I2-NeRF: Learning Neural Radiance Fields Under Physically-Grounded Media Interactions
This work addresses the challenge of accurate 3D scene reconstruction in degraded media like underwater or haze for applications in generative AI and computer vision, representing an incremental advancement over existing NeRF methods.
The paper tackles the problem of enhancing 3D perception in neural radiance fields under media degradation by proposing I2-NeRF, which improves reconstruction fidelity and physical plausibility through a reverse-stratified upsampling strategy and a radiative formulation for 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 composing the 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.