Point cloud annotation device, method, and program

    公开(公告)号:US12106438B2

    公开(公告)日:2024-10-01

    申请号:US17608735

    申请日:2019-05-08

    Abstract: Annotation can be easily performed on a three-dimensional point cloud and a working time can be reduced. An interface unit 22 displays a point cloud indicating a three-dimensional point on an object, and receives designation of a three-dimensional point indicating an annotation target object and designation of a three-dimensional point not indicating the annotation target object. A candidate cluster calculation unit 32 calculates a value of a predetermined evaluation function indicating a likelihood of a point cloud cluster being the annotation target object based on the designation of a three-dimensional point for point cloud clusters obtained by clustering the point clouds. A cluster selection and storage designation unit 34 causes the interface unit 22 to display the point cloud clusters in descending order of the value of the evaluation function, and receives a selection of a point cloud cluster to be annotated. An annotation execution unit 36 executes annotation indicating the annotation target object for each three-dimensional point included in the selected point cloud cluster.

    Label estimation device, label estimation method, and label estimation program

    公开(公告)号:US12136252B2

    公开(公告)日:2024-11-05

    申请号:US17628071

    申请日:2019-07-19

    Abstract: A point group including a small number of points that have been assigned labels is taken as an input to assign labels to points that have not been assigned labels.
    In a label estimation apparatus for estimating a label to be assigned to a point that has not been labeled using a label of a point that has been labeled among points included in a point group, a confidence derivation unit 103 takes a point that has not been labeled within a point group including a point that has been labeled and the point that has not been labeled as a target point and estimates a class of the target point and a likelihood indicating a confidence of an estimation result of the class from a set of points included in the point group, a priority derivation unit 104 obtains a distance between the target point and a point that has been assigned the same label as a label corresponding to the estimated class as a priority used to determine whether the estimated class is appropriate, and a label determination unit 105 determines whether the estimated class is appropriate using at least an index based on the distance.

    Depth superresolution device, depth superresolution method, and program

    公开(公告)号:US11900622B2

    公开(公告)日:2024-02-13

    申请号:US17425916

    申请日:2020-01-27

    CPC classification number: G06T7/50 G06T3/4046 G06T2200/04 G06T2207/20084

    Abstract: Dense depth information can be generated using only a monocular image and sparse depth information.
    A depth hyper-resolving apparatus 100 includes: an input data processing unit 22 that outputs a hierarchical input image and hierarchical input depth information by resolution conversion in accordance with a predetermined number of tiers for an input image and input depth information; a depth continuity estimation unit 24 that derives hierarchical estimated depth continuity based on the hierarchical input image; a depth continuity mask deriving unit 26 that outputs a hierarchical depth continuity mask representing values of locations depending on whether a depth is continuous based on the hierarchical input image and the hierarchical estimated depth continuity; and a cost function minimization unit 30 that derives hyper-resolved depth information to minimize a cost function expressed by using the hierarchical input depth information, the hierarchical depth continuity mask, and the hyper-resolved depth information.

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