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		98.87 1	99.20 2	98.48 1	99.32 11	99.85 2	99.55 7	96.20 6	99.48 3	96.78 3	98.51 15	99.99 1	99.36 1	98.98 8	97.59 29	99.67 19	99.99 3

DVP-MVS++		98.75 2	99.11 7	98.33 3	99.41 4	99.85 2	99.61 3	96.22 5	99.32 9	95.80 5	98.27 18	99.97 4	99.22 4	98.95 9	97.48 33	99.71 17	99.98 5

MSP-MVS		98.75 2	99.27 1	98.15 8	99.21 17	99.82 7	99.58 5	96.09 13	99.32 9	95.16 9	98.79 6	99.55 9	99.05 6	99.54 1	97.88 21	99.84 3	99.99 3
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
CNVR-MVS		98.73 4	99.17 5	98.22 5	99.47 3	99.85 2	99.57 6	96.23 3	99.30 11	94.90 11	98.65 10	98.93 20	99.36 1	99.46 3	98.21 11	99.81 6	99.80 33

DPE-MVS		98.69 5	99.14 6	98.16 7	99.37 7	99.82 7	99.66 2	96.26 1	99.18 17	95.02 10	98.62 12	99.98 3	98.88 11	98.90 12	97.51 32	99.75 10	99.97 8
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
DVP-MVS		98.65 6	98.87 13	98.38 2	99.30 13	99.85 2	99.14 22	96.23 3	99.51 2	97.16 1	96.01 33	99.99 1	98.90 10	98.89 13	97.88 21	99.56 51	99.98 5
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
APDe-MVS		98.60 7	98.97 10	98.18 6	99.38 6	99.78 12	99.35 14	96.14 9	99.24 14	95.66 7	98.19 20	99.01 17	98.66 17	98.77 15	97.80 24	99.86 2	99.97 8

SF-MVS		98.55 8	98.75 15	98.32 4	99.48 1	99.68 21	99.51 9	96.24 2	99.08 21	95.94 4	98.64 11	99.30 13	99.02 8	97.94 29	96.86 51	99.75 10	99.76 36

SMA-MVS		98.47 9	99.06 8	97.77 11	99.48 1	99.78 12	99.37 11	96.14 9	99.29 12	93.03 19	97.59 28	99.97 4	99.03 7	98.94 10	98.30 9	99.60 32	99.58 64
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
NCCC		98.41 10	99.18 3	97.52 15	99.36 8	99.84 6	99.55 7	96.08 15	99.33 8	91.77 24	98.79 6	99.46 11	98.59 19	99.15 7	98.07 18	99.73 13	99.64 53

SD-MVS		98.33 11	99.01 9	97.54 14	97.17 49	99.77 14	99.14 22	96.09 13	99.34 7	94.06 15	97.91 25	99.89 6	99.18 5	97.99 28	98.21 11	99.63 26	99.95 13
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
APD-MVS		98.28 12	98.69 16	97.80 10	99.31 12	99.62 27	99.31 17	96.15 8	99.19 16	93.60 16	97.28 29	98.35 26	98.72 16	98.27 21	98.22 10	99.73 13	99.89 25
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MCST-MVS		98.20 13	99.18 3	97.06 21	99.27 15	99.87 1	99.37 11	96.11 11	99.37 5	89.29 32	98.76 8	99.50 10	98.37 24	99.23 5	97.64 27	99.95 1	99.87 29

HPM-MVS++		98.16 14	98.87 13	97.32 17	99.39 5	99.70 19	99.18 20	96.10 12	99.09 20	91.14 26	98.02 23	99.89 6	98.44 22	98.75 16	97.03 46	99.67 19	99.63 56

MSLP-MVS++		98.12 15	98.23 28	97.99 9	99.28 14	99.72 16	99.59 4	95.27 28	98.61 34	94.79 12	96.11 32	97.79 35	99.27 3	96.62 67	98.96 5	99.77 9	99.80 33

HFP-MVS		98.02 16	98.55 20	97.40 16	99.11 20	99.69 20	99.41 10	95.41 26	98.79 30	91.86 23	98.61 13	98.16 28	99.02 8	97.87 33	97.40 35	99.60 32	99.35 83

TSAR-MVS + MP.		97.98 17	98.62 19	97.23 19	97.08 50	99.55 33	99.17 21	95.69 21	99.40 4	93.04 18	96.68 31	98.96 19	98.58 20	98.82 14	96.95 48	99.81 6	99.96 10
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
SteuartSystems-ACMMP		97.86 18	98.91 11	96.64 25	98.89 26	99.79 9	99.34 15	95.20 30	98.48 36	89.91 30	98.58 14	98.69 22	96.84 47	98.92 11	98.16 15	99.66 21	99.74 39
Skip Steuart: Steuart Systems R&D Blog.
CP-MVS		97.81 19	98.26 27	97.28 18	99.00 23	99.65 23	99.10 24	95.32 27	98.38 42	92.21 22	98.33 17	97.74 36	98.50 21	97.66 42	96.55 59	99.57 45	99.48 73

ACMMPR		97.78 20	98.28 25	97.20 20	99.03 22	99.68 21	99.37 11	95.24 29	98.86 29	91.16 25	97.86 26	97.26 38	98.79 14	97.64 44	97.40 35	99.60 32	99.25 89

DeepC-MVS_fast	95.01 1	97.67 21	98.22 29	97.02 22	99.00 23	99.79 9	99.10 24	95.82 18	99.05 23	89.53 31	93.54 48	96.77 41	98.83 12	99.34 4	99.44 2	99.82 4	99.63 56
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
AdaColmap		97.54 22	97.35 38	97.77 11	99.17 18	99.55 33	98.57 30	95.76 20	99.04 24	94.66 13	97.94 24	94.39 55	98.82 13	96.21 77	94.78 86	99.62 28	99.52 69

ACMMP_NAP		97.51 23	98.27 26	96.63 26	99.34 9	99.72 16	99.25 18	95.94 17	98.11 47	87.10 46	96.98 30	98.50 24	98.61 18	98.58 18	96.83 52	99.56 51	99.14 97

MP-MVS		97.46 24	98.30 24	96.48 27	98.93 25	99.43 42	99.20 19	95.42 25	98.43 38	87.60 42	98.19 20	98.01 34	98.09 26	98.05 26	96.67 56	99.64 24	99.35 83
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
train_agg		97.42 25	98.88 12	95.71 32	98.46 33	99.60 30	99.05 26	95.16 31	99.10 19	84.38 64	98.47 16	98.85 21	97.61 30	98.54 19	97.66 26	99.62 28	99.93 19

CPTT-MVS		97.32 26	97.60 37	96.99 23	98.29 36	99.31 54	99.04 27	94.67 35	97.99 53	93.12 17	98.03 22	98.26 27	98.77 15	96.08 80	94.26 94	98.07 180	99.27 88

X-MVS		97.20 27	98.42 23	95.77 30	99.04 21	99.64 24	98.95 29	95.10 33	98.16 45	83.97 70	98.27 18	98.08 31	97.95 27	97.89 30	97.46 34	99.58 41	99.47 74

PHI-MVS		97.09 28	98.69 16	95.22 36	97.99 41	99.59 32	97.56 43	92.16 39	98.41 40	87.11 45	98.70 9	99.42 12	96.95 43	96.88 60	98.16 15	99.56 51	99.70 45

