MODEL ALIGNMENT METHOD
    2.
    发明公开

    公开(公告)号:US20240362875A1

    公开(公告)日:2024-10-31

    申请号:US18335204

    申请日:2023-06-15

    IPC分类号: G06T19/20 G06T7/13 G06T7/60

    摘要: A computer-implemented method of aligning a source model with a target model includes receiving the source model and the target model, identifying geometric features in each of the source model and target model, assigning a feature vector to each feature, defining an associated geometry type, position, direction and magnitude for the feature, pairing each feature vector in the source model with each other feature vector in the source model and pairing each feature vector in the target model with each other feature vector in the target model, calculating a pair vector for each pairing, defining the geometry type of each feature vector in the pairing, the dimension of each feature vector in the pairing, a relative orientation and separation distance, identifying matching pair vectors between the source model and target model; and calculating a transformation matrix between the source model and target model based on the matching pair vectors.

    System and Method for Optimizing Dynamic Point Clouds Based on Prioritized Transformations

    公开(公告)号:US20240354971A1

    公开(公告)日:2024-10-24

    申请号:US18760359

    申请日:2024-07-01

    IPC分类号: G06T7/33 G06T7/38 G06T19/20

    摘要: Apparatuses and methods are disclosed including techniques for optimizing the transmission of dynamic point clouds to a client. The disclosed techniques include obtaining a reference point cloud representing a first point cloud in a time sequence of point clouds; receiving a series of representations of changes to the reference point cloud, where each representation specifies an area of the reference point cloud and a change to be applied to the specified area, each such change comprises at least one of a 3D transformation, a set of points to be added to the reference point cloud, and a set of points to be removed from the reference point cloud; and successively applying the representations to the reference point cloud to generate a point cloud representing a second point cloud in the time sequence of point clouds.

    LEARNING-BASED POINT CLOUD COMPRESSION VIA UNFOLDING OF 3D POINT CLOUDS

    公开(公告)号:US20240282013A1

    公开(公告)日:2024-08-22

    申请号:US18571361

    申请日:2022-06-20

    IPC分类号: G06T9/00 G06T17/00

    摘要: In one implementation, we propose the UnfoldingOperator, which unfolds/flattens an unorganized input 3D point cloud onto a regular 2D grid. Given an input point cloud, an input 2D grid and the reconstructed point cloud produced by the FoldingNet, our proposal maps the input point cloud onto the 2D grid based on the reconstructed point cloud, leading to a 3-channel image. Alternatively, instead of using an image alone to represent a point cloud, the point cloud is decomposed into a codeword and a 3-channel residual image. This residual image is obtained by subtracting the reconstructed point cloud from the original input. The proposed UnfoldingOperator can be applied to point cloud compression, leading to a corresponding compression system that we call UnfoldingCompression. The UnfoldingCompression can work with the TearingCompression, where we can adaptively choose whether to use the UnfoldingCompression or TearingCompression.