@inproceedings{3abdfbacf8dd487981a439cbe49a267f,
title = "DPA-PSMNet: Stereo Matching with Dynamic Pyramid Attention for Enhanced Depth Estimation",
abstract = "Stereo image depth estimation plays a critical role in the field of autonomous driving. In most prior work, depth estimation results have often been unsatisfactory due to inadequate stereo matching accuracy. This work introduces an innovative architecture specifically designed to significantly improve stereo matching accuracy. Specifically, building upon the PSMNet framework, we introduce a Dynamic Pyramid Attention (DPA) mechanism to optimize and refine the feature extraction process. And the proposed approach undergoes comprehensive benchmarking on both Scene Flow and KITTI 2015 datasets. Empirical evidence confirms that our technique surpasses PSMNet in quantitative performance metrics.",
keywords = "DPA-PSMNet, dynamic pyramid attention, feature extraction, stereo matching",
author = "Lantao Li and Chao Wei and Hongji Wang and Ruijie Zhang and Heying Huang and Zhiqing Cao",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 2025 IEEE International Conference on Unmanned Systems, ICUS 2025 ; Conference date: 18-09-2025 Through 19-09-2025",
year = "2025",
doi = "10.1109/ICUS66297.2025.11294674",
language = "English",
series = "Proceedings of 2025 IEEE International Conference on Unmanned Systems, ICUS 2025",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "780--786",
editor = "Rong Song",
booktitle = "Proceedings of 2025 IEEE International Conference on Unmanned Systems, ICUS 2025",
address = "United States",
}