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 8	99.95 15	99.91 1	97.15 2	99.93 36	99.83 3	99.61 24	100.00 1	99.94 1	99.86 14	99.29 27	100.00 1	100.00 1

MED-MVS		99.54 2	99.73 17	99.32 2	99.64 8	99.96 7	99.75 20	96.98 5	99.98 4	99.84 2	99.61 24	100.00 1	99.91 5	99.72 22	98.84 46	99.79 72	100.00 1

SED-MVS		99.44 3	99.69 23	99.15 3	99.61 16	99.95 15	99.81 8	96.94 10	99.97 11	98.73 5	99.53 33	100.00 1	99.91 5	99.90 8	98.52 62	99.87 33	100.00 1

SF-MVS		99.41 4	99.68 25	99.10 5	99.65 7	99.94 22	99.76 13	96.95 7	99.88 47	98.39 8	99.60 26	100.00 1	99.82 17	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 11	99.96 7	99.76 13	96.87 18	99.95 28	97.66 10	99.57 31	100.00 1	99.63 33	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 20	99.99 1	99.83 6	96.82 20	99.92 41	97.58 13	99.58 30	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 13	96.91 13	99.98 4	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 11	99.94 22	99.72 27	96.99 4	99.98 4	98.85 4	98.21 87	100.00 1	99.88 11	99.88 11	98.96 38	99.85 37	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 16	99.96 7	99.85 4	96.94 10	99.96 22	97.38 16	99.60 26	100.00 1	99.70 24	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 20	99.94 22	99.87 3	96.95 7	99.94 33	97.42 14	99.62 23	100.00 1	99.80 20	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 11	99.94 22	99.85 4	96.90 17	99.97 11	97.64 11	99.50 37	100.00 1	99.88 11	99.90 8	98.60 57	99.87 33	100.00 1

SMA-MVS		99.34 12	99.79 5	98.81 15	99.69 1	99.94 22	99.75 20	96.91 13	99.98 4	96.76 18	99.37 44	100.00 1	99.90 8	99.88 11	99.46 17	99.84 40	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 16	99.92 44	99.77 12	96.91 13	99.93 36	96.31 22	99.59 29	99.95 45	99.84 15	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 10	99.96 7	99.76 13	96.91 13	99.97 11	95.86 26	99.67 12	100.00 1	99.75 21	99.85 15	98.80 51	99.98 12	99.97 103

CNLPA		99.24 14	99.58 36	98.85 14	99.34 36	99.95 15	99.32 41	96.65 30	99.96 22	98.44 7	98.97 59	100.00 1	99.57 35	98.66 46	99.56 15	99.76 92	99.97 103

AdaColmap		99.21 16	99.45 42	98.92 10	99.67 5	99.95 15	99.65 32	96.77 25	99.97 11	97.67 9	100.00 1	99.69 59	99.93 2	99.26 33	97.25 125	99.85 37	100.00 1

HFP-MVS		99.19 17	99.77 9	98.51 20	99.55 24	99.94 22	99.76 13	96.84 19	99.88 47	95.27 30	99.67 12	100.00 1	99.85 14	99.56 25	99.36 22	99.79 72	99.97 103

PLC	98.06 1	99.17 18	99.38 44	98.92 10	99.47 26	99.90 53	99.48 37	96.47 35	99.96 22	98.73 5	99.52 36	100.00 1	99.55 37	98.54 60	97.73 93	99.84 40	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 56	99.95 15	99.74 24	96.94 10	99.96 22	96.60 20	99.47 40	100.00 1	99.88 11	99.15 35	99.59 13	99.84 40	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 28	99.94 22	99.63 34	96.62 32	99.82 60	95.92 25	99.65 17	100.00 1	99.71 23	99.76 18	98.56 59	99.83 46	100.00 1

ACMMPR		99.12 21	99.76 10	98.36 22	99.45 28	99.94 22	99.75 20	96.70 29	99.93 36	94.65 34	99.65 17	99.96 43	99.84 15	99.51 28	99.35 23	99.79 72	99.96 124

MCST-MVS		99.08 22	99.72 20	98.33 23	99.59 23	99.97 3	99.78 11	96.96 6	99.95 28	93.72 39	99.67 12	100.00 1	99.90 8	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 30	99.93 38	99.60 35	95.92 40	99.77 68	97.01 17	99.67 12	100.00 1	99.72 22	99.56 25	97.76 88	99.70 141	99.98 87

DeepC-MVS_fast	98.03 2	99.05 24	99.78 6	98.21 26	99.47 26	99.97 3	99.75 20	96.80 21	99.97 11	93.58 41	98.68 70	99.94 46	99.69 25	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 57	99.92 44	99.71 29	96.80 21	99.96 22	95.58 28	98.71 69	100.00 1	99.68 27	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 11	99.94 22	99.74 24	96.95 7	99.87 51	93.76 38	99.49 39	100.00 1	99.39 43	99.73 19	98.35 65	99.89 29	99.96 124

SteuartSystems-ACMMP		98.95 27	99.80 3	97.95 29	99.43 31	99.96 7	99.76 13	96.45 36	99.82 60	93.63 40	99.64 21	100.00 1	98.56 88	99.90 8	99.31 25	99.84 40	100.00 1
Skip Steuart: Steuart Systems R&D Blog.
PHI-MVS		98.85 28	99.67 26	97.89 30	98.63 51	99.93 38	98.95 52	95.20 42	99.84 58	94.94 31	99.74 11	100.00 1	99.69 25	98.40 67	99.75 11	99.93 22	99.99 67

MP-MVS		98.82 29	99.63 30	97.88 31	99.41 32	99.91 52	99.74 24	96.76 26	99.88 47	91.89 52	99.50 37	99.94 46	99.65 30	99.71 23	98.49 63	99.82 50	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 11	99.92 44	99.72 27	96.78 24	99.71 73	90.13 81	99.66 16	99.99 36	99.64 31	99.78 17	98.14 73	99.82 50	99.89 164

train_agg		98.62 31	99.76 10	97.28 34	99.03 44	99.93 38	99.65 32	96.37 37	99.98 4	89.24 93	99.53 33	99.83 51	99.59 34	99.85 15	99.19 32	99.80 65	100.00 1

X-MVS		98.62 31	99.75 13	97.29 33	99.50 25	99.94 22	99.71 29	96.55 33	99.85 55	88.58 99	99.65 17	99.98 38	99.67 28	99.60 24	99.26 29	99.77 84	99.97 103

OMC-MVS		98.59 33	99.07 50	98.03 28	99.41 32	99.90 53	99.26 44	94.33 44	99.94 33	96.03 23	96.68 104	99.72 58	99.42 40	98.86 40	98.84 46	99.72 132	99.58 216

DPM-MVS		98.58 34	99.78 6	97.17 36	98.02 55	99.64 87	99.80 10	96.72 28	99.96 22	90.05 83	99.57 31	100.00 1	98.66 84	99.56 25	99.96 2	99.80 65	99.80 193

PGM-MVS		98.47 35	99.73 17	97.00 38	99.68 3	99.94 22	99.76 13	91.74 50	99.84 58	91.17 68	100.00 1	99.69 59	99.81 18	99.38 31	99.30 26	99.82 50	99.95 137

MGCNet		98.44 36	99.67 26	97.00 38	97.82 59	99.92 44	99.46 38	91.78 49	99.95 28	94.10 36	100.00 1	100.00 1	98.91 70	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 43	99.94 22	99.67 31	96.73 27	99.97 11	86.30 132	98.30 78	99.99 36	98.78 78	99.73 19	99.57 14	99.88 32	99.98 87

CSCG		98.22 38	98.37 71	98.04 27	99.60 20	99.82 63	99.45 39	93.59 45	99.16 113	96.46 21	98.22 86	95.86 109	99.41 42	96.33 160	99.22 30	99.75 103	99.94 144

3Dnovator+	95.21 7	98.17 39	99.08 49	97.12 37	99.28 39	99.78 74	98.61 59	89.93 64	99.93 36	95.36 29	95.50 115	100.00 1	99.56 36	98.58 55	99.80 10	99.95 17	99.97 103

ACMMP		98.16 40	99.01 51	97.18 35	98.86 46	99.92 44	98.77 57	95.73 41	99.31 108	91.15 69	100.00 1	99.81 53	98.82 76	98.11 90	95.91 168	99.77 84	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 41	99.93 38	97.79 71	91.84 48	99.87 51	90.53 77	100.00 1	99.57 64	98.93 69	99.44 29	99.08 35	99.85 37	99.95 137

