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This paper introduces a new multiplane CGH computation method to reconstruct artefact-free high-quality holograms with natural-looking defocus blur. Our method introduces a new targeting scheme and a new loss function. While the targeting scheme accounts for defocused parts of the scene at each depth plane, the new loss function analyzes focused and defocused parts separately in reconstructed images. Our method support phase-only CGH calculations using various iterative (e.g., Gerchberg-Saxton, Gradient Descent) and non-iterative (e.g., Double Phase) CGH techniques. We achieve our best image quality using a modified gradient descent-based optimization recipe where we introduce a constraint inspired by the double phase method. We validate our method experimentally using our proof-of-concept holographic display, comparing various algorithms, including multi-depth scenes with sparse and dense contents., 16 pages in total, first 9 pages are for the manuscript, remaining pages are for supplementary. For more visit: https://complightlab.com/publications/realistic_defocus_cgh For our codebase visit https://github.com/complight/realistic_defocus

Computer-Generated Holography (CGH) algorithms often fall short in matching simulations with results from a physical holographic display. Our work addresses this mismatch by learning the holographic light transport in holographic displays. Using a camera and a holographic display, we capture the image reconstructions of optimized holograms that rely on ideal simulations to generate a dataset. Inspired by the ideal simulations, we learn a complex-valued convolution kernel that can propagate given holograms to captured photographs in our dataset. Our method can dramatically improve simulation accuracy and image quality in holographic displays while paving the way for physically informed learning approaches., 11 pages. Corrected a typo in equation 3

We introduce a new gaze tracker for Head Mounted Displays (HMDs). We modify two off-the-shelf HMDs to be gaze-aware using Light Emitting Diodes (LEDs). Our key contribution is to exploit the sensing capability of LEDs to create low-power gaze tracker for virtual reality (VR) applications. This yields a simple approach using minimal hardware to achieve good accuracy and low latency using light-weight supervised Gaussian Process Regression (GPR) running on a mobile device. With our hardware, we show that Minkowski distance measure based GPR implementation outperforms the commonly used radial basis function-based support vector regression (SVR) without the need to precisely determine free parameters. We show that our gaze estimation method does not require complex dimension reduction techniques, feature extraction, or distortion corrections due to off-axis optical paths. We demonstrate two complete HMD prototypes with a sample eye-tracked application, and report on a series of subjective tests using our prototypes., Comment: 14 pages, 7 figures. THIS WORK WAS CONDUCTED IN 2015

Computer-generated holography algorithms often fall short in matching simulations with results from a physical holographic display.Our work addresses this mismatch by learning the holographic light transport in holographic displays. Using a camera and a holographic display, we capture the image reconstructions of optimized holograms that rely on ideal simulations to generate a dataset. Inspired by the ideal simulations, we learn a complex-valued convolution kernel that can propagate given holograms to captured photographs in our dataset. Our method can dramatically improve simulation accuracy and image quality in holographic displays while paving the way for physically informed learning approaches., NA

Two well-known problems of stereoscopic displays are the accommodation-convergence conflict and the lack of natural blur for defocused objects. We present a new technique that we name Super Stereoscopy (SS3D) to provide a convenient solution to these problems. Regular stereoscopic glasses are replaced by SS3D glasses which deliver at least two parallax images per eye through pinholes equipped with light selective filters. The pinholes generate blur-free retinal images so as to enable correct accommodation, while the delivery of multiple parallax images per eye creates an approximate blur effect for defocused objects. Experiments performed with cameras and human viewers indicate that the technique works as desired. In case two, pinholes equipped with color filters per eye are used; the technique can be used on a regular stereoscopic display by only uploading a new content, without requiring any change in display hardware, driver, or frame rate. Apart from some tolerable loss in display brightness and decrease in natural spatial resolution limit of the eye because of pinholes, the technique is quite promising for comfortable and realistic 3D vision, especially enabling the display of close objects that are not possible to display and comfortably view on regular 3DTV and cinema.