Dr. Song Chen Profile

Dr. Song Chen

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Proceedings Article | 10 March 2023 Poster + Paper

End-to-end sensor system modeling and validation for AR/VR/MR applications

Evan Kramer, Qing Chao, Michael Schaub, Song Chen, Wei Gao

Proceedings Volume 12415, 124150S (2023) https://doi.org/10.1117/12.2668460

KEYWORDS: Cameras, Modulation transfer functions, Simulations, Sensors, Distortion, Spatial frequencies, Monte Carlo methods, Imaging systems, Image sensors

Read Abstract +

Simultaneously prototyping several sensing architectures is costly and time consuming. Therefore, a modeling pipeline that is capable of both nanoscale electromagnetic wave and macroscale ray tracing simulations for the design, performance prediction, and validation of future AR/VR/MR sensing modalities is crucial. However, current end-to-end sensor modeling pipelines use several software packages with cumbersome interfaces between scene radiometry definition, macro optical component design, and photonics component design. In our paper, we demonstrate, implement and validate a sensor modeling pipeline with streamlined interfaces and modular component definition capabilities. The pipeline is composed of Ansys Speos for source radiometry and geometry definition, Zemax OpticStudio for camera lens system definition, and Lumerical FDTD for image sensor definition. This end-to-end pipeline is used to model a camera system developed at Reality Labs and designed for AR/VR/MR applications. The first section of this paper describes the camera system modeled in the pipeline. Validation efforts for the pipeline involved comparing simulated images, obtained from inverse Monte Carlo ray propagation simulations, to experimentally collected images with the corresponding hardware. Specifically, the distortion and modulation transfer function (MTF) were determined for the simulated and experimentally collected images using ImaTest image quality software. The second section of this paper describes these validation efforts and the obtained results. The modularity of the modeling pipeline is highlighted throughout the paper for its applicability to various sensing architectures of interest to AR/VR/MR technologies.

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