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.

SED-MVS		99.44 1	99.69 21	99.15 1	99.61 13	99.95 14	99.81 6	96.94 7	99.97 9	98.73 2	99.53 30	100.00 1	99.91 4	99.90 8	98.52 59	99.87 30	100.00 1

SF-MVS		99.41 2	99.68 23	99.10 3	99.65 6	99.94 20	99.76 10	96.95 4	99.88 43	98.39 5	99.60 24	100.00 1	99.82 14	99.43 28	98.93 38	99.99 5	100.00 1

APDe-MVS		99.40 3	99.81 2	98.92 8	99.62 8	99.96 7	99.76 10	96.87 15	99.95 26	97.66 7	99.57 28	100.00 1	99.63 29	99.88 11	99.28 26	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 4	99.76 9	98.95 6	99.60 17	99.99 1	99.83 4	96.82 17	99.92 37	97.58 10	99.58 27	100.00 1	99.93 1	98.98 35	99.86 8	99.96 13	100.00 1

CNVR-MVS		99.39 4	99.75 12	98.98 4	99.69 1	99.95 14	99.76 10	96.91 10	99.98 3	97.59 9	99.64 21	100.00 1	99.93 1	99.94 2	98.75 52	99.97 12	99.97 93

DVP-MVS		99.38 6	99.57 37	99.15 1	99.62 8	99.94 20	99.72 23	96.99 2	99.98 3	98.85 1	98.21 79	100.00 1	99.88 8	99.88 11	98.96 36	99.85 34	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 6	99.78 5	98.91 11	99.61 13	99.96 7	99.85 2	96.94 7	99.96 20	97.38 13	99.60 24	100.00 1	99.70 21	99.96 1	98.96 36	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 8	99.74 15	98.94 7	99.60 17	99.94 20	99.87 1	96.95 4	99.94 30	97.42 11	99.62 23	100.00 1	99.80 17	99.91 5	98.78 50	99.98 10	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 9	99.70 20	98.96 5	99.62 8	99.94 20	99.85 2	96.90 14	99.97 9	97.64 8	99.50 34	100.00 1	99.88 8	99.90 8	98.60 54	99.87 30	100.00 1

SMA-MVS		99.34 10	99.79 4	98.81 13	99.69 1	99.94 20	99.75 17	96.91 10	99.98 3	96.76 15	99.37 41	100.00 1	99.90 5	99.88 11	99.46 17	99.84 37	99.92 132
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 11	99.85 1	98.73 14	99.61 13	99.92 41	99.77 9	96.91 10	99.93 33	96.31 19	99.59 26	99.95 42	99.84 12	99.73 18	99.84 9	99.95 15	100.00 1
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
NCCC		99.24 12	99.75 12	98.65 15	99.63 7	99.96 7	99.76 10	96.91 10	99.97 9	95.86 23	99.67 12	100.00 1	99.75 18	99.85 14	98.80 48	99.98 10	99.97 93

CNLPA		99.24 12	99.58 34	98.85 12	99.34 33	99.95 14	99.32 37	96.65 27	99.96 20	98.44 4	98.97 56	100.00 1	99.57 31	98.66 44	99.56 15	99.76 87	99.97 93

AdaColmap		99.21 14	99.45 40	98.92 8	99.67 4	99.95 14	99.65 28	96.77 22	99.97 9	97.67 6	100.00 1	99.69 56	99.93 1	99.26 31	97.25 106	99.85 34	100.00 1

HFP-MVS		99.19 15	99.77 8	98.51 18	99.55 21	99.94 20	99.76 10	96.84 16	99.88 43	95.27 27	99.67 12	100.00 1	99.85 11	99.56 23	99.36 21	99.79 68	99.97 93

PLC	98.06 1	99.17 16	99.38 42	98.92 8	99.47 23	99.90 50	99.48 33	96.47 32	99.96 20	98.73 2	99.52 33	100.00 1	99.55 33	98.54 57	97.73 88	99.84 37	99.99 60
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
SD-MVS		99.16 17	99.73 16	98.49 19	97.93 53	99.95 14	99.74 20	96.94 7	99.96 20	96.60 17	99.47 37	100.00 1	99.88 8	99.15 33	99.59 13	99.84 37	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 18	99.67 24	98.53 17	99.45 25	99.94 20	99.63 30	96.62 29	99.82 56	95.92 22	99.65 17	100.00 1	99.71 20	99.76 17	98.56 56	99.83 43	100.00 1

ACMMPR		99.12 19	99.76 9	98.36 20	99.45 25	99.94 20	99.75 17	96.70 26	99.93 33	94.65 31	99.65 17	99.96 40	99.84 12	99.51 26	99.35 22	99.79 68	99.96 114

MCST-MVS		99.08 20	99.72 18	98.33 21	99.59 20	99.97 3	99.78 8	96.96 3	99.95 26	93.72 36	99.67 12	100.00 1	99.90 5	99.91 5	98.55 57	100.00 1	100.00 1

CPTT-MVS		99.08 20	99.53 39	98.57 16	99.44 27	99.93 35	99.60 31	95.92 37	99.77 64	97.01 14	99.67 12	100.00 1	99.72 19	99.56 23	97.76 84	99.70 124	99.98 79

DeepC-MVS_fast	98.03 2	99.05 22	99.78 5	98.21 24	99.47 23	99.97 3	99.75 17	96.80 18	99.97 9	93.58 38	98.68 64	99.94 43	99.69 22	99.93 4	99.95 3	99.96 13	99.98 79
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 23	99.61 31	98.27 22	97.88 54	99.92 41	99.71 25	96.80 18	99.96 20	95.58 25	98.71 63	100.00 1	99.68 24	99.91 5	98.78 50	99.99 5	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 24	99.62 30	98.22 23	99.62 8	99.94 20	99.74 20	96.95 4	99.87 47	93.76 35	99.49 36	100.00 1	99.39 39	99.73 18	98.35 62	99.89 26	99.96 114

SteuartSystems-ACMMP		98.95 25	99.80 3	97.95 27	99.43 28	99.96 7	99.76 10	96.45 33	99.82 56	93.63 37	99.64 21	100.00 1	98.56 80	99.90 8	99.31 24	99.84 37	100.00 1
Skip Steuart: Steuart Systems R&D Blog.
PHI-MVS		98.85 26	99.67 24	97.89 28	98.63 48	99.93 35	98.95 47	95.20 39	99.84 54	94.94 28	99.74 11	100.00 1	99.69 22	98.40 64	99.75 11	99.93 19	99.99 60

MP-MVS		98.82 27	99.63 28	97.88 29	99.41 29	99.91 49	99.74 20	96.76 23	99.88 43	91.89 49	99.50 34	99.94 43	99.65 27	99.71 21	98.49 60	99.82 47	99.97 93
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
ACMMP_NAP		98.68 28	99.58 34	97.62 30	99.62 8	99.92 41	99.72 23	96.78 21	99.71 69	90.13 72	99.66 16	99.99 33	99.64 28	99.78 16	98.14 70	99.82 47	99.89 143

train_agg		98.62 29	99.76 9	97.28 32	99.03 41	99.93 35	99.65 28	96.37 34	99.98 3	89.24 83	99.53 30	99.83 48	99.59 30	99.85 14	99.19 30	99.80 61	100.00 1

X-MVS		98.62 29	99.75 12	97.29 31	99.50 22	99.94 20	99.71 25	96.55 30	99.85 51	88.58 88	99.65 17	99.98 35	99.67 25	99.60 22	99.26 27	99.77 79	99.97 93

OMC-MVS		98.59 31	99.07 47	98.03 26	99.41 29	99.90 50	99.26 40	94.33 41	99.94 30	96.03 20	96.68 93	99.72 55	99.42 36	98.86 38	98.84 44	99.72 120	99.58 181

