标题： 基于车载3D激光雷达的深度学习在几何和语义场景理解中的应用 显示英文标题

标题： On Deep Learning for Geometric and Semantic Scene Understanding Using On-Vehicle 3D LiDAR

摘要： 3D LiDAR点云数据对于计算机视觉、机器人和自动驾驶中的场景感知至关重要。 几何和语义场景理解，涉及3D点云，对于推动自动驾驶技术的发展至关重要。 然而，仍然存在重大挑战，特别是在提高系统的整体准确性（例如，分割准确性、深度估计准确性等）和效率方面。 为了解决与基于LiDAR的任务相关的准确性挑战，我们提出了DurLAR，这是首个高保真128通道3D LiDAR数据集，具有全景环境（近红外）和反射率图像。 为了在确保准确性的前提下提高3D分割的效率，我们提出了一种新的流程，该流程采用更小的架构，在需要更少真实标注的情况下，相比现有方法实现了更优的分割准确性。 为了提高分割准确性，我们引入了范围感知点对距离分布（RAPiD）特征和相关的RAPiD-Seg架构。 所有贡献已被同行评审会议接受，突显了在自动驾驶中3D LiDAR应用的准确性和效率方面的进展。 完整摘要：https://etheses.dur.ac.uk/15738/。 显示英文摘要

摘要： 3D LiDAR point cloud data is crucial for scene perception in computer vision, robotics, and autonomous driving. Geometric and semantic scene understanding, involving 3D point clouds, is essential for advancing autonomous driving technologies. However, significant challenges remain, particularly in improving the overall accuracy (e.g., segmentation accuracy, depth estimation accuracy, etc.) and efficiency of these systems. To address the challenge in terms of accuracy related to LiDAR-based tasks, we present DurLAR, the first high-fidelity 128-channel 3D LiDAR dataset featuring panoramic ambient (near infrared) and reflectivity imagery. To improve efficiency in 3D segmentation while ensuring the accuracy, we propose a novel pipeline that employs a smaller architecture, requiring fewer ground-truth annotations while achieving superior segmentation accuracy compared to contemporary approaches. To improve the segmentation accuracy, we introduce Range-Aware Pointwise Distance Distribution (RAPiD) features and the associated RAPiD-Seg architecture. All contributions have been accepted by peer-reviewed conferences, underscoring the advancements in both accuracy and efficiency in 3D LiDAR applications for autonomous driving. Full abstract: https://etheses.dur.ac.uk/15738/.