Paper: Building Rome from a Single Image reconstructs full 3D scenes
An arXiv paper titled "Building Rome from a Single Image" proposes generating a complete 3D scene mesh, including surfaces the camera never observed, from a single image. The authors redesign the object-centric 3D generator Trellis 2 with adaptive chunking that scales with camera distance (small near chunks for detail, large chunks for distant buildings), explicit 2D-3D correspondence that distinguishes free space, observed surfaces and unobserved regions, and roughly 4,000 synthesized outdoor scenes to broaden training data. The authors report that their method outperforms all baselines in geometric accuracy and perceptual quality on Tanks and Temples, ScanNet++ and in-the-wild images; no specific numbers are given in the abstract.
Why it matters: If one photo can yield a usable indoor or outdoor scene mesh, it could cut the data-collection cost of building 3D assets and simulated environments.
Computer Science > Computer Vision and Pattern Recognition
arXiv:2610.08790 (cs)
[Submitted on 6 Oct 2026]
Title:Building Rome from a Single Image
Authors:Jiraphon Yenphraphai, Fang Li, Tianshuo Xu, Depu Meng, Quentin Herau, Yihan Hu, Raymond A. Yeh, Wei Zhan
View a PDF of the paper titled Building Rome from a Single Image, by Jiraphon Yenphraphai and 7 other authors
View PDF HTML (experimental)Abstract:Single-image scene generation aims to produce a complete 3D scene mesh from a single image, including surfaces the camera did not observe. While pretrained 3D object generators encode a strong shape prior, they are mainly designed for isolated objects in a fixed canonical volume and focus mostly on indoor scenes, since diverse 3D data for outdoor scenes are quite limited. In this work, we present a method that redesigns such an object-centric generator, e.g., Trellis 2, to work on both indoor and outdoor scenes while retaining its prior. We accomplish this by (a) partitioning the scene into adaptive chunks that scale relative to the distance to the camera; nearby chunks have a smaller size to keep the finer detail, while distant structures, e.g., buildings, are covered by large chunks; (b) making the generator capture explicit 2D-3D correspondence by lifting image features and making the model aware of the free space, observed surface, and unobserved region; (c) synthesizing around 4,000 outdoor scenes to broaden the training data, as existing scene datasets are largely indoor. Experiments on Tanks and Temples, ScanNet++, and in-the-wild images show that our method outperforms all baselines in geometric accuracy and perceptual quality across both indoor and outdoor scenes.
| Comments: | Project page: this https URL |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2610.08790 [cs.CV] |
| (or arXiv:2610.08790v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2610.08790 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jiraphon Yenphraphai [view email][v1] Tue, 6 Oct 2026 17:59:50 UTC (46,319 KB)
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