Wave Optics Rendering
Abstract
Motivated by the great impact on manufacturing costs that automatically re-adjusting machines' parameters in real-time during production could have, in this project, we propose the use of camera-based optical measurements systems to provide machines with real-time measuring feedback.
In such scenario, the application of traditional real-time computer graphics approximations is innappropiate. Their core relies on the famous rendering equation proposed by Kajiya in 1986 which has proven to be sufficient to allow real-time photorealisitc image synthesis in most situations. However, this simplified model of light transportation and surface interaction oversimplifies the role light's wave properties play in complex optical systems, where we need to accurately simulate diffraction effects in real-time. To overcome this challengue, we strive to combine knowledge from real-time constrained image synthesis research with more detailed physically-based light transport models that incorporate wave characteristics.
Publications
Modern Hardware Accelerated Point Based Holography
in OSA Optics Express, vol. 32, no. 15, Optica Publishing Group, pp. 26994-27009, July 2024.
GPU-Accelerated Point-Based Holograms
in Frontiers in Optics + Laser Sciences, Optica Publishing Group, August 2022.
pages: JW4B.53
Adaptive Gaussian Points for Faster and Better Computer-Generated Holograms
in Digital Holography and Three-Dimensional Imaging, Optica Publishing Group, pp. W3A.4 ff., August 2022.
Related Projects
Physical Parameter Estimation from Images
The goal of this project is to develop fast image-based measurement methods for optical properties, which would help to close feedback loops in adaptive manufacturing.
The introduction of novel production techniques for integrated optical components demands an increasing amount of quality control and inline feedback. Our focus in this project is the combination of fast optical measurement techniques and physics-based simulations to achieve fast and accurate feedback of physical parameters as close to the machine tool as possible.
The research on this topic is done in collaboration with the PhoenixD Cluster of Excellence. We work closely with expert researchers from other disciplines under the Task Group F2: Expert Systems for Quality Control.