SWFormer:用于点云中3D物体检测的稀疏窗口转换器

Pei Sun, Mingxing Tan, Weiyue Wang, Chenxi Liu, Fei Xia, Zhaoqi Leng, Drago Anguelov
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引用次数: 39

摘要

点云中的三维目标检测是现代机器人和自动驾驶系统的核心组成部分。3D目标检测的一个关键挑战来自于3D场景中点占用的固有稀疏性。本文提出了一种可扩展且精确的三维目标检测模型——稀疏窗口变压器(SWFormer),该模型可以充分利用点云的稀疏性。SWFormer基于基于窗口的transformer的思想,将3D点转换为稀疏体素和窗口,然后使用桶式方案有效地处理这些变长稀疏窗口。除了每个空间窗口内的自关注外,我们的SWFormer还通过多尺度特征融合和窗口移动操作捕获跨窗口相关性。为了进一步解决从稀疏特征中准确检测3D物体的独特挑战,我们提出了一种新的体素扩散技术。在Waymo开放数据集上的实验结果表明,我们的SWFormer在车辆和行人的官方测试集上实现了最先进的73.36 L2 mAPH,用于3D物体检测,优于所有以前的单级和两级模型,同时效率更高。
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SWFormer: Sparse Window Transformer for 3D Object Detection in Point Clouds
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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