  This paper presents a method, namely NeuS-PIR, for recovering relightable
neural surfaces using pre-integrated rendering from multi-view images or video.
Unlike methods based on NeRF and discrete meshes, our method utilizes implicit
neural surface representation to reconstruct high-quality geometry, which
facilitates the factorization of the radiance field into two components: a
spatially varying material field and an all-frequency lighting representation.
This factorization, jointly optimized using an adapted differentiable
pre-integrated rendering framework with material encoding regularization, in
turn addresses the ambiguity of geometry reconstruction and leads to better
disentanglement and refinement of each scene property. Additionally, we
introduced a method to distil indirect illumination fields from the learned
representations, further recovering the complex illumination effect like
inter-reflection. Consequently, our method enables advanced applications such
as relighting, which can be seamlessly integrated with modern graphics engines.
Qualitative and quantitative experiments have shown that NeuS-PIR outperforms
existing methods across various tasks on both synthetic and real datasets.
Source code is available at https://github.com/Sheldonmao/NeuSPIR
