Plane detection using semantic segmentation

    公开(公告)号:US10824864B2

    公开(公告)日:2020-11-03

    申请号:US16360732

    申请日:2019-03-21

    Applicant: Apple Inc.

    Abstract: In one implementation, a method of generating a plane hypothesis is performed by a head-mounted device (HMD) including one or more processors, non-transitory memory, and a scene camera. The method includes obtaining an image of a scene including a plurality of pixels. The method include obtaining a point cloud based on the image of the scene and generating an object classification set based on the image of the scene, each element of the object classification set including a respective plurality of pixels classified as a respective object in the scene. The method includes generating a plane hypothesis based on the point cloud and the object classification set.

    Plane detection using semantic segmentation

    公开(公告)号:US11610397B2

    公开(公告)日:2023-03-21

    申请号:US17473469

    申请日:2021-09-13

    Applicant: Apple Inc.

    Abstract: In one implementation, a method of generating a plane hypothesis is performed by a device including one or more processors, non-transitory memory, and a scene camera. The method includes obtaining an image of a scene including a plurality of pixels. The method includes obtaining a plurality of points of a point cloud based on the image of the scene. The method includes obtaining an object classification set based on the image of the scene. Each element of the object classification set includes a plurality of pixels respectively associated with a corresponding object in the scene. The method includes detecting a plane within the scene by identifying a subset of the plurality of points of the point cloud that correspond to a particular element of the object classification set.

    Plane detection using semantic segmentation

    公开(公告)号:US11132546B2

    公开(公告)日:2021-09-28

    申请号:US17032213

    申请日:2020-09-25

    Applicant: Apple Inc.

    Abstract: In one implementation, a method of generating a plane hypothesis is performed by a head-mounted device (HMD) including one or more processors, non-transitory memory, and a scene camera. The method includes obtaining an image of a scene including a plurality of pixels. The method include obtaining a point cloud based on the image of the scene and generating an object classification set based on the image of the scene, each element of the object classification set including a respective plurality of pixels classified as a respective object in the scene. The method includes generating a plane hypothesis based on the point cloud and the object classification set.

    DIRECT THIN BOUNDARY PREDICTION
    4.
    发明申请

    公开(公告)号:US20200065973A1

    公开(公告)日:2020-02-27

    申请号:US16545158

    申请日:2019-08-20

    Applicant: Apple Inc.

    Abstract: In some implementations a neural network is trained to perform to directly predict thin boundaries of objects in images based on image characteristics. A neural network can be trained to predict thin boundaries of objects without requiring subsequent computations to reduce the thickness of the boundary prediction. Instead, the network is trained to make the predicted boundaries thin by effectively suppressing non-maximum values in normal directions along what might otherwise be a thick predicted boundary. To do so, the neural network can be trained to determine normal directions and suppress non-maximum values based on those determined normal directions.

    Plane Detection Using Semantic Segmentation

    公开(公告)号:US20210406541A1

    公开(公告)日:2021-12-30

    申请号:US17473469

    申请日:2021-09-13

    Applicant: Apple Inc.

    Abstract: In one implementation, a method of generating a plane hypothesis is performed by a device including one or more processors, non-transitory memory, and a scene camera. The method includes obtaining an image of a scene including a plurality of pixels. The method includes obtaining a plurality of points of a point cloud based on the image of the scene. The method includes obtaining an object classification set based on the image of the scene. Each element of the object classification set includes a plurality of pixels respectively associated with a corresponding object in the scene. The method includes detecting a plane within the scene by identifying a subset of the plurality of points of the point cloud that correspond to a particular element of the object classification set.

    PLANE DETECTION USING SEMANTIC SEGMENTATION

    公开(公告)号:US20210012112A1

    公开(公告)日:2021-01-14

    申请号:US17032213

    申请日:2020-09-25

    Applicant: Apple Inc.

    Abstract: In one implementation, a method of generating a plane hypothesis is performed by a head-mounted device (HMD) including one or more processors, non-transitory memory, and a scene camera. The method includes obtaining an image of a scene including a plurality of pixels. The method include obtaining a point cloud based on the image of the scene and generating an object classification set based on the image of the scene, each element of the object classification set including a respective plurality of pixels classified as a respective object in the scene. The method includes generating a plane hypothesis based on the point cloud and the object classification set.

    PLANE DETECTION USING SEMANTIC SEGMENTATION
    8.
    发明申请

    公开(公告)号:US20190392213A1

    公开(公告)日:2019-12-26

    申请号:US16360732

    申请日:2019-03-21

    Applicant: Apple Inc.

    Abstract: In one implementation, a method of generating a plane hypothesis is performed by a head-mounted device (HMD) including one or more processors, non-transitory memory, and a scene camera. The method includes obtaining an image of a scene including a plurality of pixels. The method include obtaining a point cloud based on the image of the scene and generating an object classification set based on the image of the scene, each element of the object classification set including a respective plurality of pixels classified as a respective object in the scene. The method includes generating a plane hypothesis based on the point cloud and the object classification set.

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