In LiDAR-based 3D object detection for autonomous driving, the ratio of the object size to input scene size is significantly smaller compared to 2D detection cases. Overlooking this difference, many 3D detectors directly follow the common practice of 2D detectors, which downsample the feature maps even after quantizing the point clouds. In this paper, we start by rethinking how such multi-stride stereotype affects the LiDAR-based 3D object detectors. Our experiments point out that the downsampling operations bring few advantages, and lead to inevitable information loss. To remedy this issue, we propose Single-stride Sparse Transformer (SST) to maintain the original resolution from the beginning to the end of the network. Armed with transformers, our method addresses the problem of insufficient receptive field in single-stride architectures. It also cooperates well with the sparsity of point clouds and naturally avoids expensive computation. Eventually, our SST achieves state-of-the-art results on the large scale Waymo Open Dataset. It is worth mentioning that our method can achieve exciting performance (83.8 LEVEL 1 AP on validation split) on small object (pedestrian) detection due to the characteristic of single stride. Codes will be released at https://github.com/TuSimple/SST

本文研究在自动驾驶中基于LiDAR的三维物体检测问题，发现传统的下采样操作可能对信息造成不可避免的损失，提出了一种称作Single-stride Sparse Transformer (SST)的新方法，该方法利用transformers避免了单步架构中接受域不足的问题，有效处理了点云的稀疏性并显著提升了检测性能，验证集上Level-1 AP值达到了83.8，小物体（行人）检测表现尤为突出。

利用稀疏变换器的单步3D物体检测器