.58 13	99.58 29	100.00 1	99.93 2	98.98 37	99.86 8	99.96 15	100.00 1

CNVR-MVS		99.39 6	99.75 13	98.98 6	99.69 1	99.95 15	99.76 12	96.91 13	99.98 3	97.59 12	99.64 21	100.00 1	99.93 2	99.94 2	98.75 55	99.97 14	99.97 103

DVP-MVS		99.38 8	99.57 39	99.15 3	99.62 10	99.94 22	99.72 26	96.99 4	99.98 3	98.85 4	98.21 86	100.00 1	99.88 10	99.88 11	98.96 38	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 8	99.78 6	98.91 13	99.61 15	99.96 7	99.85 4	96.94 9	99.96 21	97.38 16	99.60 26	100.00 1	99.70 23	99.96 1	98.96 38	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 10	99.74 16	98.94 9	99.60 19	99.94 22	99.87 3	96.95 6	99.94 32	97.42 14	99.62 23	100.00 1	99.80 19	99.91 5	98.78 53	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 11	99.70 22	98.96 7	99.62 10	99.94 22	99.85 4	96.90 17	99.97 10	97.64 11	99.50 36	100.00 1	99.88 10	99.90 8	98.60 57	99.87 32	100.00 1

SMA-MVS		99.34 12	99.79 5	98.81 15	99.69 1	99.94 22	99.75 19	96.91 13	99.98 3	96.76 18	99.37 43	100.00 1	99.90 7	99.88 11	99.46 17	99.84 39	99.92 150
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 13	99.85 1	98.73 16	99.61 15	99.92 43	99.77 11	96.91 13	99.93 35	96.31 22	99.59 28	99.95 44	99.84 14	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 14	99.75 13	98.65 17	99.63 9	99.96 7	99.76 12	96.91 13	99.97 10	95.86 26	99.67 12	100.00 1	99.75 20	99.85 15	98.80 51	99.98 12	99.97 103

CNLPA		99.24 14	99.58 36	98.85 14	99.34 35	99.95 15	99.32 40	96.65 30	99.96 21	98.44 7	98.97 58	100.00 1	99.57 34	98.66 46	99.56 15	99.76 91	99.97 103

AdaColmap		99.21 16	99.45 42	98.92 10	99.67 4	99.95 15	99.65 31	96.77 25	99.97 10	97.67 9	100.00 1	99.69 58	99.93 2	99.26 33	97.25 125	99.85 36	100.00 1

HFP-MVS		99.19 17	99.77 9	98.51 20	99.55 23	99.94 22	99.76 12	96.84 19	99.88 46	95.27 30	99.67 12	100.00 1	99.85 13	99.56 25	99.36 22	99.79 71	99.97 103

PLC	98.06 1	99.17 18	99.38 44	98.92 10	99.47 25	99.90 52	99.48 36	96.47 35	99.96 21	98.73 5	99.52 35	100.00 1	99.55 36	98.54 60	97.73 93	99.84 39	99.99 67
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
SD-MVS		99.16 19	99.73 17	98.49 21	97.93 55	99.95 15	99.74 23	96.94 9	99.96 21	96.60 20	99.47 39	100.00 1	99.88 10	99.15 35	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 20	99.67 26	98.53 19	99.45 27	99.94 22	99.63 33	96.62 32	99.82 59	95.92 25	99.65 17	100.00 1	99.71 22	99.76 18	98.56 59	99.83 45	100.00 1

ACMMPR		99.12 21	99.76 10	98.36 22	99.45 27	99.94 22	99.75 19	96.70 29	99.93 35	94.65 34	99.65 17	99.96 42	99.84 14	99.51 28	99.35 23	99.79 71	99.96 124

MCST-MVS		99.08 22	99.72 19	98.33 23	99.59 22	99.97 3	99.78 10	96.96 5	99.95 27	93.72 39	99.67 12	100.00 1	99.90 7	99.91 5	98.55 60	100.00 1	100.00 1

CPTT-MVS		99.08 22	99.53 41	98.57 18	99.44 29	99.93 37	99.60 34	95.92 40	99.77 67	97.01 17	99.67 12	100.00 1	99.72 21	99.56 25	97.76 88	99.70 140	99.98 87

DeepC-MVS_fast	98.03 2	99.05 24	99.78 6	98.21 26	99.47 25	99.97 3	99.75 19	96.80 21	99.97 10	93.58 41	98.68 69	99.94 45	99.69 24	99.93 4	99.95 3	99.96 15	99.98 87
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 25	99.61 33	98.27 24	97.88 56	99.92 43	99.71 28	96.80 21	99.96 21	95.58 28	98.71 68	100.00 1	99.68 26	99.91 5	98.78 53	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 26	99.62 32	98.22 25	99.62 10	99.94 22	99.74 23	96.95 6	99.87 50	93.76 38	99.49 38	100.00 1	99.39 42	99.73 19	98.35 65	99.89 28	99.96 124

SteuartSystems-ACMMP		98.95 27	99.80 3	97.95 29	99.43 30	99.96 7	99.76 12	96.45 36	99.82 59	93.63 40	99.64 21	100.00 1	98.56 87	99.90 8	99.31 25	99.84 39	100.00 1
Skip Steuart: Steuart Systems R&D Blog.
PHI-MVS		98.85 28	99.67 26	97.89 30	98.63 50	99.93 37	98.95 51	95.20 42	99.84 57	94.94 31	99.74 11	100.00 1	99.69 24	98.40 67	99.75 11	99.93 21	99.99 67

MP-MVS		98.82 29	99.63 30	97.88 31	99.41 31	99.91 51	99.74 23	96.76 26	99.88 46	91.89 52	99.50 36	99.94 45	99.65 29	99.71 22	98.49 63	99.82 49	99.97 103
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
ACMMP_NAP		98.68 30	99.58 36	97.62 32	99.62 10	99.92 43	99.72 26	96.78 24	99.71 72	90.13 81	99.66 16	99.99 35	99.64 30	99.78 17	98.14 73	99.82 49	99.89 164

train_agg		98.62 31	99.76 10	97.28 34	99.03 43	99.93 37	99.65 31	96.37 37	99.98 3	89.24 92	99.53 32	99.83 50	99.59 33	99.85 15	99.19 32	99.80 64	100.00 1

X-MVS		98.62 31	99.75 13	97.29 33	99.50 24	99.94 22	99.71 28	96.55 33	99.85 54	88.58 98	99.65 17	99.98 37	99.67 27	99.60 24	99.26 29	99.77 83	99.97 103

OMC-MVS		98.59 33	99.07 50	98.03 28	99.41 31	99.90 52	99.26 43	94.33 44	99.94 32	96.03 23	96.68 102	99.72 57	99.42 39	98.86 40	98.84 46	99.72 131	99.58 215

DPM-MVS		98.58 34	99.78 6	97.17 36	98.02 54	99.64 86	99.80 9	96.72 28	99.96 21	90.05 83	99.57 30	100.00 1	98.66 83	99.56 25	99.96 2	99.80 64	99.80 192

PGM-MVS		98.47 35	99.73 17	97.00 38	99.68 3	99.94 22	99.76 12	91.74 50	99.84 57	91.17 68	100.00 1	99.69 58	99.81 17	99.38 31	99.30 26	99.82 49	99.95 137

MGCNet		98.44 36	99.67 26	97.00 38	97.82 58	99.92 43	99.46 37	91.78 49	99.95 27	94.10 36	100.00 1	100.00 1	98.91 69	98.59 54	99.22 30	99.95 17	99.99 67

TSAR-MVS + ACMM		98.30 37	99.64 29	96.74 42	99.08 42	99.94 22	99.67 30	96.73 27	99.97 10	86.30 131	98.30 77	99.99 35	98.78 77	99.73 19	99.57 14	99.88 31	99.98 87

CSCG		98.22 38	98.37 71	98.04 27	99.60 19	99.82 62	99.45 38	93.59 45	99.16 112	96.46 21	98.22 85	95.86 108	99.41 41	96.33 160	99.22 30	99.75 102	99.94 144

3Dnovator+	95.21 7	98.17 39	99.08 49	97.12 37	99.28 38	99.78 73	98.61 58	89.93 64	99.93 35	95.36 29	95.50 112	100.00 1	99.56 35	98.58 55	99.80 10	99.95 17	99.97 103

ACMMP		98.16 40	99.01 51	97.18 35	98.86 45	99.92 43	98.77 56	95.73 41	99.31 107	91.15 69	100.00 1	99.81 52	98.82 75	98.11 90	95.91 168	99.77 83	99.97 103
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 41	99.69 23	96.36 47	99.23 40	99.93 37	97.79 70	91.84 48	99.87 50	90.53 77	100.00 1	99.57 63	98.93 68	99.44 29	99.08 35	99.85 36	99.95 137

