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: https://github.com/YoushaaMurhij/RVCDet.
翻译:从LiDAR点云探测三维天体的主要挑战是在不影响网络可靠性的情况下实现实时性能,换句话说,探测网络必须对其预测有足够的信心。在本文中,我们提出了一个解决方案,以提高网络的推断速度和精确度,同时实施快速动态氧化器,在快速柱基模型上采用快速动态氧化器,如一个Voxelizer在慢速的Voxel基模型上工作一样。此外,我们提出一个轻量检测子头模型,用于对预测的物体进行分类,并过滤在可忽略的时间和计算成本大大改进模型精确度的虚假检测对象。开发的代码可在以下网站公开查阅:https://github.com/Youshaa-Murhij/RVCDet。