EE Student Information

The Department of Electrical Engineering supports Black Lives Matter. Read more.

• • • • •

EE Student Information, Spring Quarter through Academic Year 2020-2021: FAQs and Updated EE Course List.

Updates will be posted on this page, as well as emailed to the EE student mail list.

Please see Stanford University Health Alerts for course and travel updates.

As always, use your best judgement and consider your own and others' well-being at all times.

SCIEN and EE292E present "Deep Learning for Practical and Robust View Synthesis"

Deep Learning for Practical and Robust View Synthesis
Wednesday, February 26, 2020 - 4:30pm
Packard 101
Dr. Oliver Woodford (Snap)
Abstract / Description: 

I will present recent work ("Local Light Field Fusion") on a practical and robust deep learning solution for capturing and rendering novel views of complex real world scenes for virtual exploration. Our view synthesis algorithm operates on an irregular grid of sampled views, first expanding each sampled view into a local light field via a multiplane image (MPI) scene representation, then rendering novel views by blending adjacent local light fields. We extend traditional plenoptic sampling theory to derive a bound that specifies precisely how densely users should sample views of a given scene when using our algorithm. In practice, we can apply this bound to capture and render views of real world scenes that achieve the perceptual quality of Nyquist rate view sampling while using up to 4000x fewer views.


Ben Mildenhall is a PhD student at UC Berkeley. He is advised Professor Ren Ng and supported by a Hertz Foundation Fellowship. He received his bachelor's degree in CS and math from Stanford University and has worked at Pixar, Google, and Fyusion in the past. His current research focuses on applying deep learning to 3D reconstruction, view synthesis, and other inverse graphics problems.