融合激光雷达、图像与相对深度，用半监督学习提升道路分割精度
UdeerLID+: Integrating LiDAR, Image, and Relative Depth with Semi-Supervised
- 多模态融合：结合激光雷达点云、图像与图像推导的相对深度图
- 半监督学习框架在KITTI上实现93.2%交并比，优于现有方法
- 适合自动驾驶感知系统开发，尤其数据标注成本高的场景
道路分割是自动驾驶系统的关键任务，需从多种环境数据中准确分类路面。本文提出一种创新方法，融合激光雷达点云、视觉图像及由图像推导出的相对深度图。多源数据融合虽具潜力，但受限于大规模精准标注数据集的稀缺性。为此，我们构建了基于半监督学习范式的[ UdeerLID+ ]框架。在KITTI数据集上的实验结果验证了其优越性能，达到93.2%的交并比（IoU），显著优于现有方法。
原文摘要 · Abstract (English)
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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