Abstract: Techniques related to generating holographic images are discussed. Such techniques include application of a machine learning model to the target image to generate data that is used to enable the determination of a phase pattern via an iterative propagation feedback model. The iterative propagation feedback model is used to generate a feedback strength value, which is then used to generate a phase diffraction pattern for presentation at a holographic plane.

Abstract: Techniques related to generating holographic images are discussed. Such techniques include application of a machine learning model to the target image to generate data that is used to enable the determination of a phase pattern via a wave propagation model. The wave propagation model is used to generate holographic data, which is then adjusted according to one or more constraints associated with the holographic display that will be used to generate a holographic image based on the adjusted holographic data.

Abstract: Disclosed herein are systems, apparatus, methods, and articles of manufacture to present three dimensional images without glasses. An example apparatus includes a micro lens array and at least one processor. The at least one processor is to: determine a first position of a first pupil of a viewer; determine a second position of a second pupil of the viewer; align a first eye box with the first position of the first pupil; align a second eye box with the second position of the second pupil; render, for presentation on a display, at least one of a color plus depth image or a light field image based on the first position of the first pupil and the second position of the second pupil; and cause backlight to be steered through the micro lens array and alternatingly through the first eye box and the second eye box.

Abstract: Techniques related to generating holographic images are discussed. Such techniques include application of a hybrid system including a pre-trained deep neural network and a subsequent iterative process using a suitable propagation model to generate diffraction pattern image data for a target holographic image such that the diffraction pattern image data is to generate a holographic image when implemented via a holographic display.

Abstract: Techniques related to generating holographic images are discussed. Such techniques include application of a hybrid system including a pre-trained deep neural network and a subsequent iterative process using a sui table propagation model to generate diffraction pattern image data for a target holographic image such that the diffraction pattern image data is to generate a holographic image when implemented via a holographic display.

Abstract: Disclosed herein are systems, apparatus, methods, and articles of manufacture to present three dimensional images without glasses. An example apparatus includes a micro lens array and at least one processor. The at least one processor is to: determine a first position of a first pupil of a viewer; determine a second position of a second pupil of the viewer; align a first eye box with the first position of the first pupil; align a second eye box with the second position of the second pupil; render, for presentation on a display, at least one of a color plus depth image or a light field image based on the first position of the first pupil and the second position of the second pupil; and cause backlight to be steered through the micro lens array and alternatingly through the first eye box and the second eye box.

Abstract: Techniques related to generating holographic images are discussed. Such techniques include application of a machine learning model to the target image to generate data that is used to enable the determination of a phase pattern via an iterative propagation feedback model. The iterative propagation feedback model is used to generate a feedback strength value, which is then used to generate a phase diffraction pattern for presentation at a holographic plane.

Abstract: In one implementation, an apparatus includes: a display to emit light in a first wavelength range; one or more light sources to emit light in a second wavelength range; a camera to detect the light in the second wavelength range; and an eyepiece to reflect and refract the light in the first wavelength range while passing, without substantial distortion, the light in the second wavelength range, wherein the eyepiece includes two lens halves separated by a retarder that changes light in the first wavelength range from a first polarization to a second polarization different from the first polarization.

Abstract: Techniques related to generating holographic images are discussed. Such techniques include application of a pre-trained deep neural network to a target holographic image to generate a feedback strength value for error feedback in an iterative propagation feedback model and generating a diffraction pattern image corresponding to the target holographic image by applying the iterative propagation feedback model based on the target holographic image and using the feedback strength value.

Abstract: Techniques related to generating holographic images are discussed. Such techniques include application of a machine learning model to the target image to generate data that is used to enable the determination of a phase pattern via a wave propagation model. The wave propagation model is used to generate holographic data, which is then adjusted according to one or more constraints associated with the holographic display that will be used to generate a holographic image based on the adjusted holographic data.

Abstract: Methods and apparatus for fabricating light-emitting diodes (LEDs) that include polarizing or wavefront-altering optical components are described in which additional optical components are added to the LED package to filter or modify the polarization of the emitted photons. Packaged LEDs that include polarizing or wavefront-altering optical components are also described. The packaged LEDs may emit infrared light that is polarized or otherwise altered by the optical components added during the fabrication process.

