Abstract
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.
Abstract (translated)
在过去的几年里，场景理解在计算机视觉领域吸引了越来越多的关注，为机器人完成某些特定任务提供了语义和物理场景信息。在3D场景中，基于RGB的 scene understanding 方法通常会忽略场景中的丰富空间几何和拓扑信息。在这项研究中，我们提出了一种自下而上的场景理解方法，推断出场景中物体之间的支持关系。我们的方法基于场景平面对的空间拓扑信息，包括三个主要步骤。1) 对对间空间配置的检测：将基本对分为局部支持连接和局部内连接；2) 基本分类：将基本分类为组合优化方法；和 3) 支持关系推断和层次图构建：自下而上支持关系推断和场景层次图构建包含基本水平和物体水平。通过实验，我们证明了该算法在基本分类和支持关系推理方面具有优异的性能。此外，我们还证明了场景层次图包含丰富的几何和拓扑信息，具有很好的可扩展性。
URL
https://arxiv.org/abs/2404.13842
