mlsanityData to 7 Oct 2026

Point Cloud Recognition with Position-to-Structure Attention Transformers

arXiv 2210.02030 ↗First posted 5 Oct 2022cs.CVcited by 1

Not on the map. Why?

Abstract (arXiv)

In this paper, we present Position-to-Structure Attention Transformers (PS-Former), a Transformer-based algorithm for 3D point cloud recognition. PS-Former deals with the challenge in 3D point cloud representation where points are not positioned in a fixed grid structure and have limited feature description (only 3D coordinates (x, y, z) for scattered points). Existing Transformer-based architectures in this domain often require a pre-specified feature engineering step to extract point features. Here, we introduce two new aspects in PS-Former: 1) a learnable condensation layer that performs point downsampling and feature extraction; and 2) a Position-to-Structure Attention mechanism that recursively enriches the structural information with the position attention branch. Compared with the competing methods, while being generic with less heuristics feature designs, PS-Former demonstrates competitive experimental results on three 3D point cloud tasks including classification, part segmentation, and scene segmentation.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 40 of its 51 references that are in the map. First posted on arXiv 5 Oct 2022.

Builds on

40 of 51 references.

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28 more
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Citing papers per year: 2024: 1.

1 paper.

  1. 2024Rethinking Attention Module Design for Point Cloud Analysiscited by 2