Adaptively identify and refine ill-posed regions for accurate stereo matching.
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| Title: | Adaptively identify and refine ill-posed regions for accurate stereo matching. |
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| Authors: | Liu, Changlin1,2 (AUTHOR) 2021023511@m.scnu.edu.cn, Sun, Linjun1 (AUTHOR) sunlinjun@semi.ac.cn, Ning, Xin1 (AUTHOR), Xu, Jian1 (AUTHOR), Yu, Lina1 (AUTHOR), Zhang, Kaijie2 (AUTHOR), Li, Weijun1 (AUTHOR) wjli@semi.ac.cn |
| Source: | Neural Networks. Oct2024, Vol. 178, pN.PAG-N.PAG. 1p. |
| Subjects: | Generalization, Pixels, Cost, Design |
| Abstract: | Stereo matching cost constrains the consistency between pixel pairs. However, the consistency constraint becomes unreliable in ill-posed regions such as occluded or ambiguous regions of the images, making it difficult to explore hidden correspondences. To address this challenge, we introduce an Error-area Feature Refinement Mechanism (EFR) that supplies context features for ill-posed regions. In EFR, we innovatively obtain the suspected error region according to aggregation perturbations, then a simple Transformer module is designed to synthesize global context and correspondence relation with the identified error mask. To better overcome existing texture overfitting, we put forward a Dual-constraint Cost Volume (DCV) that integrates supplementary constraints. This effectively improves the robustness and diversity of disparity clues, resulting in enhanced details and structural accuracy. Finally, we propose a highly accurate stereo matching network called Error-rectify Feature Guided Stereo Matching Network (ERCNet), which is based on DCV and EFR. We evaluate our model on several benchmark datasets, achieving state-of-the-art performance and demonstrating excellent generalization across datasets. The code is available at https://github.com/dean7liu/ERCNet_2023. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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