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    MULTI-SOURCE POSE MERGING FOR DEPTH ESTIMATION
    1.
    发明公开
    MULTI-SOURCE POSE MERGING FOR DEPTH ESTIMATION 审中-公开

    公开(公告)号：US20240354979A1

    公开(公告)日：2024-10-24

    申请号：US18435458

    申请日：2024-02-07

    Applicant: QUALCOMM Incorporated

    Inventor： Sai Madhuraj Jadhav ,  Amin Ansari ,  Yunxiao Shi

    IPC: G06T7/55 ,  G06T7/70 ,  G06T11/00

    CPC classification number: G06T7/55 ,  G06T7/70 ,  G06T11/00 ,  G06T2207/20081 ,  G06T2207/20212 ,  G06T2207/30244

    Abstract: This disclosure provides systems, methods, and devices for image signal processing that support multi-source pose merging for depth estimation. In a first aspect, a method of image processing includes generating, in accordance with first image data of a first image frame and second image data of a second image frame, a first mask indicating one or more pixels determined not to change position between the first image frame and the second image frame, generating, in accordance with the first image data and the second image data, a second mask indicating one or more pixels determined not to change position between the first image frame and the second image frame, and combining the first mask with the second mask to generate a third mask. Other aspects and features are also claimed and described.

    SELF-SUPERVISED MULTI-FRAME DEPTH ESTIMATION WITH ODOMETRY FUSION
    4.
    发明公开
    SELF-SUPERVISED MULTI-FRAME DEPTH ESTIMATION WITH ODOMETRY FUSION 审中-公开

    公开(公告)号：US20240362807A1

    公开(公告)日：2024-10-31

    申请号：US18309444

    申请日：2023-04-28

    Applicant: QUALCOMM Incorporated

    Inventor： Yunxiao Shi ,  Amin Ansari ,  Sai Madhuraj Jadhav ,  Avdhut Joshi

    IPC: G06T7/55 ,  G06T7/254

    CPC classification number: G06T7/55 ,  G06T7/254 ,  G06T2207/20084 ,  G06T2207/30252

    Abstract: An example device for processing image data includes a processing unit configured to: receive, from a camera of a vehicle, a first image frame at a first time and a second image frame at a second time; receive, from an odometry unit of the vehicle, a first position of the vehicle at the first time and a second position of the vehicle at a second time; calculate a pose difference value representing a difference between the second and first positions; form a pose frame having a size corresponding to the first and second image frames and sample values including the pose difference value; and provide the first and second image frames and the pose frame to a neural networking unit configured to calculate depth for objects in the first image frame and the second image frame, the depth for the objects representing distances between the objects and the vehicle.

    USING LIDAR OBJECTNESS TO ADDRESS VEHICLE SLICING IN SUPERVISED DEPTH ESTIMATION
    6.
    发明申请
    USING LIDAR OBJECTNESS TO ADDRESS VEHICLE SLICING IN SUPERVISED DEPTH ESTIMATION 有权

    公开(公告)号：US20240367674A1

    公开(公告)日：2024-11-07

    申请号：US18310403

    申请日：2023-05-01

    Applicant: QUALCOMM Incorporated

    Inventor： Sai Madhuraj Jadhav ,  Amin Ansari ,  Yunxiao Shi ,  Avdhut Joshi

    IPC: B60W60/00 ,  G06T7/13 ,  G06T7/521

    Abstract: Example systems and techniques are described for controlling operation of a vehicle and training a machine learning model for controlling operation of a vehicle. A system includes memory configured to store point cloud data associated with the vehicle and one or more processors communicatively coupled to the memory. The one or more processors are configured to determine a depth map indicative of distance of one or more objects to the vehicle and control operation of a vehicle based on the depth map. The depth map is based on executing a machine learning model, the machine learning model being trained with a slice loss function determined from training point cloud data having a respective depth that is greater than the average depth for a set of points of the point cloud data plus a threshold.

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