HLG：基于分层布局生成的综合3D房间构建
HLG: Comprehensive 3D Room Construction via Hierarchical Layout Generation
AI总结：
针对现有3D室内场景生成方法难以捕捉细粒度物体放置的问题，提出分层布局生成方法HLG，通过粗细分层、横竖解耦及可训练优化网络提升场景真实感与合理性，性能优于现有方法。
摘要：
逼真的3D室内场景生成对虚拟现实、室内设计、具身智能和场景理解至关重要。现有方法虽在粗粒度家具摆放方面取得进展，但难以捕捉细粒度的物体放置，限制了生成环境的真实感与实用性。这一缺陷阻碍了沉浸式虚拟体验的构建，也限制了具身AI应用对场景的精细化理解。为解决这些问题，我们提出了分层布局生成（Hierarchical Layout Generation，HLG），这是一种用于细粒度3D场景生成的新方法。HLG是首个采用从粗到细分层策略的方法，从大规模家具摆放逐步细化到精细的物体排布。具体而言，我们的细粒度布局对齐模块通过垂直与水平解耦构建分层布局，有效将复杂的3D室内场景分解为多个粒度层级。此外，我们的可训练布局优化网络解决了位置错误、朝向偏差、物体相交等摆放问题，确保生成的场景结构连贯且物理合理。我们通过大量实验验证了方法的有效性，结果表明其在生成逼真室内场景方面的性能优于现有方法。本研究推动了场景生成领域的发展，为需要精细化3D环境的应用开辟了新可能。我们将在论文发表后公开代码，以推动后续研究。
英文摘要：
Realistic 3D indoor scene generation is crucial for virtual reality, interior design, embodied intelligence, and scene understanding. While existing methods have made progress in coarse-scale furniture arrangement, they struggle to capture fine-grained object placements, limiting the realism and utility of generated environments. This gap hinders immersive virtual experiences and detailed scene comprehension for embodied AI applications. To address these issues, we propose Hierarchical Layout Generation (HLG), a novel method for fine-grained 3D scene generation. HLG is the first to adopt a coarse-to-fine hierarchical approach, refining scene layouts from large-scale furniture placement to intricate object arrangements. Specifically, our fine-grained layout alignment module constructs a hierarchical layout through vertical and horizontal decoupling, effectively decomposing complex 3D indoor scenes into multiple levels of granularity. Additionally, our trainable layout optimization network addresses placement issues, such as incorrect positioning, orientation errors, and object intersections, ensuring structurally coherent and physically plausible scene generation. We demonstrate the effectiveness of our approach through extensive experiments, showing superior performance in generating realistic indoor scenes compared to existing methods. This work advances the field of scene generation and opens new possibilities for applications requiring detailed 3D environments. We will release our code upon publication to encourage future research.
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。
