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.

本文通过数据角度分析了现有的 LiDAR 3D 目标检测算法尤其是对于离传感器较远区域性能欠佳的问题，发现检测模型中存在对密集物体的学习偏差，提出了一种基于模型无关的点云密度调整预处理机制，使用迭代 MCMC 优化估计不同距离范围内点密度的最优参数，实验证明这种技术可以提高现有检测器的性能。