RelTriple: Learning Plausible Indoor Layouts by Integrating Relationship Triples into the Diffusion Process

2025-03-26

AI Summary

Existing manual specification of spatial relationships in indoor furniture layout is often incomplete and prone to yielding physically or functionally implausible arrangements. To address this, we propose a ternary-relation-driven diffusion generation framework. Our method introduces, for the first time, differentiable losses encoding object–object–region (O2O/O2R) ternary topological relations—automatically extracted via Delaunay triangulation—and integrates them into the diffusion model training pipeline; hierarchical spacing analysis further enforces geometric constraints. Unlike conventional pairwise relation modeling, our approach substantially improves multi-object collaborative placement. Evaluated on unconditional layout generation, floorplan-guided layout, and scene rearrangement, our method achieves state-of-the-art performance across all tasks: spatial relationship metrics improve by ≥12%, while spatial coherence, physical plausibility, and functional usability are all significantly enhanced.

Technology Category

• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

The generation of indoor furniture layouts has significant applications in augmented reality, smart homes, and architectural design. Successful furniture arrangement requires proper physical relationships (e.g., collision avoidance) and spacing relationships between furniture and their functional zones to be respected. However, manually defined relationships are almost always incomplete and can produce unrealistic layouts. This work instead extracts spacing relationships automatically based on a hierarchical analysis and adopts the Delaunay Triangulation to produce important triple relationships. Compared to pairwise relationship modeling, triple relationships account for interactions and space utilization among multiple objects. To this end, we introduce RelTriple, a novel approach that enhances furniture distribution by learning spacing relationships between objects and regions. We formulate triple relationships as object-to-object (O2O) losses and object-to-region (O2R) losses and integrate them directly into the training process of generative diffusion. Our approach consistently improves over existing state-of-the-art methods in visual results evaluation metrics on unconditional layout generation, floorplan-conditioned layout generation, and scene rearrangement, achieving at least 12% on the introduced spatial relationship metric and superior spatial coherence and practical usability.

Problem

• Improving indoor furniture layout generation using relationship triples
• Automating spacing relationships via hierarchical analysis and Delaunay Triangulation
• Enhancing spatial coherence and usability in layout diffusion models

Innovation

• Automatically extracts spacing relationships hierarchically
• Uses Delaunay Triangulation for triple relationships
• Integrates O2O and O2R losses into diffusion training