An apparatus (1, 5, 6) is described which includes two or more colour displays (2) arranged to provide piece-wise continuous illumination of a volume. The apparatus (1, 5, 6) also includes one or more cameras (3). Each camera (3) is arranged to image the volume. The apparatus (1, 5, 6) is configured to control the two or more colour displays (2) and the one or more cameras (3) to illuminate the volume with each of two or more illumination conditions. The apparatus (1, 5, 6) is also configured to obtain two or more sets of images. Each set of images is obtained during illumination of the volume with one or more corresponding illumination conditions. The two or more sets of images include sufficient information for calculation of a reflectance map and a photometric normal map of an object or subject (4) positioned within the volume. When viewed from the volume, the apparatus (1, 5, 6) only provides direct illumination of the volume from angles within a zone of a hemisphere. The zone is less than a hemisphere and corresponds to a first range (Δα) of latitudinal angles and a second range (Δβ) of longitudinal angles. Each of the first (Δα) and second ranges (Δβ) is no more than 17π/18.

An apparatus (1, 5, 6) is described which includes two or more colour displays (2) arranged to provide piece-wise continuous illumination of a volume. The apparatus (1, 5, 6) also includes one or more cameras (3). Each camera (3) is arranged to image the volume. The apparatus (1, 5, 6) is configured to control the two or more colour displays (2) and the one or more cameras (3) to illuminate the volume with each of two or more illumination conditions. The apparatus (1, 5, 6) is also configured to obtain two or more sets of images. Each set of images is obtained during illumination of the volume with one or more corresponding illumination conditions. The two or more sets of images include sufficient information for calculation of a reflectance map and a photometric normal map of an object or subject (4) positioned within the volume. When viewed from the volume, the apparatus (1, 5, 6) only provides direct illumination of the volume from angles within a zone of a hemisphere. The zone is less than a hemisphere and corresponds to a first range (Δα) of latitudinal angles and a second range (Δβ) of longitudinal angles. Each of the first (Δα) and second ranges (Δβ) is no more than 17π/18.

A system to determine a position of one or more objects includes a transmitter to emit a beam of photons to sequentially illuminate regions of one or more objects; multiple cameras that are spaced-apart with each camera having an array of pixels to detect photons; and one or more processor devices that execute stored instructions to perform actions of a method, including: directing the transmitter to sequentially illuminate regions of one or more objects with the beam of photons; for each of the regions, receiving, from the cameras, an array position of each pixel that detected photons of the beam reflected or scattered by the region of the one or more objects; and, for each of the regions detected by the cameras, determining a position of the regions using the received array positions of the pixels that detected the photons of the beam reflected or scattered by that region.

A system to determine a position of one or more objects includes a transmitter to emit a beam of photons to sequentially illuminate regions of one or more objects; multiple cameras that are spaced-apart with each camera having an array of pixels to detect photons; and one or more processor devices that execute stored instructions to perform actions of a method, including: directing the transmitter to sequentially illuminate regions of one or more objects with the beam of photons; for each of the regions, receiving, from the cameras, an array position of each pixel that detected photons of the beam reflected or scattered by the region of the one or more objects; and, for each of the regions detected by the cameras, determining a position of the regions using the received array positions of the pixels that detected the photons of the beam reflected or scattered by that region.

Certain aspects of the present disclosure provide techniques for machine learning. A depth output from a depth model is generated based on an input image frame. A depth loss for the depth model is determined based on the depth output and an estimated ground truth for the input image frame, the estimated ground truth comprising estimated depths for a set of pixels of the input image frame. A total loss for the depth model is determined based at least in part on the depth loss. The depth model is updated based on the total loss, and a new depth output, generated using the updated depth model, is output.

A method and apparatus with image processing are disclosed. The method includes determining a real part image, an imaginary part image, and an offset image based on input images that are dependent on infrared rays of different phases, removing noise from each of the real part image and the imaginary part image using the offset image as a noise removal guide, and generating a depth image based on an improved real part image and an improved imaginary part image corresponding to respective results of the removing.

A method and apparatus with image processing are disclosed. The method includes determining a real part image, an imaginary part image, and an offset image based on input images that are dependent on infrared rays of different phases, removing noise from each of the real part image and the imaginary part image using the offset image as a noise removal guide, and generating a depth image based on an improved real part image and an improved imaginary part image corresponding to respective results of the removing.

Methods, systems, and non-transitory computer readable media for computing physical dimensions of feet based on user-captured images are described. In at least one embodiment, an exemplary method comprises: receiving, by a server from a user device, an image of the user's foot or feet; segmenting the image to identify the user's foot or feet; computing the one or more physical parameters of the user's foot or feet.

Methods, systems, and non-transitory computer readable media for computing physical dimensions of feet based on user-captured images are described. In at least one embodiment, an exemplary method comprises: receiving, by a server from a user device, an image of the user's foot or feet; segmenting the image to identify the user's foot or feet; computing the one or more physical parameters of the user's foot or feet.

A first aspect of the invention provides a method of training a neural network for capturing volumetric video, comprising: generating a 3D model of a scene; using the 3D model to generate a high fidelity depth map; capturing a perceived depth map of the scene, having a field of view that is aligned with a field of view of the high fidelity depth map; and training the neural network based on the high fidelity depth map and the perceived depth map, wherein the high fidelity depth map has a higher fidelity to the scene than the perceived depth map has.