Daniel Gehrig
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  • Publications
    • A 25-μs/inf Event-driven Graph Neural Network Processor with Spatiotemporal Caching and Spline Convolution for Ultra-low-latency AI at the Edge
    • Neural Events: Discrete Asynchronous Autoencoders for Event-Based Vision
    • RoSHI: A Versatile Robot-oriented Suit for Human Data In-the-Wild
    • A Linear N-Point Solver for Structure and Motion from Asynchronous Tracks
    • EqNIO: Subequivariant Neural Inertial Odometry
    • ETAP: Event-based Tracking of Any Point
    • Neural Inertial Odometry from Lie Events
    • A Hybrid ANN-SNN Architecture for Low-Power and Low-Latency Visual Perception
    • An N-Point Linear Solver for Line and Motion Estimation with Event Cameras
    • Low Latency Automotive Vision with Event Cameras
    • A Unified Framework for Event-based Frame Interpolation with Ad-hoc Deblurring in the Wild
    • Deep Visual Odometry with Events and Frames
    • E-Calib: A fast, robust, and accurate calibration toolbox for event cameras
    • A 5-Point Minimal Solver for Event Camera Relative Motion Estimation
    • From Chaos Comes Order: Ordering Event Representations for Object Recognition and Detection
    • Event-based Agile Object Catching with a Quadrupedal Robot
    • Pushing the Limits of Asynchronous Graph-based Object Detection with Event Cameras
    • Exploring Event Camera-based Odometry for Planetary Robots
    • AEGNN: Asynchronous Event-based Graph Neural Networks
    • ESS: Learning Event-based Semantic Segmentation from Still Images
    • Multi-Bracket High Dynamic Range Imaging with Event Cameras
    • Time Lens++: Event-based Frame Interpolation with Parametric Non-linear Flow and Multi-scale Fusion
    • Are High-Resolution Event Cameras Really Needed?
    • Bridging the Gap between Events and Frames through Unsupervised Domain Adaptation
    • E-RAFT: Dense Optical Flow from Event Cameras
    • How to Calibrate Your Event Camera
    • Time Lens: Event-based Video Frame Interpolation
    • Combining Events and Frames using Recurrent Asynchronous Multimodal Networks for Monocular Depth Prediction
    • DSEC: A Stereo Event Camera Dataset for Driving Scenarios
    • Learning Monocular Dense Depth from Events
    • Event-based Asynchronous Sparse Convolutional Networks
    • Video to Events: Recycling Video Dataset for Event Cameras
    • Fast Image Reconstruction with an Event Camera
    • End-to-End Learning of Representations for Asynchronous Event-Based Data
    • EKLT: Asynchronous, Photometric Feature Tracking using Events and Frames
    • ESIM: an Open Event Camera Simulator
    • Asynchronous, Photometric Feature Tracking using Events and Frames
  • Recent & Upcoming Talks
    • Data-driven Event-based Perception: From cameras to IMUs
    • Geometric and Algebraic Structure in Event Camera Data
    • Efficient, Data-driven Perception with Event Cameras
    • Efficient, Data-driven Perception with Event Cameras
    • Efficient, Data-driven Perception with Event Cameras
    • Efficient, Data-driven Perception with Event Cameras
    • Efficient Data-driven Perception with Event Cameras
    • Efficient, Data-driven Perception with Event Cameras
    • Efficient, Data-driven Perception with Event Cameras
    • Efficient, Data-driven Perception with Event Cameras
    • Adaptive and Efficient Perception with Event Cameras
    • Adaptive and Efficient Perception with Event Cameras
    • Efficient, Data-driven Perception with Event Cameras
    • Efficient Data-driven Perception with Event Cameras
    • Efficient Event Processing with Geometric Deep Learning
    • Efficient, Data-driven Perception with Event Cameras
    • At the intersection of Machine Learning and Event-based Vision
  • Projects
  • Blog
    • 🎉 I started as a postdoctoral researcher at the GRASP Lab at UPenn.
    • 🧠 I successfully defended my Ph.D. with Summa Cum Laude!
  • Experience
  • Teaching
    • Guest Lecturer at "Real-World Robot Learning", University of Pennsylvania"
    • Lecturer for "Vision Algorithms for Mobile Robotics"

Efficient, Data-driven Perception with Event Cameras

Nov 6, 2023·
Daniel Gehrig
· 0 min read
Last updated on Nov 6, 2023
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