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 29	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 23	100.00 1	99.82 14	99.43 28	98.93 37	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 27	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 36	97.58 10	99.58 26	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 20	100.00 1	99.93 1	99.94 2	98.75 52	99.97 12	99.97 88

DVP-MVS		99.38 6	99.57 36	99.15 1	99.62 8	99.94 20	99.72 23	96.99 2	99.98 3	98.85 1	98.21 78	100.00 1	99.88 8	99.88 11	98.96 35	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 23	100.00 1	99.70 21	99.96 1	98.96 35	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 29	97.42 11	99.62 22	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 33	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 40	100.00 1	99.90 5	99.88 11	99.46 17	99.84 37	99.92 126
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 32	96.31 19	99.59 25	99.95 41	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 11	100.00 1	99.75 18	99.85 14	98.80 48	99.98 10	99.97 88

CNLPA		99.24 12	99.58 33	98.85 12	99.34 33	99.95 14	99.32 36	96.65 27	99.96 20	98.44 4	98.97 55	100.00 1	99.57 31	98.66 44	99.56 15	99.76 87	99.97 88

AdaColmap		99.21 14	99.45 39	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 55	99.93 1	99.26 31	97.25 100	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 11	100.00 1	99.85 11	99.56 23	99.36 21	99.79 68	99.97 88

PLC	98.06 1	99.17 16	99.38 41	98.92 8	99.47 23	99.90 49	99.48 33	96.47 32	99.96 20	98.73 2	99.52 32	100.00 1	99.55 33	98.54 57	97.73 87	99.84 37	99.99 59
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 36	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 16	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 32	94.65 31	99.65 16	99.96 39	99.84 12	99.51 26	99.35 22	99.79 68	99.96 108

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 35	99.67 11	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 38	98.57 16	99.44 27	99.93 35	99.60 31	95.92 37	99.77 64	97.01 14	99.67 11	100.00 1	99.72 19	99.56 23	97.76 84	99.70 118	99.98 75

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 37	98.68 63	99.94 42	99.69 22	99.93 4	99.95 3	99.96 13	99.98 75
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 30	98.27 22	97.88 54	99.92 41	99.71 25	96.80 18	99.96 20	95.58 25	98.71 62	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 29	98.22 23	99.62 8	99.94 20	99.74 20	96.95 4	99.87 47	93.76 34	99.49 35	100.00 1	99.39 39	99.73 18	98.35 62	99.89 26	99.96 108

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 36	99.64 20	100.00 1	98.56 79	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 10	100.00 1	99.69 22	98.40 64	99.75 11	99.93 18	99.99 59

MP-MVS		98.82 27	99.63 27	97.88 29	99.41 29	99.91 48	99.74 20	96.76 23	99.88 43	91.89 48	99.50 33	99.94 42	99.65 27	99.71 21	98.49 60	99.82 47	99.97 88
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 33	97.62 30	99.62 8	99.92 41	99.72 23	96.78 21	99.71 69	90.13 71	99.66 15	99.99 32	99.64 28	99.78 16	98.14 70	99.82 47	99.89 137

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 82	99.53 29	99.83 47	99.59 30	99.85 14	99.19 29	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 87	99.65 16	99.98 34	99.67 25	99.60 22	99.26 27	99.77 79	99.97 88

OMC-MVS		98.59 31	99.07 46	98.03 26	99.41 29	99.90 49	99.26 39	94.33 41	99.94 29	96.03 20	96.68 92	99.72 54	99.42 36	98.86 38	98.84 44	99.72 115	99.58 173

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 73	99.57 27	100.00 1	98.66 78	99.56 23	99.96 2	99.80 61	99.80 160

PGM-MVS		98.47 33	99.73 16	97.00 36	99.68 3	99.94 20	99.76 10	91.74 46	99.84 54	91.17 58	100.00 1	99.69 55	99.81 15	99.38 29	99.30 25	99.82 47	99.95 115

TSAR-MVS + ACMM		98.30 34	99.64 26	96.74 39	99.08 40	99.94 20	99.67 27	96.73 24	99.97 9	86.30 107	98.30 70	99.99 32	98.78 72	99.73 18	99.57 14	99.88 29	99.98 75

CSCG		98.22 35	98.37 68	98.04 25	99.60 17	99.82 60	99.45 34	93.59 42	99.16 105	96.46 18	98.22 77	95.86 105	99.41 38	96.33 135	99.22 28	99.75 96	99.94 120

3Dnovator+	95.21 7	98.17 36	99.08 45	97.12 35	99.28 36	99.78 71	98.61 54	89.93 60	99.93 32	95.36 26	95.50 101	100.00 1	99.56 32	98.58 52	99.80 10	99.95 15	99.97 88

ACMMP		98.16 37	99.01 47	97.18 33	98.86 43	99.92 41	98.77 52	95.73 38	99.31 101	91.15 59	100.00 1	99.81 49	98.82 70	98.11 84	95.91 135	99.77 79	99.97 88
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 38	99.69 21	96.36 44	99.23 38	99.93 35	97.79 66	91.84 45	99.87 47	90.53 67	100.00 1	99.57 60	98.93 64	99.44 27	99.08 32	99.85 34	99.95 115

EPNet		98.11 39	99.63 27	96.34 45	98.44 50	99.88 55	98.55 55	90.25 56	99.93 32	92.60 44	100.00 1	99.73 52	98.41 81	98.87 37	99.02 33	99.82 47	99.97 88
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TSAR-MVS + GP.		98.06 40	99.55 37	96.32 46	94.72 81	99.92 41	99.22 40	89.98 58	99.97 9	94.77 30	99.94 9	100.00 1	99.43 35	98.52 61	98.53 58	99.79 68	100.00 1

3Dnovator	95.01 8	97.98 41	98.89 52	96.92 38	99.36 31	99.76 74	98.72 53	89.98 58	99.98 3	93.99 33	94.60 114	99.43 65	99.50 34	98.55 54	99.91 5	99.99 5	99.98 75

MVS_111021_HR		97.94 42	99.59 31	96.02 48	99.27 37	99.97 3	97.03 93	90.44 53	99.89 40	90.75 62	100.00 1	99.73 52	98.68 77	98.67 43	98.89 41	99.95 15	99.97 88

QAPM		97.90 43	98.89 52	96.74 39	99.35 32	99.80 66	98.84 49	90.20 57	99.94 29	92.85 39	94.17 117	99.78 50	99.42 36	98.71 41	99.87 7	99.79 68	99.98 75

CDPH-MVS		97.88 44	99.59 31	95.89 49	98.90 42	99.95 14	99.40 35	92.86 44	99.86 50	85.33 113	98.62 64	99.45 64	99.06 60	99.29 30	99.94 4	99.81 56	100.00 1

