Scene flow estimation using shared features

    公开(公告)号:US10986325B2

    公开(公告)日:2021-04-20

    申请号:US16569104

    申请日:2019-09-12

    Abstract: Scene flow represents the three-dimensional (3D) structure and movement of objects in a video sequence in three dimensions from frame-to-frame and is used to track objects and estimate speeds for autonomous driving applications. Scene flow is recovered by a neural network system from a video sequence captured from at least two viewpoints (e.g., cameras), such as a left-eye and right-eye of a viewer. An encoder portion of the system extracts features from frames of the video sequence. The features are input to a first decoder to predict optical flow and a second decoder to predict disparity. The optical flow represents pixel movement in (x,y) and the disparity represents pixel movement in z (depth). When combined, the optical flow and disparity represent the scene flow.

    SCENE FLOW ESTIMATION USING SHARED FEATURES
    2.
    发明申请

    公开(公告)号:US20200084427A1

    公开(公告)日:2020-03-12

    申请号:US16569104

    申请日:2019-09-12

    Abstract: Scene flow represents the three-dimensional (3D) structure and movement of objects in a video sequence in three dimensions from frame-to-frame and is used to track objects and estimate speeds for autonomous driving applications. Scene flow is recovered by a neural network system from a video sequence captured from at least two viewpoints (e.g., cameras), such as a left-eye and right-eye of a viewer. An encoder portion of the system extracts features from frames of the video sequence. The features are input to a first decoder to predict optical flow and a second decoder to predict disparity. The optical flow represents pixel movement in (x,y) and the disparity represents pixel movement in z (depth). When combined, the optical flow and disparity represent the scene flow.

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