3D shape measurement of discontinuous specular objects based on advanced PMD with bi-telecentric lens

This paper presents an advanced phase measuring deflectometry (PMD) method based on a novel mathematical model to obtain three dimensional (3D) shape of discontinuous specular object using a bi-telecentric lens. The proposed method uses an LCD screen, a flat beam splitter, a camera with a bi-telecentric lens, and a translating stage. The LCD screen is used to display sinusoidal fringe patterns and can be moved by the stage to two different positions along the normal direction of a reference plane. The camera captures the deformed fringe patterns reflected by the measured specular surface. The splitter realizes the fringe patterns displaying and imaging from the same direction. Using the proposed advanced PMD method, the depth data can be directly calculated from absolute phase, instead of integrating gradient data. In order to calibrate the relative orientation of the LCD screen and the camera, an auxiliary plane mirror is used to reflect the pattern on the LCD screen three times. After the geometric calibration, 3D shape data of the measured specular objects are calculated from the phase differences between the reference plane and the reflected surface. The experimental results show that 3D shape of discontinuous specular object can be effectively and accurately measured from absolute phase data by the proposed advanced PMD method

Towards End-to-end Video-based Eye-Tracking

Estimating eye-gaze from images alone is a challenging task, in large parts due to un-observable person-specific factors. Achieving high accuracy typically requires labeled data from test users which may not be attainable in real applications. We observe that there exists a strong relationship between what users are looking at and the appearance of the user's eyes. In response to this understanding, we propose a novel dataset and accompanying method which aims to explicitly learn these semantic and temporal relationships. Our video dataset consists of time-synchronized screen recordings, user-facing camera views, and eye gaze data, which allows for new benchmarks in temporal gaze tracking as well as label-free refinement of gaze. Importantly, we demonstrate that the fusion of information from visual stimuli as well as eye images can lead towards achieving performance similar to literature-reported figures acquired through supervised personalization. Our final method yields significant performance improvements on our proposed EVE dataset, with up to a 28 percent improvement in Point-of-Gaze estimates (resulting in 2.49 degrees in angular error), paving the path towards high-accuracy screen-based eye tracking purely from webcam sensors. The dataset and reference source code are available at https://ait.ethz.ch/projects/2020/EVEComment: Accepted at ECCV 202