DPM-MVS		98.58 32	99.78 5	97.17 34	98.02 52	99.64 83	99.80 7	96.72 25	99.96 20	90.05 74	99.57 28	100.00 1	98.66 79	99.56 23	99.96 2	99.80 61	99.80 166

PGM-MVS		98.47 33	99.73 16	97.00 36	99.68 3	99.94 20	99.76 10	91.74 47	99.84 54	91.17 59	100.00 1	99.69 56	99.81 15	99.38 29	99.30 25	99.82 47	99.95 121

MVS_0304		98.44 34	99.67 24	97.00 36	97.82 56	99.92 41	99.46 34	91.78 46	99.95 26	94.10 33	100.00 1	100.00 1	98.91 65	98.59 51	99.22 28	99.95 15	99.99 60

TSAR-MVS + ACMM		98.30 35	99.64 27	96.74 40	99.08 40	99.94 20	99.67 27	96.73 24	99.97 9	86.30 111	98.30 71	99.99 33	98.78 73	99.73 18	99.57 14	99.88 29	99.98 79

CSCG		98.22 36	98.37 68	98.04 25	99.60 17	99.82 60	99.45 35	93.59 42	99.16 105	96.46 18	98.22 78	95.86 105	99.41 38	96.33 140	99.22 28	99.75 96	99.94 126

3Dnovator+	95.21 7	98.17 37	99.08 46	97.12 35	99.28 36	99.78 71	98.61 54	89.93 61	99.93 33	95.36 26	95.50 102	100.00 1	99.56 32	98.58 52	99.80 10	99.95 15	99.97 93

ACMMP		98.16 38	99.01 48	97.18 33	98.86 43	99.92 41	98.77 52	95.73 38	99.31 101	91.15 60	100.00 1	99.81 50	98.82 71	98.11 84	95.91 143	99.77 79	99.97 93
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 39	99.69 21	96.36 45	99.23 38	99.93 35	97.79 66	91.84 45	99.87 47	90.53 68	100.00 1	99.57 61	98.93 64	99.44 27	99.08 33	99.85 34	99.95 121

EPNet		98.11 40	99.63 28	96.34 46	98.44 50	99.88 55	98.55 55	90.25 57	99.93 33	92.60 45	100.00 1	99.73 53	98.41 83	98.87 37	99.02 34	99.82 47	99.97 93
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TSAR-MVS + GP.		98.06 41	99.55 38	96.32 47	94.72 81	99.92 41	99.22 41	89.98 59	99.97 9	94.77 30	99.94 10	100.00 1	99.43 35	98.52 61	98.53 58	99.79 68	100.00 1

3Dnovator	95.01 8	97.98 42	98.89 53	96.92 39	99.36 31	99.76 74	98.72 53	89.98 59	99.98 3	93.99 34	94.60 115	99.43 66	99.50 34	98.55 54	99.91 5	99.99 5	99.98 79

MVS_111021_HR		97.94 43	99.59 32	96.02 49	99.27 37	99.97 3	97.03 93	90.44 54	99.89 41	90.75 63	100.00 1	99.73 53	98.68 78	98.67 43	98.89 41	99.95 15	99.97 93

QAPM		97.90 44	98.89 53	96.74 40	99.35 32	99.80 66	98.84 49	90.20 58	99.94 30	92.85 40	94.17 119	99.78 51	99.42 36	98.71 41	99.87 7	99.79 68	99.98 79

CDPH-MVS		97.88 45	99.59 32	95.89 50	98.90 42	99.95 14	99.40 36	92.86 44	99.86 50	85.33 118	98.62 65	99.45 65	99.06 60	99.29 30	99.94 4	99.81 56	100.00 1

CANet		97.62 46	98.94 51	96.08 48	97.19 59	99.93 35	99.29 39	90.38 55	99.87 47	91.00 61	95.79 101	99.51 62	98.72 77	98.53 58	99.00 35	99.90 24	99.99 60

TAPA-MVS	96.62 5	97.60 47	98.46 67	96.60 43	98.73 46	99.90 50	99.30 38	94.96 40	99.46 87	87.57 97	96.05 100	98.53 78	99.26 50	98.04 89	97.33 104	99.77 79	99.88 148
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
DeepPCF-MVS	97.16 4	97.58 48	99.72 18	95.07 65	98.45 49	99.96 7	93.83 155	95.93 36	100.00 1	90.79 62	98.38 70	99.85 47	95.28 146	99.94 2	99.97 1	96.15 223	99.97 93

SPE-MVS-test		97.51 49	99.18 45	95.56 55	97.16 60	99.96 7	97.39 80	89.82 63	100.00 1	89.88 75	99.16 48	98.38 84	99.23 52	98.85 39	97.93 77	99.87 30	100.00 1

PCF-MVS	97.20 3	97.49 50	98.20 73	96.66 42	97.62 57	99.92 41	98.93 48	96.64 28	98.53 141	88.31 94	94.04 122	99.58 60	98.94 62	97.53 108	97.79 82	99.54 154	99.97 93
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
CS-MVS		97.46 51	98.98 49	95.68 54	96.74 64	99.93 35	97.62 72	89.69 64	99.98 3	91.33 56	98.53 68	97.50 92	98.77 74	98.60 50	98.35 62	99.92 21	100.00 1

MSDG		97.29 52	97.55 89	97.00 36	98.66 47	99.71 78	99.03 45	96.15 35	99.59 76	89.67 81	92.77 133	94.86 108	98.75 75	98.22 75	97.94 75	99.72 120	99.76 171

CHOSEN 280x420		97.16 53	99.58 34	94.35 80	96.95 63	99.97 3	97.19 87	81.55 156	99.92 37	91.75 50	100.00 1	100.00 1	98.84 70	98.55 54	98.65 53	99.79 68	99.97 93

DELS-MVS		97.05 54	98.05 78	95.88 52	97.09 61	99.99 1	98.82 50	90.30 56	98.44 147	91.40 54	92.91 130	96.57 98	97.68 117	98.56 53	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 54	97.59 88	96.42 44	97.37 58	99.92 41	99.10 43	96.54 31	99.34 100	86.64 108	91.93 143	93.15 119	99.11 58	99.11 34	99.68 12	99.73 112	99.97 93
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 56	98.09 77	95.81 53	94.12 86	99.80 66	97.33 83	89.48 68	98.90 125	95.99 21	99.11 51	92.84 121	98.14 95	98.14 80	98.32 66	99.82 47	99.51 186

MAR-MVS		97.03 57	98.00 80	95.89 50	99.32 34	99.74 77	96.76 101	84.89 118	99.97 9	94.86 29	98.29 72	90.58 129	99.67 25	98.02 91	99.50 16	99.82 47	99.92 132
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 58	98.85 55	94.83 72	92.71 105	97.43 165	99.03 45	85.52 111	99.82 56	92.74 43	99.15 49	99.94 43	99.19 55	98.66 44	96.99 120	99.79 68	99.98 79

EC-MVSNet		96.90 59	99.32 43	94.07 82	91.64 143	99.30 113	98.18 62	85.61 110	99.97 9	89.79 76	99.33 42	99.31 69	99.28 48	98.48 63	98.86 42	99.91 22	100.00 1

baseline1		96.87 60	98.55 61	94.91 67	92.89 104	99.45 98	96.34 106	88.54 81	98.88 128	92.82 41	98.93 58	96.58 97	99.07 59	98.19 77	98.04 72	99.80 61	99.78 168

OpenMVS	94.03 11	96.87 60	98.10 76	95.44 59	99.29 35	99.78 71	98.46 60	89.92 62	99.47 86	85.78 115	91.05 147	98.50 79	99.30 46	98.49 62	99.41 18	99.89 26	99.98 79

