Skip links to main content
Logo of Geometric Image Processing Laboratory,
Computer Science Department Home
Technion Home Page

Projects Last Projects

Learning Unique Invariant Signatures of Non-Rigid Point Clouds

Sari Hleihil and Idan Shienfeld

Supervised by Ido Imanuel


We propose a metric learning framework for the construction of invariant signatures of non-rigid 3D point clouds under the isometry transformations group. We leverage the representational power of convolutional neural networks to compute these signatures and show that in comparison with classical methods, we achieve superior results that allow for higher classification accuracy using the invariant signature, and a lower pose dependency, with the additional advantage of much lower complexity, allowing for the calculation of invariant signatures for larger point clouds with orders of magnitude less time, this is achieved without the use of edge information that is commonly used for such applications. Furthermore, our proposed training scheme allows achieves superior classification accuracy than end-to-end trained classifiers using the same architecture.

Project Learning Unique Invariant Signatures of Non-Rigid Point Clouds Picture 1
Project Report

Please, see project report.

Final Presentation

Please, see final presentation.

Copyright © 2016 by Geometric Image Processing Lab. All rights reserved.