CANet		97.62 45	98.94 50	96.08 47	97.19 58	99.93 35	99.29 38	90.38 54	99.87 47	91.00 60	95.79 100	99.51 61	98.72 76	98.53 58	99.00 34	99.90 24	99.99 59

TAPA-MVS	96.62 5	97.60 46	98.46 67	96.60 42	98.73 46	99.90 49	99.30 37	94.96 40	99.46 87	87.57 95	96.05 99	98.53 77	99.26 50	98.04 89	97.33 99	99.77 79	99.88 142
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
DeepPCF-MVS	97.16 4	97.58 47	99.72 18	95.07 65	98.45 49	99.96 7	93.83 147	95.93 36	100.00 1	90.79 61	98.38 69	99.85 46	95.28 138	99.94 2	99.97 1	96.15 214	99.97 88

CS-MVS-test		97.51 48	99.18 44	95.56 54	97.16 59	99.96 7	97.39 80	89.82 62	100.00 1	89.88 74	99.16 47	98.38 83	99.23 52	98.85 39	97.93 77	99.87 30	100.00 1

PCF-MVS	97.20 3	97.49 49	98.20 73	96.66 41	97.62 56	99.92 41	98.93 48	96.64 28	98.53 136	88.31 93	94.04 120	99.58 59	98.94 62	97.53 105	97.79 82	99.54 148	99.97 88
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
CS-MVS		97.46 50	98.98 48	95.68 53	96.74 63	99.93 35	97.62 72	89.69 63	99.98 3	91.33 55	98.53 67	97.50 92	98.77 73	98.60 51	98.35 62	99.92 21	100.00 1

MSDG		97.29 51	97.55 89	97.00 36	98.66 47	99.71 78	99.03 45	96.15 35	99.59 76	89.67 80	92.77 131	94.86 108	98.75 74	98.22 75	97.94 75	99.72 115	99.76 165

CHOSEN 280x420		97.16 52	99.58 33	94.35 80	96.95 62	99.97 3	97.19 87	81.55 148	99.92 36	91.75 49	100.00 1	100.00 1	98.84 69	98.55 54	98.65 53	99.79 68	99.97 88

DELS-MVS		97.05 53	98.05 78	95.88 51	97.09 60	99.99 1	98.82 50	90.30 55	98.44 142	91.40 53	92.91 128	96.57 98	97.68 109	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 53	97.59 88	96.42 43	97.37 57	99.92 41	99.10 43	96.54 31	99.34 100	86.64 104	91.93 136	93.15 119	99.11 58	99.11 34	99.68 12	99.73 110	99.97 88
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 55	98.09 77	95.81 52	94.12 86	99.80 66	97.33 83	89.48 68	98.90 122	95.99 21	99.11 50	92.84 121	98.14 91	98.14 80	98.32 66	99.82 47	99.51 178

MVS_0304		97.04 55	98.72 58	95.08 64	96.32 67	99.90 49	99.15 41	89.61 66	99.89 40	87.22 101	95.47 102	98.22 86	98.22 87	98.63 48	98.90 40	99.93 18	100.00 1

MAR-MVS		97.03 57	98.00 80	95.89 49	99.32 34	99.74 77	96.76 100	84.89 110	99.97 9	94.86 29	98.29 71	90.58 129	99.67 25	98.02 91	99.50 16	99.82 47	99.92 126
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 54	94.83 72	92.71 105	97.43 157	99.03 45	85.52 103	99.82 56	92.74 42	99.15 48	99.94 42	99.19 55	98.66 44	96.99 114	99.79 68	99.98 75

EC-MVSNet		96.90 59	99.32 42	94.07 82	91.64 135	99.30 108	98.18 62	85.61 102	99.97 9	89.79 75	99.33 41	99.31 68	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	96.34 104	88.54 81	98.88 125	92.82 40	98.93 57	96.58 97	99.07 59	98.19 77	98.04 72	99.80 61	99.78 162

OpenMVS	94.03 11	96.87 60	98.10 76	95.44 58	99.29 35	99.78 71	98.46 60	89.92 61	99.47 86	85.78 111	91.05 140	98.50 78	99.30 46	98.49 62	99.41 18	99.89 26	99.98 75

PatchMatch-RL		96.84 62	98.03 79	95.47 55	98.84 44	99.81 64	95.61 118	89.20 72	99.65 73	91.28 56	99.39 37	93.46 117	98.18 88	98.05 87	96.28 124	99.69 123	99.55 175

ETV-MVS		96.79 63	99.19 43	94.00 84	91.78 126	99.63 85	97.15 89	88.00 87	99.95 26	88.34 92	99.32 42	98.71 74	98.82 70	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 90	99.05 113	91.27 57	95.09 107	98.46 82	97.95 99	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	99.01 120	97.08 92	87.42 96	99.88 43	90.06 72	99.39 37	94.63 109	99.33 43	97.85 97	96.99 114	99.70 118	99.96 108

LS3D		96.44 66	97.31 96	95.41 59	97.06 61	99.87 56	99.51 32	97.48 1	99.57 77	79.00 132	95.39 103	89.19 135	99.81 15	98.55 54	98.84 44	99.62 137	99.78 162

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	85.98 110	98.82 61	96.08 103	99.33 43	98.38 67	97.40 97	99.81 56	100.00 1

EPP-MVSNet		96.29 68	98.34 69	93.90 85	91.77 127	99.38 105	95.45 123	87.25 98	99.38 96	91.36 54	94.86 113	98.49 80	97.83 103	98.01 92	98.23 68	99.75 96	99.99 59

UGNet		96.05 69	98.55 61	93.13 97	94.64 82	99.65 82	94.70 135	87.78 88	99.40 95	89.69 79	98.25 74	99.25 70	92.12 170	96.50 127	97.08 109	99.84 37	99.72 167
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 63	97.87 55	99.52 89	98.81 51	91.40 49	99.42 91	84.97 114	90.46 143	96.82 96	98.05 94	96.46 131	96.19 127	99.54 148	98.92 193
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 56	96.47 65	99.89 53	95.64 115	91.23 50	99.75 66	91.59 51	96.80 89	82.44 158	98.05 94	98.53 58	97.92 78	99.80 61	100.00 1

PVSNet_Blended		96.01 71	96.48 118	95.46 56	96.47 65	99.89 53	95.64 115	91.23 50	99.75 66	91.59 51	96.80 89	82.44 158	98.05 94	98.53 58	97.92 78	99.80 61	100.00 1

thisisatest0530		95.89 73	98.32 70	93.06 100	91.76 128	99.75 75	94.94 130	87.60 93	99.91 38	86.66 103	98.28 72	99.98 34	97.72 105	97.10 117	93.24 171	99.65 129	99.95 115

tttt0517		95.88 74	98.31 71	93.04 101	91.75 130	99.75 75	94.90 131	87.60 93	99.91 38	86.63 105	98.28 72	99.98 34	97.72 105	97.10 117	93.24 171	99.65 129	99.95 115

thres100view900		95.86 75	96.62 112	94.97 66	93.10 94	99.83 58	97.76 67	89.15 73	98.62 132	90.69 63	99.00 52	84.86 149	99.30 46	97.57 104	96.48 119	99.81 56	100.00 1

