Abstract: A pixel for an image sensor includes a photon sensor, a memory, and an accumulator. The photon sensor element portion outputs a signal to an accumulator, which then may add value stored in memory together and may transfer the result to the memory of another pixel. The pixel may be analog or digital and may be used in a pixel array for compressed sensing imaging. During imaging, a pseudo-random mask may also be used. The data collected during imaging may be used in compressed sensing and may be sent to an offline storage to be processed.

Abstract: An image sensor includes a time-resolving sensor and a processor. The time-resolving sensor outputs a first signal and a second signal pair in response detecting one or more photons that have been reflected from an object. A first ratio of a magnitude of the first signal to a sum of the magnitude of the first signal and a magnitude of the second signal is proportional to a time of flight of the one or more detected photons. A second ratio of the magnitude of the second signal to the sum of the magnitude of the first signal and the magnitude of the second signal is proportional to the time of flight of the one or more detected photons. The processor determines a surface reflectance of the object where the light pulse has been reflected based on the first signal and the second signal pair and may generate a grayscale image.

Abstract: A client device configured with a neural network includes a processor, a memory, a user interface, a communications interface, a power supply and an input device, wherein the memory includes a trained neural network received from a server system that has trained and configured the neural network for the client device. A server system and a method of training a neural network are disclosed.

Abstract: A pixel for an image sensor includes a photon sensor, a memory, and an accumulator. The photon sensor element portion outputs a signal to an accumulator, which then may add value stored in memory together and may transfer the result to the memory of another pixel. The pixel may be analog or digital and may be used in a pixel array for compressed sensing imaging. During imaging, a pseudo-random mask may also be used. The data collected during imaging may be used in compressed sensing and may be sent to an offline storage to be processed.

Abstract: A camera system. In some embodiments, the camera system includes a first laser, a camera, and a processing circuit connected to the first laser and to the camera. The first laser may be steerable, and the camera may include a pixel including a photodetector and a pixel circuit, the pixel circuit including a first time-measuring circuit.

Abstract: A system and a method determines a traveling time for a light pulse between a light pulse source and a pixel of a light sensor array based on a “Find Frequent Items in a Data Steam” technique. In one embodiment, raw timestamp data output from a pixel as a data stream may be temporarily stored, processed twice and then discarded to provide an exact determination of a traveling time estimate. In another embodiment, the raw timestamp data is processed once and discarded to provide an approximate determination of a traveling time estimate. The traveling time estimate may be updated during processing and the most-frequently occurring timestamp is available when processing the data stream is complete. There is no need to keep the raw data in a memory, thereby reducing the memory requirement associated with determining the traveling time of a light pulse.

Abstract: Provided is a 3D depth sensing system and method of providing an image based on a hybrid sensing array. The 3D sensing system including a light source configured to emit light, a hybrid sensing array comprising a 2D sensing region configured to detect ambient light reflected from an object and a 3D depth sensing region configured to detect the light emitted by the light source and reflected from the object, a metalens on the hybrid sensing array, the metalens being configured to direct the ambient light reflected from the object towards the 2D sensing region, and to direct the light emitted by the light source and reflected from the object towards the 3D depth sensing region, and a processing circuit configured to combine 2D image information provided by the 2D sensing region and 3D information provided by the 3D depth sensing region to generate a combined 3D image.

Abstract: A system and method for detecting a peak bin from among a plurality of bins in a window. In some embodiments, each of the bins has a lower limit and an upper limit and contains zero or more values. The method may include: identifying a first bin, from among a plurality of bins in a first subwindow of the window, the first bin containing n values, n being a positive integer, n being greater than or equal to the number of values in each of the other bins in the first subwindow; calculating a first height-to-area ratio, the first height-to-area ratio being equal to n divided by the number of values in the first subwindow; and comparing the first height-to-area ratio to a first threshold.

Abstract: A client device configured with a neural network includes a processor, a memory, a user interface, a communications interface, a power supply and an input device, wherein the memory includes a trained neural network received from a server system that has trained and configured the neural network for the client device. A server system and a method of training a neural network are disclosed.

Abstract: A system and a method are disclosed for a structured-light system to estimate depth in an image. An image is received in which the image is of a scene onto which a reference light pattern has been projected. The projection of the reference light pattern includes a predetermined number of particular sub-patterns. A patch of the received image and a sub-pattern of the reference light pattern are matched based on either a hardcode template matching technique or a probability that the patch corresponds to the sub-pattern. If a lookup table is used, the table may be a probability matrix, may contain precomputed correlations scores or may contain precomputed class IDs. An estimate of depth of the patch is determined based on a disparity between the patch and the sub-pattern.

