FastPillars: A Deployment-Friendly Pillar-Based 3D Detector
Sifan Zhou, Xinyu Zhang, Xiangxiang Chu, Bo Zhang, Ziyu Zhao, Xiaobo Lu
Abstract
The deployment of 3D detectors strikes one of the major challenges in real-world self-driving scenarios. Existing BEV-based (i.e., Bird Eye View) detectors favor sparse convolutions (known as SPConv) to speed up training and inference, which puts a hard barrier for deployment, especially for on-device applications. In this paper, in order to tackle the challenge of efficient 3D object detection from an industry perspective, we devise a deployment-friendly pillar-based 3D detector, termed FastPillars. Specifically, aiming to compensate the geometric information loss of pillar encoding. First, we design a novel lightweight Max-and-Attention Pillar Encoding (MAPE) module specially for enhancing small objects. Second, we propose a simple yet effective backbone design for pillar-based 3D detection, enhancing pillar representations. We construct FastPillars based on these designs, achieving high performance and low latency without SPConv. Extensive experiments on two large-scale datasets demonstrate the effectiveness and efficiency of FastPillars for on-device 3D detection regarding both performance and speed. Specifically, FastPillars delivers real-time state-of-the-art accuracy on Waymo Open Dataset with 1.8 × speed up and 3.8 mAPH/L2 improvement over CenterPoint (SPConv-based). Code will be opened soon in: https://github.com/StiphyJay/FastPillars.