RPSCF		95.86 75	96.94 108	94.61 74	96.52 64	98.67 134	98.54 56	88.43 83	99.56 78	90.51 69	99.39 37	98.70 75	97.72 105	93.77 181	92.00 187	95.93 215	96.50 210

DCV-MVSNet		95.85 77	97.53 90	93.89 86	93.20 93	97.01 163	97.14 90	84.77 111	99.16 105	90.38 70	98.96 56	93.73 114	98.23 86	96.57 126	97.37 98	99.64 133	99.93 122

baseline		95.85 77	98.13 75	93.20 96	92.29 114	99.58 87	97.49 74	84.33 120	99.44 88	87.28 99	97.00 88	94.04 113	97.93 100	98.36 69	98.47 61	99.87 30	99.99 59

sasdasda		95.80 79	97.02 101	94.37 77	92.96 100	99.47 94	97.49 74	84.58 113	99.44 88	92.05 46	98.54 65	86.65 140	99.37 40	96.18 138	98.93 37	99.77 79	99.92 126

canonicalmvs		95.80 79	97.02 101	94.37 77	92.96 100	99.47 94	97.49 74	84.58 113	99.44 88	92.05 46	98.54 65	86.65 140	99.37 40	96.18 138	98.93 37	99.77 79	99.92 126

tfpn200view9		95.78 81	96.54 115	94.89 69	93.10 94	99.82 60	97.67 68	88.85 76	98.62 132	90.69 63	99.00 52	84.86 149	99.28 48	97.41 111	96.10 128	99.76 87	99.99 59

thres200		95.77 82	96.55 114	94.86 70	93.09 96	99.82 60	97.63 71	88.85 76	98.49 137	90.66 65	98.99 54	84.86 149	99.20 53	97.41 111	96.28 124	99.76 87	100.00 1

MVS_Test		95.74 83	98.18 74	92.90 103	92.16 115	99.49 93	97.36 81	84.30 121	99.79 61	84.94 115	96.65 93	93.63 116	98.85 68	98.61 50	99.10 31	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	98.49 137	90.61 66	98.86 58	84.63 153	99.20 53	97.45 107	96.10 128	99.77 79	99.99 59

MGCFI-Net		95.71 85	96.97 107	94.25 81	92.90 103	99.44 101	97.35 82	84.44 118	99.42 91	91.70 50	98.51 68	86.56 142	99.33 43	96.09 142	98.83 46	99.77 79	99.92 126

thres600view7		95.64 86	96.38 122	94.79 73	92.96 100	99.82 60	97.48 79	88.85 76	98.38 143	90.52 68	98.84 60	84.61 154	99.15 56	97.41 111	95.60 140	99.76 87	99.99 59

Vis-MVSNet (Re-imp)		95.60 87	98.52 66	92.19 107	92.37 112	99.56 88	96.37 103	87.41 97	98.95 117	84.77 118	94.88 112	98.48 81	92.44 167	98.63 48	99.37 19	99.76 87	99.77 164

FMVSNet3		95.59 88	97.51 92	93.34 94	89.48 151	96.57 170	97.67 68	84.17 122	99.48 83	89.76 76	95.09 107	94.35 110	99.14 57	98.37 68	98.86 42	99.82 47	99.89 137

ECVR-MVS		95.46 89	95.58 135	95.31 61	94.12 86	99.80 66	97.33 83	89.48 68	98.90 122	92.99 38	87.97 155	86.41 144	98.14 91	98.14 80	98.32 66	99.82 47	99.52 177

PVSNet_Blended_VisFu		95.37 90	97.44 94	92.95 102	95.20 74	99.80 66	92.68 155	88.41 84	99.12 108	87.64 94	88.31 154	99.10 71	94.07 154	98.27 72	97.51 93	99.73 110	100.00 1

DI_MVS_plusplus_trai		95.29 91	97.02 101	93.28 95	91.76 128	99.52 89	97.84 65	85.67 101	99.08 112	87.29 98	87.76 158	97.46 93	97.31 113	97.83 98	97.48 94	99.83 43	100.00 1

ET-MVSNet_ETH3D		95.20 92	97.82 85	92.15 108	80.77 213	98.13 145	97.65 70	86.93 99	99.72 68	88.56 90	99.29 45	97.01 95	99.24 51	94.58 169	95.98 133	99.75 96	99.99 59

TSAR-MVS + COLMAP		95.20 92	95.03 142	95.41 59	96.17 68	98.69 133	99.11 42	93.40 43	99.97 9	84.89 116	98.23 76	75.01 179	99.34 42	97.27 116	96.37 123	99.58 141	99.64 172

GBi-Net		95.19 94	96.99 105	93.09 98	89.11 152	96.47 172	96.90 94	84.17 122	99.48 83	89.76 76	95.09 107	94.35 110	98.87 65	96.50 127	97.21 101	99.74 100	99.81 157

test1		95.19 94	96.99 105	93.09 98	89.11 152	96.47 172	96.90 94	84.17 122	99.48 83	89.76 76	95.09 107	94.35 110	98.87 65	96.50 127	97.21 101	99.74 100	99.81 157

test1111		95.15 96	95.18 140	95.12 62	94.07 88	99.80 66	97.20 86	89.53 67	98.80 126	92.22 45	85.44 167	86.24 145	97.89 101	98.12 82	98.34 65	99.80 61	99.51 178

test0.0.03 1		95.15 96	97.87 84	91.99 109	91.69 132	98.82 131	93.04 153	83.60 127	99.65 73	88.80 85	94.15 118	97.67 90	94.97 140	96.62 125	98.16 69	99.83 43	100.00 1

baseline2		95.13 98	98.55 61	91.15 115	90.29 147	99.00 121	94.49 139	82.00 142	99.68 71	84.82 117	96.47 94	99.30 69	95.71 132	98.24 74	97.14 107	99.57 143	100.00 1

casdiffmvs_mvg		95.10 99	96.45 121	93.53 90	92.05 118	99.42 103	97.25 85	87.66 91	97.17 162	86.09 108	91.79 138	91.27 123	98.31 84	98.06 86	97.42 96	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 56	90.78 116	98.38 51	98.47 136	96.54 102	89.36 70	99.78 63	65.65 186	99.31 43	98.24 85	94.79 143	98.28 71	99.35 22	99.93 18	98.27 197
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Anonymous20231211		94.96 101	94.99 143	94.91 67	93.01 98	99.44 101	96.85 98	88.49 82	98.78 127	92.61 43	83.94 173	90.25 131	98.94 62	95.87 148	96.77 116	99.58 141	99.89 137