DPM-MVS		97.07 29	97.99 32	96.00 29	97.25 48	99.16 64	99.67 1	95.99 16	99.08 21	85.97 51	93.00 53	98.44 25	97.47 32	99.22 6	99.62 1	99.66 21	97.44 153

PGM-MVS		97.03 30	98.14 31	95.73 31	99.34 9	99.61 29	99.34 15	89.99 45	97.70 56	87.67 41	99.44 2	96.45 44	98.44 22	97.65 43	97.09 43	99.58 41	99.06 105

PLC	94.37 2	97.03 30	96.54 44	97.60 13	98.84 27	98.64 73	98.17 35	94.99 34	99.01 25	96.80 2	93.21 52	95.64 46	97.36 33	96.37 72	94.79 85	99.41 86	98.12 138
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
TSAR-MVS + ACMM		96.90 32	98.64 18	94.88 38	98.12 39	99.47 38	99.01 28	95.43 24	99.23 15	81.98 89	95.95 34	99.16 16	95.13 67	98.61 17	98.11 17	99.58 41	99.93 19

TSAR-MVS + GP.		96.47 33	98.45 22	94.17 43	92.12 86	99.29 55	97.76 39	88.05 55	99.36 6	90.26 29	97.82 27	99.21 14	97.21 36	96.78 65	96.74 54	99.63 26	99.94 16

EPNet		96.23 34	97.89 34	94.29 41	97.62 44	99.44 41	97.14 50	88.63 51	98.16 45	88.14 37	99.46 1	94.15 58	94.61 77	97.20 52	97.23 39	99.57 45	99.59 61
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CNLPA		96.14 35	95.43 54	96.98 24	98.55 30	99.41 46	95.91 56	95.15 32	99.00 26	95.71 6	84.21 103	94.55 53	97.25 34	95.50 102	96.23 65	99.28 107	99.09 104

MVS_111021_LR		96.07 36	97.94 33	93.88 46	97.86 42	99.43 42	95.70 59	89.65 48	98.73 31	84.86 61	99.38 3	94.08 59	95.78 65	97.81 36	96.73 55	99.43 82	99.42 77

ACMMP		96.05 37	96.70 43	95.29 35	98.01 40	99.43 42	97.60 42	94.33 37	97.62 59	86.17 50	98.92 4	92.81 67	96.10 58	95.67 92	93.33 114	99.55 56	99.12 100
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
3Dnovator+	90.72 7	95.99 38	96.42 46	95.50 34	98.18 38	99.33 53	97.44 45	87.73 60	97.93 54	92.36 21	84.67 96	97.33 37	97.55 31	97.32 48	98.47 8	99.72 16	99.88 26

DeepPCF-MVS	94.02 3	95.92 39	98.47 21	92.95 56	97.57 45	99.79 9	91.45 116	94.42 36	99.76 1	86.48 49	92.88 54	98.12 30	92.62 98	99.49 2	99.32 3	95.15 204	99.95 13

CDPH-MVS		95.90 40	97.77 36	93.72 49	98.28 37	99.43 42	98.40 31	91.30 43	98.34 43	78.62 107	94.80 40	95.74 45	96.11 57	97.86 34	98.67 7	99.59 36	99.56 66

CSCG		95.77 41	95.35 56	96.26 28	99.13 19	99.60 30	98.14 36	91.89 42	96.57 75	92.61 20	89.65 63	91.74 75	96.96 41	93.69 126	96.58 58	98.86 134	99.63 56

OMC-MVS		95.75 42	95.84 51	95.64 33	98.52 32	99.34 52	97.15 49	92.02 41	98.94 28	90.45 28	88.31 69	94.64 51	96.35 53	96.02 83	95.99 73	99.34 96	97.65 149

MVS_111021_HR		95.70 43	98.16 30	92.83 57	97.57 45	99.77 14	94.78 73	88.05 55	98.61 34	82.29 84	98.85 5	94.66 50	94.63 75	97.80 37	97.63 28	99.64 24	99.79 35

3Dnovator	90.31 8	95.67 44	96.16 49	95.11 37	98.59 29	99.37 51	97.50 44	87.98 57	98.02 52	89.09 33	85.36 95	94.62 52	97.66 28	97.10 56	98.90 6	99.82 4	99.73 41

CANet		95.40 45	96.27 47	94.40 40	96.25 55	99.62 27	98.37 32	88.59 52	98.09 48	87.58 43	84.57 98	95.54 48	95.87 62	98.12 24	98.03 20	99.73 13	99.90 24

QAPM		95.17 46	96.05 50	94.14 44	98.55 30	99.49 36	97.41 46	87.88 58	97.72 55	84.21 67	84.59 97	95.60 47	97.21 36	97.10 56	98.19 14	99.57 45	99.65 51

CS-MVS-test		95.06 47	96.98 41	92.82 59	95.83 58	99.40 47	93.23 96	85.29 83	99.27 13	85.89 53	93.86 47	92.70 69	97.19 38	97.70 40	96.18 67	99.49 65	99.76 36

CS-MVS		94.82 48	96.19 48	93.22 52	95.19 63	99.24 57	95.10 68	85.07 86	98.72 32	87.33 44	91.35 56	89.98 84	97.06 40	98.01 27	96.28 63	99.60 32	99.72 42

MVSTER		94.75 49	96.50 45	92.70 61	90.91 102	94.51 144	97.37 47	83.37 96	98.40 41	89.04 34	93.23 51	97.04 40	95.91 61	97.73 38	95.59 82	99.61 30	99.01 107

TAPA-MVS	92.04 6	94.72 50	95.13 59	94.24 42	97.72 43	99.17 62	97.61 41	92.16 39	97.66 58	81.99 88	87.84 74	93.94 61	96.50 50	95.74 89	94.27 93	99.46 76	97.31 156
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
DeepC-MVS	92.23 5	94.53 51	94.26 72	94.86 39	96.73 52	99.50 35	97.85 38	95.45 23	96.22 82	82.73 79	80.68 112	88.02 89	96.92 44	97.49 47	98.20 13	99.47 70	99.69 48
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CHOSEN 280x420		94.51 52	97.78 35	90.70 83	95.54 62	99.49 36	94.14 84	74.91 159	98.43 38	85.32 58	94.78 41	99.19 15	94.95 71	97.02 58	96.18 67	99.35 92	99.36 82

ETV-MVS		94.49 53	97.23 40	91.29 76	90.43 112	98.55 76	93.41 94	84.53 89	99.16 18	83.13 75	94.72 42	94.08 59	96.61 49	97.72 39	96.60 57	99.61 30	99.81 32

MVS_0304		94.35 54	95.66 53	92.83 57	94.82 65	99.46 40	98.19 34	87.75 59	97.32 65	81.83 92	83.50 105	93.19 66	94.71 73	98.24 23	98.07 18	99.68 18	99.83 31

DROMVSNet		94.33 55	96.88 42	91.36 74	90.12 119	97.70 95	95.20 67	80.27 120	98.63 33	85.97 51	93.92 46	93.85 64	97.09 39	97.54 46	96.81 53	99.49 65	99.70 45

