Computational Visual Media· 2021Q1
PCT: Point cloud transformer
- 1,800citations
- Q1SCImago
- 2021year
Short summary
A novel Point Cloud Transformer (PCT) framework, adapted from natural language processing, achieves state-of-the-art performance on point cloud learning tasks including shape classification and segmentation.
AI-generated from the title and abstract; the full text is not read.
Key points
- Introduces the Point Cloud Transformer (PCT) framework for point cloud learning.
- PCT utilizes the permutation-invariant nature of Transformers, originally successful in NLP.
- Input embeddings are enhanced with farthest point sampling and nearest neighbor search for local context.
- Achieves state-of-the-art performance on shape classification, part segmentation, semantic segmentation, and normal estimation.
AI-generated from the title and abstract; the full text is not read.
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.
The authors' abstract, as published at the source. Computational Visual Media, 2021 · DOI ↗
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Field: Computational Mechanics
Computational MechanicsEngineering