Point clouds are gaining prominence as a method for representing 3D shapes, but their irregular structure poses a challenge for deep learning methods. In this paper we propose CloudWalker, a novel method for learning 3D shapes using random walks. Previous works attempt to adapt Convolutional Neural Networks (CNNs) or impose a grid or mesh structure to 3D point clouds. This work presents a different approach for representing and learning the shape from a given point set. The key idea is to impose structure on the point set by multiple random walks through the cloud for exploring different regions of the 3D object. Then we learn a per-point and per-walk representation and aggregate multiple walk predictions at inference. Our approach achieves state-of-the-art results for two 3D shape analysis tasks: classification and retrieval.

翻译：点云作为三维形状表示方法正日益受到关注，但其非结构化特性对深度学习方法提出了挑战。本文提出CloudWalker——一种利用随机游走学习三维形状的新方法。以往的研究尝试改编卷积神经网络（CNN），或对三维点云施加网格结构。本工作提出另一种从给定点集表示和学习形状的方法，其核心思想是通过在点云中执行多次随机游走来探索三维物体的不同区域，从而对点集施加结构。随后，我们学习逐点和逐游走的表示，并在推理阶段聚合多个游走的预测结果。该方法在两项三维形状分析任务（分类与检索）中均取得了最先进的性能。