MAR-MVS		94.18 56	95.12 60	93.09 55	98.40 35	99.17 62	94.20 83	81.92 105	98.47 37	86.52 48	90.92 58	84.21 109	98.12 25	95.88 86	97.59 29	99.40 87	99.58 64
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
PCF-MVS	92.56 4	93.95 57	93.82 75	94.10 45	96.07 57	99.25 56	96.82 52	95.51 22	92.00 128	81.51 93	82.97 108	93.88 63	95.63 66	94.24 114	94.71 88	99.09 117	99.70 45
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
DELS-MVS		93.82 58	93.82 75	93.81 48	96.34 54	99.47 38	97.26 48	88.53 53	92.13 125	87.80 40	79.67 115	88.01 90	93.14 90	98.28 20	99.22 4	99.80 8	99.98 5
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
OpenMVS	88.43 11	93.49 59	93.62 78	93.34 50	98.46 33	99.39 48	97.00 51	87.66 62	95.37 89	81.21 95	75.96 132	91.58 77	96.21 56	96.37 72	97.10 42	99.52 60	99.54 68

EIA-MVS		93.32 60	95.32 57	90.99 80	90.45 111	98.53 79	93.46 93	84.68 88	97.56 62	81.38 94	91.04 57	87.37 93	96.39 52	97.27 49	95.73 78	99.59 36	99.76 36

PVSNet_BlendedMVS		93.30 61	93.46 82	93.10 53	95.60 60	99.38 49	93.59 91	88.70 49	98.09 48	88.10 38	86.96 82	75.02 133	93.08 91	97.89 30	96.90 49	99.56 51	100.00 1

PVSNet_Blended		93.30 61	93.46 82	93.10 53	95.60 60	99.38 49	93.59 91	88.70 49	98.09 48	88.10 38	86.96 82	75.02 133	93.08 91	97.89 30	96.90 49	99.56 51	100.00 1

test2506		93.08 63	93.40 84	92.70 61	92.76 79	99.20 59	94.67 76	86.82 67	92.58 121	90.81 27	86.28 87	85.24 104	91.69 107	96.85 61	96.33 61	99.45 80	97.34 155

PMMVS		93.05 64	95.40 55	90.31 87	91.41 94	97.54 101	92.62 107	83.25 98	98.08 51	79.44 105	95.18 38	88.52 88	96.43 51	95.70 90	93.88 97	98.68 150	98.91 110

LS3D		92.70 65	92.23 94	93.26 51	96.24 56	98.72 68	97.93 37	96.17 7	96.41 76	72.46 122	81.39 111	80.76 122	97.66 28	95.69 91	95.62 80	99.07 119	97.02 164

baseline1		92.67 66	93.62 78	91.55 70	91.16 97	97.15 104	93.92 89	85.97 75	94.76 97	84.07 69	87.17 78	86.89 96	94.62 76	96.72 66	95.90 76	99.57 45	96.79 168

IS_MVSNet		92.67 66	94.99 62	89.96 92	91.17 96	98.54 77	92.77 102	84.00 90	92.72 119	81.90 91	85.67 93	92.47 70	90.39 121	97.82 35	97.81 23	99.51 61	99.91 23

TSAR-MVS + COLMAP		92.56 68	92.44 92	92.71 60	94.61 67	97.69 96	97.69 40	91.09 44	98.96 27	76.71 112	94.68 43	69.41 160	96.91 45	95.80 88	94.18 95	99.26 108	96.33 172

baseline		92.56 68	94.38 68	90.43 86	90.71 106	98.23 86	95.07 69	80.73 119	97.52 63	82.45 83	87.34 77	85.91 100	94.07 84	96.29 76	95.94 75	99.58 41	99.47 74

canonicalmvs		92.54 70	93.28 85	91.68 67	91.44 93	98.24 85	95.45 64	81.84 109	95.98 86	84.85 62	90.69 59	78.53 127	96.96 41	92.97 132	97.06 44	99.57 45	99.47 74

PatchMatch-RL		92.54 70	92.82 91	92.21 63	96.57 53	98.74 67	91.85 113	86.30 70	96.23 81	85.18 59	95.21 37	73.58 139	94.22 83	95.40 105	93.08 118	99.14 114	97.49 152

MVS_Test		92.42 72	94.43 64	90.08 91	90.69 107	98.26 84	94.78 73	80.81 118	97.27 66	78.76 106	87.06 80	84.25 108	95.84 63	97.67 41	97.56 31	99.59 36	98.93 109

thisisatest0530		92.31 73	95.14 58	89.02 101	90.02 120	98.45 81	91.30 117	83.58 93	96.90 71	77.90 109	90.45 61	94.33 56	91.98 104	95.57 96	91.43 141	99.31 102	98.81 113

tttt0517		92.29 74	95.12 60	88.99 102	90.02 120	98.44 83	91.19 120	83.58 93	96.88 72	77.86 110	90.45 61	94.32 57	91.98 104	95.54 98	91.43 141	99.31 102	98.78 115

EPP-MVSNet		92.29 74	94.35 70	89.88 93	90.36 114	97.69 96	90.89 122	83.31 97	93.39 112	83.47 74	85.56 94	93.92 62	91.93 106	95.49 103	94.77 87	99.34 96	99.62 59

HQP-MVS		91.94 76	93.03 87	90.66 85	93.69 69	96.48 118	95.92 55	89.73 46	97.33 64	72.65 120	95.37 35	73.56 140	92.75 97	94.85 111	94.12 96	99.23 111	99.51 70

MSDG		91.93 77	90.28 122	93.85 47	97.36 47	97.12 105	95.88 57	94.07 38	94.52 101	84.13 68	76.74 125	80.89 121	92.54 99	93.97 122	93.61 108	99.14 114	95.10 181

UGNet		91.71 78	94.43 64	88.53 104	92.72 81	98.00 89	90.22 129	84.81 87	94.45 102	83.05 76	87.65 76	92.74 68	81.04 173	94.51 113	94.45 90	99.32 101	99.21 93
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
thres100view900		91.69 79	91.52 100	91.88 66	91.61 88	98.89 65	95.49 62	86.96 64	93.24 113	80.82 97	87.90 71	71.15 151	96.88 46	96.00 84	93.51 110	99.51 61	99.95 13

CLD-MVS		91.67 80	91.30 105	92.10 64	91.25 95	96.59 115	95.93 54	87.25 63	96.86 73	85.55 57	87.08 79	73.01 141	93.26 89	93.07 130	92.84 124	99.34 96	99.68 49
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
ET-MVSNet_ETH3D		91.59 81	94.96 63	87.65 107	72.75 211	97.24 103	95.29 65	82.73 101	96.81 74	78.49 108	95.30 36	90.48 83	97.23 35	91.60 147	94.31 91	99.43 82	99.01 107

tfpn200view9		91.47 82	91.31 103	91.65 68	91.61 88	98.69 70	95.03 70	86.17 71	93.24 113	80.82 97	87.90 71	71.15 151	96.80 48	95.53 99	92.82 126	99.47 70	99.88 26

CANet_DTU		91.36 83	95.75 52	86.23 118	92.31 85	98.71 69	95.60 61	78.41 134	98.20 44	56.48 180	94.38 44	87.96 91	95.11 68	96.89 59	96.07 69	99.48 68	98.01 142

thres200		91.36 83	91.19 107	91.55 70	91.60 90	98.69 70	94.98 71	86.17 71	92.16 124	80.76 99	87.66 75	71.15 151	96.35 53	95.53 99	93.23 116	99.47 70	99.92 22

