High-fidelity 3D scene reconstruction has been substantially advanced by recent progress in neural fields. However, most existing methods train a separate network from scratch for each individual scene. This is not scalable, inefficient, and unable to yield good results given limited views. While learning-based multi-view stereo methods alleviate this issue to some extent, their multi-view setting makes it less flexible to scale up and to broad applications. Instead, we introduce training generalizable Neural Fields incorporating scene Priors (NFPs). The NFP network maps any single-view RGB-D image into signed distance and radiance values. A complete scene can be reconstructed by merging individual frames in the volumetric space WITHOUT a fusion module, which provides better flexibility. The scene priors can be trained on large-scale datasets, allowing for fast adaptation to the reconstruction of a new scene with fewer views. NFP not only demonstrates SOTA scene reconstruction performance and efficiency, but it also supports single-image novel-view synthesis, which is underexplored in neural fields. More qualitative results are available at: https://oasisyang.github.io/neural-prior

翻译：神经场的最新进展显著推动了高保真三维场景重建的发展。然而，现有方法大多针对每个独立场景从头训练单独的网络，这导致可扩展性差、效率低下，且在视图有限时难以获得理想结果。尽管基于学习的多视图立体方法在一定程度上缓解了此问题，但其多视图设定使得扩展和广泛应用的灵活性不足。为此，我们提出引入场景先验的可泛化神经场（NFPs）训练方法。NFP网络可将任意单视图RGB-D图像映射为符号距离与辐射值。通过无需融合模块的体素空间单帧合并即可完成完整场景重建，这提供了更优的灵活性。场景先验可在大规模数据集上进行训练，从而快速适应新场景的少视图重建任务。NFP不仅展现了最优的场景重建性能与效率，还支持单图像新视角合成——这一神经场中尚待深入探索的方向。更多定性结果参见：https://oasisyang.github.io/neural-prior