UA-Net		94.95 102	98.66 59	90.63 118	94.60 84	98.94 127	96.03 108	85.28 105	98.01 154	78.92 133	97.42 86	99.96 39	89.09 194	98.95 36	98.80 48	99.82 47	98.57 195

CANet_DTU		94.90 103	98.98 48	90.13 126	94.74 80	99.81 64	98.53 57	82.23 141	99.97 9	66.76 183	100.00 1	98.50 78	98.74 75	97.52 106	97.19 106	99.76 87	99.88 142

FC-MVSNet-train		94.61 104	96.27 123	92.68 105	92.35 113	97.14 161	93.45 151	87.73 89	98.93 118	87.31 97	96.42 95	89.35 133	95.67 133	96.06 146	96.01 132	99.56 145	99.98 75

diffmvs		94.60 105	95.63 134	93.41 92	91.98 121	99.30 108	96.86 97	87.62 92	99.30 102	86.07 109	94.12 119	81.63 163	98.16 89	97.43 108	97.60 91	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		94.54 106	95.56 136	93.36 93	91.84 124	99.46 97	95.92 110	87.54 95	98.45 140	86.57 106	90.51 142	84.72 152	98.49 80	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		94.53 107	94.45 151	94.61 74	93.85 90	98.36 138	98.12 63	89.68 64	99.35 99	89.62 81	95.19 105	77.08 170	96.66 123	95.51 152	95.67 138	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
FMVSNet2		94.48 108	95.95 128	92.77 104	89.11 152	96.47 172	96.90 94	83.38 130	99.11 109	88.64 86	87.50 163	92.26 122	98.87 65	97.91 95	98.60 54	99.74 100	99.81 157

HQP-MVS		94.48 108	95.39 138	93.42 91	95.10 75	98.35 139	98.19 61	91.41 48	99.77 64	79.79 129	99.30 44	77.08 170	96.25 126	96.93 119	96.28 124	99.76 87	99.99 59

FA-MVS(training)		94.33 110	97.52 91	90.60 120	92.42 110	99.77 73	96.13 107	68.75 199	99.05 113	88.49 91	91.95 134	99.48 62	98.12 93	98.39 65	94.02 163	99.68 125	99.98 75

MDTV_nov1_ep13		94.32 111	98.77 57	89.14 136	91.70 131	99.52 89	95.21 126	72.09 197	99.80 59	78.91 134	96.32 96	99.62 57	97.71 108	98.39 65	97.71 88	99.22 197	100.00 1

CDS-MVSNet		94.32 111	97.00 104	91.19 114	89.82 150	98.71 132	95.51 120	85.14 109	96.85 163	82.33 124	92.48 132	96.40 101	94.71 144	96.86 122	97.76 84	99.63 135	99.92 126
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
dps		94.29 113	97.33 95	90.75 117	92.02 119	99.21 112	94.31 141	66.97 204	99.50 82	95.61 24	96.22 98	98.64 76	96.08 128	93.71 183	94.03 162	99.52 152	99.98 75

ACMM	94.44 10	94.26 114	94.62 147	93.84 87	94.86 79	97.73 152	93.48 150	90.76 52	99.27 103	87.46 96	99.04 51	76.60 172	96.76 121	96.37 134	93.76 166	99.74 100	99.55 175
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMP	94.49 9	94.19 115	94.74 146	93.56 89	94.25 85	98.32 141	96.02 109	89.35 71	98.90 122	87.28 99	99.14 49	76.41 175	94.94 141	96.07 145	94.35 159	99.49 159	99.99 59
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
EPMVS		94.08 116	98.54 65	88.87 137	92.51 108	99.47 94	94.18 143	66.53 205	99.68 71	82.40 123	95.24 104	99.40 66	97.86 102	98.12 82	97.99 74	99.75 96	99.88 142

test-LLR		93.71 117	97.23 97	89.60 130	91.69 132	99.10 117	94.68 137	83.60 127	99.36 97	71.94 160	93.82 122	96.51 99	95.96 130	97.42 109	94.37 156	99.74 100	99.99 59

CHOSEN 1792x2688		93.69 118	94.89 145	92.28 106	96.17 68	99.84 57	95.69 114	83.17 133	98.54 135	82.04 125	77.58 201	91.15 125	96.90 116	98.36 69	98.82 47	99.73 110	99.98 75

LGP-MVS_train		93.60 119	95.05 141	91.90 110	94.90 78	98.29 142	97.93 64	88.06 86	99.14 107	74.83 149	99.26 46	76.50 173	96.07 129	96.31 136	95.90 137	99.59 139	99.97 88

SCA		93.53 120	98.90 51	87.27 154	92.01 120	99.30 108	93.43 152	65.72 209	99.80 59	75.20 148	97.66 84	99.74 51	97.44 111	98.21 76	97.62 90	99.84 37	100.00 1

FMVSNet5		93.53 120	96.09 127	90.56 121	86.74 167	92.84 212	92.64 156	77.50 175	99.41 94	88.97 84	98.02 80	97.81 88	98.00 97	94.85 164	95.43 142	99.50 158	94.25 214

OPM-MVS		93.50 122	93.00 161	94.07 82	95.82 71	98.26 143	98.49 59	91.62 47	94.69 183	81.93 126	92.82 130	76.18 177	96.82 118	96.12 141	94.57 150	99.74 100	98.39 196
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
CostFormer		93.50 122	96.50 117	90.00 127	91.69 132	98.65 135	93.88 146	67.64 202	98.97 115	89.16 83	97.79 82	88.92 136	97.97 98	95.14 161	96.06 130	99.63 135	100.00 1

IterMVS-LS		93.50 122	96.22 124	90.33 124	90.93 138	95.50 197	94.83 133	80.54 152	98.92 119	79.11 131	90.64 141	93.70 115	96.79 119	96.93 119	97.85 80	99.78 75	99.99 59
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PatchmatchNet		93.48 125	98.84 55	87.22 155	91.93 122	99.39 104	92.55 157	66.06 207	99.71 69	75.61 145	98.24 75	99.59 58	97.35 112	97.87 96	97.64 89	99.83 43	99.43 181
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MS-PatchMatch		93.46 126	95.91 130	90.61 119	95.48 72	99.31 107	95.62 117	77.23 177	99.42 91	81.88 127	88.92 151	96.06 104	93.80 156	96.45 133	93.11 175	99.65 129	98.10 201

dmvs_re		93.34 127	94.59 148	91.88 111	87.97 163	99.14 116	95.29 125	88.61 79	98.09 151	82.71 122	97.34 87	78.96 165	96.98 114	94.62 167	93.98 164	99.73 110	99.98 75

tpm cat1		93.29 128	96.53 116	89.50 132	91.84 124	99.18 114	94.70 135	67.70 201	98.38 143	86.67 102	89.16 148	99.38 67	96.66 123	94.33 170	95.30 143	99.43 175	100.00 1

