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Fast Point Transformer
Cited 44 time in
Web of Science
Cited 57 time in Scopus
- Authors
- Issue Date
- 2022
- Publisher
- IEEE
- Citation
- Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol.2022-June, pp.16928-16937
- Abstract
- The recent success of neural networks enables a better interpretation of 3D point clouds, but processing a large-scale 3D scene remains a challenging problem. Most current approaches divide a large-scale scene into small regions and combine the local predictions together. However, this scheme inevitably involves additional stages for pre- and post-processing and may also degrade the final output due to predictions in a local perspective. This paper introduces Fast Point Transformer that consists of a new lightweight self-attention layer. Our approach encodes continuous 3D coordinates, and the voxel hashing-based architecture boosts computational efficiency. The proposed method is demonstrated with 3D semantic segmentation and 3D detection. The accuracy of our approach is competitive to the best voxel-based method, and our network achieves 129 times faster inference time than the state-of-the-art, Point Transformer, with a reasonable accuracy trade-off in 3D semantic segmentation on S3DIS dataset.
- ISSN
- 1063-6919
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