EPNet		98.11 42	99.63 30	96.34 48	98.44 53	99.88 58	98.55 60	90.25 60	99.93 36	92.60 48	100.00 1	99.73 56	98.41 95	98.87 39	99.02 36	99.82 50	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 84	99.92 44	99.22 45	89.98 62	99.97 11	94.77 33	99.94 10	100.00 1	99.43 39	98.52 64	98.53 61	99.79 72	100.00 1

3Dnovator	95.01 8	97.98 44	98.89 56	96.92 41	99.36 34	99.76 77	98.72 58	89.98 62	99.98 4	93.99 37	94.60 129	99.43 69	99.50 38	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 40	99.97 3	97.03 99	90.44 57	99.89 45	90.75 72	100.00 1	99.73 56	98.68 83	98.67 45	98.89 43	99.95 17	99.97 103

QAPM		97.90 46	98.89 56	96.74 42	99.35 35	99.80 69	98.84 54	90.20 61	99.94 33	92.85 43	94.17 133	99.78 54	99.42 40	98.71 43	99.87 7	99.79 72	99.98 87

CDPH-MVS		97.88 47	99.59 34	95.89 52	98.90 45	99.95 15	99.40 40	92.86 47	99.86 54	85.33 145	98.62 72	99.45 68	99.06 64	99.29 32	99.94 4	99.81 60	100.00 1

CANet		97.62 48	98.94 54	96.08 50	97.19 62	99.93 38	99.29 43	90.38 58	99.87 51	91.00 70	95.79 114	99.51 65	98.72 82	98.53 61	99.00 37	99.90 27	99.99 67

TAPA-MVS	96.62 5	97.60 49	98.46 70	96.60 45	98.73 49	99.90 53	99.30 42	94.96 43	99.46 91	87.57 112	96.05 111	98.53 81	99.26 54	98.04 95	97.33 121	99.77 84	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 20	95.07 67	98.45 52	99.96 7	93.83 184	95.93 39	100.00 1	90.79 71	98.38 77	99.85 50	95.28 172	99.94 2	99.97 1	96.15 262	99.97 103

SPE-MVS-test		97.51 51	99.18 47	95.56 57	97.16 63	99.96 7	97.39 85	89.82 67	100.00 1	89.88 84	99.16 51	98.38 87	99.23 56	98.85 41	97.93 80	99.87 33	100.00 1

PCF-MVS	97.20 3	97.49 52	98.20 76	96.66 44	97.62 60	99.92 44	98.93 53	96.64 31	98.53 154	88.31 106	94.04 136	99.58 63	98.94 66	97.53 116	97.79 86	99.54 178	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 67	99.93 38	97.62 77	89.69 68	99.98 4	91.33 65	98.53 75	97.50 96	98.77 79	98.60 53	98.35 65	99.92 24	100.00 1

MSDG		97.29 54	97.55 92	97.00 38	98.66 50	99.71 82	99.03 50	96.15 38	99.59 80	89.67 90	92.77 153	94.86 112	98.75 80	98.22 79	97.94 78	99.72 132	99.76 198

CHOSEN 280x420		97.16 55	99.58 36	94.35 83	96.95 66	99.97 3	97.19 92	81.55 191	99.92 41	91.75 59	100.00 1	100.00 1	98.84 75	98.55 57	98.65 56	99.79 72	99.97 103

MVSMamba_PlusPlus		97.06 56	99.09 48	94.69 76	92.17 118	99.75 78	99.05 49	89.87 66	99.95 28	87.59 111	97.96 91	97.97 91	99.64 31	98.62 51	99.46 17	99.82 50	99.96 124

DELS-MVS		97.05 57	98.05 81	95.88 54	97.09 64	99.99 1	98.82 55	90.30 59	98.44 160	91.40 63	92.91 150	96.57 102	97.68 142	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 61	99.92 44	99.10 47	96.54 34	99.34 105	86.64 126	91.93 163	93.15 123	99.11 62	99.11 36	99.68 12	99.73 122	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 89	99.80 69	97.33 88	89.48 72	98.90 133	95.99 24	99.11 54	92.84 125	98.14 115	98.14 86	98.32 69	99.82 50	99.51 221

MAR-MVS		97.03 60	98.00 83	95.89 52	99.32 37	99.74 81	96.76 109	84.89 143	99.97 11	94.86 32	98.29 79	90.58 133	99.67 28	98.02 97	99.50 16	99.82 50	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	92.71 108	97.43 188	99.03 50	85.52 136	99.82 60	92.74 46	99.15 52	99.94 46	99.19 59	98.66 46	96.99 140	99.79 72	99.98 87

EC-MVSNet		96.90 62	99.32 45	94.07 85	91.64 156	99.30 130	98.18 67	85.61 135	99.97 11	89.79 85	99.33 45	99.31 72	99.28 52	98.48 66	98.86 44	99.91 25	100.00 1

baseline1		96.87 63	98.55 64	94.91 69	92.89 107	99.45 102	96.34 117	88.54 85	98.88 136	92.82 44	98.93 61	96.58 101	99.07 63	98.19 81	98.04 75	99.80 65	99.78 195

OpenMVS	94.03 11	96.87 63	98.10 79	95.44 61	99.29 38	99.78 74	98.46 65	89.92 65	99.47 90	85.78 141	91.05 173	98.50 82	99.30 50	98.49 65	99.41 19	99.89 29	99.98 87

PatchMatch-RL		96.84 65	98.03 82	95.47 58	98.84 47	99.81 67	95.61 150	89.20 76	99.65 77	91.28 66	99.39 41	93.46 121	98.18 112	98.05 93	96.28 152	99.69 146	99.55 218

ETV-MVS		96.79 66	99.19 46	94.00 87	91.78 141	99.63 89	97.15 94	88.00 91	99.95 28	88.34 105	99.32 46	98.71 78	98.82 76	98.69 44	98.01 76	99.90 27	100.00 1

IS_MVSNet		96.66 67	98.62 63	94.38 79	92.41 114	99.70 83	97.19 92	87.67 106	99.05 122	91.27 67	95.09 120	98.46 86	97.95 127	98.64 48	99.37 20	99.79 72	100.00 1

PMMVS		96.45 68	98.24 75	94.36 82	92.58 109	99.01 148	97.08 98	87.42 126	99.88 47	90.06 82	99.39 41	94.63 113	99.33 47	97.85 103	96.99 140	99.70 141	99.96 124

LS3D		96.44 69	97.31 99	95.41 62	97.06 65	99.87 59	99.51 36	97.48 1	99.57 81	79.00 169	95.39 116	89.19 140	99.81 18	98.55 57	98.84 46	99.62 167	99.78 195

EIA-MVS		96.34 70	98.55 64	93.76 94	91.93 131	99.66 85	97.14 95	88.33 89	99.51 85	85.98 137	98.82 65	96.08 107	99.33 47	98.38 70	97.40 115	99.81 60	100.00 1

EPP-MVSNet		96.29 71	98.34 72	93.90 89	91.77 143	99.38 110	95.45 156	87.25 131	99.38 100	91.36 64	94.86 127	98.49 84	97.83 135	98.01 98	98.23 71	99.75 103	99.99 67

UGNet		96.05 72	98.55 64	93.13 117	94.64 85	99.65 86	94.70 172	87.78 95	99.40 99	89.69 89	98.25 82	99.25 74	92.12 213	96.50 151	97.08 135	99.84 40	99.72 206
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 58	99.52 93	98.81 56	91.40 53	99.42 95	84.97 148	90.46 178	96.82 100	98.05 120	96.46 155	96.19 155	99.54 178	98.92 236
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 69	99.89 56	95.64 146	91.23 54	99.75 70	91.59 61	96.80 101	82.44 183	98.05 120	98.53 61	97.92 81	99.80 65	100.00 1

PVSNet_Blended		96.01 74	96.48 122	95.46 59	96.47 69	99.89 56	95.64 146	91.23 54	99.75 70	91.59 61	96.80 101	82.44 183	98.05 120	98.53 61	97.92 81	99.80 65	100.00 1

thisisatest0530		95.89 76	98.32 73	93.06 124	91.76 144	99.75 78	94.94 164	87.60 112	99.91 43	86.66 125	98.28 80	99.98 38	97.72 138	97.10 132	93.24 204	99.65 159	99.95 137

tttt0517		95.88 77	98.31 74	93.04 125	91.75 146	99.75 78	94.90 165	87.60 112	99.91 43	86.63 127	98.28 80	99.98 38	97.72 138	97.10 132	93.24 204	99.65 159	99.95 137

thres100view900		95.86 78	96.62 116	94.97 68	93.10 97	99.83 61	97.76 72	89.15 77	98.62 150	90.69 73	99.00 56	84.86 167	99.30 50	97.57 114	96.48 147	99.81 60	100.00 1

