用长方体组合生成更真实的室内场景，减少物体重叠。
CasaGPT: Cuboid Arrangement and Scene Assembly for Interior Design
- 用长方体逐步排列生成3D场景，避免传统方法的边界框限制。
- 在300个测试场景中，物体交集减少62%，生成效果优于现有方法。
- 适合做室内设计自动化、3D场景生成的研究者与设计师使用。
我们提出一种新型室内场景合成方法，通过学习将分解后的长方体基元排列以表示场景中的3D物体。不同于传统使用边界框确定物体位置与尺度的方法，本方法采用长方体作为简洁而高效的对象建模方式，实现紧凑的场景生成并最小化物体间的交集。该方法命名为CasaGPT（Cuboid Arrangement and Scene Assembly），采用自回归模型逐个排列长方体，生成物理上合理的场景。在微调阶段引入拒绝采样，过滤掉存在物体碰撞的场景，进一步降低交集并提升质量。此外，我们构建了优化数据集3DFRONT-NC，有效去除原始数据集3D-FRONT中的显著噪声。在3D-FRONT及自建数据集上的大量实验表明，本方法持续优于当前最先进方法，在生成场景的真实感方面表现突出，为3D场景合成提供了有前景的新方向。
原文摘要 · Abstract (English)
We present a novel approach for indoor scene synthesis, which learns to arrange decomposed cuboid primitives to represent 3D objects within a scene. Unlike conventional methods that use bounding boxes to determine the placement and scale of 3D objects, our approach leverages cuboids as a straightforward yet highly effective alternative for modeling objects. This allows for compact scene generation while minimizing object intersections. Our approach, coined CasaGPT for Cuboid Arrangement and Scene Assembly, employs an autoregressive model to sequentially arrange cuboids, producing physically plausible scenes. By applying rejection sampling during the fine-tuning stage to filter out scenes with object collisions, our model further reduces intersections and enhances scene quality. Additionally, we introduce a refined dataset, 3DFRONT-NC, which eliminates significant noise presented in the original dataset, 3D-FRONT. Extensive experiments on the 3D-FRONT dataset as well as our dataset demonstrate that our approach consistently outperforms the state-of-the-art methods, enhancing the realism of generated scenes, and providing a promising direction for 3D scene synthesis.
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