Modern extended reality XR systems provide rich analysis of image data and fusion of sensor input and demand AR/VR applications that can reason about 3D scenes in a semantic manner. We present a spatial reasoning framework that bridges geometric facts with symbolic predicates and relations to handle key tasks such as determining how 3D objects are arranged among each other ('on', 'behind', 'near', etc.). Its foundation relies on oriented 3D bounding box representations, enhanced by a comprehensive set of spatial predicates, ranging from topology and connectivity to directionality and orientation, expressed in a formalism related to natural language. The derived predicates form a spatial knowledge graph and, in combination with a pipeline-based inference model, enable spatial queries and dynamic rule evaluation. Implementations for client- and server-side processing demonstrate the framework's capability to efficiently translate geometric data into actionable knowledge, ensuring scalable and technology-independent spatial reasoning in complex 3D environments. The Spatial Reasoner framework is fostering the creation of spatial ontologies, and seamlessly integrates with and therefore enriches machine learning, natural language processing, and rule systems in XR applications.

本研究针对现代XR系统在3D场景语义推理中的不足，提出了一种空间推理框架，将几何事实与符号谓词和关系结合，以处理3D物体之间的定位关系。研究结果表明，该框架能有效将几何数据转换为可操作的知识，推动空间本体的创建，并增强机器学习、自然语言处理和规则系统在XR应用中的整合能力。

空间推理器：XR应用的3D推理管道