In recent years, much progress has been made in LiDAR-based 3D object detection mainly due to advances in detector architecture designs and availability of large-scale LiDAR datasets. Existing 3D object detectors tend to perform well on the point cloud regions closer to the LiDAR sensor as opposed to on regions that are farther away. In this paper, we investigate this problem from the data perspective instead of detector architecture design. We observe that there is a learning bias in detection models towards the dense objects near the sensor and show that the detection performance can be improved by simply manipulating the input point cloud density at different distance ranges without modifying the detector architecture and without data augmentation. We propose a model-free point cloud density adjustment pre-processing mechanism that uses iterative MCMC optimization to estimate optimal parameters for altering the point density at different distance ranges. We conduct experiments using four state-of-the-art LiDAR 3D object detectors on two public LiDAR datasets, namely Waymo and ONCE. Our results demonstrate that our range-based point cloud density manipulation technique can improve the performance of the existing detectors, which in turn could potentially inspire future detector designs.

翻译：近年来，基于激光雷达的三维目标检测取得了显著进展，这主要归功于检测器架构设计的进步和大规模激光雷达数据集的出现。现有三维目标检测器在靠近激光雷达传感器的点云区域表现良好，而在较远区域性能较差。本文从数据角度而非检测器架构设计出发研究这一问题。我们观察到检测模型存在对传感器附近密集物体的学习偏差，并证明无需修改检测器架构或进行数据增强，仅通过调整不同距离范围内的输入点云密度即可提升检测性能。我们提出一种无模型点云密度调整预处理机制，该机制采用迭代MCMC优化来估计不同距离范围内调整点密度的最优参数。我们在两个公开激光雷达数据集（Waymo和ONCE）上，使用四种最新激光雷达三维目标检测器进行实验。结果表明，我们基于距离的点云密度操控技术能够提升现有检测器的性能，进而可能启发未来的检测器设计。