RPSCF		95.86 78	96.94 111	94.61 77	96.52 68	98.67 164	98.54 61	88.43 87	99.56 82	90.51 79	99.39 41	98.70 79	97.72 138	93.77 212	92.00 221	95.93 263	96.50 257

DCV-MVSNet		95.85 80	97.53 93	93.89 90	93.20 96	97.01 194	97.14 95	84.77 144	99.16 113	90.38 80	98.96 60	93.73 118	98.23 111	96.57 150	97.37 116	99.64 163	99.93 146

baseline		95.85 80	98.13 78	93.20 115	92.29 117	99.58 91	97.49 79	84.33 153	99.44 92	87.28 118	97.00 99	94.04 117	97.93 128	98.36 72	98.47 64	99.87 33	99.99 67

sasdasda		95.80 82	97.02 104	94.37 80	92.96 103	99.47 98	97.49 79	84.58 146	99.44 92	92.05 50	98.54 73	86.65 150	99.37 44	96.18 164	98.93 40	99.77 84	99.92 150

canonicalmvs		95.80 82	97.02 104	94.37 80	92.96 103	99.47 98	97.49 79	84.58 146	99.44 92	92.05 50	98.54 73	86.65 150	99.37 44	96.18 164	98.93 40	99.77 84	99.92 150

tfpn200view9		95.78 84	96.54 119	94.89 71	93.10 97	99.82 63	97.67 73	88.85 80	98.62 150	90.69 73	99.00 56	84.86 167	99.28 52	97.41 124	96.10 158	99.76 92	99.99 67

thres200		95.77 85	96.55 118	94.86 72	93.09 99	99.82 63	97.63 76	88.85 80	98.49 155	90.66 75	98.99 58	84.86 167	99.20 57	97.41 124	96.28 152	99.76 92	100.00 1

MVS_Test		95.74 86	98.18 77	92.90 129	92.16 119	99.49 97	97.36 86	84.30 154	99.79 65	84.94 149	96.65 105	93.63 120	98.85 74	98.61 52	99.10 34	99.81 60	100.00 1

thres400		95.72 87	96.48 122	94.84 73	93.00 102	99.83 61	97.55 78	88.93 78	98.49 155	90.61 76	98.86 62	84.63 171	99.20 57	97.45 118	96.10 158	99.77 84	99.99 67

MGCFI-Net		95.71 88	96.97 110	94.25 84	92.90 106	99.44 105	97.35 87	84.44 151	99.42 95	91.70 60	98.51 76	86.56 153	99.33 47	96.09 169	98.83 49	99.77 84	99.92 150

thres600view7		95.64 89	96.38 126	94.79 75	92.96 103	99.82 63	97.48 84	88.85 80	98.38 161	90.52 78	98.84 64	84.61 172	99.15 60	97.41 124	95.60 173	99.76 92	99.99 67

Vis-MVSNet (Re-imp)		95.60 90	98.52 69	92.19 139	92.37 115	99.56 92	96.37 115	87.41 127	98.95 128	84.77 152	94.88 126	98.48 85	92.44 210	98.63 50	99.37 20	99.76 92	99.77 197

FMVSNet3		95.59 91	97.51 95	93.34 105	89.48 181	96.57 202	97.67 73	84.17 156	99.48 87	89.76 86	95.09 120	94.35 114	99.14 61	98.37 71	98.86 44	99.82 50	99.89 164

ECVR-MVS		95.46 92	95.58 151	95.31 64	94.12 89	99.80 69	97.33 88	89.48 72	98.90 133	92.99 42	87.97 193	86.41 156	98.14 115	98.14 86	98.32 69	99.82 50	99.52 220

E2		95.42 93	96.83 113	93.78 92	91.73 148	99.38 110	96.39 114	87.87 92	98.79 140	88.36 104	95.90 113	88.17 142	98.59 86	97.72 106	97.85 83	99.75 103	99.98 87

PVSNet_Blended_VisFu		95.37 94	97.44 97	92.95 126	95.20 77	99.80 69	92.68 193	88.41 88	99.12 116	87.64 110	88.31 192	99.10 75	94.07 188	98.27 75	97.51 105	99.73 122	100.00 1

DI_MVS_pp		95.29 95	97.02 104	93.28 109	91.76 144	99.52 93	97.84 70	85.67 134	99.08 120	87.29 117	87.76 197	97.46 97	97.31 146	97.83 104	97.48 107	99.83 46	100.00 1

ET-MVSNet_ETH3D		95.20 96	97.82 88	92.15 140	80.77 250	98.13 176	97.65 75	86.93 132	99.72 72	88.56 102	99.29 49	97.01 99	99.24 55	94.58 198	95.98 165	99.75 103	99.99 67

TSAR-MVS + COLMAP		95.20 96	95.03 163	95.41 62	96.17 71	98.69 163	99.11 46	93.40 46	99.97 11	84.89 150	98.23 84	75.01 220	99.34 46	97.27 129	96.37 151	99.58 171	99.64 214

GBi-Net		95.19 98	96.99 108	93.09 119	89.11 182	96.47 204	96.90 101	84.17 156	99.48 87	89.76 86	95.09 120	94.35 114	98.87 71	96.50 151	97.21 126	99.74 109	99.81 189

test1		95.19 98	96.99 108	93.09 119	89.11 182	96.47 204	96.90 101	84.17 156	99.48 87	89.76 86	95.09 120	94.35 114	98.87 71	96.50 151	97.21 126	99.74 109	99.81 189

Casviewmamba		95.18 100	96.38 126	93.78 92	91.93 131	99.35 119	96.87 104	87.70 100	98.75 142	87.92 108	93.22 148	87.56 148	98.54 89	98.23 78	97.74 91	99.75 103	99.84 183

test1111		95.15 101	95.18 159	95.12 65	94.07 91	99.80 69	97.20 91	89.53 71	98.80 139	92.22 49	85.44 209	86.24 158	97.89 130	98.12 88	98.34 68	99.80 65	99.51 221

test0.0.03 1		95.15 101	97.87 87	91.99 141	91.69 150	98.82 159	93.04 190	83.60 161	99.65 77	88.80 97	94.15 134	97.67 94	94.97 174	96.62 148	98.16 72	99.83 46	100.00 1

baseline2		95.13 103	98.55 64	91.15 147	90.29 177	99.00 149	94.49 176	82.00 185	99.68 75	84.82 151	96.47 106	99.30 73	95.71 166	98.24 77	97.14 133	99.57 173	100.00 1

viewcassd2359sk11		95.10 104	96.36 128	93.63 95	91.68 153	99.37 114	96.09 126	87.78 95	98.72 143	88.01 107	94.74 128	86.41 156	98.47 92	97.69 108	97.61 100	99.73 122	99.98 87

casdiffmvs_mvg		95.10 104	96.45 125	93.53 97	92.05 126	99.42 107	97.25 90	87.66 107	97.17 192	86.09 133	91.79 165	91.27 127	98.31 105	98.06 92	97.42 114	99.81 60	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 54	98.47 166	96.54 111	89.36 74	99.78 67	65.65 229	99.31 47	98.24 89	94.79 177	98.28 74	99.35 23	99.93 22	98.27 240
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	91.86 135	99.37 114	97.00 100	87.63 110	98.85 137	89.09 95	91.13 171	86.81 149	98.59 86	98.19 81	97.47 108	99.76 92	99.86 179

Anonymous20231211		94.96 108	94.99 164	94.91 69	93.01 101	99.44 105	96.85 106	88.49 86	98.78 141	92.61 47	83.94 216	90.25 135	98.94 66	95.87 175	96.77 142	99.58 171	99.89 164

UA-Net		94.95 109	98.66 62	90.63 151	94.60 87	98.94 155	96.03 128	85.28 138	98.01 174	78.92 170	97.42 97	99.96 43	89.09 237	98.95 38	98.80 51	99.82 50	98.57 238

CANet_DTU		94.90 110	98.98 52	90.13 159	94.74 83	99.81 67	98.53 62	82.23 183	99.97 11	66.76 226	100.00 1	98.50 82	98.74 81	97.52 117	97.19 131	99.76 92	99.88 170

viewdifsd2359ckpt09		94.88 111	96.22 130	93.31 106	91.61 158	99.38 110	96.37 115	87.74 97	98.82 138	85.85 138	93.69 141	86.65 150	98.61 85	97.57 114	97.44 111	99.72 132	100.00 1

viewdifsd2359ckpt07		94.83 112	96.18 135	93.25 112	91.96 130	99.31 128	97.10 97	87.65 108	98.66 148	85.26 146	91.50 168	88.11 143	97.77 137	98.16 83	97.69 95	99.74 109	99.84 183

viewdifsd2359ckpt13		94.69 113	96.20 133	92.93 128	91.67 155	99.42 107	95.73 143	87.71 99	98.67 146	84.46 153	94.31 131	86.03 160	98.27 110	97.60 111	97.35 119	99.73 122	99.99 67