PatchMatch-RL		96.84 62	98.03 79	95.47 56	98.84 44	99.81 64	95.61 124	89.20 72	99.65 73	91.28 57	99.39 38	93.46 117	98.18 92	98.05 87	96.28 130	99.69 129	99.55 183

ETV-MVS		96.79 63	99.19 44	94.00 84	91.78 130	99.63 85	97.15 89	88.00 87	99.95 26	88.34 93	99.32 43	98.71 75	98.82 71	98.69 42	98.01 73	99.90 24	100.00 1

IS_MVSNet		96.66 64	98.62 60	94.38 76	92.41 111	99.70 79	97.19 87	87.67 93	99.05 114	91.27 58	95.09 107	98.46 83	97.95 105	98.64 46	99.37 19	99.79 68	100.00 1

PMMVS		96.45 65	98.24 72	94.36 79	92.58 106	99.01 126	97.08 92	87.42 104	99.88 43	90.06 73	99.39 38	94.63 109	99.33 43	97.85 97	96.99 120	99.70 124	99.96 114

LS3D		96.44 66	97.31 96	95.41 60	97.06 62	99.87 56	99.51 32	97.48 1	99.57 77	79.00 140	95.39 103	89.19 135	99.81 15	98.55 54	98.84 44	99.62 143	99.78 168

EIA-MVS		96.34 67	98.55 61	93.76 88	91.93 122	99.66 81	97.14 90	88.33 85	99.51 81	85.98 114	98.82 62	96.08 103	99.33 43	98.38 67	97.40 101	99.81 56	100.00 1

EPP-MVSNet		96.29 68	98.34 69	93.90 85	91.77 132	99.38 106	95.45 129	87.25 106	99.38 96	91.36 55	94.86 113	98.49 81	97.83 111	98.01 92	98.23 68	99.75 96	99.99 60

UGNet		96.05 69	98.55 61	93.13 100	94.64 82	99.65 82	94.70 143	87.78 89	99.40 95	89.69 80	98.25 75	99.25 71	92.12 178	96.50 132	97.08 115	99.84 37	99.72 175
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 70	96.83 110	95.11 64	97.87 55	99.52 89	98.81 51	91.40 50	99.42 91	84.97 119	90.46 150	96.82 96	98.05 99	96.46 136	96.19 133	99.54 154	98.92 201
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 71	96.48 118	95.46 57	96.47 66	99.89 53	95.64 121	91.23 51	99.75 66	91.59 52	96.80 90	82.44 163	98.05 99	98.53 58	97.92 78	99.80 61	100.00 1

PVSNet_Blended		96.01 71	96.48 118	95.46 57	96.47 66	99.89 53	95.64 121	91.23 51	99.75 66	91.59 52	96.80 90	82.44 163	98.05 99	98.53 58	97.92 78	99.80 61	100.00 1

thisisatest0530		95.89 73	98.32 70	93.06 104	91.76 133	99.75 75	94.94 136	87.60 97	99.91 39	86.66 107	98.28 73	99.98 35	97.72 113	97.10 121	93.24 179	99.65 135	99.95 121

tttt0517		95.88 74	98.31 71	93.04 105	91.75 135	99.75 75	94.90 137	87.60 97	99.91 39	86.63 109	98.28 73	99.98 35	97.72 113	97.10 121	93.24 179	99.65 135	99.95 121

thres100view900		95.86 75	96.62 112	94.97 66	93.10 94	99.83 58	97.76 67	89.15 73	98.62 137	90.69 64	99.00 53	84.86 152	99.30 46	97.57 107	96.48 125	99.81 56	100.00 1

RPSCF		95.86 75	96.94 108	94.61 74	96.52 65	98.67 142	98.54 56	88.43 83	99.56 78	90.51 70	99.39 38	98.70 76	97.72 113	93.77 187	92.00 195	95.93 224	96.50 218

DCV-MVSNet		95.85 77	97.53 90	93.89 86	93.20 93	97.01 171	97.14 90	84.77 119	99.16 105	90.38 71	98.96 57	93.73 114	98.23 91	96.57 131	97.37 102	99.64 139	99.93 128

baseline		95.85 77	98.13 75	93.20 98	92.29 114	99.58 87	97.49 74	84.33 128	99.44 88	87.28 101	97.00 89	94.04 113	97.93 106	98.36 69	98.47 61	99.87 30	99.99 60

sasdasda		95.80 79	97.02 101	94.37 77	92.96 100	99.47 94	97.49 74	84.58 121	99.44 88	92.05 47	98.54 66	86.65 140	99.37 40	96.18 143	98.93 38	99.77 79	99.92 132

canonicalmvs		95.80 79	97.02 101	94.37 77	92.96 100	99.47 94	97.49 74	84.58 121	99.44 88	92.05 47	98.54 66	86.65 140	99.37 40	96.18 143	98.93 38	99.77 79	99.92 132

tfpn200view9		95.78 81	96.54 115	94.89 69	93.10 94	99.82 60	97.67 68	88.85 76	98.62 137	90.69 64	99.00 53	84.86 152	99.28 48	97.41 114	96.10 136	99.76 87	99.99 60

thres200		95.77 82	96.55 114	94.86 70	93.09 96	99.82 60	97.63 71	88.85 76	98.49 142	90.66 66	98.99 55	84.86 152	99.20 53	97.41 114	96.28 130	99.76 87	100.00 1

MVS_Test		95.74 83	98.18 74	92.90 108	92.16 115	99.49 93	97.36 81	84.30 129	99.79 61	84.94 120	96.65 94	93.63 116	98.85 69	98.61 49	99.10 32	99.81 56	100.00 1

thres400		95.72 84	96.48 118	94.84 71	93.00 99	99.83 58	97.55 73	88.93 74	98.49 142	90.61 67	98.86 59	84.63 156	99.20 53	97.45 110	96.10 136	99.77 79	99.99 60

MGCFI-Net		95.71 85	96.97 107	94.25 81	92.90 103	99.44 101	97.35 82	84.44 126	99.42 91	91.70 51	98.51 69	86.56 142	99.33 43	96.09 148	98.83 46	99.77 79	99.92 132

thres600view7		95.64 86	96.38 122	94.79 73	92.96 100	99.82 60	97.48 79	88.85 76	98.38 148	90.52 69	98.84 61	84.61 157	99.15 56	97.41 114	95.60 148	99.76 87	99.99 60

Vis-MVSNet (Re-imp)		95.60 87	98.52 66	92.19 115	92.37 112	99.56 88	96.37 105	87.41 105	98.95 120	84.77 123	94.88 112	98.48 82	92.44 175	98.63 48	99.37 19	99.76 87	99.77 170

FMVSNet3		95.59 88	97.51 92	93.34 96	89.48 159	96.57 178	97.67 68	84.17 130	99.48 83	89.76 77	95.09 107	94.35 110	99.14 57	98.37 68	98.86 42	99.82 47	99.89 143

ECVR-MVS		95.46 89	95.58 138	95.31 62	94.12 86	99.80 66	97.33 83	89.48 68	98.90 125	92.99 39	87.97 163	86.41 144	98.14 95	98.14 80	98.32 66	99.82 47	99.52 185

PVSNet_Blended_VisFu		95.37 90	97.44 94	92.95 106	95.20 74	99.80 66	92.68 163	88.41 84	99.12 108	87.64 96	88.31 162	99.10 72	94.07 162	98.27 72	97.51 96	99.73 112	100.00 1

DI_MVS_pp		95.29 91	97.02 101	93.28 97	91.76 133	99.52 89	97.84 65	85.67 109	99.08 112	87.29 100	87.76 166	97.46 93	97.31 121	97.83 98	97.48 97	99.83 43	100.00 1

