Point-based object localization from images

    公开(公告)号:US11373332B2

    公开(公告)日:2022-06-28

    申请号:US17142195

    申请日:2021-01-05

    Abstract: Techniques and systems are provided for determining features of one or more objects in one or more images. For example, an image of an object and a three-dimensional model associated with the object can be obtained. From the image, a sample point on the object can be determined. A depth and an angle of the sample point of the object can be determined. A pose and a shape of the three-dimensional model associated with the object can be determined based on the depth and the angle.

    VEHICLE SHAPE AND POSE DETERMINATION FOR VEHICLE APPLICATIONS

    公开(公告)号:US20240394914A1

    公开(公告)日:2024-11-28

    申请号:US18321616

    申请日:2023-05-22

    Abstract: This disclosure provides systems, methods, and devices for vehicle driving assistance systems that support image processing. In a first aspect, a method includes receiving images of a vehicle and determining locations of key points of the vehicle within the keyframes. Pose estimations may then be determined based on the key point locations, and a three-dimensional contour of the vehicle may be determined based on the pose estimations. A model may then be trained based on the three-dimensional contour of the vehicle. Other aspects and features are also claimed and described.

    Hybrid lane estimation using both deep learning and computer vision

    公开(公告)号:US11544940B2

    公开(公告)日:2023-01-03

    申请号:US16733111

    申请日:2020-01-02

    Abstract: Disclosed are techniques for lane estimation. In aspects, a method includes receiving a plurality of camera frames captured by a camera sensor of a vehicle, assigning a first subset of the plurality of camera frames to a deep learning (DL) detector and a second subset of the plurality of camera frames to a computer vision (CV) detector based on availability of the DL and CV detectors, identifying a first set of lane boundary lines in a first camera frame processed by the DL detector, identifying a second set of lane boundary lines in a second camera frame processed by the CV detector, generating first and second sets of lane models based on the first and second sets of lane boundary lines, and updating a set of previously identified lane models based on the first set of lane models and/or the second set of lane models.

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