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.

为了成功商业化自动驾驶汽车，开发基于 LiDAR 的高性能实时架构对于至关重要。本文介绍了一种名为 FG-PFE 的新型 pillar 编码架构，它利用 STV 网格来捕捉每个 pillar 内 LiDAR 点云的细粒度分布。通过 STV 网格，每个 pillar 内的点云分别使用垂直、时间和水平维度进行编码，并通过 Attentive Pillar Aggregation 方法进行聚合。实验结果表明，FG-PFE 相较于基线模型如 PointPillar，CenterPoint-Pillar 和 PillarNet，在计算开销仅略微增加的情况下取得了显著的性能改进。