  Developing high-performance, real-time architectures for LiDAR-based 3D
object detectors is essential for the successful commercialization of
autonomous vehicles. Pillar-based methods stand out as a practical choice for
onboard deployment due to their computational efficiency. However, despite
their efficiency, these methods can sometimes underperform compared to
alternative point encoding techniques such as Voxel-encoding or PointNet++. We
argue that current pillar-based methods have not sufficiently captured the
fine-grained distributions of LiDAR points within each pillar structure.
Consequently, there exists considerable room for improvement in pillar feature
encoding. In this paper, we introduce a novel pillar encoding architecture
referred to as Fine-Grained Pillar Feature Encoding (FG-PFE). FG-PFE utilizes
Spatio-Temporal Virtual (STV) grids to capture the distribution of point clouds
within each pillar across vertical, temporal, and horizontal dimensions.
Through STV grids, points within each pillar are individually encoded using
Vertical PFE (V-PFE), Temporal PFE (T-PFE), and Horizontal PFE (H-PFE). These
encoded features are then aggregated through an Attentive Pillar Aggregation
method. Our experiments conducted on the nuScenes dataset demonstrate that
FG-PFE achieves significant performance improvements over baseline models such
as PointPillar, CenterPoint-Pillar, and PillarNet, with only a minor increase
in computational overhead.