Effi-MVS+-dtu		93.13 129	97.13 99	88.47 145	88.86 158	99.19 113	96.79 99	79.08 165	99.64 75	70.01 170	97.51 85	89.38 132	96.53 125	97.60 102	96.55 118	99.57 143	100.00 1

HyFIR lowres test		93.13 129	94.48 150	91.56 112	96.12 70	99.68 80	93.52 149	79.98 156	97.24 160	81.73 128	72.66 210	95.74 106	98.29 85	98.27 72	97.79 82	99.70 118	100.00 1

Vis-MVSNet		93.08 131	96.76 111	88.78 141	91.14 137	99.63 85	94.85 132	83.34 131	97.19 161	74.78 150	91.92 137	93.15 119	88.81 197	97.59 103	98.35 62	99.78 75	99.49 180
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
Effi-MVS+		93.06 132	95.94 129	89.70 129	90.82 139	99.45 98	95.71 113	78.94 166	98.72 128	74.71 151	97.92 81	80.73 164	98.35 82	97.72 100	97.05 112	99.70 118	100.00 1

ADS-MVSNet		92.91 133	97.97 81	87.01 157	92.07 117	99.27 111	92.70 154	65.39 212	99.85 51	75.40 146	94.93 111	98.26 84	96.86 117	96.09 142	97.52 92	99.65 129	99.84 153

GeoE		92.88 134	95.20 139	90.18 125	90.59 143	99.18 114	96.31 105	78.36 171	97.52 159	78.53 136	87.11 164	88.01 137	97.63 110	97.79 99	96.76 117	99.66 127	100.00 1

TESTMET0.1,1		92.87 135	97.23 97	87.79 151	86.96 166	99.10 117	94.68 137	77.46 176	99.36 97	71.94 160	93.82 122	96.51 99	95.96 130	97.42 109	94.37 156	99.74 100	99.99 59

FC-MVSNet-test		92.78 136	96.19 126	88.80 140	88.00 162	97.54 154	93.60 148	82.36 140	98.16 147	79.71 130	91.55 139	95.41 107	89.65 189	96.09 142	95.23 144	99.49 159	99.31 184

Fast-Effi-MVS+-dtu		92.73 137	97.62 87	87.02 156	88.91 156	98.83 130	95.79 111	73.98 191	99.89 40	68.62 175	97.73 83	93.30 118	95.21 139	97.67 101	95.96 134	99.59 139	100.00 1

IB-MVS	90.59 15	92.70 138	95.70 132	89.21 135	94.62 83	99.45 98	83.77 205	88.92 75	99.53 79	92.82 40	98.86 58	86.08 146	75.24 216	92.81 197	93.17 173	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		92.67 139	97.13 99	87.47 153	86.72 168	99.07 119	94.28 142	76.90 178	99.21 104	71.53 164	93.63 124	96.32 102	95.67 133	97.32 114	94.36 158	99.74 100	99.99 59

RPMNet		92.64 140	97.88 83	86.53 162	90.79 140	98.95 125	95.13 127	64.44 216	99.09 110	72.36 156	93.58 125	99.01 72	96.74 122	98.05 87	96.45 121	99.71 117	100.00 1

FMVSNet1		92.55 141	93.66 156	91.26 113	87.91 164	96.12 179	94.75 134	81.69 147	97.67 157	85.63 112	80.56 186	87.88 139	98.15 90	96.50 127	97.21 101	99.41 180	99.71 168

tpmrst		92.52 142	97.45 93	86.77 160	92.15 116	99.36 106	92.53 158	65.95 208	99.53 79	72.50 154	92.22 133	99.83 47	97.81 104	95.18 160	96.05 131	99.69 123	100.00 1

testgi		92.47 143	95.68 133	88.73 142	90.68 141	98.35 139	91.67 165	79.50 161	98.96 116	77.12 141	95.17 106	85.84 147	93.95 155	95.75 150	96.47 120	99.45 170	99.21 187

TAMVS		92.43 144	94.21 154	90.35 123	88.68 159	98.85 129	94.15 144	81.53 149	95.58 173	83.61 120	87.05 165	86.45 143	94.71 144	96.27 137	95.91 135	99.42 178	99.38 183

CR-MVSNet		92.32 145	97.97 81	85.74 171	90.63 142	98.95 125	95.46 121	65.50 210	99.09 110	67.51 179	94.20 116	98.18 87	95.59 136	98.16 78	97.20 104	99.74 100	100.00 1

CVMVSNet		92.13 146	95.40 137	88.32 148	91.29 136	97.29 159	91.85 162	86.42 100	96.71 165	71.84 162	89.56 146	91.18 124	88.98 196	96.17 140	97.76 84	99.51 156	99.14 189

Fast-Effi-MVS+		92.11 147	94.33 152	89.52 131	89.06 155	99.00 121	95.13 127	76.72 180	98.59 134	78.21 138	89.99 144	77.35 169	98.34 83	97.97 93	97.44 95	99.67 126	99.96 108

ACMH+	92.61 13	91.80 148	93.03 159	90.37 122	93.03 97	98.17 144	94.00 145	84.13 125	98.12 149	77.39 140	91.95 134	74.62 180	94.36 151	94.62 167	93.82 165	99.32 189	99.87 147

IterMVS-SCA-FT		91.75 149	96.87 109	85.78 169	90.34 145	95.93 186	95.06 129	73.85 192	98.91 120	61.01 199	89.21 147	98.87 73	94.66 147	98.09 85	97.12 108	99.76 87	99.99 59

IterMVS		91.65 150	96.62 112	85.85 168	90.27 148	95.80 188	95.32 124	74.15 188	98.91 120	60.95 200	88.79 153	97.76 89	94.69 146	98.04 89	97.07 110	99.73 110	100.00 1
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ACMH	92.34 14	91.59 151	93.02 160	89.92 128	93.97 89	97.98 149	90.10 180	84.70 112	98.46 139	76.80 142	93.38 126	71.94 191	94.39 149	95.34 156	94.04 161	99.54 148	100.00 1
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs4		91.41 152	93.05 158	89.49 133	85.85 176	96.52 171	91.70 164	82.49 138	98.14 148	83.17 121	87.57 160	81.76 162	94.39 149	95.47 153	92.62 181	99.33 187	99.29 185

PatchT		91.06 153	97.66 86	83.36 193	90.32 146	98.96 124	82.30 209	64.72 215	98.45 140	67.51 179	93.28 127	97.60 91	95.59 136	98.16 78	97.20 104	99.70 118	100.00 1

