AGGREGATING FEATURES FROM MULTIPLE IMAGES TO GENERATE HISTORICAL DATA FOR A CAMERA

    公开(公告)号:US20240144526A1

    公开(公告)日:2024-05-02

    申请号:US17978526

    申请日:2022-11-01

    CPC classification number: G06T7/74 G06T7/251 G06T19/006

    Abstract: Techniques for generating an aggregated set of features from multiple images generated by a camera are disclosed. A first image generated by the camera is accessed, where the first image was generated at a first time. A first set of features are identified from within the first image. A second image generated by the camera is accessed, where the second image is generated at a subsequent, second time. A second set of features are identified from within the second image. Movement data is obtained. This movement data details a movement of the camera between the first and second times. The movement data is used to reproject a pose embodied in the first image to correspond to a pose embodied in the second image. The embodiments aggregate the two sets of features to generate the aggregated set of features. The aggregated set of features for the camera are then cached.

    SYSTEMS AND METHODS FOR STRUCTURED LIGHT DEPTH COMPUTATION USING SINGLE PHOTON AVALANCHE DIODES

    公开(公告)号:US20230254603A1

    公开(公告)日:2023-08-10

    申请号:US18299201

    申请日:2023-04-12

    CPC classification number: H04N25/705 G02B27/0172 G02B2027/0138

    Abstract: A system for structured light depth computation using single photon avalanche diodes (SPADs) is configurable to, over a frame capture time period, selectively activate the illuminator to perform interleaved structured light illumination operations. The interleaved structured light illumination operations comprise alternately emitting at least a first structured light pattern from the illuminator and emitting at least a second structured light pattern from the illuminator. The system is also configurable to, over the frame capture time period, perform a plurality of sequential shutter operations to configure each SPAD pixel of the SPAD array to enable photon detection. The plurality of sequential shutter operations generates, for each SPAD pixel of the SPAD array, a plurality of binary counts indicating whether a photon was detected during each of the plurality of sequential shutter operations.

    GENERATE SUPER-RESOLUTION IMAGES FROM SPARSE COLOR INFORMATION

    公开(公告)号:US20230214962A1

    公开(公告)日:2023-07-06

    申请号:US18108788

    申请日:2023-02-13

    CPC classification number: G06T3/4061 H04N23/10 H04N23/951 G06T3/4007

    Abstract: Techniques for generating a high resolution full color output image from lower resolution sparse color input images are disclosed. A camera generates images. The camera's sensor has a sparse Bayer pattern. While the camera is generating the images, IMU data for each image is acquired. The IMU data indicates a corresponding pose the camera was in while the camera generated each image. The images and IMU data are fed into a motion model, which performs temporal filtering on the images and uses the IMU data to generate a red-only image, a green-only image, a blue-only image, and a monochrome image. The color images are up-sampled to match the resolution of the monochrome image. A high resolution output color image is generated by combining the up-sampled images and the monochrome image.

    RAPID TARGET ACQUISITION USING GRAVITY AND NORTH VECTORS

    公开(公告)号:US20230148231A1

    公开(公告)日:2023-05-11

    申请号:US17524270

    申请日:2021-11-11

    CPC classification number: G06T15/205 G06T7/33 G06T7/97

    Abstract: Techniques for aligning images generated by two cameras are disclosed. This alignment is performed by computing a relative 3D orientation between the two cameras. A first gravity vector for a first camera and a second gravity vector for a second camera are determined. A first camera image is obtained from the first camera, and a second camera image is obtained from the second camera. A first alignment process is performed to partially align the first camera's orientation with the second camera's orientation. This process is performed by aligning the gravity vectors, thereby resulting in two degrees of freedom of the relative 3D orientation being eliminated. Visual correspondences between the two images are identified. A second alignment process is performed to fully align the orientations. This process is performed by using the identified visual correspondences to identify and eliminate a third degree of freedom of the relative 3D orientation.

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