非曼哈顿环境下的文本驱动3D室内场景合成
Text-Driven 3D Indoor Scene Synthesis in Non-Manhattan Environments
AI总结：
针对非曼哈顿环境中现有方法难以建模非正交空间关系导致几何违规和物理保真度低的问题，提出SPG-Layout框架，利用物体分布统计先验和层次化布局策略，实现语义真实感与物理合理性的平衡优化。
摘要：
大型语言模型（LLM）在曼哈顿环境的3D室内合成中展现出显著能力。然而，现有方法通常无法捕捉非曼哈顿设置中合理的物体布局模式，主要原因是它们难以建模非正交空间关系，导致高几何违规和低物理保真度。为解决这一挑战，我们提出SPG-Layout，一种新颖的文本驱动框架，旨在在复杂的非曼哈顿环境中生成物理上合理的室内场景。具体来说，我们首先利用物体分布的统计先验来指导训练过程，增强环境理解和保真度。此外，模仿人类设计工作流程，我们采用层次化布局策略，优先放置大型物体，从而大幅减少布局违规。通过协同这些组件，SPG-Layout实现了语义真实感和物理合理性的平衡优化。为评估在这些复杂环境中的性能，我们构建了一个包含500个多样化非曼哈顿环境的新基准。大量实验表明，SPG-Layout在曼哈顿和非曼哈顿环境中均持续且显著优于现有方法。代码将公开发布。
英文摘要：
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 priors of object distributions to guide the training process, enhancing environmental understanding and fidelity. Furthermore, mirroring human design workflows, we adopt a hierarchical layout strategy that prioritizes the placement of large objects, thereby substantially minimizing layout violations. By synergizing these components, SPG-Layout achieves a balanced optimization of semantic realism and physical plausibility. To evaluate performance in these complex settings, we constructed a new benchmark comprising 500 diverse non-Manhattan environments. Extensive experiments demonstrate that SPG-Layout consistently and significantly outperforms existing methods across both Manhattan and non-Manhattan environments. The code will be publicly released.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
