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!
- Our work “Learning Event-Based Height From Plane and Parallax” was accepted to IROS 2019.
- I will be attending CVPR next week, and presenting at these events:
- Poster at the Deep Learning for Semantic Visual Navigation Workshop
- Talk, poster and demo at the Workshop on Event-based Vision and Smart Cameras
- Poster for Unsupervised Event-based Learning of Optical Flow, Depth, and Egomotion at 10:15am June 18th, #88.
- 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.
- 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.