EPNet		98.11 42	99.63 30	96.34 48	98.44 52	99.88 57	98.55 59	90.25 60	99.93 35	92.60 48	100.00 1	99.73 55	98.41 94	98.87 39	99.02 36	99.82 49	99.97 103
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TSAR-MVS + GP.		98.06 43	99.55 40	96.32 49	94.72 83	99.92 43	99.22 44	89.98 62	99.97 10	94.77 33	99.94 10	100.00 1	99.43 38	98.52 64	98.53 61	99.79 71	100.00 1

3Dnovator	95.01 8	97.98 44	98.89 56	96.92 41	99.36 33	99.76 76	98.72 57	89.98 62	99.98 3	93.99 37	94.60 126	99.43 68	99.50 37	98.55 57	99.91 5	99.99 7	99.98 87

MVS_111021_HR		97.94 45	99.59 34	96.02 51	99.27 39	99.97 3	97.03 98	90.44 57	99.89 44	90.75 72	100.00 1	99.73 55	98.68 82	98.67 45	98.89 43	99.95 17	99.97 103

QAPM		97.90 46	98.89 56	96.74 42	99.35 34	99.80 68	98.84 53	90.20 61	99.94 32	92.85 43	94.17 130	99.78 53	99.42 39	98.71 43	99.87 7	99.79 71	99.98 87

CDPH-MVS		97.88 47	99.59 34	95.89 52	98.90 44	99.95 15	99.40 39	92.86 47	99.86 53	85.33 144	98.62 71	99.45 67	99.06 63	99.29 32	99.94 4	99.81 59	100.00 1

CANet		97.62 48	98.94 54	96.08 50	97.19 61	99.93 37	99.29 42	90.38 58	99.87 50	91.00 70	95.79 111	99.51 64	98.72 81	98.53 61	99.00 37	99.90 26	99.99 67

TAPA-MVS	96.62 5	97.60 49	98.46 70	96.60 45	98.73 48	99.90 52	99.30 41	94.96 43	99.46 90	87.57 111	96.05 109	98.53 80	99.26 53	98.04 95	97.33 121	99.77 83	99.88 170
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
DeepPCF-MVS	97.16 4	97.58 50	99.72 19	95.07 67	98.45 51	99.96 7	93.83 183	95.93 39	100.00 1	90.79 71	98.38 76	99.85 49	95.28 171	99.94 2	99.97 1	96.15 260	99.97 103

SPE-MVS-test		97.51 51	99.18 47	95.56 57	97.16 62	99.96 7	97.39 84	89.82 67	100.00 1	89.88 84	99.16 50	98.38 86	99.23 55	98.85 41	97.93 80	99.87 32	100.00 1

PCF-MVS	97.20 3	97.49 52	98.20 76	96.66 44	97.62 59	99.92 43	98.93 52	96.64 31	98.53 153	88.31 105	94.04 133	99.58 62	98.94 65	97.53 116	97.79 86	99.54 177	99.97 103
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
CS-MVS		97.46 53	98.98 52	95.68 56	96.74 66	99.93 37	97.62 76	89.69 68	99.98 3	91.33 65	98.53 74	97.50 95	98.77 78	98.60 53	98.35 65	99.92 23	100.00 1

MSDG		97.29 54	97.55 92	97.00 38	98.66 49	99.71 81	99.03 49	96.15 38	99.59 79	89.67 90	92.77 150	94.86 111	98.75 79	98.22 79	97.94 78	99.72 131	99.76 197

CHOSEN 280x420		97.16 55	99.58 36	94.35 83	96.95 65	99.97 3	97.19 91	81.55 191	99.92 40	91.75 59	100.00 1	100.00 1	98.84 74	98.55 57	98.65 56	99.79 71	99.97 103

MVSMamba_PlusPlus		97.06 56	99.09 48	94.69 76	92.17 117	99.75 77	99.05 48	89.87 66	99.95 27	87.59 110	97.96 90	97.97 90	99.64 30	98.62 51	99.46 17	99.82 49	99.96 124

DELS-MVS		97.05 57	98.05 81	95.88 54	97.09 63	99.99 1	98.82 54	90.30 59	98.44 159	91.40 63	92.91 147	96.57 101	97.68 141	98.56 56	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 57	97.59 91	96.42 46	97.37 60	99.92 43	99.10 46	96.54 34	99.34 104	86.64 125	91.93 160	93.15 122	99.11 61	99.11 36	99.68 12	99.73 121	99.97 103
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 59	98.09 80	95.81 55	94.12 88	99.80 68	97.33 87	89.48 72	98.90 132	95.99 24	99.11 53	92.84 124	98.14 114	98.14 86	98.32 69	99.82 49	99.51 220

MAR-MVS		97.03 60	98.00 83	95.89 52	99.32 36	99.74 80	96.76 108	84.89 143	99.97 10	94.86 32	98.29 78	90.58 132	99.67 27	98.02 97	99.50 16	99.82 49	99.92 150
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 61	98.85 58	94.83 74	92.71 107	97.43 187	99.03 49	85.52 136	99.82 59	92.74 46	99.15 51	99.94 45	99.19 58	98.66 46	96.99 140	99.79 71	99.98 87

EC-MVSNet		96.90 62	99.32 45	94.07 85	91.64 155	99.30 129	98.18 66	85.61 135	99.97 10	89.79 85	99.33 44	99.31 71	99.28 51	98.48 66	98.86 44	99.91 24	100.00 1

baseline1		96.87 63	98.55 64	94.91 69	92.89 106	99.45 101	96.34 116	88.54 85	98.88 135	92.82 44	98.93 60	96.58 100	99.07 62	98.19 81	98.04 75	99.80 64	99.78 194

OpenMVS	94.03 11	96.87 63	98.10 79	95.44 61	99.29 37	99.78 73	98.46 64	89.92 65	99.47 89	85.78 140	91.05 170	98.50 81	99.30 49	98.49 65	99.41 19	99.89 28	99.98 87

PatchMatch-RL		96.84 65	98.03 82	95.47 58	98.84 46	99.81 66	95.61 149	89.20 76	99.65 76	91.28 66	99.39 40	93.46 120	98.18 111	98.05 93	96.28 152	99.69 145	99.55 217

ETV-MVS		96.79 66	99.19 46	94.00 87	91.78 140	99.63 88	97.15 93	88.00 91	99.95 27	88.34 104	99.32 45	98.71 77	98.82 75	98.69 44	98.01 76	99.90 26	100.00 1

IS_MVSNet		96.66 67	98.62 63	94.38 79	92.41 113	99.70 82	97.19 91	87.67 106	99.05 121	91.27 67	95.09 117	98.46 85	97.95 126	98.64 48	99.37 20	99.79 71	100.00 1

PMMVS		96.45 68	98.24 75	94.36 82	92.58 108	99.01 147	97.08 97	87.42 126	99.88 46	90.06 82	99.39 40	94.63 112	99.33 46	97.85 103	96.99 140	99.70 140	99.96 124

LS3D		96.44 69	97.31 99	95.41 62	97.06 64	99.87 58	99.51 35	97.48 1	99.57 80	79.00 168	95.39 113	89.19 139	99.81 17	98.55 57	98.84 46	99.62 166	99.78 194

EIA-MVS		96.34 70	98.55 64	93.76 94	91.93 130	99.66 84	97.14 94	88.33 89	99.51 84	85.98 136	98.82 64	96.08 106	99.33 46	98.38 70	97.40 115	99.81 59	100.00 1

EPP-MVSNet		96.29 71	98.34 72	93.90 89	91.77 142	99.38 109	95.45 155	87.25 131	99.38 99	91.36 64	94.86 124	98.49 83	97.83 134	98.01 98	98.23 71	99.75 102	99.99 67

UGNet		96.05 72	98.55 64	93.13 117	94.64 84	99.65 85	94.70 171	87.78 95	99.40 98	89.69 89	98.25 81	99.25 73	92.12 212	96.50 151	97.08 135	99.84 39	99.72 205
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 73	96.83 113	95.11 66	97.87 57	99.52 92	98.81 55	91.40 53	99.42 94	84.97 147	90.46 175	96.82 99	98.05 119	96.46 155	96.19 155	99.54 177	98.92 235
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 74	96.48 122	95.46 59	96.47 68	99.89 55	95.64 145	91.23 54	99.75 69	91.59 61	96.80 99	82.44 181	98.05 119	98.53 61	97.92 81	99.80 64	100.00 1