FMVSNet3		91.25 85	92.13 96	90.21 88	85.64 151	93.14 153	95.29 65	80.09 121	96.40 77	85.74 54	77.13 120	86.81 97	94.98 70	97.19 53	97.11 41	99.55 56	97.13 161

thres400		91.24 86	91.01 113	91.50 73	91.56 91	98.77 66	94.66 78	86.41 69	91.87 130	80.56 100	87.05 81	71.01 154	96.35 53	95.67 92	92.82 126	99.48 68	99.88 26

PVSNet_Blended_VisFu		91.20 87	92.89 90	89.23 99	93.41 72	98.61 75	89.80 131	85.39 80	92.84 117	82.80 78	74.21 136	91.38 79	84.64 152	97.22 51	96.04 72	99.34 96	99.93 19

DCV-MVSNet		91.15 88	92.00 97	90.17 90	90.78 104	92.23 170	93.70 90	81.17 116	95.16 92	82.98 77	89.46 65	83.31 111	93.98 86	91.79 146	92.87 121	98.41 168	99.18 95

DI_MVS_plusplus_trai		91.11 89	91.47 101	90.68 84	90.01 122	97.77 93	95.87 58	83.56 95	94.72 98	82.12 86	68.46 155	87.46 92	93.07 93	96.46 71	95.73 78	99.47 70	99.71 44

diffmvs		91.05 90	91.15 108	90.93 81	90.15 117	97.79 92	94.05 85	85.45 77	95.63 87	81.95 90	80.45 114	73.01 141	94.47 79	95.56 97	95.89 77	99.49 65	99.72 42
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
Vis-MVSNet (Re-imp)		91.05 90	94.43 64	87.11 109	91.05 99	97.99 90	92.53 109	83.82 92	92.71 120	76.28 113	84.50 99	92.43 71	79.52 178	97.24 50	97.68 25	99.43 82	98.45 126

thres600view7		90.97 92	90.70 115	91.30 75	91.53 92	98.69 70	94.33 79	86.17 71	91.75 132	80.19 101	86.06 91	70.90 155	96.10 58	95.53 99	92.08 134	99.47 70	99.86 30

baseline2		90.91 93	94.40 67	86.84 112	87.54 142	96.83 111	89.95 130	79.22 129	96.00 85	77.04 111	88.68 66	89.73 85	88.01 141	96.35 74	93.51 110	99.29 104	99.68 49

casdiffmvs_mvg		90.83 94	90.52 119	91.20 78	90.56 108	97.67 98	94.96 72	85.45 77	90.72 137	82.03 87	76.70 126	77.08 128	94.61 77	96.57 69	95.62 80	99.57 45	99.28 87
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
ACMP	89.80 9	90.72 95	91.15 108	90.21 88	92.55 83	96.52 117	92.63 106	85.71 76	94.65 99	81.06 96	93.32 49	70.56 157	90.52 120	92.68 136	91.05 146	98.76 142	99.31 86
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
casdiffmvs		90.69 96	90.56 118	90.85 82	90.14 118	97.81 91	92.94 100	85.30 81	93.47 111	82.50 82	76.34 130	74.12 137	94.67 74	96.51 70	96.26 64	99.55 56	99.42 77
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
FA-MVS(training)		90.67 97	93.03 87	87.92 106	90.95 101	98.45 81	92.61 108	66.04 193	94.90 94	84.47 63	77.52 119	91.74 75	94.07 84	97.11 55	92.46 132	99.40 87	99.03 106

ACMM	89.40 10	90.58 98	90.02 125	91.23 77	93.30 74	94.75 140	90.69 125	88.22 54	95.20 90	82.70 80	88.54 67	71.40 149	93.48 88	93.64 127	90.94 147	98.99 125	95.72 177
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GBi-Net		90.49 99	91.12 111	89.75 95	84.99 154	92.73 158	93.94 86	80.09 121	96.40 77	85.74 54	77.13 120	86.81 97	94.42 80	94.12 116	93.73 99	99.35 92	96.90 165

test1		90.49 99	91.12 111	89.75 95	84.99 154	92.73 158	93.94 86	80.09 121	96.40 77	85.74 54	77.13 120	86.81 97	94.42 80	94.12 116	93.73 99	99.35 92	96.90 165

ECVR-MVS		90.37 101	88.96 134	92.01 65	92.76 79	99.20 59	94.67 76	86.82 67	92.58 121	86.71 47	68.95 154	71.46 148	91.69 107	96.85 61	96.33 61	99.45 80	97.38 154

LGP-MVS_train		90.34 102	91.63 99	88.83 103	93.31 73	96.14 123	95.49 62	85.24 84	93.91 106	68.71 134	93.96 45	71.63 146	91.12 116	93.82 124	92.79 128	99.07 119	99.16 96

test1111		90.01 103	88.67 136	91.57 69	92.68 82	99.20 59	94.25 82	86.90 66	92.03 127	85.04 60	67.79 159	71.21 150	91.12 116	96.83 63	96.34 60	99.42 85	97.28 157

EPNet_dtu		89.82 104	94.18 73	84.74 128	96.87 51	95.54 133	92.65 105	86.91 65	96.99 68	54.17 191	92.41 55	88.54 87	78.35 181	96.15 79	96.05 71	99.47 70	93.60 189
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
RPSCF		89.81 105	89.75 126	89.88 93	93.22 76	93.99 147	94.78 73	85.23 85	94.01 105	82.52 81	95.00 39	87.23 94	92.01 103	85.16 199	83.48 204	91.54 209	89.38 203

MDTV_nov1_ep13		89.63 106	94.38 68	84.09 135	88.76 134	97.53 102	89.37 139	68.46 191	96.95 69	70.27 129	87.88 73	93.67 65	91.04 118	93.12 128	93.83 98	96.62 198	97.68 148

UA-Net		89.56 107	93.03 87	85.52 124	92.46 84	97.55 100	91.92 112	81.91 106	85.24 164	71.39 124	83.57 104	96.56 43	76.01 192	96.81 64	97.04 45	99.46 76	94.41 184

FMVSNet2		89.51 108	89.63 127	89.38 97	84.99 154	92.73 158	93.94 86	79.28 128	93.73 108	84.28 66	69.36 153	82.32 114	94.42 80	96.16 78	96.22 66	99.35 92	96.90 165

CostFormer		89.42 109	91.67 98	86.80 114	89.99 123	96.33 120	90.75 123	64.79 195	95.17 91	83.62 73	86.20 89	82.15 116	92.96 94	89.22 169	92.94 119	98.68 150	99.65 51

FC-MVSNet-train		89.37 110	89.62 128	89.08 100	90.48 110	94.16 146	89.45 135	83.99 91	91.09 135	80.09 102	82.84 109	74.52 136	91.44 113	93.79 125	91.57 140	99.01 123	99.35 83

OPM-MVS		89.33 111	87.45 145	91.53 72	94.49 68	96.20 122	96.47 53	89.72 47	82.77 171	75.43 114	80.53 113	70.86 156	93.80 87	94.00 120	91.85 138	99.29 104	95.91 175
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
test-LLR		89.31 112	93.60 80	84.30 132	88.08 138	96.98 107	88.10 144	78.00 135	94.83 95	62.43 154	84.29 101	90.96 80	89.70 126	95.63 94	92.86 122	99.51 61	99.64 53

EPMVS		89.31 112	93.70 77	84.18 134	91.10 98	98.10 87	89.17 141	62.71 199	96.24 80	70.21 131	86.46 86	92.37 72	92.79 95	91.95 144	93.59 109	99.10 116	97.19 158

