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:
this https URL

在高保真度 3D 场景重建方面，神经场的最新进展已经有了实质性的提升。然而，大多数现有方法为每个独立场景训练单独的神经网络，这不可扩展、低效且对有限视角下的结果不理想。本研究引入了训练可推广的神经场，将场景先验结合其中，从而更好地解决上述问题，并支持单张图像的新视角合成。