Zeyuan Chen, Yafei Xie, Jinkun Li, Song Wang, Yingqiang Ding*
CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073846
- 10 February 2026
Abstract Stereo matching is a pivotal task in computer vision, enabling precise depth estimation from stereo image pairs, yet it encounters challenges in regions with reflections, repetitive textures, or fine structures. In this paper, we propose a Semantic-Guided Parallax Attention Stereo Matching Network (SGPASMnet) that can be trained in unsupervised manner, building upon the Parallax Attention Stereo Matching Network (PASMnet). Our approach leverages unsupervised learning to address the scarcity of ground truth disparity in stereo matching datasets, facilitating robust training across diverse scene-specific datasets and enhancing generalization. SGPASMnet incorporates two novel components: a Cross-Scale Feature Interaction… More >
Login
Register