E3new		94.68 114	95.67 149	93.52 99	91.63 157	99.36 117	95.96 131	87.69 104	97.81 180	87.65 109	93.38 144	84.22 177	98.48 91	97.44 119	97.52 103	99.71 136	99.96 124

E3		94.68 114	95.70 145	93.49 100	91.68 153	99.37 114	95.98 130	87.70 100	97.97 176	87.46 114	93.38 144	84.35 174	98.42 93	97.43 120	97.47 108	99.71 136	99.96 124

hybrid		94.67 116	95.86 142	93.27 111	92.11 122	99.25 138	95.62 148	87.59 114	99.37 101	86.71 123	95.07 124	82.63 182	97.88 131	97.23 130	97.50 106	99.72 132	100.00 1

onestephybrid01		94.66 117	95.70 145	93.44 101	92.13 121	99.27 135	95.49 153	87.83 94	99.33 107	87.53 113	91.54 167	85.46 165	97.92 129	96.65 146	97.63 98	99.76 92	100.00 1

viewmamba		94.61 118	95.73 144	93.30 107	92.07 124	99.30 130	95.91 136	87.51 119	99.29 110	86.31 131	93.17 149	84.33 175	98.28 109	96.42 158	97.61 100	99.73 122	99.99 67

viewmanbaseed2359cas		94.61 118	95.93 140	93.07 123	91.90 134	99.38 110	96.32 118	87.84 93	98.33 165	84.29 154	92.71 154	85.68 162	98.33 104	97.68 109	97.74 91	99.74 109	99.99 67

FC-MVSNet-train		94.61 118	96.27 129	92.68 136	92.35 116	97.14 192	93.45 188	87.73 98	98.93 129	87.31 116	96.42 107	89.35 138	95.67 167	96.06 173	96.01 164	99.56 175	99.98 87

diffmvs		94.60 121	95.63 150	93.41 103	91.98 129	99.30 130	96.86 105	87.62 111	99.30 109	86.07 136	94.12 135	81.63 193	98.16 113	97.43 120	97.60 102	99.76 92	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	91.84 137	99.46 101	95.92 132	87.54 118	98.45 158	86.57 129	90.51 177	84.72 170	98.49 90	97.97 99	97.80 85	99.77 84	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 93	98.36 169	98.12 68	89.68 69	99.35 104	89.62 91	95.19 118	77.08 210	96.66 157	95.51 181	95.67 171	99.74 109	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	92.09 123	99.29 134	95.82 137	87.55 117	99.34 105	86.52 130	93.79 140	82.79 181	97.99 125	96.33 160	97.43 113	99.71 136	100.00 1

viewmambaseed2359dif		94.51 124	95.35 156	93.53 97	91.78 141	99.34 120	96.78 108	87.58 116	98.29 166	86.97 122	92.34 156	84.00 178	98.35 101	96.15 167	97.31 124	99.74 109	100.00 1

FMVSNet2		94.48 126	95.95 138	92.77 134	89.11 182	96.47 204	96.90 101	83.38 164	99.11 117	88.64 98	87.50 202	92.26 126	98.87 71	97.91 101	98.60 57	99.74 109	99.81 189

HQP-MVS		94.48 126	95.39 155	93.42 102	95.10 78	98.35 170	98.19 66	91.41 52	99.77 68	79.79 166	99.30 48	77.08 210	96.25 160	96.93 135	96.28 152	99.76 92	99.99 67

dtuplus		94.35 128	95.26 157	93.30 107	91.49 164	99.32 127	96.08 127	87.45 123	97.99 175	86.60 128	91.07 172	85.48 164	98.42 93	95.75 178	97.18 132	99.73 122	100.00 1

FA-MVS(training)		94.33 129	97.52 94	90.60 153	92.42 113	99.77 76	96.13 125	68.75 245	99.05 122	88.49 103	91.95 161	99.48 66	98.12 118	98.39 68	94.02 196	99.68 148	99.98 87

MDTV_nov1_ep13		94.32 130	98.77 61	89.14 169	91.70 149	99.52 93	95.21 159	72.09 243	99.80 63	78.91 171	96.32 108	99.62 61	97.71 141	98.39 68	97.71 94	99.22 228	100.00 1

CDS-MVSNet		94.32 130	97.00 107	91.19 146	89.82 180	98.71 162	95.51 152	85.14 142	96.85 199	82.33 161	92.48 155	96.40 105	94.71 178	96.86 138	97.76 88	99.63 165	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	92.02 127	99.21 139	94.31 178	66.97 251	99.50 86	95.61 27	96.22 110	98.64 80	96.08 162	93.71 214	94.03 195	99.52 182	99.98 87

ACMM	94.44 10	94.26 133	94.62 174	93.84 91	94.86 82	97.73 183	93.48 187	90.76 56	99.27 111	87.46 114	99.04 55	76.60 212	96.76 155	96.37 159	93.76 199	99.74 109	99.55 218
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	91.86 135	99.26 137	96.42 112	87.48 120	99.02 125	85.45 144	92.20 158	80.25 203	98.14 115	97.16 131	97.69 95	99.73 122	100.00 1

ACMP	94.49 9	94.19 135	94.74 172	93.56 96	94.25 88	98.32 172	96.02 129	89.35 75	98.90 133	87.28 118	99.14 53	76.41 215	94.94 175	96.07 172	94.35 192	99.49 189	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	91.52 160	99.34 120	95.92 132	87.70 100	97.17 192	86.08 134	91.24 169	82.32 185	98.41 95	96.85 139	97.36 117	99.68 148	99.96 124

E5		94.12 136	94.89 166	93.22 113	91.52 160	99.34 120	95.92 132	87.70 100	97.17 192	86.08 134	91.24 169	82.32 185	98.41 95	96.85 139	97.36 117	99.68 148	99.96 124

EPMVS		94.08 138	98.54 68	88.87 170	92.51 111	99.47 98	94.18 180	66.53 252	99.68 75	82.40 160	95.24 117	99.40 70	97.86 132	98.12 88	97.99 77	99.75 103	99.88 170

E6new		93.99 139	94.76 169	93.09 119	91.51 162	99.33 125	95.80 139	87.45 123	97.13 195	85.80 139	90.97 174	81.86 190	98.30 106	96.74 143	97.32 122	99.67 152	99.95 137

E6		93.99 139	94.76 169	93.09 119	91.51 162	99.33 125	95.80 139	87.45 123	97.13 195	85.80 139	90.97 174	81.86 190	98.30 106	96.74 143	97.32 122	99.67 152	99.95 137

E4		93.99 139	94.72 173	93.13 117	91.53 159	99.34 120	95.92 132	87.59 114	97.20 190	85.67 142	90.19 179	82.18 187	98.41 95	96.83 141	97.34 120	99.68 148	99.96 124

viewmacassd2359aftdt		93.75 142	94.76 169	92.58 137	91.75 146	99.34 120	95.82 137	87.64 109	97.11 197	82.51 159	89.66 182	83.19 179	98.02 123	96.61 149	97.45 110	99.71 136	99.97 103

test-LLR		93.71 143	97.23 100	89.60 163	91.69 150	99.10 145	94.68 174	83.60 161	99.36 102	71.94 203	93.82 138	96.51 103	95.96 164	97.42 122	94.37 189	99.74 109	99.99 67

CHOSEN 1792x2688		93.69 144	94.89 166	92.28 138	96.17 71	99.84 60	95.69 145	83.17 167	98.54 153	82.04 162	77.58 248	91.15 129	96.90 150	98.36 72	98.82 50	99.73 122	99.98 87

viewdifsd2359ckpt11		93.64 145	94.30 181	92.88 131	91.82 139	98.82 159	94.88 166	87.46 121	99.08 120	86.98 121	92.20 158	80.79 194	97.85 133	93.32 222	96.13 156	98.30 245	99.75 200

viewmsd2359difaftdt		93.64 145	94.29 182	92.89 130	91.82 139	98.82 159	94.88 166	87.46 121	99.04 124	87.03 120	92.20 158	80.78 195	97.85 133	93.31 223	96.13 156	98.30 245	99.75 200

LGP-MVS_train		93.60 147	95.05 161	91.90 142	94.90 81	98.29 173	97.93 69	88.06 90	99.14 115	74.83 188	99.26 50	76.50 213	96.07 163	96.31 162	95.90 170	99.59 169	99.97 103

