ML Anthology

ICPIK: Inverse Kinematics Based Articulated-ICP

Shachar Fleishman, Mark Kliger, Alon Lerner, Gershom Kutliroff
CVPRW 2015 pp. 28-35
doi:10.1109/CVPRW.2015.7301345 /cvprw/2015/fleishman2015cvprw-icpik/

Abstract

In this paper we address the problem of matching a kinematic model of an articulated body to a point cloud obtained from a consumer grade 3D sensor. We present the ICPIK algorithm - an Articulated Iterative Closest Point algorithm based on a solution to the Inverse Kinematic problem. The main virtue of the presented algorithm is its computational efficiency, achieved by relying on inverse-kinematics framework for analytical derivation of the Jacobian matrix, and the enforcement of kinematic constraints. We demonstrate the performance of the ICPIK algorithm by integrating it into a real-time hand tracking system. The presented algorithm achieves similar accuracy as state of the art methods, while significantly reducing computation time.

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Cite

Text

Fleishman et al. "ICPIK: Inverse Kinematics Based Articulated-ICP." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2015. doi:10.1109/CVPRW.2015.7301345

Markdown

[Fleishman et al. "ICPIK: Inverse Kinematics Based Articulated-ICP." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2015.](https://mlanthology.org/cvprw/2015/fleishman2015cvprw-icpik/) doi:10.1109/CVPRW.2015.7301345

BibTeX

@inproceedings{fleishman2015cvprw-icpik,
  title     = {{ICPIK: Inverse Kinematics Based Articulated-ICP}},
  author    = {Fleishman, Shachar and Kliger, Mark and Lerner, Alon and Kutliroff, Gershom},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2015},
  pages     = {28-35},
  doi       = {10.1109/CVPRW.2015.7301345},
  url       = {https://mlanthology.org/cvprw/2015/fleishman2015cvprw-icpik/}
}