Fast and accurate neural reflectance transformation imaging through knowledge distillation

• URL: http://arxiv.org/abs/2510.24486v1
• Date: Tue, 28 Oct 2025 15:00:07 GMT
• Title: Fast and accurate neural reflectance transformation imaging through knowledge distillation
• Authors: Tinsae G. Dulecha, Leonardo Righetto, Ruggero Pintus, Enrico Gobbetti, Andrea Giachetti,
• Abstract summary: Reflectance Transformation Imaging (RTI) is very popular for its ability to visually analyze surfaces.<n>Traditional methods like Polynomial Texture Maps (PTM) and Hemispherical Harmonics (HSH) are compact and fast.<n>We propose to reduce its computational cost through a novel solution based on Knowledge Distillation (DisK-NeuralRTI)
• Score: 3.8135470187943556
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reflectance Transformation Imaging (RTI) is very popular for its ability to visually analyze surfaces by enhancing surface details through interactive relighting, starting from only a few tens of photographs taken with a fixed camera and variable illumination. Traditional methods like Polynomial Texture Maps (PTM) and Hemispherical Harmonics (HSH) are compact and fast, but struggle to accurately capture complex reflectance fields using few per-pixel coefficients and fixed bases, leading to artifacts, especially in highly reflective or shadowed areas. The NeuralRTI approach, which exploits a neural autoencoder to learn a compact function that better approximates the local reflectance as a function of light directions, has been shown to produce superior quality at comparable storage cost. However, as it performs interactive relighting with custom decoder networks with many parameters, the rendering step is computationally expensive and not feasible at full resolution for large images on limited hardware. Earlier attempts to reduce costs by directly training smaller networks have failed to produce valid results. For this reason, we propose to reduce its computational cost through a novel solution based on Knowledge Distillation (DisK-NeuralRTI). ...

Related papers

• 3DPR: Single Image 3D Portrait Relight using Generative Priors [101.74130664920868]
  3DPR is an image-based relighting model that leverages generative priors learnt from multi-view One-Light-at-A-Time (OLAT) images.<n>We leverage the latent space of a pre-trained generative head model that provides a rich prior over face geometry learnt from in-the-wild image datasets.<n>Our reflectance network operates in the latent space of the generative head model, crucially enabling a relatively small number of lightstage images to train the reflectance model.
  arXiv  Detail & Related papers  (2025-10-17T17:37:42Z)
• DPCS: Path Tracing-Based Differentiable Projector-Camera Systems [49.69815958689441]
  Projector-camera systems (ProCams) simulation aims to model the physical project-and-capture process and associated scene parameters of a ProCams.<n>Recent advances use an end-to-end neural network to learn the project-and-capture process.<n>We introduce a novel path tracing-based differentiable projector-camera systems (DPCS), offering a differentiable ProCams simulation method.
  arXiv  Detail & Related papers  (2025-03-15T15:31:18Z)
• Low-Light Image Enhancement with Illumination-Aware Gamma Correction and Complete Image Modelling Network [69.96295927854042]
  Low-light environments usually lead to less informative large-scale dark areas. We propose to integrate the effectiveness of gamma correction with the strong modelling capacities of deep networks. Because exponential operation introduces high computational complexity, we propose to use Taylor Series to approximate gamma correction.
  arXiv  Detail & Related papers  (2023-08-16T08:46:51Z)
• High Dynamic Range and Super-Resolution from Raw Image Bursts [52.341483902624006]
  This paper introduces the first approach to reconstruct high-resolution, high-dynamic range color images from raw photographic bursts captured by a handheld camera with exposure bracketing. The proposed algorithm is fast, with low memory requirements compared to state-of-the-art learning-based approaches to image restoration. Experiments demonstrate its excellent performance with super-resolution factors of up to $times 4$ on real photographs taken in the wild with hand-held cameras.
  arXiv  Detail & Related papers  (2022-07-29T13:31:28Z)
• Mip-NeRF RGB-D: Depth Assisted Fast Neural Radiance Fields [0.696125353550498]
  Neural scene representations, such as neural radiance fields (NeRF), are based on training a multilayer perceptron (MLP) using a set of color images with known poses. An increasing number of devices now produce RGB-D information, which has been shown to be very important for a wide range of tasks. This paper investigates what improvements can be made to these promising implicit representations by incorporating depth information with the color images.
  arXiv  Detail & Related papers  (2022-05-19T07:11:42Z)
• RBSRICNN: Raw Burst Super-Resolution through Iterative Convolutional Neural Network [23.451063587138393]
  We propose a Raw Burst Super-Resolution Iterative Convolutional Neural Network (RBSRICNN) The proposed network produces the final output by an iterative refinement of the intermediate SR estimates. We demonstrate the effectiveness of our proposed approach in quantitative and qualitative experiments.
  arXiv  Detail & Related papers  (2021-10-25T19:01:28Z)
• Thermal Image Processing via Physics-Inspired Deep Networks [21.094006629684376]
  DeepIR combines physically accurate sensor modeling with deep network-based image representation. DeepIR requires neither training data nor periodic ground-truth calibration with a known black body target. Simulated and real data experiments demonstrate that DeepIR can perform high-quality non-uniformity correction with as few as three images.
  arXiv  Detail & Related papers  (2021-08-18T04:57:48Z)
• Universal and Flexible Optical Aberration Correction Using Deep-Prior Based Deconvolution [51.274657266928315]
  We propose a PSF aware plug-and-play deep network, which takes the aberrant image and PSF map as input and produces the latent high quality version via incorporating lens-specific deep priors. Specifically, we pre-train a base model from a set of diverse lenses and then adapt it to a given lens by quickly refining the parameters.
  arXiv  Detail & Related papers  (2021-04-07T12:00:38Z)
• Deep Generative Adversarial Residual Convolutional Networks for Real-World Super-Resolution [31.934084942626257]
  We propose a deep Super-Resolution Residual Convolutional Generative Adversarial Network (SRResCGAN) It follows the real-world degradation settings by adversarial training the model with pixel-wise supervision in the HR domain from its generated LR counterpart. The proposed network exploits the residual learning by minimizing the energy-based objective function with powerful image regularization and convex optimization techniques.
  arXiv  Detail & Related papers  (2020-05-03T00:12:38Z)
• Single image reflection removal via learning with multi-image constraints [50.54095311597466]
  We propose a novel learning-based solution that combines the advantages of the aforementioned approaches and overcomes their drawbacks. Our algorithm works by learning a deep neural network to optimize the target with joint constraints enhanced among multiple input images. Our algorithm runs in real-time and state-of-the-art reflection removal performance on real images.
  arXiv  Detail & Related papers  (2019-12-08T06:10:49Z)

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.