一张照片重建3D场景，不用再逐场景训练
以前让AI从照片重建3D场景，要么慢到要针对每个场景单独训练几小时，要么只能处理小范围、细节糊掉。这篇把「先搭骨架、再补细节」的思路做成了推理时自动级联：先用低分辨率定出整个场景的布局，再逐层细化到玻璃、反光这类难搞的表面，全程只用一个固定分辨率的生成器，却能在单物体到整座城市之间自由缩放。它在室内外和城市级场景上都追平甚至超过了逐场景优化的老方法，而且不用重新训练。它不是你明天就能用的工具，但意味着「拍一圈照片、几分钟拿到完整高保真3D模型」离现实又近了一步。
📄 原文摘要(英文)
We reconstruct high-fidelity 3D scene meshes from posed multi-view images without per-scene optimization, across scales ranging from single objects to large outdoor scenes. Per-scene optimization methods lack the learned 3D prior needed when observations are sparse or surfaces are glossy or transparent. Existing generative methods leverage such priors to complete geometry in sparsely observed regions, but typically operate at a fixed resolution over a limited spatial extent, trading spatial coverage against detail. Reconstructing a large scene therefore often requires partitioning it into independently processed overlapping local regions, making it difficult to maintain global geometric consistency. To address these issues, we propose T3lescope, which applies a single fixed-resolution generator across scene scales in an inference-time coarse-to-fine cascade. A coarse level establishes the scene layout, and finer levels perturb and denoise geometry inherited from the coarser level within progressively finer spatial cells to recover surface detail. The model is trained on individual cells at multiple scales and shares its weights across all levels, so no hierarchy is fixed during training, and the number of levels, cell scales, and cell locations are determined at inference time. On indoor, outdoor, and city-scale scenes, T3lescope outperforms feed-forward and generative baselines, matches or surpasses per-scene optimization, and recovers fine structures as well as glossy and transparent surfaces. These results show that our method generalizes across diverse scenes, view counts, and image resolutions. Project page: https://pfnet-research.github.io/t3lescope/