Abstract: An image capture device is described. The image capture device includes an array of pixels, each pixel including a photodetector. A Bayer pattern color filter is disposed over a 4×4 subset of pixels in the array of pixels. The Bayer pattern color filter defines a first 2×2 subset of pixels sensitive to red light; a second 2×2 subset of pixels sensitive to green light; a third 2×2 subset of pixels sensitive to green light; and a fourth 2×2 subset of pixels sensitive to blue light. A set of 1×1 on-chip lenses (OCLs) includes a different 1×1 OCL disposed over each pixel in the second 2×2 subset of pixels and the third 2×2 subset of pixels. A set of 2×1 OCLs or 2×2 OCLs includes a 2×1 OCL or a 2×2 OCL disposed over each pixel in the first 2×2 subset of pixels and the fourth 2×2 subset of pixels.

Abstract: A client device configured with a neural network includes a processor, a memory, a user interface, a communications interface, a power supply and an input device, wherein the memory includes a trained neural network received from a server system that has trained and configured the neural network for the client device. A server system and a method of training a neural network are disclosed.

Abstract: A system and a method are disclosed for a structured-light system to estimate depth in an image. An image is received in which the image is of a scene onto which a reference light pattern has been projected. The projection of the reference light pattern includes a predetermined number of particular sub-patterns. A patch of the received image and a sub-pattern of the reference light pattern are matched based on either a hardcode template matching technique or a probability that the patch corresponds to the sub-pattern. If a lookup table is used, the table may be a probability matrix, may contain precomputed correlations scores or may contain precomputed class IDs. An estimate of depth of the patch is determined based on a disparity between the patch and the sub-pattern.

Abstract: A camera system. In some embodiments, the camera system includes a first laser, a camera, and a processing circuit connected to the first laser and to the camera. The first laser may be steerable, and the camera may include a pixel including a photodetector and a pixel circuit, the pixel circuit including a first time-measuring circuit.

Abstract: A structured-light pattern for a structured-light system includes a base light pattern that includes a row of a plurality of sub-patterns extending in a first direction. Each sub-pattern is adjacent to at least one other sub-pattern, and each sub-pattern is different from each other sub-pattern. Each sub-pattern includes a first number of portions in a sub-row and a second number of portions in a sub-column. Each sub-row extends in the first direction and each sub-column extends in a second direction that is substantially orthogonal to the first direction. Each portion may be a first-type portion or a second-type portion. A size of a first-type portion is larger in the first direction and in the second direction than a size of a second-type portion in the first direction and in the second direction. In one embodiment, a first-type portion is a black portion and the second-type portion is a white portion.

Abstract: A camera system. In some embodiments, the camera system includes a first laser, a camera, and a processing circuit connected to the first laser and to the camera. The first laser may be steerable, and the camera may include a pixel including a photodetector and a pixel circuit, the pixel circuit including a first time-measuring circuit.

Abstract: A method is disclosed to determine a traveling time for a plurality of received light pulses that reflected and returned from an object. Each returned light pulse is associated with a timestamp indicating a time between a transmission time of a corresponding light pulse and a time of arrival of the returned light pulse. For each timestamp, a number C is determined of time stamps that are subsequent to the timestamp and within a predetermined time window after the timestamp. A maximum number C is determined, and an index i is determined for the maximum number C. A traveling time is determined for the plurality of light pulses as an average of the timestamp having a same index as the maximum number C and timestamps that are within the predetermined time window after the timestamp having the same index as the maximum number C.

Abstract: A pixel for an image sensor includes a photon sensor, a memory, and an accumulator. The photon sensor element portion outputs a signal to an accumulator, which then may add value stored in memory together and may transfer the result to the memory of another pixel. The pixel may be analog or digital and may be used in a pixel array for compressed sensing imaging. During imaging, a pseudo-random mask may also be used. The data collected during imaging may be used in compressed sensing and may be sent to an offline storage to be processed.

Abstract: A pixel of an image sensor includes a pinned photodiode (PPD), a switching device and an output circuit. A first terminal of the switching device is coupled to the PPD. A second terminal of the switching device is coupled to a floating diffusion (FD). A third terminal of the switching device is coupled to a first enable signal and a second enable signal. The switching device is responsive to the first enable signal to transfer a first charge on the PPD to the FD, and responsive to the second enable signal to transfer a second charge on the PPD to the FD. The output circuit outputs a first voltage based on the first charge and outputs a second voltage based on the second charge in which the first voltage corresponds to a time of flight of one or more detected photons and the second voltage corresponds to a greyscale image.

Abstract: An image sensor includes a time-resolving sensor and a processor. The time-resolving sensor outputs a first signal and a second signal pair in response detecting one or more photons that have been reflected from an object. A first ratio of a magnitude of the first signal to a sum of the magnitude of the first signal and a magnitude of the second signal is proportional to a time of flight of the one or more detected photons. A second ratio of the magnitude of the second signal to the sum of the magnitude of the first signal and the magnitude of the second signal is proportional to the time of flight of the one or more detected photons. The processor determines a surface reflectance of the object where the light pulse has been reflected based on the first signal and the second signal pair and may generate a grayscale image.