# Attention Models for Point Clouds in Deep Learning: A Survey

Source: [Attention Models for Point Clouds in Deep Learning: A Survey](<https://www.sota2.com/research/paper/68fa695a1e209131cea21bdc>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:58:51.104Z

Authors: Xu Wang, Yi Jin, Yigang Cen, Tao Wang, Yidong Li

Published: 2021-02-22T05:50:22.000Z

## Abstract

Recently, the advancement of 3D point clouds in deep learning has attracted intensive research in different application domains such as computer vision and robotic tasks. However, creating feature representation of robust, discriminative from unordered and irregular point clouds is challenging. In this paper, our ultimate goal is to provide a comprehensive overview of the point clouds feature representation which uses attention models. More than 75+ key contributions in the recent three years are summarized in this survey, including the 3D objective detection, 3D semantic segmentation, 3D pose estimation, point clouds completion etc. We provide a detailed characterization (1) the role of attention mechanisms, (2) the usability of attention models into different tasks, (3) the development trend of key technology.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2102.10788>)

[PDF](<https://arxiv.org/pdf/2102.10788.pdf>)