  Accurate 3D object detection in LiDAR based point clouds suffers from the
challenges of data sparsity and irregularities. Existing methods strive to
organize the points regularly, e.g. voxelize, pass them through a designed
2D/3D neural network, and then define object-level anchors that predict offsets
of 3D bounding boxes using collective evidences from all the points on the
objects of interest. Contrary to the state-of-the-art anchor-based methods,
based on the very nature of data sparsity, we observe that even points on an
individual object part are informative about semantic information of the
object. We thus argue in this paper for an approach opposite to existing
methods using object-level anchors. Inspired by compositional models, which
represent an object as parts and their spatial relations, we propose to
represent an object as composition of its interior non-empty voxels, termed
hotspots, and the spatial relations of hotspots. This gives rise to the
representation of Object as Hotspots (OHS). Based on OHS, we further propose an
anchor-free detection head with a novel ground truth assignment strategy that
deals with inter-object point-sparsity imbalance to prevent the network from
biasing towards objects with more points. Experimental results show that our
proposed method works remarkably well on objects with a small number of points.
Notably, our approach ranked 1st on KITTI 3D Detection Benchmark for cyclist
and pedestrian detection, and achieved state-of-the-art performance on NuScenes
3D Detection Benchmark.
