Abstract: Systems and methods for generating a panorama image. Captured images are coarsely aligned, and then finely aligned based on a combination of constraint values. The panorama image is generated from the finely aligned images.

Abstract: A method and system for generating a virtual representation of a physical scene, including receiving scene data corresponding to the physical scene, processing the scene data to determine scene components and scene priors corresponding to the scene components, generating, by a plurality of neural networks, dense geometric representations based at least in part on the scene priors, where each dense geometric representation corresponds to a scene component in the scene components, generating a virtual model of the physical scene based at least in part on the dense geometric representations, and generating a virtual representation of the physical scene based at least in part on the scene data, the virtual representation being aligned with the virtual model.

Abstract: System and method for rendering virtual objects onto an image.

Abstract: Systems and methods for generating a panorama image. Captured images are coarsely aligned, and then finely aligned based on a combination of constraint values. The panorama image is generated from the finely aligned images.

Abstract: System and method for rendering virtual objects onto an image.

Abstract: A method and system for generating a virtual representation of a physical scene, including receiving scene data corresponding to the physical scene, processing the scene data to determine scene components and scene priors corresponding to the scene components, generating, by a plurality of neural networks, dense geometric representations based at least in part on the scene priors, where each dense geometric representation corresponds to a scene component in the scene components, generating a virtual model of the physical scene based at least in part on the dense geometric representations, and generating a virtual representation of the physical scene based at least in part on the scene data, the virtual representation being aligned with the virtual model.

Abstract: A machine learning model can be trained using a first set of degraded images for each of a plurality of combinations and corresponding reference images, where a number of degraded images in the first set corresponding to a particular combination of the plurality of combinations is selected in accordance with a probability value associated with the particular combination. A validation process can be used to determine a loss value for each of the plurality of combinations of degradations. Updates to the probability values associated with the plurality of combinations can be calculated based on the loss values. The machine learning model can be updated using a second set of degraded images for each of the plurality of combinations, and the corresponding reference images, where a number of degraded images in the second set corresponding to the particular combination is selected based on the updated probability value.

Abstract: A method and system for generating a virtual representation of a physical scene, including receiving scene data corresponding to the physical scene, processing the scene data to determine scene components and scene priors corresponding to the scene components, generating, by a plurality of neural networks, dense geometric representations based at least in part on the scene priors, where each dense geometric representation corresponds to a scene component in the scene components, generating a virtual model of the physical scene based at least in part on the dense geometric representations, and generating a virtual representation of the physical scene based at least in part on the scene data, the virtual representation being aligned with the virtual model.

Abstract: A method for determining a visual scene virtual representation and a highly accurate visual scene-aligned geometric representation for virtual interaction.

Abstract: System and method for rendering virtual objects onto an image.

Abstract: Systems and methods for generating a panorama image. Captured images are coarsely aligned, and then finely aligned based on a combination of constraint values. The panorama image is generated from the finely aligned images.

Abstract: A method for determining a visual scene virtual representation and a highly accurate visual scene-aligned geometric representation for virtual interaction.

Abstract: A machine learning model can be trained using a first set of degraded images for each of a plurality of combinations and corresponding reference images, where a number of degraded images in the first set corresponding to a particular combination of the plurality of combinations is selected in accordance with a probability value associated with the particular combination. A validation process can be used to determine a loss value for each of the plurality of combinations of degradations. Updates to the probability values associated with the plurality of combinations can be calculated based on the loss values. The machine learning model can be updated using a second set of degraded images for each of the plurality of combinations, and the corresponding reference images, where a number of degraded images in the second set corresponding to the particular combination is selected based on the updated probability value.

Abstract: A display system with temperature compensation includes (a) a backlight unit containing a light emitting diode (LED) array, (b) a liquid crystal display (LCD) containing a plurality of pixels for spatially modulating, according to respective LCD drive values of the pixels, transmission of light generated by the LED array, (c) a plurality of temperature probes mounted to the backlight unit for measuring a respective plurality of temperatures at the LED array, (d) a light-field simulator for simulating, at least in part based upon the temperatures, a light field at the LCD as generated by the LED array, and (e) an LCD drive solver for processing a target image and the light field simulated by the light-field simulator, to determine the LCD drive values required to display the target image as compensated for temperatures of the LED array.

Abstract: A display system with temperature compensation includes (a) a backlight unit containing a light emitting diode (LED) array, (b) a liquid crystal display (LCD) containing a plurality of pixels for spatially modulating, according to respective LCD drive values of the pixels, transmission of light generated by the LED array, (c) a plurality of temperature probes mounted to the backlight unit for measuring a respective plurality of temperatures at the LED array, (d) a light-field simulator for simulating, at least in part based upon the temperatures, a light field at the LCD as generated by the LED array, and (e) an LCD drive solver for processing a target image and the light field simulated by the light-field simulator, to determine the LCD drive values required to display the target image as compensated for temperatures of the LED array.

Abstract: The present invention provides a method for converting a low dynamic range (LDR) image to a high dynamic range (HDR) image, including obtaining an input LDR image; and in an HDR image database, selecting one or more HDR images that match the input LDR image as candidate images. Further, the candidate images are warped according to a spatial correspondence between the candidate images and the input LDR image. The input LDR image is decomposed to an illumination component and a texture component. The illumination component and the texture component are reconstructed respectively based on the warped candidate images. The reconstructed illumination component and the reconstructed texture component are combined to produce an output HDR image.

Abstract: The present invention provides a method for converting a low dynamic range (LDR) image to a high dynamic range (HDR) image, including obtaining an input LDR image; and in an HDR image database, selecting one or more HDR images that match the input LDR image as candidate images. Further, the candidate images are warped according to a spatial correspondence between the candidate images and the input LDR image. The input LDR image is decomposed to an illumination component and a texture component. The illumination component and the texture component are reconstructed respectively based on the warped candidate images. The reconstructed illumination component and the reconstructed texture component are combined to produce an output HDR image.