MIMVSNet		91.01 154	96.22 124	84.93 179	85.24 180	98.09 146	90.40 175	64.96 214	97.55 158	72.65 152	96.23 97	90.81 127	96.79 119	96.69 123	97.06 111	99.52 152	97.09 207

UniMVSNet_NR-MVSNet		90.50 155	92.31 164	88.38 146	85.04 186	96.34 175	90.94 167	85.32 104	95.87 172	75.69 143	87.68 159	78.49 166	93.78 157	93.21 192	94.60 149	99.53 151	99.97 88

UniMVSNet (Re)		90.41 156	91.96 166	88.59 144	85.71 177	96.73 167	90.82 169	84.11 126	95.23 179	78.54 135	88.91 152	76.41 175	92.84 164	93.40 190	93.05 176	99.55 147	100.00 1

GA-MVS		90.38 157	94.59 148	85.46 175	88.30 161	98.44 137	92.18 159	83.30 132	97.89 156	58.05 208	92.86 129	84.25 156	91.27 180	96.65 124	92.61 182	99.66 127	99.43 181

USDC		90.36 158	91.68 167	88.82 139	92.58 106	98.02 147	96.27 106	79.83 157	98.37 145	70.61 169	89.05 149	67.50 208	94.17 152	95.77 149	94.43 154	99.46 167	98.62 194

thisisatest0515		90.28 159	94.32 153	85.57 174	85.23 181	97.23 160	85.44 201	83.09 134	96.80 164	72.41 155	89.82 145	90.87 126	87.93 202	95.27 159	90.39 204	99.33 187	99.88 142

TinyColmap		89.94 160	90.88 173	88.84 138	92.43 109	97.91 150	95.59 119	80.10 155	98.12 149	71.33 166	84.56 169	67.46 209	94.15 153	95.57 151	94.27 160	99.43 175	98.26 198

pm-mvs1		89.68 161	92.00 165	86.96 158	86.23 172	96.62 169	90.36 176	83.05 135	93.97 191	72.15 159	81.77 181	82.10 160	90.69 186	95.38 155	94.50 152	99.29 193	99.65 170

tpm		89.60 162	94.93 144	83.39 191	89.94 149	97.11 162	90.09 181	65.28 213	98.67 130	60.03 204	96.79 91	84.38 155	95.66 135	91.90 201	95.65 139	99.32 189	99.98 75

NR-MVSNet		89.52 163	90.71 174	88.14 150	86.19 173	96.20 177	92.07 160	84.58 113	95.54 174	75.27 147	87.52 161	67.96 206	91.24 181	94.33 170	93.45 169	99.49 159	99.97 88

DU-MVS		89.49 164	90.60 175	88.19 149	84.71 190	96.20 177	90.94 167	84.58 113	95.54 174	75.69 143	87.52 161	68.74 205	93.78 157	91.10 205	95.13 146	99.47 165	99.97 88

Baseline_NR-MVSNet		89.13 165	89.53 187	88.66 143	84.71 190	94.43 205	91.79 163	84.49 117	95.54 174	78.28 137	78.52 198	72.46 190	93.29 161	91.10 205	94.82 148	99.42 178	99.86 150

tfpnnormal		89.09 166	89.71 181	88.38 146	87.37 165	96.78 166	91.46 166	85.20 107	90.33 212	72.35 157	83.45 174	69.30 203	94.45 148	95.29 157	92.86 178	99.44 174	99.93 122

TranMVSNet+NR-MVSNet		88.88 167	89.90 180	87.69 152	84.06 202	95.68 189	91.88 161	85.23 106	95.16 180	72.54 153	83.06 177	70.14 200	92.93 163	90.81 208	94.53 151	99.48 163	99.89 137

WR-MVS_H		88.47 168	90.55 176	86.04 164	85.13 183	96.07 181	89.86 187	79.80 158	94.37 188	72.32 158	83.12 176	74.44 183	89.60 190	93.52 187	92.40 183	99.51 156	99.96 108

SixPastTwentyTwo		88.35 169	91.51 169	84.66 181	85.39 179	96.96 164	86.57 197	79.62 160	96.57 166	63.73 193	87.86 157	75.18 178	93.43 160	94.03 174	90.37 205	99.24 196	99.58 173

TransMVSNet (Re)		88.33 170	89.55 186	86.91 159	86.65 169	95.56 194	90.48 173	84.44 118	92.02 211	71.07 168	80.13 188	72.48 189	89.41 191	95.05 163	94.44 153	99.39 182	97.14 206

MVS-HIRNet		88.27 171	94.05 155	81.51 199	88.90 157	98.93 128	83.38 207	60.52 223	98.06 152	63.78 192	80.67 185	90.36 130	92.94 162	97.29 115	96.41 122	99.56 145	96.66 209

WR-MVS		88.23 172	90.15 178	86.00 166	84.39 197	95.64 190	89.96 184	81.80 144	94.46 186	71.60 163	82.10 179	74.36 184	88.76 198	92.48 198	92.20 185	99.46 167	99.83 155

CP-MVSNet		88.09 173	89.57 184	86.36 163	84.63 193	95.46 199	89.48 189	80.53 153	93.42 198	71.26 167	81.25 183	69.90 201	92.78 165	93.30 191	93.69 167	99.47 165	99.96 108

pmnet_mix02		88.07 174	92.32 163	83.10 194	86.14 174	96.23 176	81.90 212	83.05 135	98.04 153	57.59 210	84.93 168	82.02 161	90.87 185	93.54 186	91.53 196	99.06 204	99.97 88

UniMVSNet_ETH3D		88.05 175	87.01 205	89.27 134	88.53 160	97.49 155	90.35 177	83.48 129	94.57 184	77.87 139	70.08 214	61.75 220	96.22 127	90.17 209	95.21 145	99.16 201	99.82 156

anonymousdsp		87.98 176	92.38 162	82.85 195	83.68 206	96.79 165	90.78 170	74.06 190	95.29 178	57.91 209	83.33 175	83.12 157	91.15 183	95.96 147	92.37 184	99.52 152	99.76 165

LTVRE_ROB	88.65 16	87.87 177	91.11 172	84.10 188	86.64 170	97.47 156	94.40 140	78.41 170	96.13 170	52.02 217	87.95 156	65.92 214	93.59 159	95.29 157	95.09 147	99.52 152	99.95 115
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		87.84 178	89.42 189	85.99 167	85.16 182	96.01 183	90.52 172	81.78 146	94.43 187	67.59 177	81.32 182	71.87 192	91.48 178	91.25 204	91.16 200	99.43 175	99.92 126

TDRefinement		87.79 179	88.76 196	86.66 161	93.54 91	98.02 147	95.76 112	85.18 108	96.57 166	67.90 176	80.51 187	66.51 213	78.37 213	93.20 193	89.73 206	99.22 197	96.75 208