Anonymous20231211		89.22 114	87.56 143	91.16 79	90.23 116	96.62 114	93.22 97	85.44 79	92.89 116	84.37 65	60.13 175	81.25 119	96.02 60	90.61 154	92.01 135	97.70 188	99.41 79

Effi-MVS+		88.96 115	91.13 110	86.43 116	89.12 130	97.62 99	93.15 98	75.52 153	93.90 107	66.40 138	86.23 88	70.51 158	95.03 69	95.89 85	94.28 92	99.37 89	99.51 70

SCA		88.76 116	94.29 71	82.30 151	89.33 128	96.81 112	87.68 146	61.52 204	96.95 69	64.68 144	88.35 68	94.80 49	91.58 110	92.23 138	93.21 117	98.99 125	97.70 147

test0.0.03 1		88.71 117	92.22 95	84.63 130	88.08 138	94.71 142	85.91 169	78.00 135	95.54 88	72.96 118	86.10 90	85.88 102	83.59 160	92.95 134	93.24 115	99.25 110	97.09 162

PatchmatchNet		88.67 118	94.10 74	82.34 150	89.38 127	97.72 94	87.24 152	62.18 202	97.00 67	64.79 143	87.97 70	94.43 54	91.55 111	91.21 152	92.77 129	98.90 130	97.60 151
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
dps		88.66 119	90.19 123	86.88 111	89.94 124	96.48 118	89.56 133	64.08 197	94.12 104	89.00 35	83.39 106	82.56 113	90.16 124	86.81 191	89.26 165	98.53 163	98.71 117

TESTMET0.1,1		88.63 120	93.60 80	82.84 147	84.07 161	96.98 107	88.10 144	73.22 173	94.83 95	62.43 154	84.29 101	90.96 80	89.70 126	95.63 94	92.86 122	99.51 61	99.64 53

CHOSEN 1792x2688		88.63 120	89.01 132	88.19 105	94.83 64	99.21 58	92.66 104	79.85 125	92.40 123	72.18 123	56.38 195	80.22 124	90.24 122	97.64 44	97.28 38	99.37 89	99.94 16

CDS-MVSNet		88.59 122	90.13 124	86.79 115	86.98 147	95.43 134	92.03 111	81.33 114	85.54 161	74.51 117	77.07 123	85.14 105	87.03 146	93.90 123	95.18 83	98.88 132	98.67 119
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IB-MVS	84.67 14	88.34 123	90.61 117	85.70 121	92.99 78	98.62 74	78.85 195	86.07 74	94.35 103	88.64 36	85.99 92	75.69 131	68.09 205	88.21 172	91.43 141	99.55 56	99.96 10
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		88.25 124	93.27 86	82.38 149	83.89 162	96.86 110	87.10 156	72.80 175	94.58 100	61.85 159	83.21 107	90.65 82	89.18 130	95.43 104	92.58 131	99.46 76	99.61 60

COLMAP_ROB	84.42 15	88.24 125	87.32 146	89.32 98	95.83 58	95.82 127	92.81 101	87.68 61	92.09 126	72.64 121	72.34 144	79.96 125	88.79 132	89.54 164	89.46 161	98.16 177	92.00 195
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
IterMVS-LS		87.95 126	89.40 130	86.26 117	88.79 133	90.93 185	91.23 119	76.05 150	90.87 136	71.07 126	75.51 133	81.18 120	91.21 115	94.11 119	95.01 84	99.20 113	98.23 133
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
HyFIR lowres test		87.86 127	88.25 139	87.40 108	94.67 66	98.54 77	90.33 128	76.51 149	89.60 145	70.89 127	51.43 206	85.69 103	92.79 95	96.59 68	95.96 74	99.22 112	99.94 16

Vis-MVSNet		87.60 128	91.31 103	83.27 142	89.14 129	98.04 88	90.35 127	79.42 126	87.23 150	66.92 137	79.10 118	84.63 107	74.34 199	95.81 87	96.06 70	99.46 76	98.32 130
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
GeoE		87.55 129	88.17 140	86.82 113	88.74 135	96.32 121	92.75 103	74.93 158	90.13 142	72.73 119	69.47 152	74.03 138	92.51 100	93.99 121	93.62 107	99.29 104	99.59 61

RPMNet		87.35 130	92.41 93	81.45 155	88.85 132	96.06 124	89.42 138	59.59 211	93.57 109	61.81 160	76.48 129	91.48 78	90.18 123	96.32 75	93.37 113	98.87 133	99.59 61

tpm cat1		87.34 131	88.52 138	85.95 119	89.83 125	95.80 128	90.73 124	64.91 194	92.99 115	82.21 85	71.19 150	82.68 112	90.13 125	86.38 192	90.87 149	97.90 185	99.74 39

MS-PatchMatch		87.19 132	88.59 137	85.55 123	93.15 77	96.58 116	92.35 110	74.19 166	91.97 129	70.33 128	71.42 148	85.89 101	84.28 154	93.12 128	89.16 167	99.00 124	91.99 196

Effi-MVS+-dtu		87.18 133	90.48 120	83.32 141	86.51 148	95.76 130	91.16 121	74.28 165	90.44 141	61.31 163	86.72 85	72.68 144	91.25 114	95.01 109	93.64 102	95.45 203	99.12 100

FMVSNet5		87.06 134	89.52 129	84.20 133	79.92 199	86.57 205	87.11 155	72.37 177	96.06 83	75.41 115	84.33 100	91.76 74	91.60 109	91.51 148	91.22 144	98.77 139	85.16 208

Fast-Effi-MVS+-dtu		86.94 135	91.27 106	81.89 152	86.27 149	95.06 135	90.68 126	68.93 188	91.76 131	57.18 178	89.56 64	75.85 130	89.19 129	94.56 112	92.84 124	99.07 119	99.23 90

Fast-Effi-MVS+		86.94 135	87.88 142	85.84 120	86.99 146	95.80 128	91.24 118	73.48 172	92.75 118	69.22 132	72.70 142	65.71 166	94.84 72	94.98 110	94.71 88	99.26 108	98.48 125

tpmrst		86.78 137	90.29 121	82.69 148	90.55 109	96.95 109	88.49 143	62.58 200	95.09 93	63.52 150	76.67 128	84.00 110	92.05 102	87.93 175	91.89 137	98.98 127	99.50 72

CR-MVSNet		86.73 138	91.47 101	81.20 158	88.56 136	96.06 124	89.43 136	61.37 205	93.57 109	60.81 165	72.89 141	88.85 86	88.13 139	96.03 81	93.64 102	98.89 131	99.22 91

ADS-MVSNet		86.68 139	90.79 114	81.88 153	90.38 113	96.81 112	86.90 157	60.50 209	96.01 84	63.93 147	81.67 110	84.72 106	90.78 119	87.03 185	91.67 139	98.77 139	97.63 150

FMVSNet1		85.85 140	84.91 156	86.96 110	82.70 167	91.39 179	91.54 115	77.45 141	85.29 163	79.56 104	60.70 172	72.68 144	92.37 101	94.12 116	93.73 99	98.12 178	96.44 169

FC-MVSNet-test		85.51 141	89.08 131	81.35 156	85.31 153	93.35 149	87.65 147	77.55 140	90.01 143	64.07 146	79.63 116	81.83 118	74.94 196	92.08 141	90.83 151	98.55 160	95.81 176

