Technical Papers

Date/Time: 04 – 13 December 2020
All presentations are available in the virtual platform on-demand.

Lecturer(s):
Quan Zheng, Max-Planck-Institut für Informatik, Germany
Vahid Babaei, Max-Planck-Institut für Informatik, Germany
Gordon Wetzstein, Stanford University, United States of America
Hans-Peter Seidel, Max-Planck-Institut für Informatik, Germany
Matthias Zwicker, University of Maryland College Park, United States of America
Gurprit Singh, Max-Planck-Institut für Informatik, Germany

Bio:

Description: Modern 3D printers are capable of printing large-size light-field displays at high-resolutions. However, optimizing such displays in full 3D volume for a given light-field imagery is still a challenging task. Existing light field displays optimize over relatively small resolutions using a few co-planar layers in a 2.5D fashion to keep the problem tractable. In this paper, we propose a novel end-to-end optimization approach that encodes input light field imagery as a continuous-space implicit representation in a neural network. This allows fabricating high-resolution, attenuation-based volumetric displays that exhibit the target light fields. In addition, we incorporate the physical constraints of the material to the optimization such that the result can be printed in practice. Our simulation experiments demonstrate that our approach brings significant visual quality improvement compared to the multi-layer and uniform grid-based approaches. We validate our simulations with fabricated prototypes and demonstrate that our pipeline is flexible enough to print both planar and non-planar displays.