SPS Webinar: Differentiable Ray Tracing for End-to-end Optical Designs and Modeling
Date: 30 January 2024
Time: 8:00 AM ET (New York Time)
Presenter(s): Dr. Congli Wang, Dr. Wolfgang Heidrich
Original article: Download Open Access article
Abstract
Traditionally, the design and modeling of optical systems have heavily relied on accurately calculating ray intersections through a physically accurate simulation of light transport within an optical system. However, it is also crucial to understand how selected merit functions evolve concerning component parameters. Mathematically, this requires differentiability, involving the computation of derivatives for an optical system. Emerging as a technique, differentiable ray tracing facilitates the tracing of rays and their variations concerning parameters of interest. The application of differentiability streamlines the solution of inverse problems, akin to the training of neural networks, enabling direct optimization through gradient descent. Differentiability also facilitates the simultaneous optimization of front-end optical system modeling (hardware) and back-end image processing algorithms (software), such as neural networks, making end-to-end hardware-software joint design feasible from a numerical standpoint.
The first half of this webinar covers the basic principles of differentiable ray tracing. The presenters will delve into its applications in various areas, including freeform and lens designs, sensitivity analysis, AI-driven designs (or deep lens), and its broader utility in addressing inverse problems within optical system modeling. The second half of this webinar will cover more advanced recent developments of differentiable ray tracing in automatic lens design, end-to-end designs, and more. Future perspectives on end-to-end designs will also be presented.
Biography
Congli Wang received the B.Eng. degree in electrical engineering from Tianjin University, China, in 2015, and the M.Sc. in electrical engineering and the Ph.D. in electrical and computer engineering from King Abdullah University of Science and Technology, Saudi Arabia, in 2016 and 2021, respectively, under the supervision of Wolfgang Heidrich.
He is currently a postdoctoral scholar of electrical engineering and computer sciences at the University of California, Berkeley, USA, affiliated with the Berkeley AI Research Lab, working with Ren Ng and Austin Roorda. His research interests are computational imaging, visual computing, and optics, with a specific emphasis on building machine-intelligent optical instruments.
Dr. Wang was awarded the OSA Student Paper Award at the 2021 OSA Optical Design and Fabrication Congress. He is a reviewer for reputable conferences and journals in computer graphics, computer vision, and applied optics.
Wolfgang Heidrich (F ‘21) received the Ph.D. in computer science from the University of Erlangen in 1999, and then worked as a Research Associate in the Computer Graphics Group of the Max-Planck-Institute for Computer Science in Saarbrucken, Germany, before joining UBC in 2000.
He is currently a Professor of Computer Science and Electrical and Computer Engineering in the KAUST Visual Computing Center, for which he also served as director from 2014 to 2021. Prof. Heidrich joined King Abdullah University of Science and Technology (KAUST) in 2014, after 13 years as a faculty member at the University of British Columbia. His more recent interest is in computational imaging, focusing on hardware-software co-design of the next generation of imaging systems, with applications such as High-Dynamic Range imaging, compact computational cameras, hyperspectral cameras, to name just a few.
Prof. Heidrich’s work on High Dynamic Range Displays served as the basis for the technology behind Brightside Technologies, which was acquired by Dolby in 2007. Prof. Heidrich is a Fellow of the IEEE, AAIA, and Eurographics, and the recipient of a Humboldt Research Award as well as the ACM SIGGRAPH Computer Graphics Achievement Award.
