Abstract
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.
Abstract (translated)
生成现实主义的三维室内场景对于虚拟现实、室内设计、具身智能和场景理解至关重要。尽管现有的方法在粗略规模的家具布局方面已取得进展，但在捕捉细微的对象放置细节上仍存在困难，这限制了生成环境的真实性和实用性。这种差距阻碍了沉浸式虚拟体验的发展以及对复杂AI应用场景中详细场景的理解。为了解决这些问题，我们提出了一种名为层级布局生成（HLG）的新方法，用于精细级的3D场景生成。HLG首次采用了一个从粗到细的分层方式，通过细化大型家具放置来实现复杂的对象安排布局。 具体而言，我们的精细化布局对齐模块通过对垂直和水平方向进行解耦来构建一个层级式布局，有效地将复杂的三维室内场景分解为多个级别的详细程度。此外，我们可训练的布局优化网络解决了摆放问题，例如不正确的定位、方向错误以及物体之间的碰撞，确保生成的场景在结构上是连贯且物理上可信的。 通过广泛的实验，我们展示了这种方法的有效性，在生成逼真的室内场景方面优于现有的方法。这项工作推动了场景生成领域的发展，并为需要详细3D环境的应用开辟了新的可能性。我们将会在发表时发布我们的代码以促进未来的相关研究。
URL
https://arxiv.org/abs/2508.17832