ET-MVSNet_ETH3D		95.20 92	97.82 85	92.15 116	80.77 221	98.13 153	97.65 70	86.93 107	99.72 68	88.56 91	99.29 46	97.01 95	99.24 51	94.58 175	95.98 141	99.75 96	99.99 60

TSAR-MVS + COLMAP		95.20 92	95.03 147	95.41 60	96.17 68	98.69 141	99.11 42	93.40 43	99.97 9	84.89 121	98.23 77	75.01 187	99.34 42	97.27 119	96.37 129	99.58 147	99.64 180

GBi-Net		95.19 94	96.99 105	93.09 101	89.11 160	96.47 180	96.90 94	84.17 130	99.48 83	89.76 77	95.09 107	94.35 110	98.87 66	96.50 132	97.21 107	99.74 100	99.81 163

test1		95.19 94	96.99 105	93.09 101	89.11 160	96.47 180	96.90 94	84.17 130	99.48 83	89.76 77	95.09 107	94.35 110	98.87 66	96.50 132	97.21 107	99.74 100	99.81 163

test1111		95.15 96	95.18 144	95.12 63	94.07 88	99.80 66	97.20 86	89.53 67	98.80 129	92.22 46	85.44 175	86.24 146	97.89 107	98.12 82	98.34 65	99.80 61	99.51 186

test0.0.03 1		95.15 96	97.87 84	91.99 117	91.69 138	98.82 137	93.04 161	83.60 135	99.65 73	88.80 86	94.15 120	97.67 90	94.97 148	96.62 129	98.16 69	99.83 43	100.00 1

baseline2		95.13 98	98.55 61	91.15 123	90.29 155	99.00 127	94.49 147	82.00 150	99.68 71	84.82 122	96.47 95	99.30 70	95.71 140	98.24 74	97.14 113	99.57 149	100.00 1

viewcassd2359sk11		95.10 99	96.36 123	93.63 89	91.68 141	99.37 108	96.09 111	87.78 89	98.72 131	88.01 95	94.74 114	86.41 144	98.47 82	97.69 101	97.61 93	99.73 112	99.98 79

casdiffmvs_mvg		95.10 99	96.45 121	93.53 91	92.05 118	99.42 103	97.25 85	87.66 94	97.17 169	86.09 112	91.79 145	91.27 123	98.31 88	98.06 86	97.42 100	99.81 56	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 99	98.81 57	90.78 124	98.38 51	98.47 144	96.54 103	89.36 70	99.78 63	65.65 194	99.31 44	98.24 86	94.79 151	98.28 71	99.35 22	99.93 19	98.27 205
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Anonymous20231211		94.96 102	94.99 148	94.91 67	93.01 98	99.44 101	96.85 98	88.49 82	98.78 130	92.61 44	83.94 181	90.25 131	98.94 62	95.87 154	96.77 122	99.58 147	99.89 143

UA-Net		94.95 103	98.66 59	90.63 126	94.60 84	98.94 133	96.03 112	85.28 113	98.01 161	78.92 141	97.42 87	99.96 40	89.09 202	98.95 36	98.80 48	99.82 47	98.57 203

CANet_DTU		94.90 104	98.98 49	90.13 134	94.74 80	99.81 64	98.53 57	82.23 149	99.97 9	66.76 191	100.00 1	98.50 79	98.74 76	97.52 109	97.19 112	99.76 87	99.88 148

viewdifsd2359ckpt13		94.69 105	96.20 127	92.93 107	91.67 142	99.42 103	95.73 118	87.71 92	98.67 134	84.46 124	94.31 117	86.03 148	98.27 90	97.60 104	97.35 103	99.73 112	99.99 60

viewmanbaseed2359cas		94.61 106	95.93 132	93.07 103	91.90 124	99.38 106	96.32 107	87.84 88	98.33 152	84.29 125	92.71 134	85.68 150	98.33 87	97.68 102	97.74 87	99.74 100	99.99 60

FC-MVSNet-train		94.61 106	96.27 124	92.68 112	92.35 113	97.14 169	93.45 159	87.73 91	98.93 121	87.31 99	96.42 96	89.35 133	95.67 141	96.06 152	96.01 140	99.56 151	99.98 79

diffmvs		94.60 108	95.63 137	93.41 94	91.98 121	99.30 113	96.86 97	87.62 96	99.30 102	86.07 113	94.12 121	81.63 168	98.16 93	97.43 111	97.60 94	99.76 87	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		94.54 109	95.56 139	93.36 95	91.84 126	99.46 97	95.92 114	87.54 100	98.45 145	86.57 110	90.51 149	84.72 155	98.49 81	97.97 93	97.80 81	99.77 79	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		94.53 110	94.45 157	94.61 74	93.85 90	98.36 146	98.12 63	89.68 65	99.35 99	89.62 82	95.19 105	77.08 178	96.66 131	95.51 158	95.67 146	99.74 100	100.00 1
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
viewmambaseed2359dif		94.51 111	95.35 142	93.53 91	91.78 130	99.34 110	96.78 100	87.58 99	98.29 153	86.97 105	92.34 136	84.00 160	98.35 84	96.15 146	97.31 105	99.74 100	100.00 1

FMVSNet2		94.48 112	95.95 130	92.77 111	89.11 160	96.47 180	96.90 94	83.38 138	99.11 109	88.64 87	87.50 171	92.26 122	98.87 66	97.91 95	98.60 54	99.74 100	99.81 163

HQP-MVS		94.48 112	95.39 141	93.42 93	95.10 75	98.35 147	98.19 61	91.41 49	99.77 64	79.79 137	99.30 45	77.08 178	96.25 134	96.93 123	96.28 130	99.76 87	99.99 60

FA-MVS(training)		94.33 114	97.52 91	90.60 128	92.42 110	99.77 73	96.13 110	68.75 208	99.05 114	88.49 92	91.95 141	99.48 63	98.12 98	98.39 65	94.02 171	99.68 131	99.98 79

MDTV_nov1_ep13		94.32 115	98.77 58	89.14 144	91.70 137	99.52 89	95.21 132	72.09 206	99.80 59	78.91 142	96.32 97	99.62 58	97.71 116	98.39 65	97.71 89	99.22 203	100.00 1

CDS-MVSNet		94.32 115	97.00 104	91.19 122	89.82 158	98.71 140	95.51 126	85.14 117	96.85 171	82.33 132	92.48 135	96.40 101	94.71 152	96.86 126	97.76 84	99.63 141	99.92 132
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
dps		94.29 117	97.33 95	90.75 125	92.02 119	99.21 118	94.31 149	66.97 213	99.50 82	95.61 24	96.22 99	98.64 77	96.08 136	93.71 189	94.03 170	99.52 158	99.98 79

ACMM	94.44 10	94.26 118	94.62 153	93.84 87	94.86 79	97.73 160	93.48 158	90.76 53	99.27 103	87.46 98	99.04 52	76.60 180	96.76 129	96.37 139	93.76 174	99.74 100	99.55 183
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
diffmvs_AUTHOR		94.21 119	95.08 145	93.18 99	91.86 125	99.26 117	96.42 104	87.48 101	99.02 117	85.45 117	92.20 138	80.25 172	98.14 95	97.16 120	97.69 90	99.73 112	100.00 1

ACMP	94.49 9	94.19 120	94.74 152	93.56 90	94.25 85	98.32 149	96.02 113	89.35 71	98.90 125	87.28 101	99.14 50	76.41 183	94.94 149	96.07 151	94.35 167	99.49 165	99.99 60
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
EPMVS		94.08 121	98.54 65	88.87 145	92.51 108	99.47 94	94.18 151	66.53 214	99.68 71	82.40 131	95.24 104	99.40 67	97.86 108	98.12 82	97.99 74	99.75 96	99.88 148

viewmacassd2359aftdt		93.75 122	94.76 151	92.58 113	91.75 135	99.34 110	95.82 115	87.64 95	97.11 170	82.51 130	89.66 153	83.19 161	98.02 102	96.61 130	97.45 98	99.71 122	99.97 93

test-LLR		93.71 123	97.23 97	89.60 138	91.69 138	99.10 123	94.68 145	83.60 135	99.36 97	71.94 168	93.82 124	96.51 99	95.96 138	97.42 112	94.37 164	99.74 100	99.99 60

