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Point Transformer
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AI
DOI：10.1109/ACCESS.2021.3116304.png)
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Abstract
En 中文
In this work, we present Point Transformer, a deep neural network that operates directly on unordered and unstructured point sets. We design Point Transformer to extract local and global features and relate both representations by introducing the local-global attention mechanism, which aims to capture spatial point relations and shape information. For that purpose, we propose SortNet, as part of the Point Transformer, which induces input permutation invariance by selecting points based on a learned score. The output of Point Transformer is a sorted and permutation invariant feature list that can directly be incorporated into common computer vision applications. We evaluate our approach on standard classification and part segmentation benchmarks to demonstrate competitive results compared to the prior work.
Keywords:
Shape
Transformers
Three-dimensional displays
Standards
Task analysis
Feature extraction
Computer vision
3D point processing
artificial neural networks
computer vision
feedforward neural networks
transformer
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W
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Cited Papers
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