Abstract: The heart of a camera and one of the pillars for computer vision is the digital photodetector, a device that forms images by collecting billions of photons traveling through the physical world and into the lens of a camera.  While the photodetectors used by cellphones or professional DSLR cameras are designed to aggregate as many photons as possible, I will talk about a different type of sensor, known as a SPAD, designed to detect and timestamp individual photon events.  By developing computational algorithms and hardware systems around these sensors, we can perform new imaging feats, including the ability to (1) image the propagation of light through a scene at trillions of frames per second, (2) form dense 3D measurements from extremely low photon counts, and (3) reconstruct the shape and reflectance of objects hidden from view.

Bio: Matthew O’Toole is an Assistant Professor with the School of Computer Science at Carnegie Mellon University.  His research focus is on computational imaging, a highly multi-disciplinary topic that uses novel combinations of computation, electronics, and optics to overcome the limitations of conventional imaging systems.  He was a Banting Postdoctoral Fellow with the Department of Electrical Engineering at Stanford University.  Prior to that, he completed his Ph.D. at the University of Toronto with the Department of Computer Science, where his thesis received the ACM SIGGRAPH Outstanding Dissertation Honorable Mention award.  His work also received two runner-up best paper awards (CVPR 2014, ICCV 2007) and two best demo awards (CVPR 2015, ICCP 2015).

Host: Ioannis Gkioulekas

Point of Contact: Stephanie Matvey