Mr. Simon C. Stock Profile

Simon C. Stock

at Karlsruher Institut für Technologie

SPIE Involvement:

Author

Publications (2)

Proceedings Article | 1 April 2020 Presentation + Paper

A system approach for closed-loop assessment of neuro-visual function based on convolutional neural network analysis of EEG signals

Simon Stock, Alexandre Armengol-Urpi, Bálint Kovács, Heiko Maier, Marius Gerdes, Wilhelm Stork, Sanjay Sarma

Proceedings Volume 11360, 1136008 (2020) https://doi.org/10.1117/12.2554417

KEYWORDS: Electroencephalography, Electrodes, Convolutional neural networks, Virtual reality, Neural networks, Human-machine interfaces, Signal processing, Signal analyzers

Read Abstract +

Proceedings Article | 1 April 2020 Presentation + Paper

Highly parallelized rendering of the retinal image through a computer-simulated human eye for the design of virtual reality head-mounted displays

Con Tran Vu, Simon Stock, Lintao Toni Fan, Wilhelm Stork

Proceedings Volume 11353, 1135316 (2020) https://doi.org/10.1117/12.2555872

KEYWORDS: Computer simulations, Parallel computing, GPU based image processing, Optical design, Optical simulations, Eye, Eye models, Ray tracing, Retina, Point spread functions, Geometrical optics, Virtual reality, Image processing

Read Abstract +

For long, the VR optics designer has been taking advantage of the fact that the eye is not optically perfect and striving towards creating “perfectly imperfect” VR optics. These cheaper and lighter lens systems could have high off-axis aberration, but with negligible impact on visual quality. A good way to test and validate these designs could be simulating the rendered image of the pixel array on the retina. Earlier attempts used the principle of virtual ray tracing, which is inherently a Monte Carlo method and therefore subjects to noise at sharp transition edges. Furthermore, this approach cannot render the Point Spread Function (PSF). We took the reverse approach of physical ray tracing. Rays are generated from each illuminating point and traced forward to the retina. After the tracing process, each light source point generates a number of ray-retina intersections. We propose a novel rasterization algorithm to render the radiometric image on the virtual retina. For the radiometric-correct determination of pixel value, we used the Intersection-over-Area metric. This metric is an indicator for how much the back-projected area of each retinal cell overlaps the triangle formed by the cone of light flux falling on the retina. The geometry of the overlapping was found with the Sutherland-Hodgman algorithm. All images of source points were then superposed to find the resulting image. With the proposed software, it is possible to simulate different VR-optics in combination with eye models. The most significant challenge of the physical ray tracing was the huge calculation workload. Our software overcame this by utilizing parallel GPU computing capability offered by the CUDA platform. For evaluation, we used the software with different schematic models of the eye, including those from Gullstrand, Le Grand, Koojiman and Navarro. We found that the former two, so-called paraxial, eye models predict the eye has higher sagittal resolution than tangential. On the other hand, the latter two (so-called finite eye models) predict the opposite. With this simulation software, optical designers can validate their VR-optics-design and gain more insights about the image formation process. This tool can be used as a virtual optical test bench for the simulation of the imaging result before building a costly prototype.

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Keywords/Phrases

Search In:

Publication Years