Khodadad Mohammad, Shiraee Kasmaee Ali, Mahyar Hamidreza, Rezanejad Morteza
Department of Computational Science and Engineering, McMaster University, Hamilton, ON, Canada.
Rosalind and Morris Goodman Cancer Institute, McGill University, Montreal, QC, Canada.
Front Artif Intell. 2024 Sep 20;7:1439340. doi: 10.3389/frai.2024.1439340. eCollection 2024.
With the rapid advancement of 3D acquisition technologies, 3D sensors such as LiDARs, 3D scanners, and RGB-D cameras have become increasingly accessible and cost-effective. These sensors generate 3D point cloud data that require efficient algorithms for tasks such as 3D model classification and segmentation. While deep learning techniques have proven effective in these areas, existing models often rely on complex architectures, leading to high computational costs that are impractical for real-time applications like augmented reality and robotics. In this work, we propose the Multi-level Graph Convolutional Neural Network (MLGCN), an ultra-efficient model for 3D point cloud analysis. The MLGCN model utilizes shallow Graph Neural Network (GNN) blocks to extract features at various spatial locality levels, leveraging precomputed KNN graphs shared across GCN blocks. This approach significantly reduces computational overhead and memory usage, making the model well-suited for deployment on low-memory and low-CPU devices. Despite its efficiency, MLGCN achieves competitive performance in object classification and part segmentation tasks, demonstrating results comparable to state-of-the-art models while requiring up to a thousand times fewer floating-point operations and significantly less storage. The contributions of this paper include the introduction of a lightweight, multi-branch graph-based network for 3D shape analysis, the demonstration of the model's efficiency in both computation and storage, and a thorough theoretical and experimental evaluation of the model's performance. We also conduct ablation studies to assess the impact of different branches within the model, providing valuable insights into the role of specific components.
随着3D采集技术的迅速发展，诸如激光雷达、3D扫描仪和RGB-D相机等3D传感器已变得越来越容易获得且性价比更高。这些传感器生成3D点云数据，对于诸如3D模型分类和分割等任务需要高效的算法。虽然深度学习技术在这些领域已被证明是有效的，但现有模型通常依赖复杂的架构，导致高计算成本，这对于诸如增强现实和机器人技术等实时应用来说是不切实际的。在这项工作中，我们提出了多级图卷积神经网络（MLGCN），这是一种用于3D点云分析的超高效模型。MLGCN模型利用浅层图神经网络（GNN）块在不同的空间局部性级别提取特征，利用跨GCN块共享的预先计算的KNN图。这种方法显著减少了计算开销和内存使用，使该模型非常适合在低内存和低CPU设备上部署。尽管MLGCN效率很高，但在对象分类和部件分割任务中仍能实现有竞争力的性能，其结果与最先进的模型相当，同时所需的浮点运算次数减少多达一千倍，存储量也显著减少。本文的贡献包括引入了一种用于3D形状分析的轻量级、基于多分支图的网络，展示了该模型在计算和存储方面的效率，以及对该模型性能进行了全面的理论和实验评估。我们还进行了消融研究，以评估模型内不同分支的影响，为特定组件的作用提供了有价值的见解。
