I2-NeRF: Learning Neural Radiance Fields Under Physically-Grounded Media Interactions

Participating in efforts to endow generative AI with the 3D physical world perception, we propose I2-NeRF, a novel neural radiance field framework that enhances isometric and isotropic metric perception under media degradation. While existing NeRF models predominantly rely on object-centric sampling, I2-NeRF introduces a reverse-stratified upsampling strategy to achieve near-uniform sampling across 3D space, thereby preserving isometry. We further present a general radiative formulation for media degradation that unifies emission, absorption, and scattering into a particle model governed by the Beer–Lambert attenuation law. By matting direct and media-induced in-scatter radiance, this formulation extends naturally to complex media environments such as underwater, haze, and even low-light scenes. By treating light propagation uniformly in both vertical and horizontal directions, I2-NeRF enables isotropic metric perception and can even estimate medium properties such as water depth. Experiments on real-world datasets demonstrate that our method significantly improves both reconstruction fidelity and physical plausibility compared to existing approaches. The source code is available at https: //github. com/ShuhongLL/I2-NeRF.

Authors

• Shuhong Liu The University of Tokyo
• Lin Gu RIKEN AIP, Tokyo103-0027, Japan The University of Tokyo, Japan
• Ziteng Cui The University of Tokyo Shanghai AI Laboratory
• Xuangeng Chu
• Tatsuya Harada The University of Tokyo RIKEN AIP

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