UdeerLID+: Integrating LiDAR, Image, and Relative Depth with Semi-Supervised

• URL: http://arxiv.org/abs/2409.06197v1
• Date: Tue, 10 Sep 2024 03:57:30 GMT
• Title: UdeerLID+: Integrating LiDAR, Image, and Relative Depth with Semi-Supervised
• Authors: Tao Ni, Xin Zhan, Tao Luo, Wenbin Liu, Zhan Shi, JunBo Chen,
• Abstract summary: Road segmentation is a critical task for autonomous driving systems. Our work introduces an innovative approach that integrates LiDAR point cloud data, visual image, and relative depth maps. One of the primary challenges is the scarcity of large-scale, accurately labeled datasets.
• Score: 12.440461420762265
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Road segmentation is a critical task for autonomous driving systems, requiring accurate and robust methods to classify road surfaces from various environmental data. Our work introduces an innovative approach that integrates LiDAR point cloud data, visual image, and relative depth maps derived from images. The integration of multiple data sources in road segmentation presents both opportunities and challenges. One of the primary challenges is the scarcity of large-scale, accurately labeled datasets that are necessary for training robust deep learning models. To address this, we have developed the [UdeerLID+] framework under a semi-supervised learning paradigm. Experiments results on KITTI datasets validate the superior performance.

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