MDTV_nov1_ep13_2view		87.75 180	93.32 157	81.26 201	83.74 205	96.64 168	85.66 200	66.20 206	98.36 146	61.61 197	84.34 171	87.95 138	91.12 184	94.01 175	92.66 180	99.22 197	99.27 186

v8		87.54 181	89.33 190	85.45 176	85.41 178	95.50 197	90.32 178	78.94 166	94.35 189	66.93 182	81.90 180	70.99 197	91.62 176	91.49 203	91.22 199	99.48 163	99.87 147

v1144		87.49 182	89.64 182	84.97 178	84.73 189	95.84 187	90.17 179	79.30 162	93.96 192	64.65 190	78.83 195	73.38 188	91.51 177	93.77 181	91.77 191	99.45 170	99.93 122

v2v482		87.46 183	88.90 194	85.78 169	84.58 194	95.95 185	89.90 186	82.43 139	94.19 190	65.65 186	79.80 190	69.12 204	92.67 166	91.88 202	91.46 197	99.45 170	99.93 122

v10		87.40 184	89.62 183	84.80 180	84.93 187	95.07 203	90.44 174	75.63 184	94.51 185	66.52 184	78.87 194	73.47 187	91.86 174	93.69 184	91.87 190	99.45 170	99.86 150

pmmvs5		87.33 185	90.01 179	84.20 186	84.31 199	96.04 182	87.63 195	76.59 181	93.17 203	65.35 189	84.30 172	71.68 193	91.91 173	95.41 154	91.37 198	99.39 182	98.13 199

N_pmnet		87.31 186	91.51 169	82.41 198	85.13 183	95.57 193	80.59 214	81.79 145	96.20 168	58.52 207	78.62 196	85.66 148	89.36 192	94.64 166	92.14 186	99.08 203	97.72 205

PS-CasMVS		87.24 187	88.52 199	85.73 172	84.58 194	95.35 201	89.03 192	80.17 154	93.11 204	68.86 174	77.71 200	66.89 210	92.30 168	93.13 194	93.50 168	99.46 167	99.96 108

EU-MVSNet		87.20 188	90.47 177	83.38 192	85.11 185	93.85 210	86.10 199	79.76 159	93.30 202	65.39 188	84.41 170	78.43 167	85.04 209	92.20 200	93.03 177	98.86 206	98.05 202

PEN-MVS		87.20 188	88.22 200	86.01 165	84.01 204	94.93 204	90.00 183	81.52 151	93.46 197	69.29 172	79.69 191	65.51 215	91.72 175	91.01 207	93.12 174	99.49 159	99.84 153

EG-PatchMatch MVS		86.96 190	89.56 185	83.93 189	86.29 171	97.61 153	90.75 171	73.31 195	95.43 177	66.08 185	75.88 207	71.31 194	87.55 204	94.79 165	92.74 179	99.61 138	99.13 190

v1192		86.93 191	89.01 192	84.50 182	84.46 196	95.51 196	89.93 185	78.65 169	93.75 193	62.29 195	77.19 202	70.88 198	92.28 169	93.84 178	91.96 188	99.38 184	99.90 134

v1921920		86.81 192	88.93 193	84.33 185	84.23 200	95.41 200	90.09 181	78.10 172	93.74 194	62.17 196	76.98 204	71.14 195	92.05 171	93.69 184	91.69 194	99.32 189	99.88 142

v144192		86.80 193	88.90 194	84.35 183	84.33 198	95.56 194	89.34 190	77.74 174	93.60 195	64.03 191	77.82 199	70.76 199	91.28 179	92.91 196	91.74 193	99.37 185	99.90 134

DTE-MVSNet		86.70 194	87.66 204	85.58 173	83.30 207	94.29 206	89.74 188	81.53 149	92.77 207	68.93 173	80.13 188	64.00 218	90.62 187	89.45 210	93.34 170	99.32 189	99.67 169

gg-mvs-nofinetune		86.69 195	91.30 171	81.30 200	90.42 144	99.64 83	98.50 58	61.68 221	79.23 221	40.35 224	66.58 216	97.14 94	96.92 115	98.64 46	97.94 75	99.91 22	99.97 88

v148		86.63 196	87.79 202	85.28 177	84.65 192	95.97 184	86.46 198	82.84 137	92.91 206	71.52 165	78.99 193	66.74 212	86.83 206	89.28 211	90.69 202	99.41 180	99.94 120

v1240		86.24 197	88.56 198	83.54 190	84.05 203	95.21 202	89.27 191	76.76 179	93.42 198	60.68 203	75.99 206	69.80 202	91.21 182	93.83 180	91.76 192	99.29 193	99.91 133

pmmvs6		85.75 198	86.97 206	84.34 184	84.88 188	95.59 192	87.41 196	79.19 164	87.81 217	67.56 178	63.05 219	77.76 168	89.15 193	93.45 189	91.90 189	97.83 212	99.21 187

v7n		85.39 199	87.70 203	82.70 196	82.77 209	95.64 190	88.27 194	74.83 186	92.30 209	62.58 194	76.37 205	64.80 217	88.38 200	94.29 172	90.61 203	99.34 186	99.87 147

gm-plane-assit		84.93 200	91.61 168	77.14 209	84.14 201	91.29 216	66.18 224	69.70 198	85.22 220	47.95 221	78.58 197	89.24 134	94.90 142	98.82 40	98.12 71	99.99 5	100.00 1

CMPMVS	65.66 17	84.62 201	85.02 208	84.15 187	95.40 73	97.79 151	88.35 193	79.22 163	89.66 215	60.71 202	72.20 211	73.94 185	87.32 205	86.73 213	84.55 215	93.90 217	90.31 218
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test_method		84.44 202	89.04 191	79.08 204	81.15 212	92.82 213	82.06 211	61.92 219	96.17 169	59.38 205	74.47 209	67.52 207	91.96 172	96.92 121	95.53 141	97.98 211	99.85 152

Anonymous20231206		84.28 203	89.53 187	78.17 206	82.31 211	94.16 208	82.57 208	76.51 182	93.38 201	52.98 215	79.47 192	73.74 186	75.45 215	95.07 162	94.41 155	99.18 200	96.46 211

new_pmnet		84.12 204	87.89 201	79.72 203	80.43 214	94.14 209	80.26 215	74.14 189	96.01 171	56.30 214	74.94 208	76.45 174	88.59 199	93.11 195	89.31 207	98.59 209	91.27 217

test20.03		83.86 205	88.73 197	78.16 207	82.60 210	93.00 211	81.61 213	74.68 187	92.36 208	57.50 211	83.01 178	74.48 182	73.30 217	92.40 199	91.14 201	99.29 193	94.75 213

pmmvs-eth3d		82.92 206	83.31 211	82.47 197	76.97 217	91.76 215	83.79 204	76.10 183	90.33 212	69.95 171	71.04 213	48.09 223	89.02 195	93.85 177	89.14 208	99.02 205	98.96 192

