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A Lightweight Target-Driven Network of Stereo Matching for Inland Waterways by Su, Jing; Zhou, Yiqing; Zhang, Yu; Wang, Chao; Wei, Yi is a scholarly article available to read on EtoBox.

What is A Lightweight Target-Driven Network of Stereo Matching for Inland Waterways about?

Stereo matching for inland waterways is one of the key technologies for the autonomous navigation of Unmanned Surface Vehicles (USVs), which involves dividing the stereo images into reference images and target images for pixel-level matching. However, due to the challenges of the inland waterway environment, such as blurred textures, large spatial scales, and computational resource constraints of the USVs platform, the participation of geometric features from the target image is required for efficient target-driven matching. Based on this target-driven concept, we propose a lightweight target-driven stereo matching neural network, named LTNet. Specifically, a lightweight and efficient 4D cost volume, named the Geometry Target Volume (GTV), is designed to fully utilize the geometric information of target features by employing the shifted target features as the filtered feature volume. Subsequently, to address the substantial texture interference and object occlusions present in the waterway environment, a Left-Right Consistency Refinement (LRR) module is proposed. The \text{LRR} utilizes the pixel-level differences in left and right disparities to introduce soft constraints, thereby

Author

Su, Jing; Zhou, Yiqing; Zhang, Yu; Wang, Chao; Wei, Yi

Published

2024

Language

EN