ACMH	85.22 13	85.40 142	85.73 153	85.02 126	91.76 87	94.46 145	84.97 175	81.54 112	85.18 165	65.22 142	76.92 124	64.22 167	88.58 135	90.17 156	90.25 157	98.03 181	98.90 111
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
TAMVS		85.35 143	86.00 152	84.59 131	84.97 157	95.57 132	88.98 142	77.29 144	81.44 176	71.36 125	71.48 147	75.00 135	87.03 146	91.92 145	92.21 133	97.92 184	94.40 185

ACMH+	85.62 12	85.27 144	84.96 155	85.64 122	90.84 103	94.78 139	87.46 149	81.30 115	86.94 151	67.35 136	74.56 135	64.09 168	88.70 133	88.14 173	89.00 168	98.22 176	97.19 158

USDC		85.11 145	85.35 154	84.83 127	89.45 126	94.93 138	92.98 99	77.30 143	90.53 139	61.80 161	76.69 127	59.62 178	88.90 131	92.78 135	90.79 153	98.53 163	92.12 193

IterMVS		85.02 146	88.98 133	80.41 164	87.03 145	90.34 193	89.78 132	69.45 185	89.77 144	54.04 192	73.71 138	82.05 117	83.44 163	95.11 107	93.64 102	98.75 143	98.22 135
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS-SCA-FT		84.91 147	88.90 135	80.25 167	87.04 144	90.27 194	89.23 140	69.25 187	89.17 146	54.04 192	73.65 139	82.22 115	83.23 168	95.11 107	93.63 106	98.73 144	98.23 133

PatchT		84.89 148	90.67 116	78.13 187	87.83 141	94.99 137	72.46 207	60.22 210	91.74 133	60.81 165	72.16 145	86.95 95	88.13 139	96.03 81	93.64 102	99.36 91	99.22 91

pmmvs4		84.88 149	84.67 157	85.13 125	82.80 166	92.37 163	87.29 150	79.08 130	90.51 140	74.94 116	70.37 151	62.49 171	88.17 138	92.01 143	88.51 173	98.49 166	96.44 169

CVMVSNet		84.01 150	86.91 147	80.61 162	88.39 137	93.29 150	86.06 165	82.29 103	83.13 169	54.29 188	72.68 143	79.59 126	75.11 195	91.23 151	92.91 120	97.54 192	95.58 178

tpm		83.97 151	87.97 141	79.31 177	87.35 143	93.21 152	86.00 167	61.90 203	90.69 138	54.01 194	79.42 117	75.61 132	88.65 134	87.18 183	90.48 155	97.95 183	99.21 93

GA-MVS		83.83 152	86.63 148	80.58 163	85.40 152	94.73 141	87.27 151	78.76 133	86.49 153	49.57 202	74.21 136	67.67 163	83.38 164	95.28 106	90.92 148	99.08 118	97.09 162

UniMVSNet_NR-MVSNet		83.83 152	83.70 160	83.98 136	81.41 177	92.56 162	86.54 160	82.96 99	85.98 158	66.27 139	66.16 163	63.63 169	87.78 143	87.65 178	90.81 152	98.94 128	99.13 98

UniMVSNet (Re)		83.28 154	83.16 161	83.42 140	81.93 172	93.12 154	86.27 163	80.83 117	85.88 159	68.23 135	64.56 166	60.58 173	84.25 155	89.13 170	89.44 163	99.04 122	99.40 80

thisisatest0515		83.17 155	86.49 149	79.30 178	82.04 170	93.12 154	78.70 196	77.92 137	86.43 154	63.05 151	74.91 134	73.01 141	75.56 194	92.10 140	88.05 186	98.50 165	97.76 146

TinyColmap		83.03 156	82.24 165	83.95 137	88.88 131	93.22 151	89.48 134	76.89 146	87.53 149	62.12 156	68.46 155	55.03 194	88.43 137	90.87 153	89.65 159	97.89 186	90.91 199

testgi		82.88 157	86.14 151	79.08 180	86.05 150	92.20 171	81.23 192	74.77 161	88.70 147	57.63 177	86.73 84	61.53 172	76.83 189	90.33 155	89.43 164	97.99 182	94.05 186

DU-MVS		82.87 158	82.16 166	83.70 139	80.77 186	92.24 167	86.54 160	81.91 106	86.41 155	66.27 139	63.95 167	55.66 192	87.78 143	86.83 188	90.86 150	98.94 128	99.13 98

MIMVSNet		82.87 158	86.17 150	79.02 181	77.23 207	92.88 157	84.88 176	60.62 208	86.72 152	64.16 145	73.58 140	71.48 147	88.51 136	94.14 115	93.50 112	98.72 146	90.87 200

NR-MVSNet		82.37 160	81.95 168	82.85 146	82.56 169	92.24 167	87.49 148	81.91 106	86.41 155	65.51 141	63.95 167	52.93 203	80.80 175	89.41 166	89.61 160	98.85 135	99.10 103

Baseline_NR-MVSNet		82.08 161	80.64 175	83.77 138	80.77 186	88.50 200	86.88 158	81.71 110	85.58 160	68.80 133	58.20 187	57.75 184	86.16 148	86.83 188	88.68 170	98.33 173	98.90 111

TranMVSNet+NR-MVSNet		82.07 162	81.36 171	82.90 145	80.43 192	91.39 179	87.16 154	82.75 100	84.28 167	62.98 152	62.28 171	56.01 191	85.30 151	86.06 194	90.69 154	98.80 136	98.80 114

pm-mvs1		81.68 163	81.70 169	81.65 154	82.61 168	92.26 166	85.54 173	78.95 131	76.29 198	63.81 148	58.43 186	66.33 165	80.63 176	92.30 137	89.93 158	98.37 172	96.39 171

TDRefinement		81.49 164	80.08 181	83.13 144	91.02 100	94.53 143	91.66 114	82.43 102	81.70 174	62.12 156	62.30 170	59.32 179	73.93 200	87.31 181	85.29 197	97.61 189	90.14 201

anonymousdsp		81.29 165	84.52 159	77.52 189	79.83 200	92.62 161	82.61 187	70.88 182	80.76 180	50.82 199	68.35 157	68.76 161	82.45 171	93.00 131	89.45 162	98.55 160	98.69 118

gg-mvs-nofinetune		81.27 166	84.65 158	77.32 190	87.96 140	98.48 80	95.64 60	56.36 214	59.35 216	32.80 220	47.96 210	92.11 73	91.49 112	98.12 24	97.00 47	99.65 23	99.56 66

tfpnnormal		81.11 167	79.33 189	83.19 143	84.23 159	92.29 165	86.76 159	82.27 104	72.67 204	62.02 158	56.10 197	53.86 200	85.35 150	92.06 142	89.23 166	98.49 166	99.11 102

UniMVSNet_ETH3D		80.95 168	77.71 197	84.74 128	84.45 158	93.11 156	86.45 162	79.97 124	75.21 200	70.22 130	51.24 207	50.26 209	89.55 128	84.47 201	91.12 145	97.81 187	98.53 123