PVSNet_Blended		96.01 74	96.48 122	95.46 59	96.47 68	99.89 55	95.64 145	91.23 54	99.75 69	91.59 61	96.80 99	82.44 181	98.05 119	98.53 61	97.92 81	99.80 64	100.00 1

thisisatest0530		95.89 76	98.32 73	93.06 124	91.76 143	99.75 77	94.94 163	87.60 112	99.91 42	86.66 124	98.28 79	99.98 37	97.72 137	97.10 132	93.24 204	99.65 158	99.95 137

tttt0517		95.88 77	98.31 74	93.04 125	91.75 145	99.75 77	94.90 164	87.60 112	99.91 42	86.63 126	98.28 79	99.98 37	97.72 137	97.10 132	93.24 204	99.65 158	99.95 137

thres100view900		95.86 78	96.62 116	94.97 68	93.10 96	99.83 60	97.76 71	89.15 77	98.62 149	90.69 73	99.00 55	84.86 165	99.30 49	97.57 114	96.48 147	99.81 59	100.00 1

RPSCF		95.86 78	96.94 111	94.61 77	96.52 67	98.67 163	98.54 60	88.43 87	99.56 81	90.51 79	99.39 40	98.70 78	97.72 137	93.77 211	92.00 221	95.93 261	96.50 255

DCV-MVSNet		95.85 80	97.53 93	93.89 90	93.20 95	97.01 193	97.14 94	84.77 144	99.16 112	90.38 80	98.96 59	93.73 117	98.23 110	96.57 150	97.37 116	99.64 162	99.93 146

baseline		95.85 80	98.13 78	93.20 115	92.29 116	99.58 90	97.49 78	84.33 153	99.44 91	87.28 117	97.00 98	94.04 116	97.93 127	98.36 72	98.47 64	99.87 32	99.99 67

sasdasda		95.80 82	97.02 104	94.37 80	92.96 102	99.47 97	97.49 78	84.58 146	99.44 91	92.05 50	98.54 72	86.65 149	99.37 43	96.18 164	98.93 40	99.77 83	99.92 150

canonicalmvs		95.80 82	97.02 104	94.37 80	92.96 102	99.47 97	97.49 78	84.58 146	99.44 91	92.05 50	98.54 72	86.65 149	99.37 43	96.18 164	98.93 40	99.77 83	99.92 150

tfpn200view9		95.78 84	96.54 119	94.89 71	93.10 96	99.82 62	97.67 72	88.85 80	98.62 149	90.69 73	99.00 55	84.86 165	99.28 51	97.41 124	96.10 158	99.76 91	99.99 67

thres200		95.77 85	96.55 118	94.86 72	93.09 98	99.82 62	97.63 75	88.85 80	98.49 154	90.66 75	98.99 57	84.86 165	99.20 56	97.41 124	96.28 152	99.76 91	100.00 1

MVS_Test		95.74 86	98.18 77	92.90 129	92.16 118	99.49 96	97.36 85	84.30 154	99.79 64	84.94 148	96.65 103	93.63 119	98.85 73	98.61 52	99.10 34	99.81 59	100.00 1

thres400		95.72 87	96.48 122	94.84 73	93.00 101	99.83 60	97.55 77	88.93 78	98.49 154	90.61 76	98.86 61	84.63 169	99.20 56	97.45 118	96.10 158	99.77 83	99.99 67

MGCFI-Net		95.71 88	96.97 110	94.25 84	92.90 105	99.44 104	97.35 86	84.44 151	99.42 94	91.70 60	98.51 75	86.56 152	99.33 46	96.09 169	98.83 48	99.77 83	99.92 150

thres600view7		95.64 89	96.38 126	94.79 75	92.96 102	99.82 62	97.48 83	88.85 80	98.38 160	90.52 78	98.84 63	84.61 170	99.15 59	97.41 124	95.60 173	99.76 91	99.99 67

Vis-MVSNet (Re-imp)		95.60 90	98.52 69	92.19 139	92.37 114	99.56 91	96.37 114	87.41 127	98.95 127	84.77 151	94.88 123	98.48 84	92.44 209	98.63 50	99.37 20	99.76 91	99.77 196

FMVSNet3		95.59 91	97.51 95	93.34 105	89.48 180	96.57 201	97.67 72	84.17 156	99.48 86	89.76 86	95.09 117	94.35 113	99.14 60	98.37 71	98.86 44	99.82 49	99.89 164

ECVR-MVS		95.46 92	95.58 151	95.31 64	94.12 88	99.80 68	97.33 87	89.48 72	98.90 132	92.99 42	87.97 189	86.41 154	98.14 114	98.14 86	98.32 69	99.82 49	99.52 219

E2		95.42 93	96.83 113	93.78 92	91.73 147	99.38 109	96.39 113	87.87 92	98.79 139	88.36 103	95.90 110	88.17 141	98.59 85	97.72 106	97.85 83	99.75 102	99.98 87

PVSNet_Blended_VisFu		95.37 94	97.44 97	92.95 126	95.20 76	99.80 68	92.68 192	88.41 88	99.12 115	87.64 109	88.31 188	99.10 74	94.07 187	98.27 75	97.51 105	99.73 121	100.00 1

DI_MVS_pp		95.29 95	97.02 104	93.28 109	91.76 143	99.52 92	97.84 69	85.67 134	99.08 119	87.29 116	87.76 193	97.46 96	97.31 145	97.83 104	97.48 107	99.83 45	100.00 1

ET-MVSNet_ETH3D		95.20 96	97.82 88	92.15 140	80.77 248	98.13 175	97.65 74	86.93 132	99.72 71	88.56 101	99.29 48	97.01 98	99.24 54	94.58 198	95.98 165	99.75 102	99.99 67

TSAR-MVS + COLMAP		95.20 96	95.03 163	95.41 62	96.17 70	98.69 162	99.11 45	93.40 46	99.97 10	84.89 149	98.23 83	75.01 218	99.34 45	97.27 129	96.37 151	99.58 170	99.64 213

GBi-Net		95.19 98	96.99 108	93.09 119	89.11 181	96.47 203	96.90 100	84.17 156	99.48 86	89.76 86	95.09 117	94.35 113	98.87 70	96.50 151	97.21 126	99.74 108	99.81 188

test1		95.19 98	96.99 108	93.09 119	89.11 181	96.47 203	96.90 100	84.17 156	99.48 86	89.76 86	95.09 117	94.35 113	98.87 70	96.50 151	97.21 126	99.74 108	99.81 188

Casviewmamba		95.18 100	96.38 126	93.78 92	91.93 130	99.35 118	96.87 103	87.70 100	98.75 141	87.92 107	93.22 145	87.56 147	98.54 88	98.23 78	97.74 91	99.75 102	99.84 182

test1111		95.15 101	95.18 159	95.12 65	94.07 90	99.80 68	97.20 90	89.53 71	98.80 138	92.22 49	85.44 205	86.24 156	97.89 129	98.12 88	98.34 68	99.80 64	99.51 220

test0.0.03 1		95.15 101	97.87 87	91.99 141	91.69 149	98.82 158	93.04 189	83.60 161	99.65 76	88.80 96	94.15 131	97.67 93	94.97 173	96.62 148	98.16 72	99.83 45	100.00 1

baseline2		95.13 103	98.55 64	91.15 147	90.29 176	99.00 148	94.49 175	82.00 185	99.68 74	84.82 150	96.47 104	99.30 72	95.71 165	98.24 77	97.14 133	99.57 172	100.00 1

viewcassd2359sk11		95.10 104	96.36 128	93.63 95	91.68 152	99.37 113	96.09 125	87.78 95	98.72 142	88.01 106	94.74 125	86.41 154	98.47 91	97.69 108	97.61 100	99.73 121	99.98 87

casdiffmvs_mvg		95.10 104	96.45 125	93.53 97	92.05 125	99.42 106	97.25 89	87.66 107	97.17 190	86.09 132	91.79 162	91.27 126	98.31 104	98.06 92	97.42 114	99.81 59	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		95.10 104	98.81 60	90.78 149	98.38 53	98.47 165	96.54 110	89.36 74	99.78 66	65.65 228	99.31 46	98.24 88	94.79 176	98.28 74	99.35 23	99.93 21	98.27 239
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
hybridcas		95.05 107	95.96 137	93.99 88	91.86 134	99.37 113	97.00 99	87.63 110	98.85 136	89.09 94	91.13 168	86.81 148	98.59 85	98.19 81	97.47 108	99.76 91	99.86 178

Anonymous20231211		94.96 108	94.99 164	94.91 69	93.01 100	99.44 104	96.85 105	88.49 86	98.78 140	92.61 47	83.94 212	90.25 134	98.94 65	95.87 175	96.77 142	99.58 170	99.89 164

UA-Net		94.95 109	98.66 62	90.63 151	94.60 86	98.94 154	96.03 127	85.28 138	98.01 173	78.92 169	97.42 96	99.96 42	89.09 236	98.95 38	98.80 51	99.82 49	98.57 237

