R$^3$L: A Framework for Reliable 3D Layout Generation from Relative Spatial Relations

A new framework called R$^3$L has been introduced by researchers to enhance the reliability and consistency of relative spatial reasoning in 3D layout generation. This study tackles the issue where Multimodal Large Language Models (MLLMs) frequently yield unreliable spatial relationships, which are usually corrected through post-hoc heuristics. The primary observation is that multi-hop reasoning entails repeated transformations of reference frames, causing semantic and metric drift. To address this challenge, R$^3$L incorporates invariant spatial decomposition to separate linked relation chains, employs consistent spatial imagination via an imagine-and-revise loop, and utilizes supportive spatial optimization to facilitate pose optimization. The findings are available on arXiv under ID 2605.06758.