Abstract: Examples are disclosed relating to performing signal processing on time-of-flight sensor data using pixelwise temporal metrics. One example provides a computing system comprising a logic machine, and a storage machine holding instructions executable by the logic machine to obtain temporal phase data for a plurality of pixels as acquired by a time-of-flight image sensor, the temporal phase data comprising phase data for a plurality of light modulation frequencies, determine temporal active brightness data for the pixel, and, for each pixel of the plurality of pixels, determine a statistical metric for the temporal active brightness data. The instructions are further executable to perform phase unwrapping on the temporal phase data for the plurality of pixels to obtain a depth image, based on the statistical metric for the temporal active brightness data, perform a denoising operation on at least some pixels of the depth image, and output the depth image.

Abstract: Examples are disclosed relating to performing denoising and adaptive precision control on time-of-flight sensor data using noise metrics. One example provides a computing system, comprising, a logic machine, and a storage machine holding instructions executable by the logic machine to obtain time-of-flight (ToF) image data comprising a plurality of pixels, for each pixel of the ToF image data, determine one or more noise metrics by applying a spatial kernel, segment the ToF image data based on the one or more noise metrics to obtain differently classified pixels, during a denoising phase, process pixels of a first classification differently than pixels of a second classification, after the denoising phase, determine a depth image, and output the depth image.

Abstract: Examples are provided relating to recovering depth data from noisy phase data of low-signal pixels. One example provides a computing system, comprising a logic machine, and a storage machine holding instructions executable by the logic machine to process depth data by obtaining depth image data and active brightness image data for a plurality of pixels, the depth image data comprising phase data for a plurality of frequencies, and identifying low-signal pixels based at least on the active brightness image data. The instructions are further executable to apply a denoising filter to phase data of the low-signal pixels to obtain denoised phase data and not applying the denoising filter to phase data of other pixels. The instructions are further executable to, after applying the denoising filter, perform phase unwrapping on the phase data for the plurality of frequencies to obtain a depth image, and output the depth image.

Abstract: Examples are disclosed relating to performing denoising and adaptive precision control on time-of-flight sensor data using noise metrics. One example provides a computing system, comprising, a logic machine, and a storage machine holding instructions executable by the logic machine to obtain time-of-flight (ToF) image data comprising a plurality of pixels, for each pixel of the ToF image data, determine one or more noise metrics by applying a spatial kernel, segment the ToF image data based on the one or more noise metrics to obtain differently classified pixels, during a denoising phase, process pixels of a first classification differently than pixels of a second classification, after the denoising phase, determine a depth image, and output the depth image.

Abstract: Examples are disclosed relating to performing signal processing on time-of-flight sensor data using pixelwise temporal metrics. One example provides a computing system comprising a logic machine, and a storage machine holding instructions executable by the logic machine to obtain temporal phase data for a plurality of pixels as acquired by a time-of-flight image sensor, the temporal phase data comprising phase data for a plurality of light modulation frequencies, determine temporal active brightness data for the pixel, and, for each pixel of the plurality of pixels, determine a statistical metric for the temporal active brightness data. The instructions are further executable to perform phase unwrapping on the temporal phase data for the plurality of pixels to obtain a depth image, based on the statistical metric for the temporal active brightness data, perform a denoising operation on at least some pixels of the depth image, and output the depth image.

Abstract: Examples are provided relating to recovering depth data from noisy phase data of low-signal pixels. One example provides a computing system, comprising a logic machine, and a storage machine holding instructions executable by the logic machine to process depth data by obtaining depth image data and active brightness image data for a plurality of pixels, the depth image data comprising phase data for a plurality of frequencies, and identifying low-signal pixels based at least on the active brightness image data. The instructions are further executable to apply a denoising filter to phase data of the low-signal pixels to obtain denoised phase data and not applying the denoising filter to phase data of other pixels. The instructions are further executable to, after applying the denoising filter, perform phase unwrapping on the phase data for the plurality of frequencies to obtain a depth image, and output the depth image.