CANet_DTU		94.90 110	98.98 52	90.13 159	94.74 82	99.81 66	98.53 61	82.23 183	99.97 10	66.76 225	100.00 1	98.50 81	98.74 80	97.52 117	97.19 131	99.76 91	99.88 170

viewdifsd2359ckpt09		94.88 111	96.22 130	93.31 106	91.61 157	99.38 109	96.37 114	87.74 97	98.82 137	85.85 137	93.69 138	86.65 149	98.61 84	97.57 114	97.44 111	99.72 131	100.00 1

viewdifsd2359ckpt07		94.83 112	96.18 135	93.25 112	91.96 129	99.31 127	97.10 96	87.65 108	98.66 147	85.26 145	91.50 165	88.11 142	97.77 136	98.16 83	97.69 95	99.74 108	99.84 182

viewdifsd2359ckpt13		94.69 113	96.20 133	92.93 128	91.67 154	99.42 106	95.73 142	87.71 99	98.67 145	84.46 152	94.31 128	86.03 158	98.27 109	97.60 111	97.35 119	99.73 121	99.99 67

E3new		94.68 114	95.67 149	93.52 99	91.63 156	99.36 116	95.96 130	87.69 104	97.81 179	87.65 108	93.38 141	84.22 175	98.48 90	97.44 119	97.52 103	99.71 135	99.96 124

E3		94.68 114	95.70 145	93.49 100	91.68 152	99.37 113	95.98 129	87.70 100	97.97 175	87.46 113	93.38 141	84.35 172	98.42 92	97.43 120	97.47 108	99.71 135	99.96 124

hybrid		94.67 116	95.86 142	93.27 111	92.11 121	99.25 137	95.62 147	87.59 114	99.37 100	86.71 122	95.07 121	82.63 180	97.88 130	97.23 130	97.50 106	99.72 131	100.00 1

onestephybrid01		94.66 117	95.70 145	93.44 101	92.13 120	99.27 134	95.49 152	87.83 94	99.33 106	87.53 112	91.54 164	85.46 163	97.92 128	96.65 146	97.63 98	99.76 91	100.00 1

viewmamba		94.61 118	95.73 144	93.30 107	92.07 123	99.30 129	95.91 135	87.51 119	99.29 109	86.31 130	93.17 146	84.33 173	98.28 108	96.42 158	97.61 100	99.73 121	99.99 67

viewmanbaseed2359cas		94.61 118	95.93 140	93.07 123	91.90 133	99.38 109	96.32 117	87.84 93	98.33 164	84.29 153	92.71 151	85.68 160	98.33 103	97.68 109	97.74 91	99.74 108	99.99 67

FC-MVSNet-train		94.61 118	96.27 129	92.68 136	92.35 115	97.14 191	93.45 187	87.73 98	98.93 128	87.31 115	96.42 105	89.35 137	95.67 166	96.06 173	96.01 164	99.56 174	99.98 87

diffmvs		94.60 121	95.63 150	93.41 103	91.98 128	99.30 129	96.86 104	87.62 111	99.30 108	86.07 135	94.12 132	81.63 191	98.16 112	97.43 120	97.60 102	99.76 91	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		94.54 122	95.56 153	93.36 104	91.84 136	99.46 100	95.92 131	87.54 118	98.45 157	86.57 128	90.51 174	84.72 168	98.49 89	97.97 99	97.80 85	99.77 83	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		94.53 123	94.45 178	94.61 77	93.85 92	98.36 168	98.12 67	89.68 69	99.35 103	89.62 91	95.19 115	77.08 208	96.66 156	95.51 181	95.67 171	99.74 108	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
hybridnocas07		94.51 124	95.57 152	93.28 109	92.09 122	99.29 133	95.82 136	87.55 117	99.34 104	86.52 129	93.79 137	82.79 179	97.99 124	96.33 160	97.43 113	99.71 135	100.00 1

viewmambaseed2359dif		94.51 124	95.35 156	93.53 97	91.78 140	99.34 119	96.78 107	87.58 116	98.29 165	86.97 121	92.34 153	84.00 176	98.35 100	96.15 167	97.31 124	99.74 108	100.00 1

FMVSNet2		94.48 126	95.95 138	92.77 134	89.11 181	96.47 203	96.90 100	83.38 164	99.11 116	88.64 97	87.50 198	92.26 125	98.87 70	97.91 101	98.60 57	99.74 108	99.81 188

HQP-MVS		94.48 126	95.39 155	93.42 102	95.10 77	98.35 169	98.19 65	91.41 52	99.77 67	79.79 165	99.30 47	77.08 208	96.25 159	96.93 135	96.28 152	99.76 91	99.99 67

dtuplus		94.35 128	95.26 157	93.30 107	91.49 163	99.32 126	96.08 126	87.45 123	97.99 174	86.60 127	91.07 169	85.48 162	98.42 92	95.75 178	97.18 132	99.73 121	100.00 1

FA-MVS(training)		94.33 129	97.52 94	90.60 153	92.42 112	99.77 75	96.13 124	68.75 245	99.05 121	88.49 102	91.95 158	99.48 65	98.12 117	98.39 68	94.02 196	99.68 147	99.98 87

MDTV_nov1_ep13		94.32 130	98.77 61	89.14 169	91.70 148	99.52 92	95.21 158	72.09 243	99.80 62	78.91 170	96.32 106	99.62 60	97.71 140	98.39 68	97.71 94	99.22 227	100.00 1

CDS-MVSNet		94.32 130	97.00 107	91.19 146	89.82 179	98.71 161	95.51 151	85.14 142	96.85 197	82.33 160	92.48 152	96.40 104	94.71 177	96.86 138	97.76 88	99.63 164	99.92 150
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
dps		94.29 132	97.33 98	90.75 150	92.02 126	99.21 138	94.31 177	66.97 251	99.50 85	95.61 27	96.22 108	98.64 79	96.08 161	93.71 213	94.03 195	99.52 181	99.98 87

ACMM	94.44 10	94.26 133	94.62 174	93.84 91	94.86 81	97.73 182	93.48 186	90.76 56	99.27 110	87.46 113	99.04 54	76.60 210	96.76 154	96.37 159	93.76 199	99.74 108	99.55 217
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
diffmvs_AUTHOR		94.21 134	95.08 160	93.18 116	91.86 134	99.26 136	96.42 111	87.48 120	99.02 124	85.45 143	92.20 155	80.25 201	98.14 114	97.16 131	97.69 95	99.73 121	100.00 1

ACMP	94.49 9	94.19 135	94.74 172	93.56 96	94.25 87	98.32 171	96.02 128	89.35 75	98.90 132	87.28 117	99.14 52	76.41 213	94.94 174	96.07 172	94.35 192	99.49 188	99.99 67
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
E5new		94.12 136	94.89 166	93.22 113	91.52 159	99.34 119	95.92 131	87.70 100	97.17 190	86.08 133	91.24 166	82.32 183	98.41 94	96.85 139	97.36 117	99.68 147	99.96 124

E5		94.12 136	94.89 166	93.22 113	91.52 159	99.34 119	95.92 131	87.70 100	97.17 190	86.08 133	91.24 166	82.32 183	98.41 94	96.85 139	97.36 117	99.68 147	99.96 124

EPMVS		94.08 138	98.54 68	88.87 170	92.51 110	99.47 97	94.18 179	66.53 252	99.68 74	82.40 159	95.24 114	99.40 69	97.86 131	98.12 88	97.99 77	99.75 102	99.88 170

E6new		93.99 139	94.76 169	93.09 119	91.51 161	99.33 124	95.80 138	87.45 123	97.13 193	85.80 138	90.97 171	81.86 188	98.30 105	96.74 143	97.32 122	99.67 151	99.95 137

E6		93.99 139	94.76 169	93.09 119	91.51 161	99.33 124	95.80 138	87.45 123	97.13 193	85.80 138	90.97 171	81.86 188	98.30 105	96.74 143	97.32 122	99.67 151	99.95 137

E4		93.99 139	94.72 173	93.13 117	91.53 158	99.34 119	95.92 131	87.59 114	97.20 188	85.67 141	90.19 176	82.18 185	98.41 94	96.83 141	97.34 120	99.68 147	99.96 124

viewmacassd2359aftdt		93.75 142	94.76 169	92.58 137	91.75 145	99.34 119	95.82 136	87.64 109	97.11 195	82.51 158	89.66 179	83.19 177	98.02 122	96.61 149	97.45 110	99.71 135	99.97 103

test-LLR		93.71 143	97.23 100	89.60 163	91.69 149	99.10 144	94.68 173	83.60 161	99.36 101	71.94 202	93.82 135	96.51 102	95.96 163	97.42 122	94.37 189	99.74 108	99.99 67

