NeuS-PIR: Learning Relightable Neural Surface using Pre-Integrated Rendering

• URL: http://arxiv.org/abs/2306.07632v3
• Date: Tue, 26 Mar 2024 07:00:27 GMT
• Title: NeuS-PIR: Learning Relightable Neural Surface using Pre-Integrated Rendering
• Authors: Shi Mao, Chenming Wu, Zhelun Shen, Yifan Wang, Dayan Wu, Liangjun Zhang,
• Abstract summary: This paper presents a method, namely NeuS-PIR, for recovering relightable neural surfaces 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. Our method enables advanced applications such as relighting, which can be seamlessly integrated with modern graphics engines.
• Score: 23.482941494283978
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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

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