I am a PhD student at the University of Pennsylvania in Computer and Information Science, working in the GRASP Lab, and I am advised by Kostas Daniilidis.
My undergrad was completed at Duke University, where I was fortunate to be a part of the Robertson Scholars Leadership Program, and work with Michael Zavlanos on mobile stereo vision systems.
Before undergrad, I grew up in Auckland, New Zealand.
My research is in computer vision and robotics, with focuses in event-based cameras, 3D perception and self-supervised learning methods.
Contact me at alexzhu (at) seas.upenn.edu.
Check out my YouTube page for the latest videos of our work on event-based cameras!
News
- Our work “Unsupervised Event-based Learning of Optical Flow, Depth, and Egomotion” was accepted to CVPR 2019.
- Two new works on unsupervised learning of geometry have been released on arXiv:
Unsupervised Event-based Learning of Optical Flow, Depth, and Egomotion
In this work, we propose a pipeline for unsupervised learning of optical flow and depth and egomotion from events only – no grayscale frames or photoconsistency.
Robustness Meets Deep Learning: An End-to-End Hybrid Pipeline for Unsupervised Learning of Egomotion
This work contains a novel framework for unsupervised learning of egomotion for images. We train two networks to predict optical flow and depth from a monocular image, and then use RANSAC to estimate the pose from the network outputs. The pipeline is fully differentiable. - I will be presenting our work on “Unsupervised Event-based Optical Flow using Motion Compensation” at the What is Optical Flow for? workshop and as a demo at ECCV 2018.
- Our work “Realtime Time Synchronized Event-based Stereo” was accepted to ECCV 2018.
- Our work “EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras” was nominated for best student paper at RSS 2018!
- I will be an invited member at the Telluride 2018 Neuromorphic Cognition Engineering Workshop.
- Our work “EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras” was accepted to RSS 2018.
- The code for the feature tracking method in our works “Event-based Feature Tracking with Probabilistic Data Association” and “Event-based Visual Inertial Odometry” are now available.
https://github.com/daniilidis-group/event_feature_tracking - I will be presenting our work in RAL on the Multi Vehicle Stereo Event Camera dataset at ICRA 2018! We will also have two other works presented later this year.
- I will be presenting our work on Event-based Visual inertial Odometry at CVPR 2017.
- I will be presenting our work on Event-based Feature Tracking with Probabilistic Data Associations at ICRA 2017.