CHOSEN 1792x2688		93.69 144	94.89 166	92.28 138	96.17 70	99.84 59	95.69 144	83.17 167	98.54 152	82.04 161	77.58 244	91.15 128	96.90 149	98.36 72	98.82 50	99.73 121	99.98 87

viewdifsd2359ckpt11		93.64 145	94.30 181	92.88 131	91.82 138	98.82 158	94.88 165	87.46 121	99.08 119	86.98 120	92.20 155	80.79 192	97.85 132	93.32 221	96.13 156	98.30 243	99.75 199

viewmsd2359difaftdt		93.64 145	94.29 182	92.89 130	91.82 138	98.82 158	94.88 165	87.46 121	99.04 123	87.03 119	92.20 155	80.78 193	97.85 132	93.31 222	96.13 156	98.30 243	99.75 199

LGP-MVS_train		93.60 147	95.05 161	91.90 142	94.90 80	98.29 172	97.93 68	88.06 90	99.14 114	74.83 187	99.26 49	76.50 211	96.07 162	96.31 162	95.90 170	99.59 168	99.97 103

SCA		93.53 148	98.90 55	87.27 191	92.01 127	99.30 129	93.43 188	65.72 256	99.80 62	75.20 186	97.66 94	99.74 54	97.44 143	98.21 80	97.62 99	99.84 39	100.00 1

FMVSNet5		93.53 148	96.09 136	90.56 154	86.74 197	92.84 248	92.64 193	77.50 219	99.41 97	88.97 95	98.02 89	97.81 91	98.00 123	94.85 193	95.43 175	99.50 187	94.25 260

OPM-MVS		93.50 150	93.00 195	94.07 85	95.82 73	98.26 173	98.49 63	91.62 51	94.69 219	81.93 162	92.82 149	76.18 215	96.82 151	96.12 168	94.57 183	99.74 108	98.39 238
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
CostFormer		93.50 150	96.50 121	90.00 160	91.69 149	98.65 164	93.88 182	67.64 249	98.97 125	89.16 93	97.79 92	88.92 140	97.97 125	95.14 190	96.06 160	99.63 164	100.00 1

IterMVS-LS		93.50 150	96.22 130	90.33 157	90.93 167	95.50 231	94.83 168	80.54 195	98.92 129	79.11 167	90.64 173	93.70 118	96.79 152	96.93 135	97.85 83	99.78 79	99.99 67
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PatchmatchNet		93.48 153	98.84 59	87.22 192	91.93 130	99.39 108	92.55 194	66.06 254	99.71 72	75.61 182	98.24 82	99.59 61	97.35 144	97.87 102	97.64 97	99.83 45	99.43 223
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MS-PatchMatch		93.46 154	95.91 141	90.61 152	95.48 74	99.31 127	95.62 147	77.23 221	99.42 94	81.88 163	88.92 185	96.06 107	93.80 189	96.45 157	93.11 209	99.65 158	98.10 245

0.3-1-1-0.015		93.45 155	93.88 185	92.95 126	85.17 213	95.96 216	96.24 122	87.68 105	97.58 182	91.83 53	98.67 70	80.39 195	98.94 65	88.61 246	96.06 160	97.85 247	99.90 159

0.4-1-1-0.2		93.35 156	93.81 186	92.80 132	85.14 215	95.96 216	96.25 120	87.39 128	97.58 182	91.79 57	98.23 83	80.39 195	98.39 98	88.57 247	96.06 160	97.85 247	99.91 157

dmvs_re		93.34 157	94.59 175	91.88 143	87.97 192	99.14 143	95.29 157	88.61 83	98.09 170	82.71 157	97.34 97	78.96 202	96.98 147	94.62 196	93.98 197	99.73 121	99.98 87

0.4-1-1-0.1		93.31 158	93.77 187	92.77 134	85.13 216	95.94 219	96.21 123	87.29 129	97.58 182	91.79 57	98.11 88	80.39 195	98.36 99	88.54 248	95.98 165	97.82 250	99.89 164

tpm cat1		93.29 159	96.53 120	89.50 165	91.84 136	99.18 141	94.70 171	67.70 248	98.38 160	86.67 123	89.16 182	99.38 70	96.66 156	94.33 199	95.30 176	99.43 205	100.00 1

casdiffseed414692147		93.14 160	93.44 190	92.79 133	91.46 164	99.20 139	95.06 161	87.27 130	96.60 201	85.16 146	87.25 199	77.77 205	98.09 118	96.80 142	96.57 145	99.67 151	99.90 159

Effi-MVS+-dtu		93.13 161	97.13 102	88.47 179	88.86 187	99.19 140	96.79 106	79.08 208	99.64 78	70.01 212	97.51 95	89.38 136	96.53 158	97.60 111	96.55 146	99.57 172	100.00 1

HyFIR lowres test		93.13 161	94.48 177	91.56 144	96.12 72	99.68 83	93.52 185	79.98 199	97.24 187	81.73 164	72.66 253	95.74 109	98.29 107	98.27 75	97.79 86	99.70 140	100.00 1

Vis-MVSNet		93.08 163	96.76 115	88.78 174	91.14 166	99.63 88	94.85 167	83.34 165	97.19 189	74.78 188	91.92 161	93.15 122	88.81 239	97.59 113	98.35 65	99.78 79	99.49 222
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
Effi-MVS+		93.06 164	95.94 139	89.70 162	90.82 168	99.45 101	95.71 143	78.94 209	98.72 142	74.71 189	97.92 91	80.73 194	98.35 100	97.72 106	97.05 138	99.70 140	100.00 1

ADS-MVSNet		92.91 165	97.97 84	87.01 194	92.07 123	99.27 134	92.70 191	65.39 259	99.85 54	75.40 183	94.93 122	98.26 87	96.86 150	96.09 169	97.52 103	99.65 158	99.84 182

GeoE		92.88 166	95.20 158	90.18 158	90.59 172	99.18 141	96.31 118	78.36 214	97.52 186	78.53 172	87.11 200	88.01 143	97.63 142	97.79 105	96.76 143	99.66 156	100.00 1

TESTMET0.1,1		92.87 167	97.23 100	87.79 187	86.96 196	99.10 144	94.68 173	77.46 220	99.36 101	71.94 202	93.82 135	96.51 102	95.96 163	97.42 122	94.37 189	99.74 108	99.99 67

FC-MVSNet-test		92.78 168	96.19 134	88.80 173	88.00 191	97.54 184	93.60 184	82.36 182	98.16 166	79.71 166	91.55 163	95.41 110	89.65 231	96.09 169	95.23 177	99.49 188	99.31 226

Fast-Effi-MVS+-dtu		92.73 169	97.62 90	87.02 193	88.91 185	98.83 157	95.79 140	73.98 237	99.89 44	68.62 217	97.73 93	93.30 121	95.21 172	97.67 110	95.96 167	99.59 168	100.00 1

IB-MVS	90.59 15	92.70 170	95.70 145	89.21 168	94.62 85	99.45 101	83.77 248	88.92 79	99.53 82	92.82 44	98.86 61	86.08 157	75.24 262	92.81 229	93.17 207	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		92.67 171	97.13 102	87.47 190	86.72 198	99.07 146	94.28 178	76.90 222	99.21 111	71.53 206	93.63 139	96.32 105	95.67 166	97.32 127	94.36 191	99.74 108	99.99 67

RPMNet		92.64 172	97.88 86	86.53 199	90.79 169	98.95 152	95.13 159	64.44 263	99.09 117	72.36 198	93.58 140	99.01 75	96.74 155	98.05 93	96.45 149	99.71 135	100.00 1

FMVSNet1		92.55 173	93.66 189	91.26 145	87.91 194	96.12 210	94.75 170	81.69 190	97.67 180	85.63 142	80.56 229	87.88 145	98.15 113	96.50 151	97.21 126	99.41 210	99.71 208

tpmrst		92.52 174	97.45 96	86.77 197	92.15 119	99.36 116	92.53 195	65.95 255	99.53 82	72.50 196	92.22 154	99.83 50	97.81 135	95.18 189	96.05 163	99.69 145	100.00 1

testgi		92.47 175	95.68 148	88.73 175	90.68 170	98.35 169	91.67 202	79.50 204	98.96 126	77.12 178	95.17 116	85.84 159	93.95 188	95.75 178	96.47 148	99.45 200	99.21 229

TAMVS		92.43 176	94.21 183	90.35 156	88.68 188	98.85 156	94.15 180	81.53 192	95.58 209	83.61 155	87.05 201	86.45 153	94.71 177	96.27 163	95.91 168	99.42 208	99.38 225

CR-MVSNet		92.32 177	97.97 84	85.74 208	90.63 171	98.95 152	95.46 153	65.50 257	99.09 117	67.51 221	94.20 129	98.18 89	95.59 169	98.16 83	97.20 129	99.74 108	100.00 1