PM-MVS		82.79 207	84.51 209	80.77 202	77.22 216	92.13 214	83.61 206	73.31 195	93.50 196	61.06 198	77.15 203	46.52 226	90.55 188	94.14 173	89.05 210	98.85 207	99.12 191

pmmvs3		80.91 208	85.62 207	75.42 211	75.01 219	89.09 219	75.31 218	68.70 200	86.99 218	46.74 223	81.18 184	62.91 219	87.95 201	93.84 178	89.06 209	98.80 208	96.23 212

MIMVSNet1		80.64 209	83.97 210	76.76 210	68.91 222	91.15 218	78.32 217	75.47 185	89.58 216	56.64 213	65.10 217	65.17 216	82.14 210	93.51 188	91.64 195	99.10 202	91.66 216

MDA-MVSNet-bldmvs		80.30 210	82.83 212	77.34 208	69.16 221	94.29 206	72.16 219	81.97 143	90.14 214	57.32 212	94.01 121	47.97 224	86.81 207	68.74 221	86.82 212	96.63 213	97.86 203

new-patchmatchnet		78.17 211	80.82 214	75.07 212	76.93 218	91.20 217	71.90 220	73.32 194	86.59 219	48.91 218	67.11 215	47.85 225	81.19 211	88.18 212	87.02 211	98.19 210	97.79 204

FPMVS		73.80 212	74.62 215	72.84 213	83.09 208	84.44 221	83.89 203	73.64 193	92.20 210	48.50 219	72.19 212	59.51 221	63.16 219	69.13 220	66.26 224	84.74 222	78.59 225

WB-MVS		71.64 213	82.10 213	59.45 217	79.66 215	78.44 224	55.66 228	78.80 168	93.01 205	19.20 230	86.36 166	71.05 196	39.18 226	85.26 215	81.08 216	84.19 223	79.49 224

Gipuma		71.02 214	72.60 218	69.19 214	71.31 220	75.11 225	66.36 223	61.65 222	94.93 181	47.29 222	38.74 224	38.52 227	75.52 214	86.09 214	85.92 214	93.01 218	88.87 220
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
GG-mvs-BLEND		69.85 215	99.39 40	35.39 223	3.67 231	99.94 20	99.10 43	1.69 227	99.85 51	3.19 232	98.13 79	99.46 63	4.92 227	99.23 32	99.14 30	99.80 61	100.00 1

PMMVS2		65.18 216	68.25 219	61.59 215	61.37 225	79.72 223	59.18 227	61.80 220	64.72 224	37.33 225	53.82 221	35.59 228	54.46 224	73.94 219	80.52 217	95.40 216	89.43 219

PMVS	60.14 18	62.67 217	64.05 220	61.06 216	68.32 223	53.27 231	52.23 229	67.63 203	75.07 223	48.30 220	58.27 220	57.43 222	49.99 225	67.20 222	62.42 225	79.87 226	74.68 226
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
testmvs		61.76 218	72.90 217	48.76 220	21.21 229	68.61 226	66.11 225	37.38 225	94.83 182	33.06 226	64.31 218	29.72 229	86.08 208	74.44 218	78.71 218	48.74 228	99.65 170

E-PMN		55.33 219	55.79 222	54.81 219	59.81 227	57.23 229	38.83 230	63.59 217	64.06 226	24.66 228	35.33 226	26.40 231	58.69 221	55.41 224	70.54 221	83.26 224	81.56 223

EMVS		55.14 220	55.29 223	54.97 218	60.87 226	57.52 228	38.58 231	63.57 218	64.54 225	23.36 229	36.96 225	27.99 230	60.69 220	51.17 225	66.61 223	82.73 225	82.25 222

MVE	58.81 19	52.07 221	55.15 224	48.48 221	42.45 228	62.35 227	36.41 232	54.70 224	49.88 227	27.65 227	29.98 227	18.08 232	54.87 223	65.93 223	77.26 219	74.79 227	82.59 221
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test123		48.14 222	58.11 221	36.51 222	8.71 230	56.81 230	59.55 226	24.08 226	77.50 222	14.41 231	49.20 222	11.94 234	80.98 212	41.62 226	69.81 222	31.32 229	99.90 134

uanet_test		0.00 223	0.00 225	0.00 224	0.00 232	0.00 232	0.00 233	0.00 228	0.00 228	0.00 233	0.00 228	0.00 235	0.00 228	0.00 227	0.00 226	0.00 230	0.00 227

sosnet-low-res		0.00 223	0.00 225	0.00 224	0.00 232	0.00 232	0.00 233	0.00 228	0.00 228	0.00 233	0.00 228	0.00 235	0.00 228	0.00 227	0.00 226	0.00 230	0.00 227

sosnet		0.00 223	0.00 225	0.00 224	0.00 232	0.00 232	0.00 233	0.00 228	0.00 228	0.00 233	0.00 228	0.00 235	0.00 228	0.00 227	0.00 226	0.00 230	0.00 227

TPM-MVS					99.67 4	99.96 7	99.82 5			94.63 32	99.65 16	100.00 1	99.90 5			99.99 5	99.80 160
Ray Leroy Khuboni and Hongjun Xu: Textureless Resilient Propagation Matching in Multiple View Stereosis (TPM-MVS). SATNAC 2025
RE-MVS-def										52.74 216

9.14												100.00 1

SR-MVS					99.61 13			96.80 18				100.00 1

Anonymous202405211			95.78 131		93.26 92	99.52 89	96.70 101	88.55 80	97.93 155		88.99 150	90.68 128	98.99 61	96.46 131	97.02 113	99.64 133	99.89 137

our_test_3					85.89 175	96.09 180	82.15 210

ambc			74.33 216		66.84 224	84.26 222	84.17 202		93.39 200	58.99 206	45.93 223	18.06 233	70.61 218	93.94 176	86.62 213	92.61 220	98.13 199

MTAPA										96.61 16		100.00 1

MTMP										97.42 11		100.00 1

Patchmatch-RL test							68.01 222

tmp_tt				78.81 205	98.80 45	85.73 220	70.08 221	77.87 173	98.68 129	83.71 119	99.53 29	74.55 181	54.97 222	78.28 217	72.43 220	87.45 221

XVS					95.09 76	99.94 20	97.49 74			88.58 87		99.98 34				99.78 75

X-MVStestdata					95.09 76	99.94 20	97.49 74			88.58 87		99.98 34				99.78 75

mPP-MVS					99.23 38							99.87 45

NP-MVS									99.79 61

Patchmtry						99.00 121	95.46 121	65.50 210		67.51 179

DeepMVS_CX						97.31 158	79.48 216	89.65 65	98.66 131	60.89 201	94.40 115	66.89 210	87.65 203	81.69 216		92.76 219	94.24 215

High-resolution multi-view benchmark