论文概要
研究领域: CV 作者: Jerred Chen, Simon Weber, Ronald Clark 发布时间: 2026-09-09 arXiv: 2609.10531
中文摘要
图像到3D模型可以从单张RGB图像生成视觉上引人注目的3D资产，但其几何形状通常仅由可用观测松散约束，限制了它们在需要几何保真度的应用中的使用。然而，在许多现实场景中，测试时可能可以获得物体的部分几何观测。本文提出一种无需训练的框架，将此类证据整合到预训练的图像到3D生成模型中，无需重新训练或微调。为此，作者使用在模型占据表示上定义的射线一致观测似然来引导生成，结合表面占据和自由空间证据。应用于SAM 3D及其多视图扩展，该方法在不同可观测水平上大幅改善几何保真度和视觉质量。结果表明，预训练图像到3D模型可以通过显式测试时引导有效整合部分几何观测，补充其学习的生成先验而无需修改底层模型。
原文摘要
Image-to-3D models can generate visually compelling 3D assets from a single RGB image, but their geometry is often only loosely constrained by the available observations, limiting their use in applications that require geometric fidelity. In many real-world settings, however, partial geometric observations of the object may be available at test time. We introduce a training-free framework for incorporating such evidence into pretrained image-to-3D generative models without retraining or finetuning. To do this, we guide generation using a ray-consistent observation likelihood defined over the model’s occupancy representation, combining surface occupancy and free-space evidence. Applied to SAM 3D and its multi-view extension, our approach substantially improves geometric fidelity across different levels of observability, as well as visual quality. Our results demonstrate that pretrained image-to-3D models can effectively integrate partial geometric observations through explicit test-time guidance, complementing their learned generative prior without modifying the underlying model.
— 自动采集于 2026-09-11
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