CVMVSNet		92.13 178	95.40 154	88.32 182	91.29 165	97.29 189	91.85 199	86.42 133	96.71 199	71.84 204	89.56 180	91.18 127	88.98 238	96.17 166	97.76 88	99.51 185	99.14 231

Fast-Effi-MVS+		92.11 179	94.33 179	89.52 164	89.06 184	99.00 148	95.13 159	76.72 224	98.59 151	78.21 174	89.99 177	77.35 207	98.34 102	97.97 99	97.44 111	99.67 151	99.96 124

ACMH+	92.61 13	91.80 180	93.03 193	90.37 155	93.03 99	98.17 174	94.00 181	84.13 159	98.12 168	77.39 176	91.95 158	74.62 222	94.36 184	94.62 196	93.82 198	99.32 219	99.87 175

IterMVS-SCA-FT		91.75 181	96.87 112	85.78 206	90.34 174	95.93 220	95.06 161	73.85 238	98.91 130	61.01 242	89.21 181	98.87 76	94.66 180	98.09 91	97.12 134	99.76 91	99.99 67

dtuonly		91.72 182	95.05 161	87.83 186	87.94 193	98.44 166	94.83 168	82.15 184	96.62 200	65.07 232	86.58 202	90.12 135	97.30 146	97.08 134	96.74 144	99.67 151	99.81 188

IterMVS		91.65 183	96.62 116	85.85 205	90.27 177	95.80 222	95.32 156	74.15 234	98.91 130	60.95 243	88.79 187	97.76 92	94.69 179	98.04 95	97.07 136	99.73 121	100.00 1
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ACMH	92.34 14	91.59 184	93.02 194	89.92 161	93.97 91	97.98 179	90.10 223	84.70 145	98.46 156	76.80 179	93.38 141	71.94 234	94.39 182	95.34 185	94.04 194	99.54 177	100.00 1
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs4		91.41 185	93.05 192	89.49 166	85.85 206	96.52 202	91.70 201	82.49 179	98.14 167	83.17 156	87.57 195	81.76 190	94.39 182	95.47 182	92.62 215	99.33 217	99.29 227

blend_shiyan4		91.06 186	91.01 208	91.13 148	85.43 208	91.84 251	96.41 112	82.84 175	97.61 181	91.83 53	98.80 65	80.39 195	92.83 200	85.75 252	82.95 253	97.42 251	99.73 201

PatchT		91.06 186	97.66 89	83.36 235	90.32 175	98.96 151	82.30 253	64.72 262	98.45 157	67.51 221	93.28 144	97.60 94	95.59 169	98.16 83	97.20 129	99.70 140	100.00 1

usedtu_dtu_shiyan1		91.03 188	93.73 188	87.88 185	80.10 250	96.73 197	93.00 190	84.24 155	97.91 177	77.39 176	84.98 206	87.83 146	93.08 196	95.84 176	93.18 206	99.46 196	99.63 214

MIMVSNet		91.01 189	96.22 130	84.93 217	85.24 211	98.09 176	90.40 218	64.96 261	97.55 185	72.65 194	96.23 107	90.81 130	96.79 152	96.69 145	97.06 137	99.52 181	97.09 252

UniMVSNet_NR-MVSNet		90.50 190	92.31 198	88.38 180	85.04 220	96.34 206	90.94 205	85.32 137	95.87 208	75.69 180	87.68 194	78.49 203	93.78 190	93.21 224	94.60 182	99.53 180	99.97 103

UniMVSNet (Re)		90.41 191	91.96 200	88.59 178	85.71 207	96.73 197	90.82 208	84.11 160	95.23 215	78.54 171	88.91 186	76.41 213	92.84 199	93.40 220	93.05 210	99.55 176	100.00 1

GA-MVS		90.38 192	94.59 175	85.46 212	88.30 190	98.44 166	92.18 196	83.30 166	97.89 178	58.05 253	92.86 148	84.25 174	91.27 222	96.65 146	92.61 216	99.66 156	99.43 223

USDC		90.36 193	91.68 201	88.82 172	92.58 108	98.02 177	96.27 119	79.83 200	98.37 162	70.61 211	89.05 183	67.50 251	94.17 185	95.77 177	94.43 187	99.46 196	98.62 236

thisisatest0515		90.28 194	94.32 180	85.57 211	85.23 212	97.23 190	85.44 244	83.09 168	96.80 198	72.41 197	89.82 178	90.87 129	87.93 244	95.27 188	90.39 238	99.33 217	99.88 170

TinyColmap		89.94 195	90.88 209	88.84 171	92.43 111	97.91 180	95.59 150	80.10 198	98.12 168	71.33 208	84.56 208	67.46 252	94.15 186	95.57 180	94.27 193	99.43 205	98.26 240

pm-mvs1		89.68 196	92.00 199	86.96 195	86.23 202	96.62 200	90.36 219	83.05 169	93.97 227	72.15 201	81.77 224	82.10 186	90.69 228	95.38 184	94.50 185	99.29 223	99.65 211

tpm		89.60 197	94.93 165	83.39 233	89.94 178	97.11 192	90.09 224	65.28 260	98.67 145	60.03 247	96.79 101	84.38 171	95.66 168	91.90 233	95.65 172	99.32 219	99.98 87

NR-MVSNet		89.52 198	90.71 210	88.14 184	86.19 203	96.20 208	92.07 197	84.58 146	95.54 210	75.27 185	87.52 196	67.96 249	91.24 223	94.33 199	93.45 202	99.49 188	99.97 103

DU-MVS		89.49 199	90.60 211	88.19 183	84.71 224	96.20 208	90.94 205	84.58 146	95.54 210	75.69 180	87.52 196	68.74 248	93.78 190	91.10 238	95.13 179	99.47 194	99.97 103

usedtu_blend_shiyan5		89.34 200	89.98 216	88.60 177	70.40 259	91.71 254	96.25 120	82.93 171	90.83 249	91.83 53	98.80 65	80.39 195	92.83 200	85.63 253	82.75 254	97.39 252	99.73 201

Baseline_NR-MVSNet		89.13 201	89.53 225	88.66 176	84.71 224	94.43 240	91.79 200	84.49 150	95.54 210	78.28 173	78.52 241	72.46 233	93.29 194	91.10 238	94.82 181	99.42 208	99.86 178

tfpnnormal		89.09 202	89.71 219	88.38 180	87.37 195	96.78 196	91.46 203	85.20 140	90.33 255	72.35 199	83.45 217	69.30 246	94.45 181	95.29 186	92.86 212	99.44 204	99.93 146

FE-MVSNET3		88.92 203	89.98 216	87.69 188	70.40 259	91.71 254	90.75 210	82.93 171	90.83 249	91.83 53	98.80 65	80.39 195	92.83 200	85.63 253	82.75 254	97.39 252	99.72 205

TranMVSNet+NR-MVSNet		88.88 204	89.90 218	87.69 188	84.06 236	95.68 223	91.88 198	85.23 139	95.16 216	72.54 195	83.06 220	70.14 243	92.93 198	90.81 241	94.53 184	99.48 192	99.89 164

WR-MVS_H		88.47 205	90.55 212	86.04 201	85.13 216	96.07 212	89.86 230	79.80 201	94.37 224	72.32 200	83.12 219	74.44 226	89.60 232	93.52 217	92.40 217	99.51 185	99.96 124

SixPastTwentyTwo		88.35 206	91.51 203	84.66 219	85.39 210	96.96 194	86.57 240	79.62 203	96.57 202	63.73 236	87.86 191	75.18 217	93.43 193	94.03 203	90.37 239	99.24 226	99.58 215

TransMVSNet (Re)		88.33 207	89.55 224	86.91 196	86.65 199	95.56 228	90.48 216	84.44 151	92.02 247	71.07 210	80.13 231	72.48 232	89.41 233	95.05 192	94.44 186	99.39 212	97.14 251

MVS-HIRNet		88.27 208	94.05 184	81.51 241	88.90 186	98.93 155	83.38 250	60.52 270	98.06 171	63.78 235	80.67 228	90.36 133	92.94 197	97.29 128	96.41 150	99.56 174	96.66 254

WR-MVS		88.23 209	90.15 214	86.00 203	84.39 231	95.64 224	89.96 227	81.80 187	94.46 222	71.60 205	82.10 222	74.36 227	88.76 240	92.48 230	92.20 219	99.46 196	99.83 186

CP-MVSNet		88.09 210	89.57 222	86.36 200	84.63 227	95.46 233	89.48 232	80.53 196	93.42 234	71.26 209	81.25 226	69.90 244	92.78 203	93.30 223	93.69 200	99.47 194	99.96 124

pmnet_mix02		88.07 211	92.32 197	83.10 236	86.14 204	96.23 207	81.90 256	83.05 169	98.04 172	57.59 256	84.93 207	82.02 187	90.87 227	93.54 216	91.53 230	99.06 236	99.97 103

