面向三维室内场景合成的直接数值布局生成：基于空间推理
Direct Numerical Layout Generation for 3D Indoor Scene Synthesis via Spatial Reasoning
AI总结：
DirectLayout利用大语言模型的空间推理，分三阶段直接生成三维室内场景数值布局，通过思维链激活与奖励机制提升泛化，实现语义一致且物理合理的开放词汇布局生成。
摘要：
逼真的三维室内场景合成对于具身智能和数字内容创作至关重要。它可自然地分为两个子任务：物体生成和布局生成。尽管最近的生成模型在物体级别的质量和可控性方面取得了显著进展，但布局生成由于数据集有限仍具挑战性。现有方法要么过拟合这些数据集，要么依赖预定义约束来优化数值布局，从而牺牲了灵活性。因此，它们无法生成既开放词汇又符合细粒度用户指令的场景。我们提出了DirectLayout，一个利用大语言模型（LLMs）的可泛化空间推理直接从文本描述生成数值三维布局的框架。DirectLayout将生成过程分解为三个阶段：生成鸟瞰图（BEV）布局、将其提升至三维空间，以及优化物体放置。为了启用显式空间推理并帮助模型掌握物体放置的基本原则，我们基于3D-Front数据集采用了思维链（CoT）激活。此外，我们设计了基于思维链的生成式布局奖励，以增强泛化能力和空间规划。在推理过程中，DirectLayout通过上下文学习中的迭代资产-布局对齐来解决资产与布局不匹配的问题。大量实验表明，DirectLayout实现了令人印象深刻的语义一致性、泛化能力和物理合理性。
英文摘要：
Realistic 3D indoor scene synthesis is vital for embodied AI and digital content creation. It can be naturally divided into two subtasks: object generation and layout generation. While recent generative models have significantly advanced object-level quality and controllability, layout generation remains challenging due to limited datasets. Existing methods either overfit to these datasets or rely on predefined constraints to optimize numerical layout that sacrifice flexibility. As a result, they fail to generate scenes that are both open-vocabulary and aligned with fine-grained user instructions. We introduce DirectLayout, a framework that directly generates numerical 3D layouts from text descriptions using generalizable spatial reasoning of large language models (LLMs). DirectLayout decomposes the generation into three stages: producing a Bird's-Eye View (BEV) layout, lifting it into 3D space, and refining object placements. To enable explicit spatial reasoning and help the model grasp basic principles of object placement, we employ Chain-of-Thought (CoT) Activation based on the 3D-Front dataset. Additionally, we design CoT-Grounded Generative Layout Reward to enhance generalization and spatial planning. During inference, DirectLayout addresses asset-layout mismatches via Iterative Asset-Layout Alignment through in-context learning. Extensive experiments demonstrate that DirectLayout achieves impressive semantic consistency, generalization and physical plausibility.
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。
