论文：单张图像重建完整3D场景

Hugging Face · Papers10-05 20:00Jiraphon Yenphraphai, Fang Li, Tianshuo Xu, Depu Meng, Quentin Herau, Yihan Hu, Raymond A. Yeh, Wei Zhan

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AI 总结

一篇提交至 arXiv 的论文提出"Building Rome from a Single Image"方法，目标是从单张图像生成完整的三维场景网格，包括相机未观测到的表面。作者改造以物体为中心的三维生成器 Trellis 2，通过按相机距离自适应分块（近处小块保留细节、远处建筑用大块覆盖）、显式建模二维—三维对应关系并区分自由空间/已观测表面/未观测区域，同时合成了约 4000 个室外场景以补充训练数据。论文称在 Tanks and Temples、ScanNet++ 以及真实拍摄图像上的几何精度与感知质量均优于所有对比方法（该结论为作者自述，未提供具体数值）。

为什么重要：若单图即可产出可用的室内外完整场景网格，将降低三维内容与仿真环境构建的数据采集成本。

Trellis 2

来源原文Hugging Face · Papers · 约 3 分钟读完

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

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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)

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From: Jiraphon Yenphraphai [view email]
[v1] Tue, 6 Oct 2026 17:59:50 UTC (46,319 KB)

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