让神经辐射场更真实地模拟光在复杂介质中的传播。
I2-NeRF: Learning Neural Radiance Fields Under Physically-Grounded Media Interactions
- 采用反向分层采样，实现三维空间均匀采样，保持几何一致性。
- 统一吸收、散射与发射过程，可处理水下、雾霾、暗光等场景。
- 能估计介质属性如水深，适合需要物理真实感的3D生成任务。
为提升生成式AI对三维物理世界的感知能力，本文提出I2-NeRF，一种新型神经辐射场框架，可在介质退化条件下增强等距与各向同性度量感知。现有NeRF模型多依赖物体中心采样，I2-NeRF引入反向分层上采样策略，实现近似均匀的三维空间采样，从而保持等距性。我们进一步提出一种通用辐射建模方法，将发射、吸收与散射统一为受朗伯-比尔定律控制的粒子模型。通过组合直接辐射与介质诱导的散射辐射，该模型可自然拓展至水下、雾霾及低光照等复杂介质环境。通过在垂直与水平方向统一建模光传播，I2-NeRF实现各向同性度量感知，并可估计介质属性如水深。在真实数据集上的实验表明，相比现有方法，本方法显著提升了重建保真度与物理合理性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。