ML Anthology

KPPF: Keypoint-Based Point-Pair-Feature for Scalable Automatic Global Registration of Large RGB-D Scans

Lucas Malleus, Thomas Fisichella, Diane Lingrand, Frédéric Precioso, N. Gros, Y. Noutary, L. Robert, Lirone Samoun
ICCVW 2017 pp. 2495-2502
doi:10.1109/ICCVW.2017.294 /iccvw/2017/malleus2017iccvw-kppf/
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Cite

Text

Malleus et al. "KPPF: Keypoint-Based Point-Pair-Feature for Scalable Automatic Global Registration of Large RGB-D Scans." IEEE/CVF International Conference on Computer Vision Workshops, 2017. doi:10.1109/ICCVW.2017.294

Markdown

[Malleus et al. "KPPF: Keypoint-Based Point-Pair-Feature for Scalable Automatic Global Registration of Large RGB-D Scans." IEEE/CVF International Conference on Computer Vision Workshops, 2017.](https://mlanthology.org/iccvw/2017/malleus2017iccvw-kppf/) doi:10.1109/ICCVW.2017.294

BibTeX

@inproceedings{malleus2017iccvw-kppf,
  title     = {{KPPF: Keypoint-Based Point-Pair-Feature for Scalable Automatic Global Registration of Large RGB-D Scans}},
  author    = {Malleus, Lucas and Fisichella, Thomas and Lingrand, Diane and Precioso, Frédéric and Gros, N. and Noutary, Y. and Robert, L. and Samoun, Lirone},
  booktitle = {IEEE/CVF International Conference on Computer Vision Workshops},
  year      = {2017},
  pages     = {2495-2502},
  doi       = {10.1109/ICCVW.2017.294},
  url       = {https://mlanthology.org/iccvw/2017/malleus2017iccvw-kppf/}
}