HierRelTriple: Guiding Indoor Layout Generation With Hierarchical Relationship Triplet Losses.

Area of Science:

• Computer Vision
• Artificial Intelligence
• Computational Geometry

Background:

• Current indoor scene generation methods struggle with complex multi-object spatial relationships.
• Existing approaches often rely on simplified pairwise interactions and handcrafted rules.
• This leads to unrealistic arrangements, including overcrowding and physical implausibility.

Purpose of the Study:

• To introduce HierRelTriple, a novel hierarchical framework for learning intricate spatial relationships in indoor scenes.
• To improve the physical realism and reduce collisions in generated indoor layouts.
• To develop a method that automatically extracts multiple levels of spatial relationships.

Main Methods:

• HierRelTriple employs a hierarchical approach, partitioning functional regions to extract object-to-region (O2R), object-to-object (O2O), and corner-to-corner (C2C) relationships.
• Geometric triplets represent these relationships, with Delaunay Triangulation establishing spatial priors.
• IoU-based losses between denoised and ground-truth triplets are integrated into a diffusion denoising process.

Main Results:

• HierRelTriple demonstrated significant improvements in spatial-relation metrics, exceeding 15%.
• The method substantially reduced collisions and boundary violations in generated scenes.
• Experiments covered unconditional layout synthesis, floorplan-conditioned generation, and scene rearrangement.

Conclusions:

• HierRelTriple effectively models complex, multi-object spatial relationships for realistic indoor scene generation.
• The hierarchical triplet-based approach enhances physical plausibility and reduces common generation errors.
• This framework offers a robust solution for advanced indoor layout synthesis and manipulation.