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.

本文提出了一种新方法，使用预积分渲染有效地学习可重构的神经表面，同时在神经隐式场内学习几何、材料和照明等物理属性，并在协作上进行优化，实现了在合成和真实数据集中超越现有技术的效果。

NeuS-PIR: 使用预积分渲染学习可重新照明的神经表面