Generation of High-Level Concepts in 3D Scene Graphs via Autoregressive Diffusion
This work proposes a unified autoregressive diffusion-based graph generative model that jointly learns structure and features, constructing complete 3DSGs bottom-up from observed vertical planes across arbitrary hierarchy depths, and proposes an adaptation of the Fused Gromov--Wasserstein distance for principled graph-level evaluation of generated 3DSGs against ground truth.
J. A. Millan-Romera, Samuel Cognolato, Holger Voos et al.
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