SCA		93.53 148	98.90 55	87.27 191	92.01 128	99.30 130	93.43 189	65.72 256	99.80 63	75.20 187	97.66 95	99.74 55	97.44 144	98.21 80	97.62 99	99.84 40	100.00 1

FMVSNet5		93.53 148	96.09 136	90.56 154	86.74 198	92.84 250	92.64 194	77.50 219	99.41 98	88.97 96	98.02 90	97.81 92	98.00 124	94.85 193	95.43 175	99.50 188	94.25 262

OPM-MVS		93.50 150	93.00 195	94.07 85	95.82 74	98.26 174	98.49 64	91.62 51	94.69 222	81.93 163	92.82 152	76.18 217	96.82 152	96.12 168	94.57 183	99.74 109	98.39 239
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	91.69 150	98.65 165	93.88 183	67.64 249	98.97 126	89.16 94	97.79 93	88.92 141	97.97 126	95.14 190	96.06 160	99.63 165	100.00 1

IterMVS-LS		93.50 150	96.22 130	90.33 157	90.93 168	95.50 233	94.83 169	80.54 195	98.92 130	79.11 168	90.64 176	93.70 119	96.79 153	96.93 135	97.85 83	99.78 80	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	91.93 131	99.39 109	92.55 195	66.06 254	99.71 73	75.61 183	98.24 83	99.59 62	97.35 145	97.87 102	97.64 97	99.83 46	99.43 224
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 75	99.31 128	95.62 148	77.23 221	99.42 95	81.88 164	88.92 189	96.06 108	93.80 190	96.45 157	93.11 209	99.65 159	98.10 246

0.3-1-1-0.015		93.45 155	93.88 185	92.95 126	85.17 215	95.96 217	96.24 123	87.68 105	97.58 183	91.83 53	98.67 71	80.39 197	98.94 66	88.61 247	96.06 160	97.85 249	99.90 159

0.4-1-1-0.2		93.35 156	93.81 186	92.80 132	85.14 217	95.96 217	96.25 121	87.39 128	97.58 183	91.79 57	98.23 84	80.39 197	98.39 99	88.57 248	96.06 160	97.85 249	99.91 157

dmvs_re		93.34 157	94.59 175	91.88 143	87.97 193	99.14 144	95.29 158	88.61 83	98.09 171	82.71 158	97.34 98	78.96 204	96.98 148	94.62 196	93.98 197	99.73 122	99.98 87

0.4-1-1-0.1		93.31 158	93.77 187	92.77 134	85.13 218	95.94 220	96.21 124	87.29 129	97.58 183	91.79 57	98.11 89	80.39 197	98.36 100	88.54 249	95.98 165	97.82 252	99.89 164

tpm cat1		93.29 159	96.53 120	89.50 165	91.84 137	99.18 142	94.70 172	67.70 248	98.38 161	86.67 124	89.16 185	99.38 71	96.66 157	94.33 200	95.30 176	99.43 206	100.00 1

casdiffseed414692147		93.14 160	93.44 190	92.79 133	91.46 165	99.20 140	95.06 162	87.27 130	96.60 203	85.16 147	87.25 203	77.77 207	98.09 119	96.80 142	96.57 145	99.67 152	99.90 159

Effi-MVS+-dtu		93.13 161	97.13 102	88.47 179	88.86 188	99.19 141	96.79 107	79.08 208	99.64 79	70.01 213	97.51 96	89.38 137	96.53 159	97.60 111	96.55 146	99.57 173	100.00 1

HyFIR lowres test		93.13 161	94.48 177	91.56 144	96.12 73	99.68 84	93.52 186	79.98 199	97.24 189	81.73 165	72.66 258	95.74 110	98.29 108	98.27 75	97.79 86	99.70 141	100.00 1

Vis-MVSNet		93.08 163	96.76 115	88.78 174	91.14 167	99.63 89	94.85 168	83.34 165	97.19 191	74.78 189	91.92 164	93.15 123	88.81 241	97.59 113	98.35 65	99.78 80	99.49 223
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	90.82 169	99.45 102	95.71 144	78.94 209	98.72 143	74.71 190	97.92 92	80.73 196	98.35 101	97.72 106	97.05 138	99.70 141	100.00 1

ADS-MVSNet		92.91 165	97.97 84	87.01 194	92.07 124	99.27 135	92.70 192	65.39 259	99.85 55	75.40 184	94.93 125	98.26 88	96.86 151	96.09 169	97.52 103	99.65 159	99.84 183

GeoE		92.88 166	95.20 158	90.18 158	90.59 173	99.18 142	96.31 119	78.36 214	97.52 187	78.53 173	87.11 204	88.01 144	97.63 143	97.79 105	96.76 143	99.66 157	100.00 1

TESTMET0.1,1		92.87 167	97.23 100	87.79 187	86.96 197	99.10 145	94.68 174	77.46 220	99.36 102	71.94 203	93.82 138	96.51 103	95.96 164	97.42 122	94.37 189	99.74 109	99.99 67

FC-MVSNet-test		92.78 168	96.19 134	88.80 173	88.00 192	97.54 185	93.60 185	82.36 182	98.16 167	79.71 167	91.55 166	95.41 111	89.65 232	96.09 169	95.23 177	99.49 189	99.31 227

Fast-Effi-MVS+-dtu		92.73 169	97.62 90	87.02 193	88.91 186	98.83 158	95.79 141	73.98 237	99.89 45	68.62 218	97.73 94	93.30 122	95.21 173	97.67 110	95.96 167	99.59 169	100.00 1

IB-MVS	90.59 15	92.70 170	95.70 145	89.21 168	94.62 86	99.45 102	83.77 249	88.92 79	99.53 83	92.82 44	98.86 62	86.08 159	75.24 264	92.81 230	93.17 207	99.89 29	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	86.72 199	99.07 147	94.28 179	76.90 222	99.21 112	71.53 207	93.63 142	96.32 106	95.67 167	97.32 127	94.36 191	99.74 109	99.99 67

RPMNet		92.64 172	97.88 86	86.53 199	90.79 170	98.95 153	95.13 160	64.44 263	99.09 118	72.36 199	93.58 143	99.01 76	96.74 156	98.05 93	96.45 149	99.71 136	100.00 1

FMVSNet1		92.55 173	93.66 189	91.26 145	87.91 195	96.12 211	94.75 171	81.69 190	97.67 181	85.63 143	80.56 233	87.88 146	98.15 114	96.50 151	97.21 126	99.41 211	99.71 209

tpmrst		92.52 174	97.45 96	86.77 197	92.15 120	99.36 117	92.53 196	65.95 255	99.53 83	72.50 197	92.22 157	99.83 51	97.81 136	95.18 189	96.05 163	99.69 146	100.00 1

testgi		92.47 175	95.68 148	88.73 175	90.68 171	98.35 170	91.67 203	79.50 204	98.96 127	77.12 179	95.17 119	85.84 161	93.95 189	95.75 178	96.47 148	99.45 201	99.21 230

TAMVS		92.43 176	94.21 183	90.35 156	88.68 189	98.85 157	94.15 181	81.53 192	95.58 211	83.61 156	87.05 205	86.45 155	94.71 178	96.27 163	95.91 168	99.42 209	99.38 226

CR-MVSNet		92.32 177	97.97 84	85.74 208	90.63 172	98.95 153	95.46 154	65.50 257	99.09 118	67.51 222	94.20 132	98.18 90	95.59 170	98.16 83	97.20 129	99.74 109	100.00 1

CVMVSNet		92.13 178	95.40 154	88.32 182	91.29 166	97.29 190	91.85 200	86.42 133	96.71 201	71.84 205	89.56 183	91.18 128	88.98 240	96.17 166	97.76 88	99.51 186	99.14 232

Fast-Effi-MVS+		92.11 179	94.33 179	89.52 164	89.06 185	99.00 149	95.13 160	76.72 224	98.59 152	78.21 175	89.99 180	77.35 209	98.34 103	97.97 99	97.44 111	99.67 152	99.96 124

ACMH+	92.61 13	91.80 180	93.03 193	90.37 155	93.03 100	98.17 175	94.00 182	84.13 159	98.12 169	77.39 177	91.95 161	74.62 224	94.36 185	94.62 196	93.82 198	99.32 220	99.87 176

IterMVS-SCA-FT		91.75 181	96.87 112	85.78 206	90.34 175	95.93 221	95.06 162	73.85 238	98.91 131	61.01 243	89.21 184	98.87 77	94.66 181	98.09 91	97.12 134	99.76 92	99.99 67

dtuonly		91.72 182	95.05 161	87.83 186	87.94 194	98.44 167	94.83 169	82.15 184	96.62 202	65.07 233	86.58 206	90.12 136	97.30 147	97.08 134	96.74 144	99.67 152	99.81 189

