[Paper Review] Attention Models for Point Clouds in Deep Learning: A Survey

Xu Wang, Yi Jin+3 more|arXiv (Cornell University)|Feb 22, 2021

3D Shape Modeling and Analysis38 references4 citations

TL;DR

This survey provides a comprehensive overview of attention mechanisms in deep learning for 3D point cloud processing, covering over 75 recent works across computer vision, robotics, and miscellaneous applications. It systematically categorizes attention models by task, analyzes their architectural components, and identifies key trends and challenges in robust, discriminative feature learning from unordered, irregular point clouds.

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.

Motivation & Objective

• To provide a comprehensive, up-to-date survey focused exclusively on attention mechanisms in deep learning for 3D point cloud representation.
• To analyze the role of attention mechanisms in improving feature representation for unordered and irregular point clouds.
• To categorize and compare attention-based methods across key tasks such as 3D detection, segmentation, pose estimation, and completion.
• To identify emerging trends and open challenges in designing efficient, multimodal, and energy-aware attention models for point clouds.
• To serve as a reference for researchers by summarizing more than 75 key contributions from the past three years in the field.

Proposed method

• Categorizes attention mechanisms into self-attention, co-attention, soft-attention, hard-attention, and local-attention based on their operational principles.
• Reviews architectural designs of attention modules, including multi-head self-attention, channel-wise and point-wise attention, and temperature-adjusted attention modules.
• Analyzes the integration of attention mechanisms in various deep learning frameworks such as PointNet, PointNet++, and graph neural networks.
• Examines the use of attention in multimodal fusion, particularly for point-cloud and image feature alignment using soft-attention masks.
• Evaluates attention-based methods in point cloud upsampling, completion, and normal estimation using hierarchical and recursive attention modules.
• Discusses architectural trade-offs, including computational cost, memory footprint, and energy efficiency, especially for deployment on edge devices.

Experimental results

Research questions

• RQ1 How do different types of attention mechanisms (e.g., self-attention, co-attention, soft/hard attention) enhance feature representation in 3D point clouds?
• RQ2 What are the key architectural components and design patterns used in state-of-the-art attention models for point cloud tasks?
• RQ3 How do attention mechanisms improve performance in downstream tasks such as 3D detection, semantic segmentation, and normal estimation?
• RQ4 What are the major challenges in scaling attention mechanisms to large-scale point clouds while maintaining efficiency?
• RQ5 How can attention-based models be effectively adapted for multimodal fusion and energy-efficient deployment in real-world applications?

Key findings

• Self-attention mechanisms significantly improve long-range feature dependency modeling in point clouds, as demonstrated in methods like the multi-head self-attention module in TMHSA.
• Dual attention (point-wise and channel-wise) mechanisms enhance feature representation by dynamically weighting both spatial points and feature channels, improving recognition accuracy.
• Attention-based fusion techniques, such as soft-attention masks, enable effective multimodal feature integration between point clouds and images, improving performance in 3D recognition tasks.
• Hierarchical self-attention in models like L2G-AE enables multi-level feature aggregation across point, scale, and region levels, improving unsupervised upsampling quality.
• Channel attention mechanisms in PUI-Net enhance discriminative feature extraction, leading to improved performance in point cloud completion and upsampling.
• Despite performance gains, many attention-based models face scalability and computational cost issues, especially with large point sets, highlighting the need for lightweight, energy-efficient designs.