UniMVSNet_ETH3D		88.05 212	87.01 246	89.27 167	88.53 189	97.49 185	90.35 220	83.48 163	94.57 220	77.87 175	70.08 257	61.75 263	96.22 160	90.17 242	95.21 178	99.16 231	99.82 187

anonymousdsp		87.98 213	92.38 196	82.85 237	83.68 240	96.79 195	90.78 209	74.06 236	95.29 214	57.91 255	83.33 218	83.12 178	91.15 225	95.96 174	92.37 218	99.52 181	99.76 197

LTVRE_ROB	88.65 16	87.87 214	91.11 207	84.10 230	86.64 200	97.47 186	94.40 176	78.41 213	96.13 206	52.02 264	87.95 190	65.92 257	93.59 192	95.29 186	95.09 180	99.52 181	99.95 137
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		87.84 215	89.42 227	85.99 204	85.16 214	96.01 214	90.52 215	81.78 189	94.43 223	67.59 219	81.32 225	71.87 235	91.48 220	91.25 237	91.16 234	99.43 205	99.92 150

TDRefinement		87.79 216	88.76 234	86.66 198	93.54 93	98.02 177	95.76 141	85.18 141	96.57 202	67.90 218	80.51 230	66.51 256	78.37 258	93.20 225	89.73 240	99.22 227	96.75 253

MDTV_nov1_ep13_2view		87.75 217	93.32 191	81.26 243	83.74 239	96.64 199	85.66 243	66.20 253	98.36 163	61.61 240	84.34 210	87.95 144	91.12 226	94.01 204	92.66 214	99.22 227	99.27 228

v8		87.54 218	89.33 228	85.45 213	85.41 209	95.50 231	90.32 221	78.94 209	94.35 225	66.93 224	81.90 223	70.99 240	91.62 218	91.49 236	91.22 233	99.48 192	99.87 175

v1144		87.49 219	89.64 220	84.97 216	84.73 223	95.84 221	90.17 222	79.30 205	93.96 228	64.65 233	78.83 238	73.38 231	91.51 219	93.77 211	91.77 225	99.45 200	99.93 146

v2v482		87.46 220	88.90 232	85.78 206	84.58 228	95.95 218	89.90 229	82.43 181	94.19 226	65.65 228	79.80 233	69.12 247	92.67 204	91.88 234	91.46 231	99.45 200	99.93 146

v10		87.40 221	89.62 221	84.80 218	84.93 221	95.07 237	90.44 217	75.63 229	94.51 221	66.52 226	78.87 237	73.47 230	91.86 216	93.69 214	91.87 224	99.45 200	99.86 178

pmmvs5		87.33 222	90.01 215	84.20 228	84.31 233	96.04 213	87.63 238	76.59 225	93.17 239	65.35 231	84.30 211	71.68 236	91.91 215	95.41 183	91.37 232	99.39 212	98.13 243

N_pmnet		87.31 223	91.51 203	82.41 240	85.13 216	95.57 227	80.59 259	81.79 188	96.20 204	58.52 252	78.62 239	85.66 161	89.36 234	94.64 195	92.14 220	99.08 234	97.72 249

PS-CasMVS		87.24 224	88.52 237	85.73 209	84.58 228	95.35 235	89.03 235	80.17 197	93.11 240	68.86 216	77.71 243	66.89 253	92.30 210	93.13 226	93.50 201	99.46 196	99.96 124

EU-MVSNet		87.20 225	90.47 213	83.38 234	85.11 219	93.85 245	86.10 242	79.76 202	93.30 238	65.39 230	84.41 209	78.43 204	85.04 253	92.20 232	93.03 211	98.86 238	98.05 246

PEN-MVS		87.20 225	88.22 238	86.01 202	84.01 238	94.93 238	90.00 226	81.52 194	93.46 233	69.29 214	79.69 234	65.51 258	91.72 217	91.01 240	93.12 208	99.49 188	99.84 182

EG-PatchMatch MVS		86.96 227	89.56 223	83.93 231	86.29 201	97.61 183	90.75 210	73.31 241	95.43 213	66.08 227	75.88 250	71.31 237	87.55 246	94.79 194	92.74 213	99.61 167	99.13 232

v1192		86.93 228	89.01 230	84.50 224	84.46 230	95.51 230	89.93 228	78.65 212	93.75 229	62.29 238	77.19 245	70.88 241	92.28 211	93.84 208	91.96 222	99.38 214	99.90 159

v1921920		86.81 229	88.93 231	84.33 227	84.23 234	95.41 234	90.09 224	78.10 215	93.74 230	62.17 239	76.98 247	71.14 238	92.05 213	93.69 214	91.69 228	99.32 219	99.88 170

v144192		86.80 230	88.90 232	84.35 225	84.33 232	95.56 228	89.34 233	77.74 217	93.60 231	64.03 234	77.82 242	70.76 242	91.28 221	92.91 228	91.74 227	99.37 215	99.90 159

DTE-MVSNet		86.70 231	87.66 242	85.58 210	83.30 242	94.29 241	89.74 231	81.53 192	92.77 243	68.93 215	80.13 231	64.00 261	90.62 229	89.45 243	93.34 203	99.32 219	99.67 209

gg-mvs-nofinetune		86.69 232	91.30 206	81.30 242	90.42 173	99.64 86	98.50 62	61.68 268	79.23 266	40.35 271	66.58 259	97.14 97	96.92 148	98.64 48	97.94 78	99.91 24	99.97 103

v148		86.63 233	87.79 240	85.28 214	84.65 226	95.97 215	86.46 241	82.84 175	92.91 242	71.52 207	78.99 236	66.74 255	86.83 249	89.28 244	90.69 236	99.41 210	99.94 144

dtuonlycased		86.51 234	91.31 205	80.92 244	83.57 241	94.69 239	81.41 258	75.18 231	97.02 196	59.42 249	87.86 191	85.42 164	86.86 248	88.71 245	87.20 247	99.08 234	98.25 242

gbinet_0.2-2-1-0.02		86.42 235	87.47 243	85.19 215	71.78 256	91.76 252	90.97 204	82.60 178	90.87 248	75.35 184	85.62 204	76.07 216	93.09 195	85.42 259	82.55 260	97.37 257	99.98 87

v1240		86.24 236	88.56 236	83.54 232	84.05 237	95.21 236	89.27 234	76.76 223	93.42 234	60.68 246	75.99 249	69.80 245	91.21 224	93.83 210	91.76 226	99.29 223	99.91 157

wanda-best-256-512		85.94 237	87.03 244	84.66 219	70.40 259	91.71 254	90.75 210	82.93 171	90.83 249	73.88 191	83.78 213	74.80 219	92.62 205	85.63 253	82.75 254	97.39 252	99.73 201

FE-blended-shiyan7		85.94 237	87.03 244	84.66 219	70.40 259	91.71 254	90.75 210	82.93 171	90.83 249	73.88 191	83.78 213	74.80 219	92.62 205	85.63 253	82.75 254	97.39 252	99.73 201

blended_shiyan8		85.87 239	86.93 249	84.64 222	70.41 258	91.71 254	90.90 207	82.61 177	90.54 254	74.01 190	83.77 215	74.58 223	92.53 208	85.57 258	82.67 259	97.37 257	99.66 210

blended_shiyan6		85.86 240	86.98 247	84.56 223	70.38 263	91.69 259	90.72 214	82.45 180	90.79 253	73.86 193	83.58 216	74.80 219	92.57 207	85.60 257	82.69 258	97.38 256	99.72 205

pmmvs6		85.75 241	86.97 248	84.34 226	84.88 222	95.59 226	87.41 239	79.19 207	87.81 261	67.56 220	63.05 263	77.76 206	89.15 235	93.45 219	91.90 223	97.83 249	99.21 229

v7n		85.39 242	87.70 241	82.70 238	82.77 244	95.64 224	88.27 237	74.83 232	92.30 245	62.58 237	76.37 248	64.80 260	88.38 242	94.29 201	90.61 237	99.34 216	99.87 175

gm-plane-assit		84.93 243	91.61 202	77.14 253	84.14 235	91.29 260	66.18 271	69.70 244	85.22 265	47.95 268	78.58 240	89.24 138	94.90 175	98.82 42	98.12 74	99.99 7	100.00 1

CMPMVS	65.66 17	84.62 244	85.02 251	84.15 229	95.40 75	97.79 181	88.35 236	79.22 206	89.66 258	60.71 245	72.20 254	73.94 228	87.32 247	86.73 250	84.55 252	93.90 263	90.31 264
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test_method		84.44 245	89.04 229	79.08 247	81.15 247	92.82 249	82.06 255	61.92 266	96.17 205	59.38 250	74.47 252	67.52 250	91.96 214	96.92 137	95.53 174	97.98 246	99.85 181

