Real-time and high-performance 3D object detection is of critical importance
for autonomous driving. Recent top-performing 3D object detectors mainly rely
on point-based or 3D voxel-based convolutions, which are both computationally
inefficient for onboard deployment. In contrast, pillar-based methods use
solely 2D convolutions, which consume less computation resources, but they lag
far behind their voxel-based counterparts in detection accuracy. In this paper,
by examining the primary performance gap between pillar- and voxel-based
detectors, we develop a real-time and high-performance pillar-based detector,
dubbed PillarNet.The proposed PillarNet consists of a powerful encoder network
for effective pillar feature learning, a neck network for spatial-semantic
feature fusion and the commonly used detect head. Using only 2D convolutions,
PillarNet is flexible to an optional pillar size and compatible with classical
2D CNN backbones, such as VGGNet and ResNet. Additionally, PillarNet benefits
from our designed orientation-decoupled IoU regression loss along with the
IoU-aware prediction branch. Extensive experimental results on the large-scale
nuScenes Dataset and Waymo Open Dataset demonstrate that the proposed PillarNet
performs well over state-of-the-art 3D detectors in terms of effectiveness and
efficiency. Code is available at https://github.com/agent-sgs/PillarNet.

本文针对自动驾驶中实时、高性能三维物体检测的问题，提出了基于支柱的检测器 PillarNet。与现有的点云和三维卷积方法不同，PillarNet 仅使用 2D 卷积且具有高效灵活等特点，经过大规模数据集测试表明比现有方法有更好的效果和性能。