CHOSEN 1792x2688		93.69 124	94.89 150	92.28 114	96.17 68	99.84 57	95.69 120	83.17 141	98.54 140	82.04 133	77.58 209	91.15 125	96.90 124	98.36 69	98.82 47	99.73 112	99.98 79

viewdifsd2359ckpt11		93.64 125	94.30 160	92.88 110	91.82 128	98.82 137	94.88 138	87.46 102	99.08 112	86.98 104	92.20 138	80.79 169	97.85 109	93.32 197	96.13 134	98.30 217	99.75 173

viewmsd2359difaftdt		93.64 125	94.29 161	92.89 109	91.82 128	98.82 137	94.88 138	87.46 102	99.04 116	87.03 103	92.20 138	80.78 170	97.85 109	93.31 198	96.13 134	98.30 217	99.75 173

LGP-MVS_train		93.60 127	95.05 146	91.90 118	94.90 78	98.29 150	97.93 64	88.06 86	99.14 107	74.83 157	99.26 47	76.50 181	96.07 137	96.31 141	95.90 145	99.59 145	99.97 93

SCA		93.53 128	98.90 52	87.27 162	92.01 120	99.30 113	93.43 160	65.72 218	99.80 59	75.20 156	97.66 85	99.74 52	97.44 119	98.21 76	97.62 92	99.84 37	100.00 1

FMVSNet5		93.53 128	96.09 129	90.56 129	86.74 175	92.84 220	92.64 164	77.50 183	99.41 94	88.97 85	98.02 81	97.81 88	98.00 103	94.85 170	95.43 150	99.50 164	94.25 223

OPM-MVS		93.50 130	93.00 169	94.07 82	95.82 71	98.26 151	98.49 59	91.62 48	94.69 191	81.93 134	92.82 132	76.18 185	96.82 126	96.12 147	94.57 158	99.74 100	98.39 204
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
CostFormer		93.50 130	96.50 117	90.00 135	91.69 138	98.65 143	93.88 154	67.64 211	98.97 118	89.16 84	97.79 83	88.92 136	97.97 104	95.14 167	96.06 138	99.63 141	100.00 1

IterMVS-LS		93.50 130	96.22 125	90.33 132	90.93 146	95.50 205	94.83 141	80.54 160	98.92 122	79.11 139	90.64 148	93.70 115	96.79 127	96.93 123	97.85 80	99.78 75	99.99 60
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PatchmatchNet		93.48 133	98.84 56	87.22 163	91.93 122	99.39 105	92.55 165	66.06 216	99.71 69	75.61 153	98.24 76	99.59 59	97.35 120	97.87 96	97.64 91	99.83 43	99.43 189
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MS-PatchMatch		93.46 134	95.91 133	90.61 127	95.48 72	99.31 112	95.62 123	77.23 185	99.42 91	81.88 135	88.92 159	96.06 104	93.80 164	96.45 138	93.11 183	99.65 135	98.10 209

dmvs_re		93.34 135	94.59 154	91.88 119	87.97 171	99.14 122	95.29 131	88.61 79	98.09 158	82.71 129	97.34 88	78.96 173	96.98 122	94.62 173	93.98 172	99.73 112	99.98 79

tpm cat1		93.29 136	96.53 116	89.50 140	91.84 126	99.18 120	94.70 143	67.70 210	98.38 148	86.67 106	89.16 156	99.38 68	96.66 131	94.33 176	95.30 151	99.43 181	100.00 1

Effi-MVS+-dtu		93.13 137	97.13 99	88.47 153	88.86 166	99.19 119	96.79 99	79.08 173	99.64 75	70.01 178	97.51 86	89.38 132	96.53 133	97.60 104	96.55 124	99.57 149	100.00 1

HyFIR lowres test		93.13 137	94.48 156	91.56 120	96.12 70	99.68 80	93.52 157	79.98 164	97.24 167	81.73 136	72.66 218	95.74 106	98.29 89	98.27 72	97.79 82	99.70 124	100.00 1

Vis-MVSNet		93.08 139	96.76 111	88.78 149	91.14 145	99.63 85	94.85 140	83.34 139	97.19 168	74.78 158	91.92 144	93.15 119	88.81 205	97.59 106	98.35 62	99.78 75	99.49 188
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
Effi-MVS+		93.06 140	95.94 131	89.70 137	90.82 147	99.45 98	95.71 119	78.94 174	98.72 131	74.71 159	97.92 82	80.73 171	98.35 84	97.72 100	97.05 118	99.70 124	100.00 1

ADS-MVSNet		92.91 141	97.97 81	87.01 165	92.07 117	99.27 116	92.70 162	65.39 221	99.85 51	75.40 154	94.93 111	98.26 85	96.86 125	96.09 148	97.52 95	99.65 135	99.84 159

GeoE		92.88 142	95.20 143	90.18 133	90.59 151	99.18 120	96.31 108	78.36 179	97.52 166	78.53 144	87.11 172	88.01 137	97.63 118	97.79 99	96.76 123	99.66 133	100.00 1

TESTMET0.1,1		92.87 143	97.23 97	87.79 159	86.96 174	99.10 123	94.68 145	77.46 184	99.36 97	71.94 168	93.82 124	96.51 99	95.96 138	97.42 112	94.37 164	99.74 100	99.99 60

FC-MVSNet-test		92.78 144	96.19 128	88.80 148	88.00 170	97.54 162	93.60 156	82.36 148	98.16 154	79.71 138	91.55 146	95.41 107	89.65 197	96.09 148	95.23 152	99.49 165	99.31 192

Fast-Effi-MVS+-dtu		92.73 145	97.62 87	87.02 164	88.91 164	98.83 136	95.79 116	73.98 200	99.89 41	68.62 183	97.73 84	93.30 118	95.21 147	97.67 103	95.96 142	99.59 145	100.00 1

IB-MVS	90.59 15	92.70 146	95.70 135	89.21 143	94.62 83	99.45 98	83.77 213	88.92 75	99.53 79	92.82 41	98.86 59	86.08 147	75.24 225	92.81 205	93.17 181	99.89 26	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		92.67 147	97.13 99	87.47 161	86.72 176	99.07 125	94.28 150	76.90 186	99.21 104	71.53 172	93.63 126	96.32 102	95.67 141	97.32 117	94.36 166	99.74 100	99.99 60

RPMNet		92.64 148	97.88 83	86.53 170	90.79 148	98.95 131	95.13 133	64.44 225	99.09 110	72.36 164	93.58 127	99.01 73	96.74 130	98.05 87	96.45 127	99.71 122	100.00 1

FMVSNet1		92.55 149	93.66 164	91.26 121	87.91 172	96.12 187	94.75 142	81.69 155	97.67 164	85.63 116	80.56 194	87.88 139	98.15 94	96.50 132	97.21 107	99.41 186	99.71 176

tpmrst		92.52 150	97.45 93	86.77 168	92.15 116	99.36 109	92.53 166	65.95 217	99.53 79	72.50 162	92.22 137	99.83 48	97.81 112	95.18 166	96.05 139	99.69 129	100.00 1

testgi		92.47 151	95.68 136	88.73 150	90.68 149	98.35 147	91.67 173	79.50 169	98.96 119	77.12 149	95.17 106	85.84 149	93.95 163	95.75 156	96.47 126	99.45 176	99.21 195

