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About this paper
Pei Sun, Mingxing Tan, Weiyue Wang, Chenxi Liu, Fei Xia, Zhaoqi Leng, Dragomir Anguelov
SWFormer introduces a pure Transformer-based architecture for 3D object detection in sparse point clouds, efficiently processing variable-length windows and diffusing features to achieve state-of-the-art performance on the Waymo Open Dataset with 73.36 mAPH/L2. Developed by Waymo LLC, this model effectively handles the challenges of sparsity and scale inherent in LiDAR data.
Key insights
- A purely Transformer-based architecture can achieve state-of-the-art performance for 3D object detection in sparse point clouds without explicit convolutions.
- Efficient handling of variable-length sparse windows through bucketing and limiting window shifts is critical for adapting Transformers to 3D point cloud data.
- The voxel diffusion module significantly improves detection accuracy by mitigating feature sparsity around object centers, a common challenge in sparse 3D data.
Summary by alphaXiv.
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