ML Anthology

Photogeometric Structured Light: A Self-Calibrating and Multi-Viewpoint Framework for Accurate 3D Modeling

Daniel G. Aliaga, Yi Xu
CVPR 2008
doi:10.1109/CVPR.2008.4587709 /cvpr/2008/aliaga2008cvpr-photogeometric/

Abstract

Structured-light methods actively generate geometric correspondence data between projectors and cameras in order to facilitate robust 3D reconstruction. In this paper, we present photogeometric structured light whereby a standard structured light method is extended to include photometric methods. Photometric processing serves the double purpose of increasing the amount of recovered surface detail and of enabling the structured-light setup to be robustly self-calibrated. Further, our framework uses a photogeometric optimization that supports the simultaneous use of multiple cameras and projectors and yields a single and accurate multi-view 3D model which best complies with photometric and geometric data.

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Text

Aliaga and Xu. "Photogeometric Structured Light: A Self-Calibrating and Multi-Viewpoint Framework for Accurate 3D Modeling." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2008. doi:10.1109/CVPR.2008.4587709

Markdown

[Aliaga and Xu. "Photogeometric Structured Light: A Self-Calibrating and Multi-Viewpoint Framework for Accurate 3D Modeling." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2008.](https://mlanthology.org/cvpr/2008/aliaga2008cvpr-photogeometric/) doi:10.1109/CVPR.2008.4587709

BibTeX

@inproceedings{aliaga2008cvpr-photogeometric,
  title     = {{Photogeometric Structured Light: A Self-Calibrating and Multi-Viewpoint Framework for Accurate 3D Modeling}},
  author    = {Aliaga, Daniel G. and Xu, Yi},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2008},
  doi       = {10.1109/CVPR.2008.4587709},
  url       = {https://mlanthology.org/cvpr/2008/aliaga2008cvpr-photogeometric/}
}