Abstract: In one implementation, an apparatus includes a display to emit light in a first wavelength range, a camera to detect light in a second wavelength range, an eyepiece to distort light in the first wavelength range, and one or more light sources, disposed between the eyepiece and the display, to emit light in the second wavelength range. In one implementation, an apparatus includes a display to emit light in a first wavelength range, one or more light sources to emit light in a second wavelength range, a camera to detect light in a second wavelength range, and an eyepiece to reflect and refract light in the first wavelength range while passing, without substantial distortion, light in the second wavelength range.

Abstract: Disclosed herein are systems, apparatus, methods, and articles of manufacture to present three dimensional images without glasses. An example apparatus includes a micro lens array and at least one processor. The at least one processor is to: determine a first position of a first pupil of a viewer; determine a second position of a second pupil of the viewer; align a first eye box with the first position of the first pupil; align a second eye box with the second position of the second pupil; render, for presentation on a display, at least one of a color plus depth image or a light field image based on the first position of the first pupil and the second position of the second pupil; and cause backlight to be steered through the micro lens array and alternatingly through the first eye box and the second eye box.

Abstract: In some examples, a three dimensional display system includes a display (for example, a display screen or a display panel), a micro lens array, and an eye tracker to track one or more eyes of a person and to provide eye location information. The display system also includes a rendering processor to render or capture color plus depth images (for example, RGB-D images) or light field images. The display system also includes a light field processor to use the eye location information to convert the rendered color plus depth images or light field images to display images to be provided to the display.

Abstract: In some examples, a three dimensional display system includes a display (for example, a display screen or a display panel), a micro lens array, and an eye tracker to track one or more eyes of a person and to provide eye location information. The display system also includes a rendering processor to render or capture color plus depth images (for example, RGB-D images) or light field images. The display system also includes a light field processor to use the eye location information to convert the rendered color plus depth images or light field images to display images to be provided to the display.

Abstract: A head-mounted display may include a display system and an optical system in a housing. The display system may have displays that produce images. Positioners may be used to move the displays relative to the eye positions of a user's eyes. An adjustable optical system may include tunable lenses such as tunable cylindrical liquid crystal lenses. The displays may be viewed through the lenses when the user's eyes are at the eye positions. A sensor may be incorporated into the head-mounted display to measure refractive errors in the user's eyes. The sensor may include waveguides and volume holograms, and a camera for gathering light that has reflected from the retinas of the user's eyes. Viewing comfort may be enhanced by adjusting display positions relative to the eye positions and/or by adjusting lens settings based on the content being presented on the display and/or measured refractive errors.

Abstract: An apparatus to facilitate generating a multi-focal/multi-plane (MF/MP) display is disclosed. The apparatus comprises one or more processors to generate a plurality full resolution views for each frame of a three-dimension (3D) scene, perform deep neural network (DNN) inferencing using the plurality of full resolution views to select two or more presentation planes from among a plurality of available planes for display.

Abstract: Techniques related to generating holographic images are discussed. Such techniques include application of a pre-trained deep neural network to a target holographic image to generate a feedback strength value for error feedback in an iterative propagation feedback model and generating a diffraction pattern image corresponding to the target holographic image by applying the iterative propagation feedback model based on the target holographic image and using the feedback strength value.

Abstract: A reflector assembly for a solid-state luminaire is disclosed. The disclosed reflector assembly may be configured, in accordance with some embodiments, to be disposed over a given printed circuit board (PCB) of a host luminaire such that emissions of emitters populated over that PCB are reflected out of the luminaire via the reflector assembly. In some embodiments, the reflector assembly may be formed from one or more reflective members, which may be generally bar-shaped or cup-shaped, or other example configurations. In some other embodiments, the reflector assembly may be formed from a bulk body having one or more reflective cavities formed therein. The particular configuration of a given reflective member or reflective cavity, as the case may be, of the reflector assembly, as well as the particular arrangement thereof for a host luminaire, may be customized as desired for a given target application or end-use.

Abstract: Techniques related to generating holographic images are discussed. Such techniques include application of a hybrid system including a pre-trained deep neural network and a subsequent iterative process using a sui table propagation model to generate diffraction pattern image data for a target holographic image such that the diffraction pattern image data is to generate a holographic image when implemented via a holographic display.