  Advances in LiDAR sensors provide rich 3D data that supports 3D scene
understanding. However, due to occlusion and signal miss, LiDAR point clouds
are in practice 2.5D as they cover only partial underlying shapes, which poses
a fundamental challenge to 3D perception. To tackle the challenge, we present a
novel LiDAR-based 3D object detection model, dubbed Behind the Curtain Detector
(BtcDet), which learns the object shape priors and estimates the complete
object shapes that are partially occluded (curtained) in point clouds. BtcDet
first identifies the regions that are affected by occlusion and signal miss. In
these regions, our model predicts the probability of occupancy that indicates
if a region contains object shapes. Integrated with this probability map,
BtcDet can generate high-quality 3D proposals. Finally, the probability of
occupancy is also integrated into a proposal refinement module to generate the
final bounding boxes. Extensive experiments on the KITTI Dataset and the Waymo
Open Dataset demonstrate the effectiveness of BtcDet. Particularly, for the 3D
detection of both cars and cyclists on the KITTI benchmark, BtcDet surpasses
all of the published state-of-the-art methods by remarkable margins. Code is
released
(https://github.com/Xharlie/BtcDet}{https://github.com/Xharlie/BtcDet).
