Small object detection for 3D point cloud is a challenging problem because of
two limitations: (1) Perceiving small objects is much more diffcult than normal
objects due to the lack of valid points. (2) Small objects are easily blocked
which breaks the shape of their meshes in 3D point cloud. In this paper, we
propose a pillar set abstraction (PSA) and foreground point compensation (FPC)
and design a point-based detection network, PSA-Det3D, to improve the detection
performance for small object. The PSA embeds a pillar query operation on the
basis of set abstraction (SA) to expand its receptive field of the network,
which can aggregate point-wise features effectively. To locate more occluded
objects, we persent a proposal generation layer consisting of a foreground
point segmentation and a FPC module. Both the foreground points and the
estimated centers are finally fused together to generate the detection result.
The experiments on the KITTI 3D detection benchmark show that our proposed
PSA-Det3D outperforms other algorithms with high accuracy for small object
detection.

本文提出了基于柱状集合抽象（PSA）和前景点补偿（FPC）的 PSA-Det3D 点检测网络，以解决 3D 点云中小物体检测的困难问题，并在 KITTI 3D 检测基准上进行了实验验证。