Abstract: A depth estimation system is described capable of determining depth information using two images from two cameras. A first camera captures a first image and a second camera captures a second image, both images including a plurality of light channels. A scan direction is selected from a plurality of scan directions. For the selected scan direction, along each of a plurality of scanlines, the system compares pixels from the first image to pixels from the second image. The comparison is based on calculating a census transform for each pixel in the first image and a census transform for each pixel in the second image. This comparison is used to determine a stereo correspondence between the pixels in the first image and the pixels in the second image. The system generates a depth map based on the stereo correspondence.

Abstract: An eye-tracking system includes a substrate transparent to visible light and configured to be placed in a field of view of an eye of a user, a plurality of waveguides on the substrate, a light source optically coupled to the plurality of waveguides, and a plurality of polarization volume holograms (PVHs) in the field of view of the eye of the user. Each PVH of the plurality of PVHs is optically coupled to a respective waveguide of the plurality of waveguides and is configured to couple a respective light beam out of the respective waveguide towards the eye of the user.

Abstract: A printing device includes a main control board, a resin vat, a vibration generating assembly, a detection assembly and a support plate. The vibration generating assembly includes a drive component and a paddle. A drive end of the drive component is connected to the paddle, and the drive end is configured to drive the paddle to vibrate. The detection assembly includes an elastic sheet, a resistance strain gage and a detection circuit. The resistance strain gage is attached to the elastic sheet, a first end of the elastic sheet is arranged on the first face of the support plate, and a second end of the elastic sheet extends into the liquid to be detected. The detection circuit is configured to determine an output voltage according to a resistance value of the resistance strain gage. Automatic detection of the liquid level depth of the liquid to be detected is achieving.

Abstract: Methods, systems, and media for partitioning neural networks are provided. In some embodiments, a method comprises obtaining a training set. The method comprises training a plurality of neural networks using the training set, wherein neural networks differ based on dimensions of one or more layers of the neural networks and a location of a compression block positioned between a first set of layers of a neural network and a second set of layers of the neural network. The method comprises selecting a neural network based on hardware constraints of a system on which the neural network is to be implemented, wherein the first set of layers of the selected neural network are executed by one or more sensor devices of the system, and wherein the second set of layers of the selected neural network are executed by an aggregator computing device of the system.

Abstract: A printing device includes: a base; a printing platform, the printing platform being connected to the base; a printing bracket, the printing bracket being fixed on the base; and a printing head assembly, the printing head assembly being connected to the printing bracket and being arranged above the printing platform, and the printing head assembly including a bearing member, a discharging assembly and an optical assembly. The discharging assembly and the optical assembly are both arranged on the bearing member, the discharging assembly is used for providing printing materials of at least two colors for the printing platform, and the optical assembly is used for curing the printing materials on the printing platform. The printing device can implement three-dimensional color printing.

Abstract: A light homogenization method for an LCD photocuring 3D printer, including: obtaining the initial grayscale value of a pixel in image data to be printed; obtaining a preset light homogenization compensation value, and calculating the difference between the preset light homogenization compensation value and the initial grayscale value, to obtain a target grayscale value; and controlling the LCD to perform, according to the target grayscale value, photocuring printing on the image data to be printed. The method can solve the problem of uneven exposure in photocuring 3D printing in the prior art, and improve the printing quality. A light homogenization apparatus for an LCD photocuring 3D printer, and a printer are correspondingly provided.

Abstract: In one example, an apparatus comprises a first photodiode, a second photodiode, a first floating diffusion, a second floating diffusion, a quantizer, and a controller. The controller can enable the first photodiode and the second photodiode to generate and accumulate photo charge within an exposure period, and use the quantizer to quantize reset voltages at the first floating diffusion and at the second floating diffusion to generate a first digital reset value and a second digital reset value. After the exposure period ends, the controller can transfer the photo charge from the first photodiode and the second photodiode to, respectively, the first floating diffusion and the second floating diffusion to generate a first signal voltage and a second signal voltage, and quantize the signal voltages into digital signal values using the quantizer. Digital representations can be generated based on the digital reset values and the digital signal values.

Abstract: In one example, a mobile device comprises: a physical link; a plurality of image sensors, each image sensor being configured to transmit image data via the physical link; and a controller coupled to the physical link, whereby the physical link, the plurality of image sensors, and the controller form a multi-drop network. The controller is configured to: transmit a control signal to configure image sensing operations at the plurality of image sensors; receive, via the physical link, image data from at least a subset of the plurality of image sensors; combine the image data from the at least a subset of the plurality of image sensors to obtain an extended field of view (FOV); determine information of a surrounding environment of the mobile device captured within the extended FOV; and provide the information to an application to generate content based on the information.

Abstract: Methods and systems for image sensing are provided. In one example, an apparatus comprises a semiconductor substrate comprising a light incident surface to receive light, a first pinned photodiode, and a second pinned photodiode, the first pinned photodiode and the second pinned photodiode forming a stack structure in the semiconductor substrate along an axis perpendicular to the light incident surface, the stack structure enabling the first pinned photodiode and the second pinned photodiode to, respectively, convert a first component of the light and a second component of the light to first charge and second charge. The apparatus further comprises one or more capacitors formed in the semiconductor substrate and configured to generate a first voltage and a second voltage based on, respectively, the first charge and the second charge.

