基于神经场逆渲染的三维重建研究综述

In computer vision and computer graphics, 3D reconstruction aims to generate high-fidelity digital models of real objects and scenes from 2D images, videos, or other sensor data, and has long attracted attention from both academia and industry. In recent years, deep learning has continuously improved reconstruction accuracy and efficiency, enabling applications such as relighting, digital twins, and virtual reality. With the advent of Neural Radiance Fields (NeRF), neural-field-based reconstruction has become increasingly mainstream; inverse rendering has been incorporated to enhance appearance disentanglement, forming a more complete reconstruction framework. Motivated by a rapidly growing yet fragmented literature, this survey focused on static objects and scenes and organized neural-field-based inverse-rendering 3D reconstruction into two components: neural-field surface reconstruction, and neural-field material and illumination estimation. For each component, core research questions were distilled and representative methods were summarized following a “research problem–technical solution” perspective. Their key principles and derivations were analyzed to inform future algorithm design. In addition, commonly used datasets and evaluation metrics for both directions were compiled, and their practical details and applicability were discussed. Finally, open challenges and promising future directions were outlined.