| title | Easy-3Dfy: Object-Level Geometric Composition for Single-Image 3D Scene Construction |
|---|---|
| authors | Ziang Zhang, Wenyou Yi, Zehan Wang, Jiachen Xu, Xintong Han, Chunchao Guo, Zhou Zhao |
| date | 2026-10-23 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2610.32250 |
| upvotes | 11 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf |
| abstract | Single-image 3D scene construction is a fundamental problem in 3D vision. Recent methods such as SAM-3Dfy enable end-to-end reconstruction of 3D assets and their scene layouts, but still struggle with fine-grained scene geometry and asset quality. Meanwhile, specialized 3D object generators provide detailed object geometry, while 3D foundation models offer strong and generalizable spatial understanding. We propose Easy-3Dfy, a compositional workflow that decouples object generation from scene layout estimation, allowing specialized models to fully exploit their respective strengths. An RPF-based aligner then bridges these capabilities by establishing geometric correspondence between object-local meshes and partial scene pointmaps, recovering object layout for scene composition. Without requiring complete scene-level supervision, the proposed workflow consistently outperforms SAM-3Dfy across multiple geometric metrics on Scene-Maker and 3D-FUTURE200. Our results demonstrate that explicit geometric composition can effectively combine high-quality object generation with strong spatial understanding, while enabling the workflow to benefit independently from advances in both 3D object generation and foundation models. |
cite
@misc{zhang2026easy,
title={Easy-3Dfy: Object-Level Geometric Composition for Single-Image 3D Scene Construction},
author={Ziang Zhang and Wenyou Yi and Zehan Wang and Jiachen Xu and Xintong Han and Chunchao Guo and Zhou Zhao},
year={2026},
eprint={2610.32250},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2610.32250}
}