Attention aggregation learning for fast stereo matching

Long Yan; Zhiyao Li; Qiuyue Li; Fuyang Yu; Chao Dong

doi:10.1117/12.3031098

19 July 2024 Attention aggregation learning for fast stereo matching

Long Yan, Zhiyao Li, Qiuyue Li, Fuyang Yu, Chao Dong

Author Affiliations +

Proceedings Volume 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024); 131818V (2024) https://doi.org/10.1117/12.3031098
Event: Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 2024, Beijing, China

Abstract

In the stereo matching domain, deep learning has advanced significantly in extracting features from raw data. However, the complex networks yielding highly accurate disparity maps entail substantial computational expenses and longer processing time. Achieving a balance between real-time processing and accuracy poses a persistent challenge. Hence, we designed the Attention Bilateral Grid Network (ABGNet), a fast network leveraging the Attention Aggregation Module (AAM) and bilateral grid upsampling. For our network, initial contextual information is first obtained through lowresolution 3D convolution. Then, guided by a bilateral grid, edge-preserving up-sampling is applied to the coarse disparity map. Finally utilizing comprehensive contextual data, attention aggregation further refines the disparity map. This network ensures high-speed processing while maintaining specified accuracy levels.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Long Yan, Zhiyao Li, Qiuyue Li, Fuyang Yu, and Chao Dong "Attention aggregation learning for fast stereo matching", Proc. SPIE 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 131818V (19 July 2024); https://doi.org/10.1117/12.3031098

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KEYWORDS

3D image processing

Convolution

Image processing

Feature extraction

Image filtering

Tunable filters

Error analysis

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