Recent advances in neural implicit fields enables rapidly reconstructing 3D geometry from multi-view images. Beyond that, recovering physical properties such as material and illumination is essential for enabling more applications. This paper presents a new method that effectively learns relightable neural surface using pre-intergrated rendering, which simultaneously learns geometry, material and illumination within the neural implicit field. The key insight of our work is that these properties are closely related to each other, and optimizing them in a collaborative manner would lead to consistent improvements. Specifically, we propose NeuS-PIR, a method that factorizes the radiance field into a spatially varying material field and a differentiable environment cubemap, and jointly learns it with geometry represented by neural surface. Our experiments demonstrate that the proposed method outperforms the state-of-the-art method in both synthetic and real datasets.

翻译：近期神经隐式场的进展使得从多视角图像快速重建三维几何成为可能。在此基础上，恢复材质与光照等物理属性对于拓展应用至关重要。本文提出一种新方法，利用预积分渲染有效学习可再光照神经表面，在神经隐式场中同步学习几何、材质与光照。我们的关键洞察在于：这些属性彼此紧密关联，协同优化可带来一致性改进。具体而言，我们提出NeuS-PIR方法，将辐射场分解为空间变化的材质场与可微分环境立方体贴图，并与神经表面表示的几何进行联合学习。实验表明，该方法在合成与真实数据集上均优于现有最优方法。