Hou In Ivan Tam¹, Hou In Derek Pun¹, Austin T. Wang¹, Xiaohao Sun¹, Qirui Wu¹, Han-Hung Lee¹, Angel X. Chang^1,2, Manolis Savva¹

¹Simon Fraser University  ²Canada-CIFAR AI Chair, Amii

Page | Paper

Compositional 3D indoor scene generation is a long-standing problem and a rapidly evolving area of research spanning computer graphics, 3D computer vision, and machine learning. The goal is to model the complex relationships among objects and their spatial and functional arrangements within a scene, enabling the creation of rich, diverse, and useful 3D environments for a wide range of applications. This survey offers a comprehensive overview of the state of the art, formulating a unifying framework for analyzing scene generation systems and systematically categorizing existing methods according to their approaches to key components. We review recent progress, analyze the strengths and limitations of different paradigms, and highlight both major advances and open challenges. Our survey aims to serve as a resource for researchers and practitioners, offering insights into the current landscape and inspiring new ideas for future work in this area.

If you find this work helpful in your research, please cite our work:

@article{tam2025survey,
    title = {Survey on Compositional {3D} Indoor Scene Generation},
    author = {Tam, Hou In Ivan and Pun, Hou In Derek and Wang, Austin T. and Sun, Xiaohao and Wu, Qirui and Lee, Han-Hung and Chang, Angel X. and Savva, Manolis},
    year = {2025}
}

This work was funded in part by a CIFAR AI Chair, a Canada Research Chair, and NSERC Discovery Grants.