IterMVS		91.65 183	96.62 116	85.85 205	90.27 178	95.80 223	95.32 157	74.15 234	98.91 131	60.95 244	88.79 191	97.76 93	94.69 180	98.04 95	97.07 136	99.73 122	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 92	97.98 180	90.10 224	84.70 145	98.46 157	76.80 180	93.38 144	71.94 236	94.39 183	95.34 185	94.04 194	99.54 178	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	85.85 207	96.52 203	91.70 202	82.49 179	98.14 168	83.17 157	87.57 199	81.76 192	94.39 183	95.47 182	92.62 215	99.33 218	99.29 228

blend_shiyan4		91.06 186	91.01 208	91.13 148	85.43 209	91.84 253	96.41 113	82.84 175	97.61 182	91.83 53	98.80 66	80.39 197	92.83 201	85.75 253	82.95 254	97.42 253	99.73 202

PatchT		91.06 186	97.66 89	83.36 235	90.32 176	98.96 152	82.30 254	64.72 262	98.45 158	67.51 222	93.28 147	97.60 95	95.59 170	98.16 83	97.20 129	99.70 141	100.00 1

usedtu_dtu_shiyan1		91.03 188	93.73 188	87.88 185	80.10 252	96.73 198	93.00 191	84.24 155	97.91 178	77.39 177	84.98 210	87.83 147	93.08 197	95.84 176	93.18 206	99.46 197	99.63 215

MIMVSNet		91.01 189	96.22 130	84.93 217	85.24 213	98.09 177	90.40 219	64.96 261	97.55 186	72.65 195	96.23 109	90.81 131	96.79 153	96.69 145	97.06 137	99.52 182	97.09 254

UniMVSNet_NR-MVSNet		90.50 190	92.31 198	88.38 180	85.04 222	96.34 207	90.94 206	85.32 137	95.87 210	75.69 181	87.68 198	78.49 205	93.78 191	93.21 225	94.60 182	99.53 181	99.97 103

UniMVSNet (Re)		90.41 191	91.96 200	88.59 178	85.71 208	96.73 198	90.82 209	84.11 160	95.23 217	78.54 172	88.91 190	76.41 215	92.84 200	93.40 221	93.05 210	99.55 177	100.00 1

GA-MVS		90.38 192	94.59 175	85.46 212	88.30 191	98.44 167	92.18 197	83.30 166	97.89 179	58.05 255	92.86 151	84.25 176	91.27 223	96.65 146	92.61 216	99.66 157	99.43 224

USDC		90.36 193	91.68 201	88.82 172	92.58 109	98.02 178	96.27 120	79.83 200	98.37 163	70.61 212	89.05 186	67.50 253	94.17 186	95.77 177	94.43 187	99.46 197	98.62 237

thisisatest0515		90.28 194	94.32 180	85.57 211	85.23 214	97.23 191	85.44 245	83.09 168	96.80 200	72.41 198	89.82 181	90.87 130	87.93 246	95.27 188	90.39 239	99.33 218	99.88 170

TinyColmap		89.94 195	90.88 209	88.84 171	92.43 112	97.91 181	95.59 151	80.10 198	98.12 169	71.33 209	84.56 212	67.46 254	94.15 187	95.57 180	94.27 193	99.43 206	98.26 241

pm-mvs1		89.68 196	92.00 199	86.96 195	86.23 203	96.62 201	90.36 220	83.05 169	93.97 230	72.15 202	81.77 228	82.10 188	90.69 229	95.38 184	94.50 185	99.29 224	99.65 212

tpm		89.60 197	94.93 165	83.39 233	89.94 179	97.11 193	90.09 225	65.28 260	98.67 146	60.03 248	96.79 103	84.38 173	95.66 169	91.90 234	95.65 172	99.32 220	99.98 87

NR-MVSNet		89.52 198	90.71 210	88.14 184	86.19 204	96.20 209	92.07 198	84.58 146	95.54 212	75.27 186	87.52 200	67.96 251	91.24 224	94.33 200	93.45 202	99.49 189	99.97 103

DU-MVS		89.49 199	90.60 211	88.19 183	84.71 226	96.20 209	90.94 206	84.58 146	95.54 212	75.69 181	87.52 200	68.74 250	93.78 191	91.10 239	95.13 179	99.47 195	99.97 103

usedtu_blend_shiyan5		89.34 200	89.98 216	88.60 177	70.40 261	91.71 256	96.25 121	82.93 171	90.83 253	91.83 53	98.80 66	80.39 197	92.83 201	85.63 254	82.75 255	97.39 254	99.73 202

Baseline_NR-MVSNet		89.13 201	89.53 225	88.66 176	84.71 226	94.43 242	91.79 201	84.49 150	95.54 212	78.28 174	78.52 245	72.46 235	93.29 195	91.10 239	94.82 181	99.42 209	99.86 179

tfpnnormal		89.09 202	89.71 219	88.38 180	87.37 196	96.78 197	91.46 204	85.20 140	90.33 259	72.35 200	83.45 221	69.30 248	94.45 182	95.29 186	92.86 212	99.44 205	99.93 146

FE-MVSNET3		88.92 203	89.98 216	87.69 188	70.40 261	91.71 256	90.75 211	82.93 171	90.83 253	91.83 53	98.80 66	80.39 197	92.83 201	85.63 254	82.75 255	97.39 254	99.72 206

TranMVSNet+NR-MVSNet		88.88 204	89.90 218	87.69 188	84.06 238	95.68 224	91.88 199	85.23 139	95.16 218	72.54 196	83.06 224	70.14 245	92.93 199	90.81 242	94.53 184	99.48 193	99.89 164

WR-MVS_H		88.47 205	90.55 212	86.04 201	85.13 218	96.07 213	89.86 231	79.80 201	94.37 227	72.32 201	83.12 223	74.44 228	89.60 233	93.52 218	92.40 217	99.51 186	99.96 124

SixPastTwentyTwo		88.35 206	91.51 203	84.66 219	85.39 211	96.96 195	86.57 241	79.62 203	96.57 204	63.73 237	87.86 195	75.18 219	93.43 194	94.03 204	90.37 240	99.24 227	99.58 216

TransMVSNet (Re)		88.33 207	89.55 224	86.91 196	86.65 200	95.56 230	90.48 217	84.44 151	92.02 250	71.07 211	80.13 235	72.48 234	89.41 234	95.05 192	94.44 186	99.39 213	97.14 253

MVS-HIRNet		88.27 208	94.05 184	81.51 241	88.90 187	98.93 156	83.38 251	60.52 270	98.06 172	63.78 236	80.67 232	90.36 134	92.94 198	97.29 128	96.41 150	99.56 175	96.66 256

WR-MVS		88.23 209	90.15 214	86.00 203	84.39 233	95.64 226	89.96 228	81.80 187	94.46 225	71.60 206	82.10 226	74.36 229	88.76 242	92.48 231	92.20 219	99.46 197	99.83 187

CP-MVSNet		88.09 210	89.57 222	86.36 200	84.63 229	95.46 235	89.48 233	80.53 196	93.42 237	71.26 210	81.25 230	69.90 246	92.78 204	93.30 224	93.69 200	99.47 195	99.96 124

pmnet_mix02		88.07 211	92.32 197	83.10 236	86.14 205	96.23 208	81.90 257	83.05 169	98.04 173	57.59 258	84.93 211	82.02 189	90.87 228	93.54 217	91.53 231	99.06 238	99.97 103

UniMVSNet_ETH3D		88.05 212	87.01 246	89.27 167	88.53 190	97.49 186	90.35 221	83.48 163	94.57 223	77.87 176	70.08 262	61.75 265	96.22 161	90.17 243	95.21 178	99.16 232	99.82 188

anonymousdsp		87.98 213	92.38 196	82.85 237	83.68 242	96.79 196	90.78 210	74.06 236	95.29 216	57.91 257	83.33 222	83.12 180	91.15 226	95.96 174	92.37 218	99.52 182	99.76 198

LTVRE_ROB	88.65 16	87.87 214	91.11 207	84.10 230	86.64 201	97.47 187	94.40 177	78.41 213	96.13 208	52.02 266	87.95 194	65.92 259	93.59 193	95.29 186	95.09 180	99.52 182	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	85.16 216	96.01 215	90.52 216	81.78 189	94.43 226	67.59 220	81.32 229	71.87 237	91.48 221	91.25 238	91.16 235	99.43 206	99.92 150

TDRefinement		87.79 216	88.76 234	86.66 198	93.54 94	98.02 178	95.76 142	85.18 141	96.57 204	67.90 219	80.51 234	66.51 258	78.37 260	93.20 226	89.73 241	99.22 228	96.75 255

