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8 November 2024 Learnable upsampling bilateral grid refinement for stereo matching network
Wenfeng Qiu, Chihao Ma, Jianhua Li, Ren Qian, Yong Zhao
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Proceedings Volume 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024); 134161V (2024) https://doi.org/10.1117/12.3050134
Event: 2024 4th International Conference on Advanced Algorithms and Neural Networks, 2024, Qingdao, China
Abstract
Most existing binocular stereo matching algorithms require a trade-off between accuracy and speed, unable to achieve both simultaneously. One reason lies in the complexity and variability of scenes that stereo matching tasks must handle, where disparities in heavily textured, weakly textured, and occluded areas are often difficult to infer correctly. Therefore, this paper proposes the Learnable Upsampling Bilateral Grid Refinement for Stereo Matching Network (LUGNet). Through learnable bilateral grid upsampling guided by the left image, LUGNet calculates offsets for cost volume upsampling, while simultaneously leveraging the network to automatically learn interpolation weights to accommodate features of different datasets. Ultimately, LUGNet achieves error rates comparable to high-precision networks with a parameter count of 2.6M and an inference time of 58ms.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Wenfeng Qiu, Chihao Ma, Jianhua Li, Ren Qian, and Yong Zhao "Learnable upsampling bilateral grid refinement for stereo matching network", Proc. SPIE 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024), 134161V (8 November 2024); https://doi.org/10.1117/12.3050134
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KEYWORDS
Feature extraction
Data modeling
Education and training
Interpolation
3D modeling
Design
Evolutionary algorithms
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