Abstract: Methods and systems for sensing physical quantities are provided. In one example, an apparatus comprises: a plurality of sensing elements, each sensing element being configured to measure a physical quantity and to generate a measurement result; and a controller configured to: receive first measurement results from at least a subset of the plurality of sensing elements, each of the first measurement results corresponding to an event based at least in part on satisfying a relationship with a threshold; populate an event matrix based in part on the first measurement results; populate a change matrix based in part on the threshold; and generate information related to a distribution of the measurement results among the plurality of sensing elements based at least in part on the event matrix and the change matrix.

Abstract: Methods and systems for quantizing a physical quantity, such as light, are provided. In one example, an apparatus comprises an analog-to-digital (A/D) converter configured to generate raw digital outputs based on performing at least one of: (1) a first quantization operation to quantize a physical stimulus within a first intensity range based on a first A/D conversion relationship, or (2) a second quantization operation to quantize the physical stimulus within a second intensity range based on a second A/D conversion relationship; and a raw output conversion circuit configured generate a refined digital output based on a raw digital output obtained from the A/D converter and at least one predetermined conversion parameter. The at least one conversion parameter compensates for a discontinuity between the first A/D conversion relationship and the second A/D conversion relationship.

Abstract: In one example, an apparatus is provided. The apparatus includes a photodiode, a charge sensing unit, an analog-to-digital converter (ADC), and a controller. The controller is configured to: enable the photodiode to generate charge in response to incident light, accumulate at least a portion of the charge as residual charge until the photodiode becomes saturated by the residual charge, and transfer the remaining portion of the charge to the charge sensing unit as overflow charge if the photodiode becomes saturated by the residual charge. The controller is further configured to: generate, using the ADC, a first digital output based on the residual charge; after generating the first digital output, generate, using the ADC, a second digital output based on the overflow charge; and generate a digital representation of an intensity of the incident light based on at least one of the first digital output or the second digital output.

Abstract: The disclosed computer-implemented method may include (i) conditionally operating, at a first frequency, a first stage of an eye-tracking system processing pipeline that detects a region of interest and (ii) operating, at a second frequency that is substantially greater than the first frequency, a second stage of the eye-tracking system processing pipeline that predicts a gaze orientation based at least in part on the detected region of interest. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: The disclosed system may include a first layer that includes multiple digital pixel sensors configured to detect light. The system may also include a second layer that includes various image processing components configured to process the light detected by the digital pixel sensors. Still further, the system may include a third layer that includes machine learning (ML) hardware processing components. The image processing components of the second layer may be communicatively connected to the ML hardware processing components of the third layer via multiple micro through-silicon vias (uTSVs). Various other methods of manufacturing, apparatuses, and computer-readable media are also disclosed.

Abstract: Imaging systems, cameras, and image sensors of this disclosure include imaging pixels that include subpixels. Diffractive optical elements such as a metasurface lens layers or a liquid crystal polarization hologram (LCPH) are configured to focus image light to the subpixels of the imaging pixels.

Abstract: In one example, an apparatus comprises: a first sensor layer, including an array of pixel cells configured to generate pixel data; and one or more semiconductor layers located beneath the first sensor layer with the one or more semiconductor layers being electrically connected to the first sensor layer via interconnects. The one or more semiconductor layers comprises on-chip compute circuits configured to receive the pixel data via the interconnects and process the pixel data, the on-chip compute circuits comprising: a machine learning (ML) model accelerator configured to implement a convolutional neural network (CNN) model to process the pixel data; a first memory to store coefficients of the CNN model and instruction codes; a second memory to store the pixel data of a frame; and a controller configured to execute the codes to control operations of the ML model accelerator, the first memory, and the second memory.

Abstract: An eye monitoring system is included in a headset of a virtual reality system or of an augmented reality system. The eye monitoring system determines distances between the eye monitoring system and portions of a user's eye enclosed by the headset. The eye monitoring system projects a temporally periodic pattern of light onto the user's eye via a sensor. The eye monitoring system determines a distance between the eye monitoring system and locations of the user's eye based on a phase shift of the periodic pattern of light captured by each pixel of the sensor. From the determined distances, the eye monitoring system determines features of the user's eye.

Abstract: An image sensor includes imaging pixels and a patterned liquid crystal polarizer (LCP). The imaging pixel include subpixels. The patterned LCP is disposed over the subpixels and configured to direct a particular polarized portion of imaging light to particular subpixels.

Abstract: A stacked camera-image-sensor circuit may include (i) a first layer that includes a plurality of image sensing elements, (ii) a second layer that includes components that interface with the image sensing elements, and (iii) at least one additional layer that includes image-processing components. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: In one example, an apparatus comprises: a memory to store input data and weights, the input data comprising groups of data elements, each group being associated with a channel of channels, the weights comprising weight tensors, each weight tensor being associated with a channel of the channels; a data sparsity map generation circuit configured to generate, based on the input data, a channel sparsity map and a spatial sparsity map, the channel sparsity map indicating channels associated with first weights tensors to be selected, the spatial sparsity map indicating spatial locations of first data elements; a gating circuit configured to: fetch, based on the channel sparsity map and the sparsity map, the first weights tensors and the first data elements from the memory; and a processing circuit configured to perform neural network computations on the first data elements and the first weights tensors to generate a processing result.