Modeling large scenes from unconstrained images has proven to be a major challenge in computer vision. Existing methods tackling in-the-wild scene modeling operate in closed-world settings, where no conditioning on priors acquired from real-world images is present. We propose RefinedFields, which is, to the best of our knowledge, the first method leveraging pre-trained models to improve in-the-wild scene modeling. We employ pre-trained networks to refine K-Planes representations via optimization guidance using an alternating training procedure. We carry out extensive experiments and verify the merit of our method on synthetic data and real tourism photo collections. RefinedFields enhances rendered scenes with richer details and improves upon its base representation on the task of novel view synthesis in the wild. Our project page can be found at https://refinedfields.github.io.

翻译：从无约束图像中建模大规模场景是计算机视觉领域的一项重大挑战。现有针对野外场景建模的方法主要在封闭世界设定下运行，未利用从真实图像中获取的先验知识进行条件约束。我们提出RefinedFields方法——据我们所知，这是首个利用预训练模型改善野外场景建模的技术。该方法采用预训练网络，通过交替训练策略优化K-Planes表示的辐射场。通过大量实验，我们在合成数据与真实旅游照片集上验证了该方法的有效性。RefinedFields能够增强渲染场景的细节丰富度，并在野外新视角合成任务中显著优于其基础表示。项目主页详见https://refinedfields.github.io。