Spatial Reasoner: A 3D Inference Pipeline for XR Applications

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. Index T erms --spatial computing, extended reality, knowledge representation, spatial reasoning I. I NTRODUCTION Spatial computing, which includes various immersive technologies such as extended reality (XR), augmented reality (AR), virtual reality (VR) and mixed reality (MR), merges the digital and physical worlds.