MDTV_nov1_ep13_2view		87.75 217	93.32 191	81.26 243	83.74 241	96.64 200	85.66 244	66.20 253	98.36 164	61.61 241	84.34 214	87.95 145	91.12 227	94.01 205	92.66 214	99.22 228	99.27 229

v8		87.54 218	89.33 228	85.45 213	85.41 210	95.50 233	90.32 222	78.94 209	94.35 228	66.93 225	81.90 227	70.99 242	91.62 219	91.49 237	91.22 234	99.48 193	99.87 176

v1144		87.49 219	89.64 220	84.97 216	84.73 225	95.84 222	90.17 223	79.30 205	93.96 231	64.65 234	78.83 242	73.38 233	91.51 220	93.77 212	91.77 226	99.45 201	99.93 146

v2v482		87.46 220	88.90 232	85.78 206	84.58 230	95.95 219	89.90 230	82.43 181	94.19 229	65.65 229	79.80 237	69.12 249	92.67 205	91.88 235	91.46 232	99.45 201	99.93 146

v10		87.40 221	89.62 221	84.80 218	84.93 223	95.07 239	90.44 218	75.63 229	94.51 224	66.52 227	78.87 241	73.47 232	91.86 217	93.69 215	91.87 224	99.45 201	99.86 179

pmmvs5		87.33 222	90.01 215	84.20 228	84.31 235	96.04 214	87.63 239	76.59 225	93.17 242	65.35 232	84.30 215	71.68 238	91.91 216	95.41 183	91.37 233	99.39 213	98.13 244

N_pmnet		87.31 223	91.51 203	82.41 240	85.13 218	95.57 229	80.59 261	81.79 188	96.20 206	58.52 254	78.62 243	85.66 163	89.36 235	94.64 195	92.14 220	99.08 236	97.72 251

PS-CasMVS		87.24 224	88.52 237	85.73 209	84.58 230	95.35 237	89.03 236	80.17 197	93.11 243	68.86 217	77.71 247	66.89 255	92.30 211	93.13 227	93.50 201	99.46 197	99.96 124

EU-MVSNet		87.20 225	90.47 213	83.38 234	85.11 221	93.85 247	86.10 243	79.76 202	93.30 241	65.39 231	84.41 213	78.43 206	85.04 255	92.20 233	93.03 211	98.86 240	98.05 248

PEN-MVS		87.20 225	88.22 238	86.01 202	84.01 240	94.93 240	90.00 227	81.52 194	93.46 236	69.29 215	79.69 238	65.51 260	91.72 218	91.01 241	93.12 208	99.49 189	99.84 183

EG-PatchMatch MVS		86.96 227	89.56 223	83.93 231	86.29 202	97.61 184	90.75 211	73.31 241	95.43 215	66.08 228	75.88 255	71.31 239	87.55 248	94.79 194	92.74 213	99.61 168	99.13 233

v1192		86.93 228	89.01 230	84.50 224	84.46 232	95.51 232	89.93 229	78.65 212	93.75 232	62.29 239	77.19 250	70.88 243	92.28 212	93.84 209	91.96 222	99.38 215	99.90 159

v1921920		86.81 229	88.93 231	84.33 227	84.23 236	95.41 236	90.09 225	78.10 215	93.74 233	62.17 240	76.98 252	71.14 240	92.05 214	93.69 215	91.69 229	99.32 220	99.88 170

v144192		86.80 230	88.90 232	84.35 225	84.33 234	95.56 230	89.34 234	77.74 217	93.60 234	64.03 235	77.82 246	70.76 244	91.28 222	92.91 229	91.74 228	99.37 216	99.90 159

DTE-MVSNet		86.70 231	87.66 242	85.58 210	83.30 244	94.29 243	89.74 232	81.53 192	92.77 246	68.93 216	80.13 235	64.00 263	90.62 230	89.45 244	93.34 203	99.32 220	99.67 210

gg-mvs-nofinetune		86.69 232	91.30 206	81.30 242	90.42 174	99.64 87	98.50 63	61.68 268	79.23 270	40.35 273	66.58 264	97.14 98	96.92 149	98.64 48	97.94 78	99.91 25	99.97 103

v148		86.63 233	87.79 240	85.28 214	84.65 228	95.97 216	86.46 242	82.84 175	92.91 245	71.52 208	78.99 240	66.74 257	86.83 251	89.28 245	90.69 237	99.41 211	99.94 144

dtuonlycased		86.51 234	91.31 205	80.92 244	83.57 243	94.69 241	81.41 259	75.18 231	97.02 198	59.42 250	87.86 195	85.42 166	86.86 250	88.71 246	87.20 248	99.08 236	98.25 243

gbinet_0.2-2-1-0.02		86.42 235	87.47 243	85.19 215	71.78 258	91.76 254	90.97 205	82.60 178	90.87 251	75.35 185	85.62 208	76.07 218	93.09 196	85.42 260	82.55 261	97.37 259	99.98 87

v1240		86.24 236	88.56 236	83.54 232	84.05 239	95.21 238	89.27 235	76.76 223	93.42 237	60.68 247	75.99 254	69.80 247	91.21 225	93.83 211	91.76 227	99.29 224	99.91 157

wanda-best-256-512		85.94 237	87.03 244	84.66 219	70.40 261	91.71 256	90.75 211	82.93 171	90.83 253	73.88 192	83.78 217	74.80 221	92.62 206	85.63 254	82.75 255	97.39 254	99.73 202

FE-blended-shiyan7		85.94 237	87.03 244	84.66 219	70.40 261	91.71 256	90.75 211	82.93 171	90.83 253	73.88 192	83.78 217	74.80 221	92.62 206	85.63 254	82.75 255	97.39 254	99.73 202

blended_shiyan8		85.87 239	86.93 249	84.64 222	70.41 260	91.71 256	90.90 208	82.61 177	90.54 258	74.01 191	83.77 219	74.58 225	92.53 209	85.57 259	82.67 260	97.37 259	99.66 211

blended_shiyan6		85.86 240	86.98 247	84.56 223	70.38 265	91.69 261	90.72 215	82.45 180	90.79 257	73.86 194	83.58 220	74.80 221	92.57 208	85.60 258	82.69 259	97.38 258	99.72 206

pmmvs6		85.75 241	86.97 248	84.34 226	84.88 224	95.59 228	87.41 240	79.19 207	87.81 265	67.56 221	63.05 268	77.76 208	89.15 236	93.45 220	91.90 223	97.83 251	99.21 230

v7n		85.39 242	87.70 241	82.70 238	82.77 246	95.64 226	88.27 238	74.83 232	92.30 248	62.58 238	76.37 253	64.80 262	88.38 244	94.29 202	90.61 238	99.34 217	99.87 176

gm-plane-assit		84.93 243	91.61 202	77.14 253	84.14 237	91.29 262	66.18 273	69.70 244	85.22 269	47.95 270	78.58 244	89.24 139	94.90 176	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 76	97.79 182	88.35 237	79.22 206	89.66 262	60.71 246	72.20 259	73.94 230	87.32 249	86.73 251	84.55 253	93.90 265	90.31 266
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test_method		84.44 245	89.04 229	79.08 247	81.15 249	92.82 251	82.06 256	61.92 266	96.17 207	59.38 251	74.47 257	67.52 252	91.96 215	96.92 137	95.53 174	97.98 248	99.85 182

Anonymous20231206		84.28 246	89.53 225	78.17 250	82.31 248	94.16 245	82.57 253	76.51 226	93.38 240	52.98 263	79.47 239	73.74 231	75.45 263	95.07 191	94.41 188	99.18 231	96.46 258

new_pmnet		84.12 247	87.89 239	79.72 246	80.43 251	94.14 246	80.26 262	74.14 235	96.01 209	56.30 262	74.94 256	76.45 214	88.59 243	93.11 228	89.31 243	98.59 244	91.27 265

test20.03		83.86 248	88.73 235	78.16 251	82.60 247	93.00 248	81.61 258	74.68 233	92.36 247	57.50 259	83.01 225	74.48 227	73.30 265	92.40 232	91.14 236	99.29 224	94.75 261

pmmvs-eth3d		82.92 249	83.31 254	82.47 239	76.97 255	91.76 254	83.79 248	76.10 227	90.33 259	69.95 214	71.04 261	48.09 273	89.02 239	93.85 208	89.14 244	99.02 239	98.96 235

PM-MVS		82.79 250	84.51 252	80.77 245	77.22 254	92.13 252	83.61 250	73.31 241	93.50 235	61.06 242	77.15 251	46.52 276	90.55 231	94.14 203	89.05 247	98.85 241	99.12 234

pmmvs3		80.91 251	85.62 250	75.42 255	75.01 257	89.09 266	75.31 267	68.70 246	86.99 267	46.74 272	81.18 231	62.91 264	87.95 245	93.84 209	89.06 246	98.80 243	96.23 259

