  The progress of LiDAR-based 3D object detection has significantly enhanced
developments in autonomous driving and robotics. However, due to the
limitations of LiDAR sensors, object shapes suffer from deterioration in
occluded and distant areas, which creates a fundamental challenge to 3D
perception. Existing methods estimate specific 3D shapes and achieve remarkable
performance. However, these methods rely on extensive computation and memory,
causing imbalances between accuracy and real-time performance. To tackle this
challenge, we propose a novel LiDAR-based 3D object detection model named
BSH-Det3D, which applies an effective way to enhance spatial features by
estimating complete shapes from a bird's eye view (BEV). Specifically, we
design the Pillar-based Shape Completion (PSC) module to predict the
probability of occupancy whether a pillar contains object shapes. The PSC
module generates a BEV shape heatmap for each scene. After integrating with
heatmaps, BSH-Det3D can provide additional information in shape deterioration
areas and generate high-quality 3D proposals. We also design an attention-based
densification fusion module (ADF) to adaptively associate the sparse features
with heatmaps and raw points. The ADF module integrates the advantages of
points and shapes knowledge with negligible overheads. Extensive experiments on
the KITTI benchmark achieve state-of-the-art (SOTA) performance in terms of
accuracy and speed, demonstrating the efficiency and flexibility of BSH-Det3D.
The source code is available on https://github.com/mystorm16/BSH-Det3D.
