SWFormer: Sparse Window Transformer for 3D Object Detection in Point Clouds

• URL: http://arxiv.org/abs/2210.07372v1
• Date: Thu, 13 Oct 2022 21:37:53 GMT
• Title: SWFormer: Sparse Window Transformer for 3D Object Detection in Point Clouds
• Authors: Pei Sun, Mingxing Tan, Weiyue Wang, Chenxi Liu, Fei Xia, Zhaoqi Leng, and Dragomir Anguelov
• Abstract summary: 3D object detection in point clouds is a core component for modern robotics and autonomous driving systems. Key challenge in 3D object detection comes from the inherent sparse nature of point occupancy within the 3D scene. We propose Sparse Window Transformer (SWFormer), a scalable and accurate model for 3D object detection.
• Score: 44.635939022626744
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: 3D object detection in point clouds is a core component for modern robotics and autonomous driving systems. A key challenge in 3D object detection comes from the inherent sparse nature of point occupancy within the 3D scene. In this paper, we propose Sparse Window Transformer (SWFormer ), a scalable and accurate model for 3D object detection, which can take full advantage of the sparsity of point clouds. Built upon the idea of window-based Transformers, SWFormer converts 3D points into sparse voxels and windows, and then processes these variable-length sparse windows efficiently using a bucketing scheme. In addition to self-attention within each spatial window, our SWFormer also captures cross-window correlation with multi-scale feature fusion and window shifting operations. To further address the unique challenge of detecting 3D objects accurately from sparse features, we propose a new voxel diffusion technique. Experimental results on the Waymo Open Dataset show our SWFormer achieves state-of-the-art 73.36 L2 mAPH on vehicle and pedestrian for 3D object detection on the official test set, outperforming all previous single-stage and two-stage models, while being much more efficient.

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