Anonymous20231206		84.28 246	89.53 225	78.17 250	82.31 246	94.16 243	82.57 252	76.51 226	93.38 237	52.98 261	79.47 235	73.74 229	75.45 261	95.07 191	94.41 188	99.18 230	96.46 256

new_pmnet		84.12 247	87.89 239	79.72 246	80.43 249	94.14 244	80.26 260	74.14 235	96.01 207	56.30 260	74.94 251	76.45 212	88.59 241	93.11 227	89.31 242	98.59 242	91.27 263

test20.03		83.86 248	88.73 235	78.16 251	82.60 245	93.00 246	81.61 257	74.68 233	92.36 244	57.50 257	83.01 221	74.48 225	73.30 263	92.40 231	91.14 235	99.29 223	94.75 259

pmmvs-eth3d		82.92 249	83.31 254	82.47 239	76.97 253	91.76 252	83.79 247	76.10 227	90.33 255	69.95 213	71.04 256	48.09 269	89.02 237	93.85 207	89.14 243	99.02 237	98.96 234

PM-MVS		82.79 250	84.51 252	80.77 245	77.22 252	92.13 250	83.61 249	73.31 241	93.50 232	61.06 241	77.15 246	46.52 272	90.55 230	94.14 202	89.05 246	98.85 239	99.12 233

pmmvs3		80.91 251	85.62 250	75.42 255	75.01 255	89.09 264	75.31 265	68.70 246	86.99 263	46.74 270	81.18 227	62.91 262	87.95 243	93.84 208	89.06 245	98.80 241	96.23 257

MIMVSNet1		80.64 252	83.97 253	76.76 254	68.91 265	91.15 262	78.32 264	75.47 230	89.58 259	56.64 259	65.10 260	65.17 259	82.14 254	93.51 218	91.64 229	99.10 233	91.66 262

MDA-MVSNet-bldmvs		80.30 253	82.83 255	77.34 252	69.16 264	94.29 241	72.16 266	81.97 186	90.14 257	57.32 258	94.01 134	47.97 270	86.81 250	68.74 267	86.82 249	96.63 259	97.86 247

FE-MVSNET2		79.98 254	80.91 257	78.89 248	67.11 267	92.85 247	83.34 251	77.59 218	88.33 260	59.81 248	55.71 265	48.82 268	86.33 251	93.94 205	89.34 241	99.14 232	97.39 250

new-patchmatchnet		78.17 255	80.82 259	75.07 256	76.93 254	91.20 261	71.90 267	73.32 240	86.59 264	48.91 265	67.11 258	47.85 271	81.19 255	88.18 249	87.02 248	98.19 245	97.79 248

FE-MVSNET		77.93 256	80.91 257	74.45 257	61.41 269	89.15 263	78.53 263	75.91 228	87.12 262	52.74 262	63.25 262	50.07 267	79.29 257	91.87 235	89.12 244	98.81 240	95.76 258

usedtu_dtu_shiyan2		74.26 257	75.54 260	72.77 259	60.18 272	86.34 265	79.24 262	68.68 247	77.80 267	57.94 254	47.93 268	58.22 265	76.77 259	80.13 262	80.11 263	93.82 264	98.26 240

FPMVS		73.80 258	74.62 261	72.84 258	83.09 243	84.44 267	83.89 246	73.64 239	92.20 246	48.50 266	72.19 255	59.51 264	63.16 265	69.13 266	66.26 270	84.74 269	78.59 271

WB-MVS		71.64 259	82.10 256	59.45 263	79.66 251	78.44 270	55.66 275	78.80 211	93.01 241	19.20 277	86.36 203	71.05 239	39.18 272	85.26 260	81.08 261	84.19 270	79.49 270

Gipuma		71.02 260	72.60 264	69.19 260	71.31 257	75.11 271	66.36 270	61.65 269	94.93 217	47.29 269	38.74 270	38.52 273	75.52 260	86.09 251	85.92 251	93.01 265	88.87 266
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
GG-mvs-BLEND		69.85 261	99.39 43	35.39 269	3.67 277	99.94 22	99.10 46	1.69 274	99.85 54	3.19 279	98.13 87	99.46 66	4.92 273	99.23 34	99.14 33	99.80 64	100.00 1

PMMVS2		65.18 262	68.25 265	61.59 261	61.37 270	79.72 269	59.18 274	61.80 267	64.72 270	37.33 272	53.82 266	35.59 274	54.46 270	73.94 265	80.52 262	95.40 262	89.43 265

PMVS	60.14 18	62.67 263	64.05 266	61.06 262	68.32 266	53.27 277	52.23 276	67.63 250	75.07 269	48.30 267	58.27 264	57.43 266	49.99 271	67.20 268	62.42 271	79.87 273	74.68 272
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
testmvs		61.76 264	72.90 263	48.76 266	21.21 275	68.61 272	66.11 272	37.38 272	94.83 218	33.06 273	64.31 261	29.72 275	86.08 252	74.44 264	78.71 264	48.74 275	99.65 211

E-PMN		55.33 265	55.79 268	54.81 265	59.81 273	57.23 275	38.83 277	63.59 264	64.06 272	24.66 275	35.33 272	26.40 277	58.69 267	55.41 270	70.54 267	83.26 271	81.56 269

EMVS		55.14 266	55.29 269	54.97 264	60.87 271	57.52 274	38.58 278	63.57 265	64.54 271	23.36 276	36.96 271	27.99 276	60.69 266	51.17 271	66.61 269	82.73 272	82.25 268

MVE	58.81 19	52.07 267	55.15 270	48.48 267	42.45 274	62.35 273	36.41 279	54.70 271	49.88 273	27.65 274	29.98 273	18.08 278	54.87 269	65.93 269	77.26 265	74.79 274	82.59 267
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test123		48.14 268	58.11 267	36.51 268	8.71 276	56.81 276	59.55 273	24.08 273	77.50 268	14.41 278	49.20 267	11.94 280	80.98 256	41.62 272	69.81 268	31.32 276	99.90 159

uanet_test		0.00 269	0.00 271	0.00 270	0.00 278	0.00 278	0.00 280	0.00 275	0.00 274	0.00 280	0.00 274	0.00 281	0.00 274	0.00 273	0.00 272	0.00 277	0.00 273

sosnet-low-res		0.00 269	0.00 271	0.00 270	0.00 278	0.00 278	0.00 280	0.00 275	0.00 274	0.00 280	0.00 274	0.00 281	0.00 274	0.00 273	0.00 272	0.00 277	0.00 273

sosnet		0.00 269	0.00 271	0.00 270	0.00 278	0.00 278	0.00 280	0.00 275	0.00 274	0.00 280	0.00 274	0.00 281	0.00 274	0.00 273	0.00 272	0.00 277	0.00 273

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 35	99.65 17	100.00 1	99.90 7			99.99 7	99.80 192
Ray Leroy Khuboni and Hongjun Xu: Textureless Resilient Propagation Matching in Multiple View Stereosis (TPM-MVS). SATNAC 2025
RE-MVS-def										52.74 262

9.14												100.00 1

SR-MVS					99.61 15			96.80 21				100.00 1

Anonymous202405211			95.78 143		93.26 94	99.52 92	96.70 109	88.55 84	97.93 176		88.99 184	90.68 131	98.99 64	96.46 155	97.02 139	99.64 162	99.89 164

our_test_3					85.89 205	96.09 211	82.15 254

ambc			74.33 262		66.84 268	84.26 268	84.17 245		93.39 236	58.99 251	45.93 269	18.06 279	70.61 264	93.94 205	86.62 250	92.61 267	98.13 243

MTAPA										96.61 19		100.00 1

MTMP										97.42 14		100.00 1

Patchmatch-RL test							68.01 269

tmp_tt				78.81 249	98.80 47	85.73 266	70.08 268	77.87 216	98.68 144	83.71 154	99.53 32	74.55 224	54.97 268	78.28 263	72.43 266	87.45 268

XVS					95.09 78	99.94 22	97.49 78			88.58 98		99.98 37				99.78 79

X-MVStestdata					95.09 78	99.94 22	97.49 78			88.58 98		99.98 37				99.78 79

mPP-MVS					99.23 40							99.87 48

NP-MVS									99.79 64

Patchmtry						99.00 148	95.46 153	65.50 257		67.51 221

DeepMVS_CX						97.31 188	79.48 261	89.65 70	98.66 147	60.89 244	94.40 127	66.89 253	87.65 245	81.69 261		92.76 266	94.24 261

High-resolution multi-view benchmark