Abstract: A digital image detection apparatus and method of use are disclosed. The apparatus may include an array of photon detectors, configured to receive photons reflected from a target scene and a plurality of first arrival differential (FAD) units, where each FAD units is configured to receive a first input from a first photon detector in the array and a second input from a second photon detector. Each FAD unit may include a set-reset (“SR”) latch, configured to receive the first input and the second input and to determine which of the first input and the second input arrives earlier in time, and a counter control unit, configured to receive an output from the SR latch and increment a differential count based on the output.

Abstract: Methods, system, and computer storage media are provided for generating physical-based materials for rendering digital objects with an appearance of a real-world material. Images depicted the real-world material, including diffuse component images and specular component images, are captured using different lighting patterns, which may include area lights. From the captured images, approximations of one or more material maps are determined using a photometric stereo technique. Based on the approximations and the captured images, a neural network system generates a set of material maps, such as a diffuse albedo material map, a normal material map, a specular albedo material map, and a roughness material map. The material maps from the neural network may be optimized based on a comparison of the input images of the real-world material and images rendered from the material maps.

Abstract: Methods, system, and computer storage media are provided for generating physical-based materials for rendering digital objects with an appearance of a real-world material. Images depicted the real-world material, including diffuse component images and specular component images, are captured using different lighting patterns, which may include area lights. From the captured images, approximations of one or more material maps are determined using a photometric stereo technique. Based on the approximations and the captured images, a neural network system generates a set of material maps, such as a diffuse albedo material map, a normal material map, a specular albedo material map, and a roughness material map. The material maps from the neural network may be optimized based on a comparison of the input images of the real-world material and images rendered from the material maps.