MACHINE LEARNING TECHNIQUES FOR DIRECT BOUNDARY REPRESENTATION SYNTHESIS

    公开(公告)号:US20240289505A1

    公开(公告)日:2024-08-29

    申请号:US18407327

    申请日:2024-01-08

    Applicant: AUTODESK, INC.

    CPC classification number: G06F30/12 G06N7/01

    Abstract: One embodiment of the present invention sets forth a technique for generating 3D CAD model representations of three-dimensional objects. The technique includes generating a vertex list that includes a first ordered list of elements representing vertex coordinates and sampling a first index from the vertex list based on a first probability distribution. The technique also includes generating an edge list and sampling a second index from one or more indices into the edge list. The technique further includes generating an element in a face list, dereferencing the element in the face list to retrieve an element in the edge list, and dereferencing an element in the edge list to retrieve a vertex coordinate from an element in the vertex list. The technique further includes generating an indexed boundary representation for the 3D CAD model based on at least the vertex list, the edge list, and the face list.

    MACHINE LEARNING TECHNIQUES FOR DIRECT BOUNDARY REPRESENTATION SYNTHESIS

    公开(公告)号:US20240289502A1

    公开(公告)日:2024-08-29

    申请号:US18407320

    申请日:2024-01-08

    Applicant: AUTODESK, INC.

    CPC classification number: G06F30/10

    Abstract: One embodiment of the present invention sets forth a technique for generating 3D CAD model representations of three-dimensional objects in boundary representation format. The technique includes generating an indexed boundary representation of the generated 3D CAD model. The indexed boundary representation includes ordered lists of vertices, edges, and faces defining the generated 3D CAD model, where the edges are encoded as references to vertices in the vertex list and the face are encoded as references to edges in the edge list. The technique further includes converting the indexed boundary representation of the generated 3D CAD model into a boundary representation of the 3D CAD model through the application of heuristic algorithms to the indexed boundary representation. The technique is optionally guided by conditional data associated with the 3D CAD model to be generated, including a 2D image, a 3D collection of volume elements, or a 3D point cloud.

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