  LiDAR-based 3D object detection pushes forward an immense influence on
autonomous vehicles. Due to the limitation of the intrinsic properties of
LiDAR, fewer points are collected at the objects farther away from the sensor.
This imbalanced density of point clouds degrades the detection accuracy but is
generally neglected by previous works. To address the challenge, we propose a
novel two-stage 3D object detection framework, named SIENet. Specifically, we
design the Spatial Information Enhancement (SIE) module to predict the spatial
shapes of the foreground points within proposals, and extract the structure
information to learn the representative features for further box refinement.
The predicted spatial shapes are complete and dense point sets, thus the
extracted structure information contains more semantic representation. Besides,
we design the Hybrid-Paradigm Region Proposal Network (HP-RPN) which includes
multiple branches to learn discriminate features and generate accurate
proposals for the SIE module. Extensive experiments on the KITTI 3D object
detection benchmark show that our elaborately designed SIENet outperforms the
state-of-the-art methods by a large margin.
