Synthesizing realistic 3D indoor scenes is a challenging task that traditionally relies on manual arrangement and annotation by expert designers. Recent advances in autoregressive models have automated this process, but they often lack semantic understanding of the relationships and hierarchies present in real-world scenes, yielding limited performance. In this paper, we propose Forest2Seq, a framework that formulates indoor scene synthesis as an order-aware sequential learning problem. Forest2Seq organizes the inherently unordered collection of scene objects into structured, ordered hierarchical scene trees and forests. By employing a clustering-based algorithm and a breadth-first traversal, Forest2Seq derives meaningful orderings and utilizes a transformer to generate realistic 3D scenes autoregressively. Experimental results on standard benchmarks demonstrate Forest2Seq's superiority in synthesizing more realistic scenes compared to top-performing baselines, with significant improvements in FID and KL scores. Our additional experiments for downstream tasks and ablation studies also confirm the importance of incorporating order as a prior in 3D scene generation.

提出了一种将室内场景合成问题作为有序顺序学习问题的框架，通过使用聚类算法和广度优先遍历得到有意义的顺序，利用变换器自动生成逼真的三维场景。在标准基准测试上的实验结果表明，Forest2Seq相比其他优秀基线模型，在合成逼真场景方面具有显著优势，得到的FID和KL得分有大幅度提升。我们的额外实验和消融研究也证实，在三维场景生成中，引入顺序作为先验信息的重要性。

Forest2Seq: 恢复顺序优先权力，用于顺序室内场景合成