TAMVS		92.43 152	94.21 162	90.35 131	88.68 167	98.85 135	94.15 152	81.53 157	95.58 181	83.61 127	87.05 173	86.45 143	94.71 152	96.27 142	95.91 143	99.42 184	99.38 191

CR-MVSNet		92.32 153	97.97 81	85.74 179	90.63 150	98.95 131	95.46 127	65.50 219	99.09 110	67.51 187	94.20 118	98.18 87	95.59 144	98.16 78	97.20 110	99.74 100	100.00 1

CVMVSNet		92.13 154	95.40 140	88.32 156	91.29 144	97.29 167	91.85 170	86.42 108	96.71 173	71.84 170	89.56 154	91.18 124	88.98 204	96.17 145	97.76 84	99.51 162	99.14 197

Fast-Effi-MVS+		92.11 155	94.33 158	89.52 139	89.06 163	99.00 127	95.13 133	76.72 188	98.59 139	78.21 146	89.99 151	77.35 177	98.34 86	97.97 93	97.44 99	99.67 132	99.96 114

ACMH+	92.61 13	91.80 156	93.03 167	90.37 130	93.03 97	98.17 152	94.00 153	84.13 133	98.12 156	77.39 148	91.95 141	74.62 188	94.36 159	94.62 173	93.82 173	99.32 195	99.87 153

IterMVS-SCA-FT		91.75 157	96.87 109	85.78 177	90.34 153	95.93 194	95.06 135	73.85 201	98.91 123	61.01 207	89.21 155	98.87 74	94.66 155	98.09 85	97.12 114	99.76 87	99.99 60

IterMVS		91.65 158	96.62 112	85.85 176	90.27 156	95.80 196	95.32 130	74.15 197	98.91 123	60.95 208	88.79 161	97.76 89	94.69 154	98.04 89	97.07 116	99.73 112	100.00 1
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ACMH	92.34 14	91.59 159	93.02 168	89.92 136	93.97 89	97.98 157	90.10 188	84.70 120	98.46 144	76.80 150	93.38 128	71.94 199	94.39 157	95.34 162	94.04 169	99.54 154	100.00 1
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs4		91.41 160	93.05 166	89.49 141	85.85 184	96.52 179	91.70 172	82.49 146	98.14 155	83.17 128	87.57 168	81.76 167	94.39 157	95.47 159	92.62 189	99.33 193	99.29 193

PatchT		91.06 161	97.66 86	83.36 201	90.32 154	98.96 130	82.30 217	64.72 224	98.45 145	67.51 187	93.28 129	97.60 91	95.59 144	98.16 78	97.20 110	99.70 124	100.00 1

MIMVSNet		91.01 162	96.22 125	84.93 187	85.24 188	98.09 154	90.40 183	64.96 223	97.55 165	72.65 160	96.23 98	90.81 127	96.79 127	96.69 127	97.06 117	99.52 158	97.09 215

UniMVSNet_NR-MVSNet		90.50 163	92.31 172	88.38 154	85.04 194	96.34 183	90.94 175	85.32 112	95.87 180	75.69 151	87.68 167	78.49 174	93.78 165	93.21 200	94.60 157	99.53 157	99.97 93

UniMVSNet (Re)		90.41 164	91.96 174	88.59 152	85.71 185	96.73 175	90.82 177	84.11 134	95.23 187	78.54 143	88.91 160	76.41 183	92.84 172	93.40 196	93.05 184	99.55 153	100.00 1

GA-MVS		90.38 165	94.59 154	85.46 183	88.30 169	98.44 145	92.18 167	83.30 140	97.89 163	58.05 216	92.86 131	84.25 159	91.27 188	96.65 128	92.61 190	99.66 133	99.43 189

USDC		90.36 166	91.68 175	88.82 147	92.58 106	98.02 155	96.27 109	79.83 165	98.37 150	70.61 177	89.05 157	67.50 216	94.17 160	95.77 155	94.43 162	99.46 173	98.62 202

thisisatest0515		90.28 167	94.32 159	85.57 182	85.23 189	97.23 168	85.44 209	83.09 142	96.80 172	72.41 163	89.82 152	90.87 126	87.93 210	95.27 165	90.39 212	99.33 193	99.88 148

TinyColmap		89.94 168	90.88 181	88.84 146	92.43 109	97.91 158	95.59 125	80.10 163	98.12 156	71.33 174	84.56 177	67.46 217	94.15 161	95.57 157	94.27 168	99.43 181	98.26 206

pm-mvs1		89.68 169	92.00 173	86.96 166	86.23 180	96.62 177	90.36 184	83.05 143	93.97 199	72.15 167	81.77 189	82.10 165	90.69 194	95.38 161	94.50 160	99.29 199	99.65 178

tpm		89.60 170	94.93 149	83.39 199	89.94 157	97.11 170	90.09 189	65.28 222	98.67 134	60.03 212	96.79 92	84.38 158	95.66 143	91.90 209	95.65 147	99.32 195	99.98 79

NR-MVSNet		89.52 171	90.71 182	88.14 158	86.19 181	96.20 185	92.07 168	84.58 121	95.54 182	75.27 155	87.52 169	67.96 214	91.24 189	94.33 176	93.45 177	99.49 165	99.97 93

DU-MVS		89.49 172	90.60 183	88.19 157	84.71 198	96.20 185	90.94 175	84.58 121	95.54 182	75.69 151	87.52 169	68.74 213	93.78 165	91.10 214	95.13 154	99.47 171	99.97 93

Baseline_NR-MVSNet		89.13 173	89.53 195	88.66 151	84.71 198	94.43 213	91.79 171	84.49 125	95.54 182	78.28 145	78.52 206	72.46 198	93.29 169	91.10 214	94.82 156	99.42 184	99.86 156

tfpnnormal		89.09 174	89.71 189	88.38 154	87.37 173	96.78 174	91.46 174	85.20 115	90.33 220	72.35 165	83.45 182	69.30 211	94.45 156	95.29 163	92.86 186	99.44 180	99.93 128

TranMVSNet+NR-MVSNet		88.88 175	89.90 188	87.69 160	84.06 210	95.68 197	91.88 169	85.23 114	95.16 188	72.54 161	83.06 185	70.14 208	92.93 171	90.81 217	94.53 159	99.48 169	99.89 143

WR-MVS_H		88.47 176	90.55 184	86.04 172	85.13 191	96.07 189	89.86 195	79.80 166	94.37 196	72.32 166	83.12 184	74.44 191	89.60 198	93.52 193	92.40 191	99.51 162	99.96 114

SixPastTwentyTwo		88.35 177	91.51 177	84.66 189	85.39 187	96.96 172	86.57 205	79.62 168	96.57 174	63.73 201	87.86 165	75.18 186	93.43 168	94.03 180	90.37 213	99.24 202	99.58 181

TransMVSNet (Re)		88.33 178	89.55 194	86.91 167	86.65 177	95.56 202	90.48 181	84.44 126	92.02 219	71.07 176	80.13 196	72.48 197	89.41 199	95.05 169	94.44 161	99.39 188	97.14 214

MVS-HIRNet		88.27 179	94.05 163	81.51 207	88.90 165	98.93 134	83.38 215	60.52 232	98.06 159	63.78 200	80.67 193	90.36 130	92.94 170	97.29 118	96.41 128	99.56 151	96.66 217

WR-MVS		88.23 180	90.15 186	86.00 174	84.39 205	95.64 198	89.96 192	81.80 152	94.46 194	71.60 171	82.10 187	74.36 192	88.76 206	92.48 206	92.20 193	99.46 173	99.83 161

