Home Publications Research Grants Inventions & Patents Awards Additional Research Activities Faculties & Institutions Research Areas

Dense non-rigid shape correspondence using random forests

Rodola E, Bulo SR, Windheuser T, Vestner M, Cremers D (2014)

---

Publication Type: Conference contribution

Publication year: 2014

Journal

Publisher: IEEE Computer Society

Pages Range: 4177-4184

Conference Proceedings Title: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Event location: Columbus, OH, USA

ISBN: 9781479951178

DOI: 10.1109/CVPR.2014.532

Abstract

We propose a shape matching method that produces dense correspondences tuned to a specific class of shapes and deformations. In a scenario where this class is represented by a small set of example shapes, the proposed method learns a shape descriptor capturing the variability of the deformations in the given class. The approach enables the wave kernel signature to extend the class of recognized deformations from near isometries to the deformations appearing in the example set by means of a random forest classifier. With the help of the introduced spatial regularization, the proposed method achieves significant improvements over the baseline approach and obtains state-of-the-art results while keeping short computation times.

Involved external institutions

How to cite

APA:

Rodola, E., Bulo, S.R., Windheuser, T., Vestner, M., & Cremers, D. (2014). Dense non-rigid shape correspondence using random forests. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 4177-4184). Columbus, OH, USA: IEEE Computer Society.

MLA:

Rodola, Emanuele, et al. "Dense non-rigid shape correspondence using random forests." Proceedings of the 27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014, Columbus, OH, USA IEEE Computer Society, 2014. 4177-4184.

BibTeX: Download