In order to deal with the sparse and unstructured raw point clouds, LiDAR
based 3D object detection research mostly focuses on designing dedicated local
point aggregators for fine-grained geometrical modeling. In this paper, we
revisit the local point aggregators from the perspective of allocating
computational resources. We find that the simplest pillar based models perform
surprisingly well considering both accuracy and latency. Additionally, we show
that minimal adaptions from the success of 2D object detection, such as
enlarging receptive field, significantly boost the performance. Extensive
experiments reveal that our pillar based networks with modernized designs in
terms of architecture and training render the state-of-the-art performance on
the two popular benchmarks: Waymo Open Dataset and nuScenes. Our results
challenge the common intuition that the detailed geometry modeling is essential
to achieve high performance for 3D object detection.

本文提出了基于 pillar 的算法模型在 LiDAR 3D 目标检测中的效果以及在架构和训练等方面的现代化设计，通过扩大感受野等方法取得了显著提升，在 Waymo 开放数据集和 nuScenes 数据集上实现了最优性能。