Olivier Mercier

Raffles Xingqi Zhu, Charlie S. Burlingham, Olivier Mercier et al.

Stereoscopic head-mounted displays (HMDs) render and present binocular images to create an egocentric, 3D percept to the HMD user. Within this render and presentation pipeline there are potential rendering camera and viewing position errors that can induce deviations in the depth and distance that a user perceives compared to the underlying intended geometry. For example, rendering errors can arise when HMD render cameras are incorrectly positioned relative to the assumed centers of projections of the HMD displays and viewing errors can arise when users view stereo geometry from the incorrect location in the HMD eyebox. In this work we present a geometric framework that predicts errors in distance perception arising from inaccurate HMD perspective geometry and build an HMD platform to reliably simulate render and viewing error in a Quest 3 HMD with eye tracking to experimentally test these predictions. We present a series of five experiments to explore the efficacy of this geometric framework and show that errors in perspective geometry can induce both under- and over-estimations in perceived distance. We further demonstrate how real-time visual feedback can be used to dynamically recalibrate visuomotor mapping so that an accurate reach distance is achieved even if the perceived visual distance is negatively impacted by geometric error.

Marie-Pier Cote, Brian Hartman, Olivier Mercier et al.

Due to confidentiality issues, it can be difficult to access or share interesting datasets for methodological development in actuarial science, or other fields where personal data are important. We show how to design three different types of generative adversarial networks (GANs) that can build a synthetic insurance dataset from a confidential original dataset. The goal is to obtain synthetic data that no longer contains sensitive information but still has the same structure as the original dataset and retains the multivariate relationships. In order to adequately model the specific characteristics of insurance data, we use GAN architectures adapted for multi-categorical data: a Wassertein GAN with gradient penalty (MC-WGAN-GP), a conditional tabular GAN (CTGAN) and a Mixed Numerical and Categorical Differentially Private GAN (MNCDP-GAN). For transparency, the approaches are illustrated using a public dataset, the French motor third party liability data. We compare the three different GANs on various aspects: ability to reproduce the original data structure and predictive models, privacy, and ease of use. We find that the MC-WGAN-GP synthesizes the best data, the CTGAN is the easiest to use, and the MNCDP-GAN guarantees differential privacy.