Fusion of multi-scale geometric features and frequency domain decomposition for stereo matching network
Hua Hou, Diancheng Wang, Jinqian Xu, Yan Wang
TL;DR
This paper introduces a stereo matching network that improves depth estimation by combining geometric features and frequency domain decomposition, achieving high accuracy and efficiency.
Contribution
The novel fusion of multi-scale geometric features and frequency domain decomposition enhances stereo matching precision and efficiency.
Findings
The proposed method achieves state-of-the-art performance on multiple stereo matching benchmarks.
It attains 1.39% and 2.54% error rates on KITTI2015 background and foreground regions, respectively.
The method maintains real-time inference capabilities while improving edge and detail clarity.
Abstract
In learning-based stereo matching methods, a feature information-rich and concise cost volume is crucial for achieving high-precision and high-efficiency stereo matching. Aiming at the problem that the cost volume lacks global geometric information, which leads to confusing foreground and background disparity estimation and blurring at edges and details, this paper proposes a fusion of multi-scale geometric features and frequency domain decomposition stereo matching network. Firstly, the initial cost volume is processed by the multi-scale geometric extraction module, which achieves an effective conversion from local correlation to global geometric information understanding, and significantly enhances the perception of scene boundaries and occluded regions. In the cost aggregation stage, we introduce an adaptive guidance mechanism based on channel attention, which not only improves the…
Genes, proteins, chemicals, diseases, species, mutations and cell lines named across the full text — each resolved to its canonical identifier and authoritative record.
Click any figure to enlarge with its caption.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16
Figure 17
Figure 18
Figure 19
Figure 20
Figure 21
Figure 22
Figure 23
Figure 24
Figure 25
Figure 26
Figure 27
Figure 28
Figure 29
Figure 30
Figure 31
Figure 32
Figure 33
Figure 34
Figure 35
Figure 36
Figure 37
Figure 38
Figure 39
Figure 40
Figure 41
Figure 42
Figure 43
Figure 44
Figure 45
Figure 46
Figure 47Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Vision and Imaging · Advanced Image Processing Techniques · Advanced Image and Video Retrieval Techniques