The main challenge in 3D object detection from LiDAR point clouds is
achieving real-time performance without affecting the reliability of the
network. In other words, the detecting network must be confident enough about
its predictions. In this paper, we present a solution to improve network
inference speed and precision at the same time by implementing a fast dynamic
voxelizer that works on fast pillar-based models in the same way a voxelizer
works on slow voxel-based models. In addition, we propose a lightweight
detection sub-head model for classifying predicted objects and filter out false
detected objects that significantly improves model precision in a negligible
time and computing cost. The developed code is publicly available at:
this https URL.

该论文提出了一种解决方案，通过实现快速动态体素化器，以便在快速基于柱体模型中像体素化器在缓慢的基于体素模型中工作一样工作，实现网络推理速度和精度的提高。此外，作者还提出了轻型检测子头模型来分类预测的物体，并过滤出虚假的检测物体，从而显著提高模型的精度，在可忽略的时间和计算成本内。