FlatFormer: Flattened Window Attention for Efficient Point Cloud Transformer

Zhijian Liu*, Xinyu Yang*, Haotian Tang, Shang Yang, Song Han
Massachusetts Institute of Technology
(* indicates equal contribution)


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Transformer, as an alternative to CNN, has been proven effective in many modalities (e.g., texts and images). For 3D point cloud transformers, existing efforts focus primarily on pushing their accuracy to the state-of-the-art level. However, their latency lags behind sparse convolution-based models (3x slower), hindering their usage in resource-constrained, latency-sensitive applications (such as autonomous driving). This inefficiency comes from point clouds' sparse and irregular nature, whereas transformers are designed for dense, regular workloads. This paper presents FlatFormer to close this latency gap by trading spatial proximity for better computational regularity. We first flatten the point cloud with window-based sorting and partition points into groups of equal sizes rather than windows of equal shapes. This effectively avoids expensive structuring and padding overheads. We then apply self-attention within groups to extract local features, alternate sorting axis to gather features from different directions, and shift windows to exchange features across groups. FlatFormer delivers state-of-the-art accuracy on Waymo Open Dataset with 4.6x speedup over (transformer-based) SST and 1.4x speedup over (sparse convolutional) CenterPoint. This is the first point cloud transformer that achieves real-time performance on edge GPUs and is faster than sparse convolutional methods while achieving on-par or even superior accuracy on large-scale benchmarks.




title={FlatFormer: Flattened Window Attention for Efficient Point Cloud Transformer},

author={Liu, Zhijian and Yang, Xinyu and Tang, Haotian and Yang, Shang and Han, Song},

booktitle={IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},




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We would like to thank Tianwei Yin, Lue Fan and Ligeng Mao for providing detailed results of CenterPoint, SST/FSD and VoTr, and Yue Wang and Yukang Chen for their helpful discussions. This work was supported by National Science Foundation, MIT-IBM Watson AI Lab, NVIDIA, Hyundai and Ford. Zhijian Liu was partially supported by the Qualcomm Innovation Fellowship.

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