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。

通过时空虚拟网格进行细粒度柱特征编码的3D物体检测