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.

翻译：3D检测器的部署是实际自动驾驶场景中的主要挑战之一。现有基于BEV（即鸟瞰图）的检测器倾向于使用稀疏卷积（称为SPConv）来加速训练和推理，这给部署带来了硬性障碍，尤其是对于设备端应用。本文从工业角度出发，为应对高效3D物体检测的挑战，设计了一种易于部署的基于支柱的3D检测器，称为FastPillars。首先，我们引入了一种新颖的轻量级最大-注意力支柱编码（MAPE）模块，专门用于增强小型3D物体。其次，我们提出了一个简单而有效的原理，用于设计基于支柱的3D检测骨干网络。基于这些设计，我们构建了FastPillars，在不使用SPConv的情况下实现了高性能和低延迟。在两个大规模数据集上的广泛实验表明，FastPillars在设备端3D检测的性能和速度方面均具有有效性和高效性。具体而言，FastPillars在Waymo Open Dataset上达到了最先进的精度，与基于SPConv的CenterPoint相比，速度提升1.8倍，mAPH/L2提高3.8。我们的代码已公开在：https://github.com/StiphyJay/FastPillars。