CP-MVSNet		88.09 181	89.57 192	86.36 171	84.63 201	95.46 207	89.48 197	80.53 161	93.42 206	71.26 175	81.25 191	69.90 209	92.78 173	93.30 199	93.69 175	99.47 171	99.96 114

pmnet_mix02		88.07 182	92.32 171	83.10 202	86.14 182	96.23 184	81.90 220	83.05 143	98.04 160	57.59 218	84.93 176	82.02 166	90.87 193	93.54 192	91.53 204	99.06 210	99.97 93

UniMVSNet_ETH3D		88.05 183	87.01 213	89.27 142	88.53 168	97.49 163	90.35 185	83.48 137	94.57 192	77.87 147	70.08 222	61.75 228	96.22 135	90.17 218	95.21 153	99.16 207	99.82 162

anonymousdsp		87.98 184	92.38 170	82.85 203	83.68 214	96.79 173	90.78 178	74.06 199	95.29 186	57.91 217	83.33 183	83.12 162	91.15 191	95.96 153	92.37 192	99.52 158	99.76 171

LTVRE_ROB	88.65 16	87.87 185	91.11 180	84.10 196	86.64 178	97.47 164	94.40 148	78.41 178	96.13 178	52.02 226	87.95 164	65.92 222	93.59 167	95.29 163	95.09 155	99.52 158	99.95 121
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		87.84 186	89.42 197	85.99 175	85.16 190	96.01 191	90.52 180	81.78 154	94.43 195	67.59 185	81.32 190	71.87 200	91.48 186	91.25 213	91.16 208	99.43 181	99.92 132

TDRefinement		87.79 187	88.76 204	86.66 169	93.54 91	98.02 155	95.76 117	85.18 116	96.57 174	67.90 184	80.51 195	66.51 221	78.37 222	93.20 201	89.73 214	99.22 203	96.75 216

MDTV_nov1_ep13_2view		87.75 188	93.32 165	81.26 209	83.74 213	96.64 176	85.66 208	66.20 215	98.36 151	61.61 205	84.34 179	87.95 138	91.12 192	94.01 181	92.66 188	99.22 203	99.27 194

v8		87.54 189	89.33 198	85.45 184	85.41 186	95.50 205	90.32 186	78.94 174	94.35 197	66.93 190	81.90 188	70.99 205	91.62 184	91.49 212	91.22 207	99.48 169	99.87 153

v1144		87.49 190	89.64 190	84.97 186	84.73 197	95.84 195	90.17 187	79.30 170	93.96 200	64.65 198	78.83 203	73.38 196	91.51 185	93.77 187	91.77 199	99.45 176	99.93 128

v2v482		87.46 191	88.90 202	85.78 177	84.58 202	95.95 193	89.90 194	82.43 147	94.19 198	65.65 194	79.80 198	69.12 212	92.67 174	91.88 210	91.46 205	99.45 176	99.93 128

v10		87.40 192	89.62 191	84.80 188	84.93 195	95.07 211	90.44 182	75.63 193	94.51 193	66.52 192	78.87 202	73.47 195	91.86 182	93.69 190	91.87 198	99.45 176	99.86 156

pmmvs5		87.33 193	90.01 187	84.20 194	84.31 207	96.04 190	87.63 203	76.59 189	93.17 211	65.35 197	84.30 180	71.68 201	91.91 181	95.41 160	91.37 206	99.39 188	98.13 207

N_pmnet		87.31 194	91.51 177	82.41 206	85.13 191	95.57 201	80.59 222	81.79 153	96.20 176	58.52 215	78.62 204	85.66 151	89.36 200	94.64 172	92.14 194	99.08 209	97.72 213

PS-CasMVS		87.24 195	88.52 207	85.73 180	84.58 202	95.35 209	89.03 200	80.17 162	93.11 212	68.86 182	77.71 208	66.89 218	92.30 176	93.13 202	93.50 176	99.46 173	99.96 114

EU-MVSNet		87.20 196	90.47 185	83.38 200	85.11 193	93.85 218	86.10 207	79.76 167	93.30 210	65.39 196	84.41 178	78.43 175	85.04 217	92.20 208	93.03 185	98.86 212	98.05 210

PEN-MVS		87.20 196	88.22 208	86.01 173	84.01 212	94.93 212	90.00 191	81.52 159	93.46 205	69.29 180	79.69 199	65.51 223	91.72 183	91.01 216	93.12 182	99.49 165	99.84 159

EG-PatchMatch MVS		86.96 198	89.56 193	83.93 197	86.29 179	97.61 161	90.75 179	73.31 204	95.43 185	66.08 193	75.88 215	71.31 202	87.55 212	94.79 171	92.74 187	99.61 144	99.13 198

v1192		86.93 199	89.01 200	84.50 190	84.46 204	95.51 204	89.93 193	78.65 177	93.75 201	62.29 203	77.19 210	70.88 206	92.28 177	93.84 184	91.96 196	99.38 190	99.90 140

v1921920		86.81 200	88.93 201	84.33 193	84.23 208	95.41 208	90.09 189	78.10 180	93.74 202	62.17 204	76.98 212	71.14 203	92.05 179	93.69 190	91.69 202	99.32 195	99.88 148

v144192		86.80 201	88.90 202	84.35 191	84.33 206	95.56 202	89.34 198	77.74 182	93.60 203	64.03 199	77.82 207	70.76 207	91.28 187	92.91 204	91.74 201	99.37 191	99.90 140

DTE-MVSNet		86.70 202	87.66 212	85.58 181	83.30 215	94.29 214	89.74 196	81.53 157	92.77 215	68.93 181	80.13 196	64.00 226	90.62 195	89.45 219	93.34 178	99.32 195	99.67 177

gg-mvs-nofinetune		86.69 203	91.30 179	81.30 208	90.42 152	99.64 83	98.50 58	61.68 230	79.23 230	40.35 233	66.58 224	97.14 94	96.92 123	98.64 46	97.94 75	99.91 22	99.97 93

v148		86.63 204	87.79 210	85.28 185	84.65 200	95.97 192	86.46 206	82.84 145	92.91 214	71.52 173	78.99 201	66.74 220	86.83 214	89.28 220	90.69 210	99.41 186	99.94 126

v1240		86.24 205	88.56 206	83.54 198	84.05 211	95.21 210	89.27 199	76.76 187	93.42 206	60.68 211	75.99 214	69.80 210	91.21 190	93.83 186	91.76 200	99.29 199	99.91 139

pmmvs6		85.75 206	86.97 214	84.34 192	84.88 196	95.59 200	87.41 204	79.19 172	87.81 225	67.56 186	63.05 228	77.76 176	89.15 201	93.45 195	91.90 197	97.83 221	99.21 195

v7n		85.39 207	87.70 211	82.70 204	82.77 217	95.64 198	88.27 202	74.83 195	92.30 217	62.58 202	76.37 213	64.80 225	88.38 208	94.29 178	90.61 211	99.34 192	99.87 153

gm-plane-assit		84.93 208	91.61 176	77.14 217	84.14 209	91.29 224	66.18 233	69.70 207	85.22 229	47.95 230	78.58 205	89.24 134	94.90 150	98.82 40	98.12 71	99.99 5	100.00 1

CMPMVS	65.66 17	84.62 209	85.02 216	84.15 195	95.40 73	97.79 159	88.35 201	79.22 171	89.66 223	60.71 210	72.20 219	73.94 193	87.32 213	86.73 222	84.55 224	93.90 226	90.31 227
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test_method		84.44 210	89.04 199	79.08 212	81.15 220	92.82 221	82.06 219	61.92 228	96.17 177	59.38 213	74.47 217	67.52 215	91.96 180	96.92 125	95.53 149	97.98 220	99.85 158