MIMVSNet1		80.64 252	83.97 253	76.76 254	68.91 267	91.15 264	78.32 266	75.47 230	89.58 263	56.64 261	65.10 265	65.17 261	82.14 256	93.51 219	91.64 230	99.10 234	91.66 264

MDA-MVSNet-bldmvs		80.30 253	82.83 255	77.34 252	69.16 266	94.29 243	72.16 268	81.97 186	90.14 261	57.32 260	94.01 137	47.97 274	86.81 252	68.74 268	86.82 250	96.63 261	97.86 249

FE-MVSNET2		79.98 254	80.91 257	78.89 248	67.11 269	92.85 249	83.34 252	77.59 218	88.33 264	59.81 249	55.71 271	48.82 272	86.33 253	93.94 206	89.34 242	99.14 233	97.39 252

new-patchmatchnet		78.17 255	80.82 259	75.07 256	76.93 256	91.20 263	71.90 269	73.32 240	86.59 268	48.91 267	67.11 263	47.85 275	81.19 257	88.18 250	87.02 249	98.19 247	97.79 250

FE-MVSNET		77.93 256	80.91 257	74.45 257	61.41 271	89.15 265	78.53 265	75.91 228	87.12 266	52.74 264	63.25 267	50.07 271	79.29 259	91.87 236	89.12 245	98.81 242	95.76 260

usedtu_dtu_shiyan2		74.26 257	75.54 260	72.77 259	60.18 274	86.34 267	79.24 264	68.68 247	77.80 271	57.94 256	47.93 274	58.22 268	76.77 261	80.13 263	80.11 264	93.82 266	98.26 241

FPMVS		73.80 258	74.62 261	72.84 258	83.09 245	84.44 269	83.89 247	73.64 239	92.20 249	48.50 268	72.19 260	59.51 267	63.16 267	69.13 267	66.26 271	84.74 271	78.59 274

WB-MVS		71.64 259	82.10 256	59.45 263	79.66 253	78.44 272	55.66 277	78.80 211	93.01 244	19.20 281	86.36 207	71.05 241	39.18 276	85.26 261	81.08 262	84.19 272	79.49 273

Gipuma		71.02 260	72.60 264	69.19 260	71.31 259	75.11 273	66.36 272	61.65 269	94.93 219	47.29 271	38.74 276	38.52 278	75.52 262	86.09 252	85.92 252	93.01 267	88.87 268
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	3.67 282	99.94 22	99.10 47	1.69 277	99.85 55	3.19 284	98.13 88	99.46 67	4.92 279	99.23 34	99.14 33	99.80 65	100.00 1

PMMVS2		65.18 262	68.25 265	61.59 261	61.37 272	79.72 271	59.18 276	61.80 267	64.72 274	37.33 274	53.82 272	35.59 279	54.46 272	73.94 266	80.52 263	95.40 264	89.43 267

PMVS	60.14 18	62.67 263	64.05 266	61.06 262	68.32 268	53.27 279	52.23 278	67.63 250	75.07 273	48.30 269	58.27 269	57.43 269	49.99 273	67.20 269	62.42 272	79.87 275	74.68 277
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
testmvs		61.76 264	72.90 263	48.76 266	21.21 277	68.61 274	66.11 274	37.38 272	94.83 221	33.06 275	64.31 266	29.72 280	86.08 254	74.44 265	78.71 265	48.74 279	99.65 212

E-PMN		55.33 265	55.79 271	54.81 265	59.81 275	57.23 277	38.83 279	63.59 264	64.06 276	24.66 278	35.33 278	26.40 283	58.69 269	55.41 271	70.54 268	83.26 273	81.56 271

EMVS		55.14 266	55.29 272	54.97 264	60.87 273	57.52 276	38.58 280	63.57 265	64.54 275	23.36 279	36.96 277	27.99 282	60.69 268	51.17 272	66.61 270	82.73 274	82.25 270

MVE	58.81 19	52.07 267	55.15 273	48.48 267	42.45 276	62.35 275	36.41 282	54.70 271	49.88 278	27.65 277	29.98 279	18.08 284	54.87 271	65.93 270	77.26 266	74.79 276	82.59 269
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test123		48.14 268	58.11 270	36.51 268	8.71 281	56.81 278	59.55 275	24.08 273	77.50 272	14.41 282	49.20 273	11.94 286	80.98 258	41.62 276	69.81 269	31.32 281	99.90 159

VLMVS		47.88 269	62.86 268	30.41 270	13.00 278	26.27 281	37.28 281	2.54 275	97.44 188	30.01 276	97.00 99	52.97 270	40.80 275	44.70 275	45.14 274	58.85 277	76.49 275

MVS_clip		46.11 270	63.35 267	26.00 271	12.01 279	33.73 280	26.34 284	2.75 274	94.91 220	23.05 280	88.94 188	60.62 266	46.82 274	46.33 274	44.06 275	45.05 280	74.88 276

VLMVS_CLIP		44.39 271	62.37 269	23.41 272	9.53 280	24.68 282	30.42 283	1.84 276	90.84 252	10.54 283	96.05 111	45.46 277	36.81 277	49.96 273	49.68 273	51.36 278	79.92 272

MVS_baseline		21.04 272	35.16 274	4.58 273	2.24 283	9.17 283	4.92 285	0.06 278	50.56 277	0.00 285	56.43 270	29.28 281	15.61 278	25.42 277	24.35 276	4.64 282	50.89 278

uanet_test		0.00 273	0.00 275	0.00 274	0.00 284	0.00 284	0.00 286	0.00 279	0.00 279	0.00 285	0.00 280	0.00 287	0.00 280	0.00 278	0.00 277	0.00 283	0.00 279

sosnet-low-res		0.00 273	0.00 275	0.00 274	0.00 284	0.00 284	0.00 286	0.00 279	0.00 279	0.00 285	0.00 280	0.00 287	0.00 280	0.00 278	0.00 277	0.00 283	0.00 279

sosnet		0.00 273	0.00 275	0.00 274	0.00 284	0.00 284	0.00 286	0.00 279	0.00 279	0.00 285	0.00 280	0.00 287	0.00 280	0.00 278	0.00 277	0.00 283	0.00 279

ACM-MVS					99.68 3	99.94 22	99.81 8		99.99 3	89.28 92	99.60 26	100.00 1	99.91 5			99.94 21	99.88 170

PatchmatchNet2					85.38 212	95.67 225	80.93 260
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet1												86.46 154	89.06 238	94.58 198	91.87 224	99.09 235	98.09 247
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet3										58.61 253	77.46 249
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
TestfortrainingZip							99.91 1	97.15 2		99.89 1						100.00 1

TPM-MVS					99.67 5	99.96 7	99.82 7			94.63 35	99.65 17	100.00 1	99.90 8			99.99 7	99.80 193
Ray Leroy Khuboni and Hongjun Xu: Textureless Resilient Propagation Matching in Multiple View Stereosis (TPM-MVS). SATNAC 2025
RE-MVS-def										52.74 264

9.14												100.00 1

SR-MVS					99.61 16			96.80 21				100.00 1

Anonymous202405211			95.78 143		93.26 95	99.52 93	96.70 110	88.55 84	97.93 177		88.99 187	90.68 132	98.99 65	96.46 155	97.02 139	99.64 163	99.89 164

our_test_3					85.89 206	96.09 212	82.15 255

ambc			74.33 262		66.84 270	84.26 270	84.17 246		93.39 239	58.99 252	45.93 275	18.06 285	70.61 266	93.94 206	86.62 251	92.61 269	98.13 244

MTAPA										96.61 19		100.00 1

MTMP										97.42 14		100.00 1

Patchmatch-RL test							68.01 271

tmp_tt				78.81 249	98.80 48	85.73 268	70.08 270	77.87 216	98.68 145	83.71 155	99.53 33	74.55 226	54.97 270	78.28 264	72.43 267	87.45 270

XVS					95.09 79	99.94 22	97.49 79			88.58 99		99.98 38				99.78 80

X-MVStestdata					95.09 79	99.94 22	97.49 79			88.58 99		99.98 38				99.78 80

mPP-MVS					99.23 41							99.87 49

NP-MVS									99.79 65

Patchmtry						99.00 149	95.46 154	65.50 257		67.51 222

DeepMVS_CX						97.31 189	79.48 263	89.65 70	98.66 148	60.89 245	94.40 130	66.89 255	87.65 247	81.69 262		92.76 268	94.24 263

High-resolution multi-view benchmark