NeAI: A Pre-convoluted Representation for Plug-and-Play Neural Ambient Illumination

• URL: http://arxiv.org/abs/2304.08757v1
• Date: Tue, 18 Apr 2023 06:32:30 GMT
• Title: NeAI: A Pre-convoluted Representation for Plug-and-Play Neural Ambient Illumination
• Authors: Yiyu Zhuang, Qi Zhang, Xuan Wang, Hao Zhu, Ying Feng, Xiaoyu Li, Ying Shan, Xun Cao
• Abstract summary: We propose a framework named neural ambient illumination (NeAI) NeAI uses Neural Radiance Fields (NeRF) as a lighting model to handle complex lighting in a physically based way. Experiments demonstrate the superior performance of novel-view rendering compared to previous works.
• Score: 28.433403714053103
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in implicit neural representation have demonstrated the ability to recover detailed geometry and material from multi-view images. However, the use of simplified lighting models such as environment maps to represent non-distant illumination, or using a network to fit indirect light modeling without a solid basis, can lead to an undesirable decomposition between lighting and material. To address this, we propose a fully differentiable framework named neural ambient illumination (NeAI) that uses Neural Radiance Fields (NeRF) as a lighting model to handle complex lighting in a physically based way. Together with integral lobe encoding for roughness-adaptive specular lobe and leveraging the pre-convoluted background for accurate decomposition, the proposed method represents a significant step towards integrating physically based rendering into the NeRF representation. The experiments demonstrate the superior performance of novel-view rendering compared to previous works, and the capability to re-render objects under arbitrary NeRF-style environments opens up exciting possibilities for bridging the gap between virtual and real-world scenes. The project and supplementary materials are available at https://yiyuzhuang.github.io/NeAI/.

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