Anonymous20231206		84.28 211	89.53 195	78.17 214	82.31 219	94.16 216	82.57 216	76.51 190	93.38 209	52.98 223	79.47 200	73.74 194	75.45 224	95.07 168	94.41 163	99.18 206	96.46 219

new_pmnet		84.12 212	87.89 209	79.72 211	80.43 222	94.14 217	80.26 223	74.14 198	96.01 179	56.30 222	74.94 216	76.45 182	88.59 207	93.11 203	89.31 215	98.59 216	91.27 226

test20.03		83.86 213	88.73 205	78.16 215	82.60 218	93.00 219	81.61 221	74.68 196	92.36 216	57.50 219	83.01 186	74.48 190	73.30 226	92.40 207	91.14 209	99.29 199	94.75 222

pmmvs-eth3d		82.92 214	83.31 219	82.47 205	76.97 225	91.76 223	83.79 212	76.10 191	90.33 220	69.95 179	71.04 221	48.09 232	89.02 203	93.85 183	89.14 216	99.02 211	98.96 200

PM-MVS		82.79 215	84.51 217	80.77 210	77.22 224	92.13 222	83.61 214	73.31 204	93.50 204	61.06 206	77.15 211	46.52 235	90.55 196	94.14 179	89.05 219	98.85 213	99.12 199

pmmvs3		80.91 216	85.62 215	75.42 219	75.01 227	89.09 228	75.31 227	68.70 209	86.99 227	46.74 232	81.18 192	62.91 227	87.95 209	93.84 184	89.06 218	98.80 215	96.23 220

MIMVSNet1		80.64 217	83.97 218	76.76 218	68.91 230	91.15 226	78.32 226	75.47 194	89.58 224	56.64 221	65.10 225	65.17 224	82.14 218	93.51 194	91.64 203	99.10 208	91.66 225

MDA-MVSNet-bldmvs		80.30 218	82.83 220	77.34 216	69.16 229	94.29 214	72.16 228	81.97 151	90.14 222	57.32 220	94.01 123	47.97 233	86.81 215	68.74 230	86.82 221	96.63 222	97.86 211

new-patchmatchnet		78.17 219	80.82 223	75.07 220	76.93 226	91.20 225	71.90 229	73.32 203	86.59 228	48.91 227	67.11 223	47.85 234	81.19 219	88.18 221	87.02 220	98.19 219	97.79 212

FE-MVSNET		77.93 220	80.91 222	74.45 221	61.41 233	89.15 227	78.53 225	75.91 192	87.12 226	52.74 224	63.25 227	50.07 231	79.29 221	91.87 211	89.12 217	98.81 214	95.76 221

FPMVS		73.80 221	74.62 224	72.84 222	83.09 216	84.44 230	83.89 211	73.64 202	92.20 218	48.50 228	72.19 220	59.51 229	63.16 228	69.13 229	66.26 233	84.74 231	78.59 234

WB-MVS		71.64 222	82.10 221	59.45 226	79.66 223	78.44 233	55.66 237	78.80 176	93.01 213	19.20 239	86.36 174	71.05 204	39.18 235	85.26 224	81.08 225	84.19 232	79.49 233

Gipuma		71.02 223	72.60 227	69.19 223	71.31 228	75.11 234	66.36 232	61.65 231	94.93 189	47.29 231	38.74 233	38.52 236	75.52 223	86.09 223	85.92 223	93.01 227	88.87 229
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
GG-mvs-BLEND		69.85 224	99.39 41	35.39 232	3.67 240	99.94 20	99.10 43	1.69 236	99.85 51	3.19 241	98.13 80	99.46 64	4.92 236	99.23 32	99.14 31	99.80 61	100.00 1

PMMVS2		65.18 225	68.25 228	61.59 224	61.37 234	79.72 232	59.18 236	61.80 229	64.72 233	37.33 234	53.82 230	35.59 237	54.46 233	73.94 228	80.52 226	95.40 225	89.43 228

PMVS	60.14 18	62.67 226	64.05 229	61.06 225	68.32 231	53.27 240	52.23 238	67.63 212	75.07 232	48.30 229	58.27 229	57.43 230	49.99 234	67.20 231	62.42 234	79.87 235	74.68 235
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
testmvs		61.76 227	72.90 226	48.76 229	21.21 238	68.61 235	66.11 234	37.38 234	94.83 190	33.06 235	64.31 226	29.72 238	86.08 216	74.44 227	78.71 227	48.74 237	99.65 178

E-PMN		55.33 228	55.79 231	54.81 228	59.81 236	57.23 238	38.83 239	63.59 226	64.06 235	24.66 237	35.33 235	26.40 240	58.69 230	55.41 233	70.54 230	83.26 233	81.56 232

EMVS		55.14 229	55.29 232	54.97 227	60.87 235	57.52 237	38.58 240	63.57 227	64.54 234	23.36 238	36.96 234	27.99 239	60.69 229	51.17 234	66.61 232	82.73 234	82.25 231

MVE	58.81 19	52.07 230	55.15 233	48.48 230	42.45 237	62.35 236	36.41 241	54.70 233	49.88 236	27.65 236	29.98 236	18.08 241	54.87 232	65.93 232	77.26 228	74.79 236	82.59 230
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test123		48.14 231	58.11 230	36.51 231	8.71 239	56.81 239	59.55 235	24.08 235	77.50 231	14.41 240	49.20 231	11.94 243	80.98 220	41.62 235	69.81 231	31.32 238	99.90 140

uanet_test		0.00 232	0.00 234	0.00 233	0.00 241	0.00 241	0.00 242	0.00 237	0.00 237	0.00 242	0.00 237	0.00 244	0.00 237	0.00 236	0.00 235	0.00 239	0.00 236

sosnet-low-res		0.00 232	0.00 234	0.00 233	0.00 241	0.00 241	0.00 242	0.00 237	0.00 237	0.00 242	0.00 237	0.00 244	0.00 237	0.00 236	0.00 235	0.00 239	0.00 236

sosnet		0.00 232	0.00 234	0.00 233	0.00 241	0.00 241	0.00 242	0.00 237	0.00 237	0.00 242	0.00 237	0.00 244	0.00 237	0.00 236	0.00 235	0.00 239	0.00 236

TPM-MVS					99.67 4	99.96 7	99.82 5			94.63 32	99.65 17	100.00 1	99.90 5			99.99 5	99.80 166
Ray Leroy Khuboni and Hongjun Xu: Textureless Resilient Propagation Matching in Multiple View Stereosis (TPM-MVS). SATNAC 2025
RE-MVS-def										52.74 224

9.14												100.00 1

SR-MVS					99.61 13			96.80 18				100.00 1

Anonymous202405211			95.78 134		93.26 92	99.52 89	96.70 102	88.55 80	97.93 162		88.99 158	90.68 128	98.99 61	96.46 136	97.02 119	99.64 139	99.89 143

our_test_3					85.89 183	96.09 188	82.15 218

ambc			74.33 225		66.84 232	84.26 231	84.17 210		93.39 208	58.99 214	45.93 232	18.06 242	70.61 227	93.94 182	86.62 222	92.61 229	98.13 207

MTAPA										96.61 16		100.00 1

MTMP										97.42 11		100.00 1

Patchmatch-RL test							68.01 231

tmp_tt				78.81 213	98.80 45	85.73 229	70.08 230	77.87 181	98.68 133	83.71 126	99.53 30	74.55 189	54.97 231	78.28 226	72.43 229	87.45 230

XVS					95.09 76	99.94 20	97.49 74			88.58 88		99.98 35				99.78 75

X-MVStestdata					95.09 76	99.94 20	97.49 74			88.58 88		99.98 35				99.78 75

mPP-MVS					99.23 38							99.87 46

NP-MVS									99.79 61

Patchmtry						99.00 127	95.46 127	65.50 219		67.51 187

DeepMVS_CX						97.31 166	79.48 224	89.65 66	98.66 136	60.89 209	94.40 116	66.89 218	87.65 211	81.69 225		92.76 228	94.24 224

High-resolution multi-view benchmark