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    FAST MULTI-SCALE POINT CLOUD REGISTRATION WITH A HIERARCHICAL GAUSSIAN MIXTURE
    1.
    发明申请
    FAST MULTI-SCALE POINT CLOUD REGISTRATION WITH A HIERARCHICAL GAUSSIAN MIXTURE 审中-公开

    公开(公告)号：US20190319851A1

    公开(公告)日：2019-10-17

    申请号：US16351312

    申请日：2019-03-12

    Applicant: NVIDIA Corporation

    Inventor： Benjamin David Eckart ,  Kihwan Kim ,  Jan Kautz

    IPC: H04L12/24 ,  H04L29/08 ,  G06F16/22

    Abstract: Point cloud registration sits at the core of many important and challenging 3D perception problems including autonomous navigation, object/scene recognition, and augmented reality (AR). A new registration algorithm is presented that achieves speed and accuracy by registering a point cloud to a representation of a reference point cloud. A target point cloud is registered to the reference point cloud by iterating through a number of cycles of an EM algorithm where, during an Expectation step, each point in the target point cloud is associated with a node of a hierarchical tree data structure and, during a Maximization step, an estimated transformation is determined based on the association of the points with corresponding nodes of the hierarchical tree data structure. The estimated transformation is determined by solving a minimization problem associated with a sum, over a number of mixture components, over terms related to a Mahalanobis distance.

    NEURAL NETWORK ARCHITECTURE FOR IMPLICIT LEARNING OF A PARAMETRIC DISTRIBUTION OF DATA
    2.
    发明申请
    NEURAL NETWORK ARCHITECTURE FOR IMPLICIT LEARNING OF A PARAMETRIC DISTRIBUTION OF DATA 有权

    公开(公告)号：US20250111476A1

    公开(公告)日：2025-04-03

    申请号：US18890544

    申请日：2024-09-19

    Applicant: NVIDIA Corporation

    Inventor： Benjamin David Eckart ,  Anthea Li ,  Chao Liu ,  Kevin Shih ,  Jan Kautz

    IPC: G06T3/4046

    Abstract: Parametric distributions of data are one type of data model that can be used for various purposes such as for computer vision tasks that may include classification, segmentation, 3D reconstruction, etc. These parametric distributions of data may be computed from a given data set, which may be unstructured and/or which may include low-dimensional data. Current solutions for learning parametric distributions of data involve explicitly learning kernel parameters. However, this explicit learning approach is not only inefficient in that it requires a high computational cost (i.e. from a large number of floating point operations per second), but it also leaves room for improvement in terms of accuracy of the resulting learned model. The present disclosure provides a neural network architecture that implicitly learns a parametric distribution of data, which can reduce the computational cost while improve accuracy when compared with prior solutions that rely on the explicit learning design.

    Fast multi-scale point cloud registration with a hierarchical gaussian mixture
    3.
    发明授权
    Fast multi-scale point cloud registration with a hierarchical gaussian mixture 有权

    公开(公告)号：US10826786B2

    公开(公告)日：2020-11-03

    申请号：US16351312

    申请日：2019-03-12

    Applicant: NVIDIA Corporation

    Inventor： Benjamin David Eckart ,  Kihwan Kim ,  Jan Kautz

    IPC: H04L12/24 ,  G06F17/18 ,  G06F16/22 ,  H04L29/08

    Abstract: Point cloud registration sits at the core of many important and challenging 3D perception problems including autonomous navigation, object/scene recognition, and augmented reality (AR). A new registration algorithm is presented that achieves speed and accuracy by registering a point cloud to a representation of a reference point cloud. A target point cloud is registered to the reference point cloud by iterating through a number of cycles of an EM algorithm where, during an Expectation step, each point in the target point cloud is associated with a node of a hierarchical tree data structure and, during a Maximization step, an estimated transformation is determined based on the association of the points with corresponding nodes of the hierarchical tree data structure. The estimated transformation is determined by solving a minimization problem associated with a sum, over a number of mixture components, over terms related to a Mahalanobis distance.

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