PCT: Point cloud transformer | RDL Research Database

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 processing and 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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Publication Info

Year: 2021
Type: article
Volume: 7
Issue: 2
Pages: 187-199
Citations: 1529
Access: Closed

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APA Style

                            
                                
                                    Meng-Hao Guo, 
                                
                                    Jun-xiong Cai, 
                                
                                    Zheng-Ning Liu
                                
                                et al.
                            
                            (2021). 
                            PCT: Point cloud transformer. 
                            Computational Visual Media
                            , 7
                            (2)
                            , 187-199.
                            https://doi.org/10.1007/s41095-021-0229-5
                        

Identifiers

DOI: 10.1007/s41095-021-0229-5
arXiv: 2012.09688

Data Quality

Data completeness: 79%