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- W4384304019 abstract "Recent studies have shown that stereo matching can be achieved by supervised learning. In this paper, we propose a coarse-to-fine stereo matching framework based on structural information filtering to obtain an accurate disparity map. Our framework addresses the challenge of disparity estimation in ill-posed areas such as texture-less and reflective surfaces. We introduce a contextual attention feature extraction module to extract multi-scale features and contextual information to decompose complex scenes. Additionally, we design a structural attention weight generation module to add structural information to the cost volume to deal with ill-posed regions. Furthermore, we regularize the initial cost volume using a two-stage cost aggregation module that can sufficiently aggregate deep information. Finally, the dense disparity map is obtained by a disparity refine module that optimizes disparity boundaries. Our ablation experiments on various variants of the coarse-to-fine stereo matching network based on structural information filtering (Cs-Net) demonstrate the effectiveness of each module for handling ill-posed regions and complex scenes. Experiments on Scene Flow, KITTI, and DriveStereo datasets show that our method achieves remarkable accuracy." @default.
- W4384304019 created "2023-07-15" @default.
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- W4384304019 date "2023-01-01" @default.
- W4384304019 modified "2023-10-14" @default.
- W4384304019 title "Coarse-to-Fine Stereo Matching Network Based on Multi-Scale Structural Information Filtrating" @default.
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- W4384304019 doi "https://doi.org/10.1109/access.2023.3294441" @default.
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