Generation of High-Level Concepts in 3D Scene Graphs via Autoregressive Diffusion
Indoor 3D Scene Graphs (3DSGs) represent environments as multi-layer hierarchies that connect observed geometric primitives (e.g., planes) to higher-level metric-semantic concepts (e.g., rooms, floors, buildings), enabling incremental spatial reasoning for robotic perception and SLAM. However, classical high-level concept generation approaches rely on hand-crafted rules for specific concept classes, while learning-based methods require separate models for graph structure and spatial node features (e.g., centroids), which limits scalability to novel classes and more complex hierarchies. We propose a unified autoregressive diffusion-based graph generative model that jointly learns structure...
| Authors | Jose Andres Millan-Romera, Samuel Cognolato, Holger Voos et al. |
|---|---|
| Primary category | cs.RO |
| Published | 28 August 2026 |
| Last updated at source | 11 October 2026 |
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