V42		80.88 169	80.74 173	81.05 159	81.21 180	92.01 173	85.96 168	77.75 139	81.62 175	59.73 172	59.93 178	58.35 183	82.98 170	86.90 187	88.06 185	98.69 149	98.32 130

v2v482		80.86 170	80.52 179	81.25 157	80.79 185	91.85 174	85.68 171	78.78 132	81.05 177	58.09 175	60.46 173	56.08 189	85.45 149	87.27 182	88.53 172	98.73 144	98.38 129

v8		80.61 171	80.61 177	80.62 161	81.51 175	91.00 184	86.06 165	74.07 168	81.78 173	59.93 171	60.10 177	58.42 182	83.35 165	86.99 186	88.11 183	98.79 137	97.83 144

pmmvs5		80.48 172	81.43 170	79.36 176	81.50 176	92.24 167	82.07 190	74.08 167	78.10 191	55.86 183	67.72 160	54.35 197	83.91 159	92.97 132	88.65 171	98.77 139	96.01 173

v10		80.38 173	80.73 174	79.96 169	81.22 179	90.40 192	86.11 164	71.63 179	82.42 172	57.65 176	58.74 184	57.47 185	84.44 153	89.75 160	88.28 176	98.71 147	98.06 141

v1144		80.36 174	80.63 176	80.05 168	80.86 184	91.56 177	85.78 170	75.22 155	80.73 181	55.83 184	58.51 185	56.99 187	83.93 158	89.79 159	88.25 177	98.68 150	98.56 122

SixPastTwentyTwo		80.28 175	82.06 167	78.21 186	81.89 174	92.35 164	77.72 197	74.48 162	83.04 170	54.22 189	76.06 131	56.40 188	83.55 161	86.83 188	84.83 199	97.38 193	94.93 182

CP-MVSNet		79.90 176	79.49 186	80.38 165	80.72 188	90.83 186	82.98 184	75.17 156	79.70 186	61.39 162	59.74 179	51.98 206	83.31 166	87.37 180	88.38 174	98.71 147	98.45 126

v1192		79.84 177	80.05 183	79.61 172	80.49 191	91.04 183	85.56 172	74.37 164	80.73 181	54.35 187	57.07 192	54.54 196	84.23 156	89.94 157	88.38 174	98.63 154	98.61 120

WR-MVS_H		79.76 178	80.07 182	79.40 175	81.25 178	91.73 176	82.77 185	74.82 160	79.02 190	62.55 153	59.41 181	57.32 186	76.27 191	87.61 179	87.30 191	98.78 138	98.09 139

WR-MVS		79.67 179	80.25 180	79.00 182	80.65 189	91.16 181	83.31 182	76.57 148	80.97 178	60.50 170	59.20 182	58.66 181	74.38 198	85.85 196	87.76 188	98.61 155	98.14 136

v148		79.66 180	79.13 191	80.27 166	81.02 182	91.76 175	81.90 191	79.32 127	79.24 188	63.79 149	58.07 189	54.34 198	77.17 187	84.42 202	87.52 190	98.40 169	98.59 121

LTVRE_ROB	79.45 16	79.66 180	80.55 178	78.61 184	83.01 165	92.19 172	87.18 153	73.69 171	71.70 207	43.22 215	71.22 149	50.85 207	87.82 142	89.47 165	90.43 156	96.75 196	98.00 143
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
v144192		79.61 182	79.77 184	79.41 174	80.28 193	91.06 182	84.87 177	73.86 169	79.65 187	55.38 185	57.76 190	55.20 193	83.46 162	88.42 171	87.89 187	98.61 155	98.42 128

v1921920		79.55 183	79.77 184	79.30 178	80.24 194	90.77 188	85.37 174	73.75 170	80.38 183	53.78 195	56.89 194	54.18 199	84.05 157	89.55 163	88.13 182	98.59 157	98.52 124

TransMVSNet (Re)		79.51 184	78.36 193	80.84 160	83.17 163	89.72 196	84.22 180	81.45 113	73.98 203	60.79 168	57.20 191	56.05 190	77.11 188	89.88 158	88.86 169	98.30 175	92.83 191

MVS-HIRNet		79.34 185	82.56 162	75.57 195	84.11 160	95.02 136	75.03 204	57.28 213	85.50 162	55.88 182	53.00 203	70.51 158	83.05 169	92.12 139	91.96 136	98.09 179	89.83 202

PS-CasMVS		79.06 186	78.58 192	79.63 171	80.59 190	90.55 190	82.54 188	75.04 157	77.76 192	58.84 173	58.16 188	50.11 211	82.09 172	87.05 184	88.18 180	98.66 153	98.27 132

v1240		78.97 187	79.27 190	78.63 183	80.04 195	90.61 189	84.25 179	72.95 174	79.22 189	52.70 197	56.22 196	52.88 205	83.28 167	89.60 162	88.20 179	98.56 159	98.14 136

pmnet_mix02		78.91 188	81.17 172	76.28 194	81.91 173	90.82 187	74.25 205	77.87 138	86.17 157	49.04 203	67.97 158	62.93 170	77.40 185	82.75 207	82.11 206	97.18 194	95.42 179

MDTV_nov1_ep13_2view		78.83 189	82.35 163	74.73 198	78.65 202	91.51 178	79.18 194	62.52 201	84.51 166	52.51 198	67.49 161	67.29 164	78.90 179	85.52 198	86.34 194	96.62 198	93.76 187

PEN-MVS		78.80 190	78.13 195	79.58 173	80.03 196	89.67 197	83.61 181	75.83 151	77.71 194	58.41 174	60.11 176	50.00 212	81.02 174	84.08 203	88.14 181	98.59 157	97.18 160

EG-PatchMatch MVS		78.32 191	79.42 188	77.03 192	83.03 164	93.77 148	84.47 178	69.26 186	75.85 199	53.69 196	55.68 198	60.23 176	73.20 201	89.69 161	88.22 178	98.55 160	92.54 192

DTE-MVSNet		77.92 192	77.42 198	78.51 185	79.34 201	89.00 199	83.05 183	75.60 152	76.89 196	56.58 179	59.63 180	50.31 208	78.09 184	82.57 208	87.56 189	98.38 170	95.95 174

v7n		77.71 193	78.25 194	77.09 191	78.49 203	90.55 190	82.15 189	71.11 181	76.79 197	54.18 190	55.63 199	50.20 210	78.28 182	89.36 168	87.15 192	98.33 173	98.07 140

gm-plane-assit		77.20 194	82.26 164	71.30 201	81.10 181	82.00 213	54.33 218	64.41 196	63.80 215	40.93 217	59.04 183	76.57 129	87.30 145	98.26 22	97.36 37	99.74 12	98.76 116

N_pmnet		76.83 195	77.97 196	75.50 196	80.96 183	88.23 202	72.81 206	76.83 147	80.87 179	50.55 200	56.94 193	60.09 177	75.70 193	83.28 205	84.23 201	96.14 202	92.12 193

pmmvs6		76.79 196	75.69 203	78.09 188	79.95 198	89.57 198	80.92 193	74.46 163	64.79 213	60.74 169	45.71 212	60.55 174	78.37 180	88.04 174	86.00 195	94.07 206	95.15 180

CMPMVS	58.73 17	76.78 197	74.27 204	79.70 170	93.26 75	95.58 131	82.74 186	77.44 142	71.46 210	56.29 181	53.58 202	59.13 180	77.33 186	79.20 209	79.71 209	91.14 211	81.24 211
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
EU-MVSNet		76.76 198	79.47 187	73.60 199	79.99 197	87.47 203	77.39 198	75.43 154	77.62 195	47.83 206	64.78 165	60.44 175	64.80 206	86.28 193	86.53 193	96.17 201	93.19 190

