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Scalable Autonomous Driving via Fully Data-driven Simulation
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NAVSIM v2 End-to-End Driving Challenge
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Biography
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Felix Heide is a professor of Computer Science at Princeton, where he leads the Princeton Computational Imaging Lab, and he is the Head of AI at Torc Robotics which builds full autonomy stacks for self-driving trucks. He previously founded the startup Algolux which was acquired by Torc and Daimler Trucks. His group at Princeton explores imaging and computer vision approaches that allow computers to see and understand what seems invisible today — enabling super-human capabilities for the cameras in his vehicles, personal devices, microscopes, telescopes, and the instrumentation he use for fundamental research in physics. This includes today's capture and vision challenges, including harsh environmental conditions, e.g., imaging under ultra-low or high illumination or computer vision through dense fog, rain, and snow, imaging at ultra-fast or slow time scales, freezing light in motion, imaging at extreme scene scales, from super-resolution microscopy to kilometer-scale depth sensing, and imaging via proxies using nearby object surfaces as sensors instead. Researching vision systems end-to-end, his work lies at the intersection of optics, machine learning, optimization, computer graphics, and computer vision. He received his Ph.D. from the University of British Columbia, and he was a postdoc at Stanford University. His doctoral dissertation won the Alain Fournier Ph.D. Dissertation Award and the SIGGRAPH outstanding doctoral dissertation award. He was recently named SIGGRAPH Significant New Researcher, Sloan Research Fellow and Packard Fellow.
Scalable Autonomous Driving via Fully Data-driven Simulation
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Felix Heide is a professor of Computer Science at Princeton, where he leads the Princeton Computational Imaging Lab, and he is the Head of AI at Torc Robotics which builds full autonomy stacks for self-driving trucks. He previously founded the startup Algolux which was acquired by Torc and Daimler Trucks. His group at Princeton explores imaging and computer vision approaches that allow computers to see and understand what seems invisible today — enabling super-human capabilities for the cameras in his vehicles, personal devices, microscopes, telescopes, and the instrumentation he use for fundamental research in physics. This includes today's capture and vision challenges, including harsh environmental conditions, e.g., imaging under ultra-low or high illumination or computer vision through dense fog, rain, and snow, imaging at ultra-fast or slow time scales, freezing light in motion, imaging at extreme scene scales, from super-resolution microscopy to kilometer-scale depth sensing, and imaging via proxies using nearby object surfaces as sensors instead. Researching vision systems end-to-end, his work lies at the intersection of optics, machine learning, optimization, computer graphics, and computer vision. He received his Ph.D. from the University of British Columbia, and he was a postdoc at Stanford University. His doctoral dissertation won the Alain Fournier Ph.D. Dissertation Award and the SIGGRAPH outstanding doctoral dissertation award. He was recently named SIGGRAPH Significant New Researcher, Sloan Research Fellow and Packard Fellow.
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