# Modeling Point Clouds with Self-Attention and Gumbel Subset Sampling

## TL;DR

This paper introduces Point Attention Transformers with Gumbel Subset Sampling for efficient, permutation-equivariant processing of point clouds, demonstrating improved performance and a novel application to event camera streams.

## Contribution

It proposes a novel end-to-end learnable sampling method and a transformer architecture tailored for point cloud data, enhancing efficiency and task-agnostic capabilities.

## Key findings

- Effective on classification and segmentation benchmarks.
- Achieves state-of-the-art on DVS128 Gesture Dataset.
- Reduces computational cost with hierarchical subset selection.

## Abstract

Geometric deep learning is increasingly important thanks to the popularity of 3D sensors. Inspired by the recent advances in NLP domain, the self-attention transformer is introduced to consume the point clouds. We develop Point Attention Transformers (PATs), using a parameter-efficient Group Shuffle Attention (GSA) to replace the costly Multi-Head Attention. We demonstrate its ability to process size-varying inputs, and prove its permutation equivariance. Besides, prior work uses heuristics dependence on the input data (e.g., Furthest Point Sampling) to hierarchically select subsets of input points. Thereby, we for the first time propose an end-to-end learnable and task-agnostic sampling operation, named Gumbel Subset Sampling (GSS), to select a representative subset of input points. Equipped with Gumbel-Softmax, it produces a "soft" continuous subset in training phase, and a "hard" discrete subset in test phase. By selecting representative subsets in a hierarchical fashion, the networks learn a stronger representation of the input sets with lower computation cost. Experiments on classification and segmentation benchmarks show the effectiveness and efficiency of our methods. Furthermore, we propose a novel application, to process event camera stream as point clouds, and achieve a state-of-the-art performance on DVS128 Gesture Dataset.