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The irregular domain and lack of ordering make it challenging to design deep neural networks for point cloud processing. This paper presents a novel framework named Point Cloud Transformer (PCT) for point cloud learning. PCT is based on Transformer,which achieves huge success in natural language processingand displays great potential in image processing. It is inherently permutation invariant for processing a sequence of points, making it well-suited for point cloud learning. To better capture local context within the point cloud, we enhance input embedding with the support of farthest point sampling and nearest neighbor search. Extensive experiments demonstrate that the PCT achieves the state-of-the-art performance on shape classification, part segmentation, semantic segmentation, and normal estimation tasks.


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PCT: Point cloud transformer

Show Author's information Meng-Hao Guo1Jun-Xiong Cai1Zheng-Ning Liu1Tai-Jiang Mu1Ralph R. Martin2Shi-Min Hu1( )
BNRist, Department of Computer Science andTechnology, Tsinghua University, Beiing 100084, China
Cardiff University, Cardiff CF243AA, UK

Abstract

The irregular domain and lack of ordering make it challenging to design deep neural networks for point cloud processing. This paper presents a novel framework named Point Cloud Transformer (PCT) for point cloud learning. PCT is based on Transformer,which achieves huge success in natural language processingand displays great potential in image processing. It is inherently permutation invariant for processing a sequence of points, making it well-suited for point cloud learning. To better capture local context within the point cloud, we enhance input embedding with the support of farthest point sampling and nearest neighbor search. Extensive experiments demonstrate that the PCT achieves the state-of-the-art performance on shape classification, part segmentation, semantic segmentation, and normal estimation tasks.

Keywords:

3D computer vision, deep learning, point cloud processing, Transformer
Received: 04 March 2021 Accepted: 26 March 2021 Published: 10 April 2021 Issue date: June 2021
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Publication history

Received: 04 March 2021
Accepted: 26 March 2021
Published: 10 April 2021
Issue date: June 2021

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© The Author(s) 2021

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Project Number 61521002) and the Joint NSFC-DFG Research Program (Project Number 61761136018).

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