I am specifically interested in event-based vision — using event cameras, such as the Dynamic Vision Sensor (DVS).
These cameras are novel, bio-inspired vision sensors that output pixel-level brightness changes instead of standard intensity frames. They offer significant advantages over standard cameras, namely:
However, because their output is composed of a sequence of asynchronous events rather than actual intensity images, traditional vision algorithms cannot be applied.
The goal of my research is to design novel algorithms that leverage the outstanding properties of event cameras to solve fundamental vision tasks for robotics — in particular SLAM, visual odometry and mapping (3D reconstruction).
PhD student, 2015
Robotics and Perception Group, University of Zürich
M.Sc. Mathematics, Vision and Learning, 2014
Ecole Normale Supérieure de Cachan
Our paper EVO: A Geometric Approach to Event-based 6-DOF Parallel Tracking and Mapping in Real-time has been accepted for publication in the Robotics and Automation Letters (RA-L), and for presentation at ICRA’17!
Our paper EMVS: Event-based Multi-View Stereo, receives the BMVC’16 Best Industry Paper Award!