Matching 3D Point Clouds

Speakers

Shai Avidan
Professor, School of Electrical Engineering, Tel Aviv University.

Title

"Matching 3D Point Clouds"

Abstract

I will present three deep learning algorithms for registering 3D point clouds in different settings. The first is designed to find a rigid transformation between point clouds and is based on the concept of best buddies similarity. The second algorithm offers a fast method for non-rigid dense correspondence between point clouds based on structured shape construction. Finally, I extend the second algorithm to handle scene flow estimation that can be learned on a small amount of data without employing ground-truth flow supervision.

About Speakers

Shai Avidan received the PhD degree from the School of Computer Science, Hebrew University, Jerusalem, Israel, in 1999. He is currently a professor at the School of Electrical Engineering, Tel Aviv University. In between, he worked for Mobileye, Microsoft Research,  Mitsubishi Electric Research Labs, and Adobe. He has published extensively in the fields of object tracking in video and 3D object modeling from images. He is currently interested in Computational Photography and 3D data analysis.