FastPillars: A Deployment-friendly Pillar-based 3D Detector

• URL: http://arxiv.org/abs/2302.02367v6
• Date: Wed, 13 Dec 2023 08:55:27 GMT
• Title: FastPillars: A Deployment-friendly Pillar-based 3D Detector
• Authors: Sifan Zhou, Zhi Tian, Xiangxiang Chu, Xinyu Zhang, Bo Zhang, Xiaobo Lu, Chengjian Feng, Zequn Jie, Patrick Yin Chiang, Lin Ma
• Abstract summary: Existing BEV-based (i.e., Bird Eye View) detectors favor sparse convolutions (known as SPConv) to speed up training and inference. FastPillars delivers state-of-the-art accuracy on Open dataset with 1.8X speed up and 3.8 mAPH/L2 improvement over CenterPoint (SPConv-based)
• Score: 63.0697065653061
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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, to tackle the challenge of efficient 3D object detection from an industry perspective, we devise a deployment-friendly pillar-based 3D detector, termed FastPillars. First, we introduce a novel lightweight Max-and-Attention Pillar Encoding (MAPE) module specially for enhancing small 3D objects. Second, we propose a simple yet effective principle for designing a backbone in pillar-based 3D detection. 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 state-of-the-art accuracy on Waymo Open Dataset with 1.8X speed up and 3.8 mAPH/L2 improvement over CenterPoint (SPConv-based). Our code is publicly available at: https://github.com/StiphyJay/FastPillars.

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