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

• URL: http://arxiv.org/abs/2510.22161v1
• Date: Sat, 25 Oct 2025 05:13:28 GMT
• Title: I2-NeRF: Learning Neural Radiance Fields Under Physically-Grounded Media Interactions
• Authors: Shuhong Liu, Lin Gu, Ziteng Cui, Xuangeng Chu, Tatsuya Harada,
• Abstract summary: I2-NeRF is a novel neural radiance field framework that enhances isotropic metric perception under media degradation.<n>We 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.<n>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.
• Score: 48.696181442718206
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• ShinyNeRF: Digitizing Anisotropic Appearance in Neural Radiance Fields [5.851353438304085]
  We introduce ShinyNeRF, a novel framework capable of handling both isotropic and anisotropic reflections.<n>Our method is capable of jointly estimating surface normals, tangents, specular concentration, and anisotropy magnitudes of an Anisotropic Spherical Gaussian (ASG) distribution.<n> Experimental results show that ShinyNeRF not only achieves state-of-the-art performance on digitizing anisotropic specular reflections, but also offers plausible physical interpretations and editing of material properties.
  arXiv  Detail & Related papers  (2025-12-25T14:35:10Z)
• Edit2Perceive: Image Editing Diffusion Models Are Strong Dense Perceivers [55.15722080205737]
  Edit2Perceive is a unified diffusion framework that adapts editing models for depth, normal, and matting.<n>Our single-step deterministic inference yields up to faster runtime while training on relatively small datasets.
  arXiv  Detail & Related papers  (2025-11-24T01:13:51Z)
• PHASE-Net: Physics-Grounded Harmonic Attention System for Efficient Remote Photoplethysmography Measurement [63.007237197267834]
  Existing deep learning methods are mostly physiological monitoring and lack theoretical robustness.<n>We propose a physics-informed r paradigm derived from the Navier-Stokes equations of hemodynamics, showing that the pulse signal follows a second-order system.<n>This provides a theoretical justification for using a Temporal Conal Network (TCN)<n>Phase-Net achieves state-of-the-art performance with strong efficiency, offering a theoretically grounded and deployment-ready r solution.
  arXiv  Detail & Related papers  (2025-09-29T14:36:45Z)
• 3D-UIR: 3D Gaussian for Underwater 3D Scene Reconstruction via Physics Based Appearance-Medium Decoupling [30.985414238960466]
  3D Gaussian Splatting (3DGS) offers real-time rendering capabilities, but struggles with underwater inhomogeneous environments.<n>We propose a physics-based framework that disentangles object appearance from water medium effects.<n>Our approach achieves both high-quality novel view synthesis and physically accurate scene restoration.
  arXiv  Detail & Related papers  (2025-05-27T14:19:30Z)
• Stochastic Reconstruction of Gappy Lagrangian Turbulent Signals by Conditional Diffusion Models [1.7810134788247751]
  We present a method for reconstructing missing spatial and velocity data along the trajectories of small objects passively advected by turbulent flows. Our approach makes use of conditional generative diffusion models, a recently proposed data-driven machine learning technique.
  arXiv  Detail & Related papers  (2024-10-31T14:26:10Z)
• Mesh2NeRF: Direct Mesh Supervision for Neural Radiance Field Representation and Generation [51.346733271166926]
  Mesh2NeRF is an approach to derive ground-truth radiance fields from textured meshes for 3D generation tasks. We validate the effectiveness of Mesh2NeRF across various tasks.
  arXiv  Detail & Related papers  (2024-03-28T11:22:53Z)
• Towards Detailed Text-to-Motion Synthesis via Basic-to-Advanced Hierarchical Diffusion Model [60.27825196999742]
  We propose a novel Basic-to-Advanced Hierarchical Diffusion Model, named B2A-HDM, to collaboratively exploit low-dimensional and high-dimensional diffusion models for detailed motion synthesis. Specifically, the basic diffusion model in low-dimensional latent space provides the intermediate denoising result that is consistent with the textual description. The advanced diffusion model in high-dimensional latent space focuses on the following detail-enhancing denoising process.
  arXiv  Detail & Related papers  (2023-12-18T06:30:39Z)
• StableDreamer: Taming Noisy Score Distillation Sampling for Text-to-3D [88.66678730537777]
  We present StableDreamer, a methodology incorporating three advances. First, we formalize the equivalence of the SDS generative prior and a simple supervised L2 reconstruction loss. Second, our analysis shows that while image-space diffusion contributes to geometric precision, latent-space diffusion is crucial for vivid color rendition.
  arXiv  Detail & Related papers  (2023-12-02T02:27:58Z)
• Reference-Free Isotropic 3D EM Reconstruction using Diffusion Models [8.590026259176806]
  We propose a diffusion-model-based framework that overcomes the limitations of requiring reference data or prior knowledge about the degradation process. Our approach utilizes 2D diffusion models to consistently reconstruct 3D volumes and is well-suited for highly downsampled data.
  arXiv  Detail & Related papers  (2023-08-03T07:57:02Z)
• High-resolution tomographic reconstruction of optical absorbance through scattering media using neural fields [25.647287240640356]
  We propose NeuDOT, a novel DOT scheme based on neural fields (NF) NeuDOT achieves submillimetre lateral resolution and resolves complex 3D objects at 14 mm-depth, outperforming state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-04-04T10:13:13Z)
• 3D wind field profiles from hyperspectral sounders: revisiting optic-flow from a meteorological perspective [0.0]
  We present an efficient optic flow algorithm for the extraction of vertically resolved 3D atmospheric motion vector (AMV) data measures by Forecast sounders. We show that the proposed recursion is superior to state-of-the-art optical flow algorithms in the real atmospheric sounding inter-II observations.
  arXiv  Detail & Related papers  (2023-03-09T10:14:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.