A Biquadratic Reflectance Model for Radiometric Image Analysis

Abstract

Radiometric image analysis methods heavily rely on reflectance models. Due to the complexity of real materials, methods based on simple models such as the Lambertian model often suffer from inaccuracy. On the other hand, more advanced models such as the Cook-Torrance model severely complicate the analysis problem. We tackle this dilemma by focusing on the low-frequency component of the reflectance. We propose a compact biquadratic reflectance model to represent the reflectance of a broad class of materials precisely in the low-frequency domain. We validate our model by fitting to both existing parametric models and non-parametric measured data, and show that our model outperforms existing parametric diffuse models. We show applications of reflectometry using general diffuse surfaces and photometric stereo for general isotropic materials. Experimental results show the effectiveness of our biquadratic model and its usefulness in radiometric image analysis. © 2012 IEEE.

Cite

Text

Shi et al. "A Biquadratic Reflectance Model for Radiometric Image Analysis." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2012. doi:10.1109/CVPR.2012.6247680

Markdown

[Shi et al. "A Biquadratic Reflectance Model for Radiometric Image Analysis." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2012.](https://mlanthology.org/cvpr/2012/shi2012cvpr-biquadratic/) doi:10.1109/CVPR.2012.6247680

BibTeX

@inproceedings{shi2012cvpr-biquadratic,
  title     = {{A Biquadratic Reflectance Model for Radiometric Image Analysis}},
  author    = {Shi, Boxin and Tan, Ping and Matsushita, Yasuyuki and Ikeuchi, Katsushi},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2012},
  pages     = {230-237},
  doi       = {10.1109/CVPR.2012.6247680},
  url       = {https://mlanthology.org/cvpr/2012/shi2012cvpr-biquadratic/}
}