ML Anthology

Virtual Training for Multi-View Object Class Recognition

Han-Pang Chiu, Leslie Pack Kaelbling, Tomás Lozano-Pérez
CVPR 2007
doi:10.1109/CVPR.2007.383044 /cvpr/2007/chiu2007cvpr-virtual/

Abstract

Our goal is to circumvent one of the roadblocks to using existing approaches for single-view recognition for achieving multi-view recognition, namely, the need for sufficient training data for many viewpoints. We show how to construct virtual training examples for multi-view recognition using a simple model of objects (nearly planar facades centered at fixed 3D positions). We also show how the models can be learned from a few labeled images for each class.

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Cite

Text

Chiu et al. "Virtual Training for Multi-View Object Class Recognition." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2007. doi:10.1109/CVPR.2007.383044

Markdown

[Chiu et al. "Virtual Training for Multi-View Object Class Recognition." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2007.](https://mlanthology.org/cvpr/2007/chiu2007cvpr-virtual/) doi:10.1109/CVPR.2007.383044

BibTeX

@inproceedings{chiu2007cvpr-virtual,
  title     = {{Virtual Training for Multi-View Object Class Recognition}},
  author    = {Chiu, Han-Pang and Kaelbling, Leslie Pack and Lozano-Pérez, Tomás},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2007},
  doi       = {10.1109/CVPR.2007.383044},
  url       = {https://mlanthology.org/cvpr/2007/chiu2007cvpr-virtual/}
}