Giorgia Modi
RGB-only Active 3D Scene Graph Generation for Indoor Mobile Robots.
Rel. Giuseppe Bruno Averta, Daniele De Martini, Davide Buoso. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering), 2026
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Abstract
Three-dimensional scene graphs provide robots with structured, queryable representations of their surroundings by encoding objects, their attributes, and spatial relationships in a unified graph. Existing methods for constructing such graphs typically require depth sensors and predefined exploration trajectories, limiting their applicability to platforms equipped with specialised hardware and carefully planned data-collection runs. This thesis presents a complete framework for active, incremental construction of 3D scene graphs from RGB images only. The framework is based on two main contributions. First, an RGB-only 3D scene graph generation pipeline that couples MapAnything, a state-of-the-art feed-forward 3D reconstruction model, with ConceptGraphs, an open-vocabulary semantic mapping system, and replaces the original LLM-based edge inference with a deterministic geometric edge generator.
Quantitative evaluation on the Replica dataset shows that the proposed pipeline achieves scene graph quality comparable to the ground-truth-depth baseline, confirming that learned depth and pose predictions are sufficient for accurate scene graph construction
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