Text-Driven 3D Indoor Scene Synthesis in Non-Manhattan Environments
Large Language Models (LLMs) have demonstrated remarkable capabilities in 3D indoor synthesis for Manhattan environments. However, existing methods often fail to capture plausible object layout patterns in non-Manhattan settings, primarily because they struggle to model non-orthogonal spatial relationships, leading to high geometric violations and low physical fidelity. To address this challenge, we propose SPG-Layout, a novel text-driven framework designed to generate physically plausible indoor scenes within complex non-Manhattan environments. Specifically, we first utilize statistical prior
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- LinkedLinked via arxiv author · 85%Xianhui Meng →
“Text-Driven 3D Indoor Scene Synthesis in Non-Manhattan Environments”
- LinkedLinked via arxiv author · 85%Zirui Song →
“Text-Driven 3D Indoor Scene Synthesis in Non-Manhattan Environments”
- LinkedLinked via arxiv author · 85%Yuchen Zhang →
“Text-Driven 3D Indoor Scene Synthesis in Non-Manhattan Environments”
- LinkedLinked via arxiv author · 85%Li Zhang →
“Text-Driven 3D Indoor Scene Synthesis in Non-Manhattan Environments”
- LinkedLinked via arxiv author · 85%Yongxuan Lv →
“Text-Driven 3D Indoor Scene Synthesis in Non-Manhattan Environments”
- LinkedLinked via arxiv author · 85%Xiuying Chen →
“Text-Driven 3D Indoor Scene Synthesis in Non-Manhattan Environments”
- LinkedLinked via arxiv author · 85%Kun Wang →
“Text-Driven 3D Indoor Scene Synthesis in Non-Manhattan Environments”
- LinkedLinked via arxiv author · 85%Yan Luo →
“Text-Driven 3D Indoor Scene Synthesis in Non-Manhattan Environments”