Over the years, scene understanding has attracted a growing interest in computer vision, providing the semantic and physical scene information necessary for robots to complete some particular tasks autonomously. In 3D scenes, rich spatial geometric and topological information are often ignored by RGB-based approaches for scene understanding. In this study, we develop a bottom-up approach for scene understanding that infers support relations between objects from a point cloud. Our approach utilizes the spatial topology information of the plane pairs in the scene, consisting of three major steps. 1) Detection of pairwise spatial configuration: dividing primitive pairs into local support connection and local inner connection; 2) primitive classification: a combinatorial optimization method applied to classify primitives; and 3) support relations inference and hierarchy graph construction: bottom-up support relations inference and scene hierarchy graph construction containing primitive level and object level. Through experiments, we demonstrate that the algorithm achieves excellent performance in primitive classification and support relations inference. Additionally, we show that the scene hierarchy graph contains rich geometric and topological information of objects, and it possesses great scalability for scene understanding.

翻译：近年来，场景理解在计算机视觉领域引发了日益增长的研究兴趣，为机器人自主完成特定任务提供了必要的语义和物理场景信息。在三维场景中，丰富的空间几何与拓扑信息常被基于RGB的场景理解方法所忽略。本研究提出了一种自下而上的场景理解方法，通过点云推断物体间的支持关系。该方法利用场景中平面对的空间拓扑信息，包含三个主要步骤：1）成对空间构型检测：将基本图元对划分为局部支撑连接和局部内部连接；2）基本图元分类：采用组合优化方法对基本图元进行分类；3）支持关系推断与层级图构建：自下而上地推断支持关系并构建包含基本图元层和物体层的场景层级图。实验表明，该算法在基本图元分类和支持关系推断方面取得了优异性能。此外，我们证明场景层级图蕴含了丰富的物体几何与拓扑信息，并具有出色的场景理解可扩展性。