PM-MVS		75.81 199	76.11 202	75.46 197	73.81 208	85.48 207	76.42 200	70.57 183	80.05 185	54.75 186	62.33 169	39.56 218	80.59 177	87.71 177	82.81 205	96.61 200	94.81 183

pmmvs-eth3d		75.17 200	74.09 205	76.43 193	72.92 209	84.49 209	76.61 199	72.42 176	74.33 201	61.28 164	54.71 201	39.42 219	78.20 183	87.77 176	84.25 200	97.17 195	93.63 188

Anonymous20231206		74.59 201	77.00 199	71.78 200	77.89 206	87.45 204	75.14 203	72.29 178	77.76 192	46.65 208	52.14 204	52.93 203	61.10 209	89.37 167	88.09 184	97.59 190	91.30 198

test20.03		72.81 202	76.24 201	68.80 204	78.31 204	85.40 208	71.04 208	71.20 180	71.85 206	43.40 214	65.31 164	54.71 195	51.27 212	85.92 195	84.18 202	97.58 191	86.35 207

test_method		71.90 203	76.72 200	66.28 209	60.87 217	78.37 215	69.75 212	49.81 219	83.44 168	49.63 201	47.13 211	53.23 202	76.38 190	91.32 150	85.76 196	91.22 210	97.77 145

new_pmnet		71.86 204	73.67 206	69.75 203	72.56 212	84.20 210	70.95 210	66.81 192	80.34 184	43.62 213	51.60 205	53.81 201	71.24 203	82.91 206	80.93 207	93.35 208	81.92 210

MDA-MVSNet-bldmvs		69.61 205	70.36 208	68.74 205	62.88 215	88.50 200	65.40 215	77.01 145	71.60 209	43.93 210	66.71 162	35.33 221	72.47 202	61.01 215	80.63 208	90.73 212	88.75 205

pmmvs3		69.04 206	70.75 207	67.04 207	66.83 213	78.54 214	64.99 216	60.92 207	64.67 214	40.61 218	55.08 200	40.29 217	74.89 197	83.76 204	84.01 203	93.98 207	88.88 204

MIMVSNet1		68.63 207	70.24 209	66.76 208	56.86 219	83.26 211	67.93 213	70.26 184	68.05 211	46.80 207	40.44 213	48.15 213	62.01 207	84.96 200	84.86 198	96.69 197	81.93 209

GG-mvs-BLEND		67.99 208	97.35 38	33.72 216	1.22 225	99.72 16	98.30 33	0.57 223	97.61 61	1.18 226	93.26 50	96.63 42	1.74 222	97.15 54	97.14 40	99.34 96	99.96 10

new-patchmatchnet		67.66 209	68.07 210	67.18 206	72.85 210	82.86 212	63.09 217	68.61 190	66.60 212	42.64 216	49.28 208	38.68 220	61.21 208	75.84 210	75.22 211	94.67 205	88.00 206

FPMVS		63.27 210	61.31 212	65.57 210	78.25 205	74.42 218	75.23 202	68.92 189	72.33 205	43.87 211	49.01 209	43.94 215	48.64 214	61.15 214	58.81 216	78.51 218	69.49 216

Gipuma		54.59 211	53.98 213	55.30 211	59.03 218	52.63 220	47.17 220	56.08 215	71.68 208	37.54 219	20.90 219	19.00 223	52.33 211	71.69 212	75.20 212	79.64 217	66.79 217
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	49.05 18	51.88 212	50.56 215	53.42 212	64.21 214	43.30 222	42.64 221	62.93 198	50.56 217	43.72 212	37.44 214	42.95 216	35.05 217	58.76 217	54.58 217	71.95 219	66.33 218
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PMMVS2		50.69 213	52.33 214	48.78 213	51.24 220	64.81 219	47.91 219	53.79 218	44.95 218	21.75 221	29.98 217	25.90 222	31.98 219	59.95 216	65.37 214	86.00 215	75.36 214

E-PMN		37.15 214	34.82 217	39.86 214	47.53 222	35.42 224	23.79 223	55.26 216	35.18 221	14.12 223	17.38 222	14.13 225	39.73 216	32.24 219	46.98 218	58.76 220	62.39 220

EMVS		36.45 215	33.63 218	39.74 215	48.47 221	35.73 223	23.59 224	55.11 217	35.61 220	12.88 224	17.49 220	14.62 224	41.04 215	29.33 220	43.00 219	57.32 221	59.62 221

MVE	42.40 19	36.00 216	38.65 216	32.92 217	29.16 223	46.17 221	22.61 225	44.21 220	26.44 223	18.88 222	17.41 221	9.36 227	32.29 218	45.75 218	61.38 215	50.35 222	64.03 219
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		21.55 217	30.91 219	10.62 218	2.78 224	11.66 225	18.51 226	4.82 221	38.21 219	4.06 225	36.35 215	4.47 228	26.81 220	23.27 221	27.11 220	6.75 223	75.30 215

test123		16.81 218	24.80 220	7.48 219	0.82 226	8.38 226	11.92 227	2.60 222	28.96 222	1.12 227	28.39 218	1.26 229	24.51 221	8.93 222	22.19 221	3.90 224	75.49 213

uanet_test		0.00 219	0.00 221	0.00 220	0.00 227	0.00 227	0.00 228	0.00 224	0.00 224	0.00 228	0.00 223	0.00 230	0.00 223	0.00 223	0.00 222	0.00 225	0.00 222

sosnet-low-res		0.00 219	0.00 221	0.00 220	0.00 227	0.00 227	0.00 228	0.00 224	0.00 224	0.00 228	0.00 223	0.00 230	0.00 223	0.00 223	0.00 222	0.00 225	0.00 222

sosnet		0.00 219	0.00 221	0.00 220	0.00 227	0.00 227	0.00 228	0.00 224	0.00 224	0.00 228	0.00 223	0.00 230	0.00 223	0.00 223	0.00 222	0.00 225	0.00 222

RE-MVS-def										46.54 209

9.14												99.73 8

SR-MVS					99.27 15			95.82 18				99.00 18

Anonymous202405211			87.54 144		90.72 105	97.10 106	93.40 95	85.30 81	91.41 134		60.23 174	80.69 123	95.80 64	91.33 149	92.60 130	98.38 170	99.40 80

our_test_3					81.94 171	90.26 195	75.39 201

ambc			64.61 211		61.80 216	75.31 217	71.00 209		74.16 202	48.83 204	36.02 216	13.22 226	58.66 210	85.80 197	76.26 210	88.01 213	91.53 197

MTAPA										94.58 14		98.56 23

MTMP										95.24 8		98.13 29

Patchmatch-RL test							37.05 222

tmp_tt				71.24 202	90.29 115	76.39 216	65.81 214	59.43 212	97.62 59	79.65 103	90.60 60	68.71 162	49.71 213	72.71 211	65.70 213	82.54 216

XVS					93.63 70	99.64 24	94.32 80			83.97 70		98.08 31				99.59 36

X-MVStestdata					93.63 70	99.64 24	94.32 80			83.97 70		98.08 31				99.59 36

mPP-MVS					98.66 28							97.11 39

NP-MVS									97.69 57

Patchmtry						95.86 126	89.43 136	61.37 205		60.81 165

DeepMVS_CX						85.88 206	69.83 211	81.56 111	87.99 148	48.22 205	71.85 146	45.52 214	68.67 204	63.21 213		86.64 214	80.03 212

High-resolution multi-view benchmark