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.

Inventor： Pradeep Kumar JAYARAMAN ,  Nishkrit DESAI ,  Joseph George LAMBOURNE ,  Nigel Jed Wesley MORRIS ,  Aditya SANGHI ,  Karl D. D. WILLIS

IPC: G06F30/10

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.

公开(公告)号：US20220318636A1

公开(公告)日：2022-10-06

申请号：US17348314

申请日：2021-06-15

Applicant: AUTODESK, INC.

Inventor： Pradeep Kumar JAYARAMAN ,  Thomas Ryan DAVIES ,  Joseph George LAMBOURNE ,  Nigel Jed Wesley MORRIS ,  Aditya SANGHI ,  Hooman SHAYANI

IPC: G06N3/08 ,  G06N3/04 ,  G06F30/10 ,  G06F16/901

Abstract: In various embodiments, a training application trains machine learning models to perform tasks associated with 3D CAD objects that are represented using B-reps. In operation, the training application computes a preliminary result via a machine learning model based on a representation of a 3D CAD object that includes a graph and multiple 2D UV-grids. Based on the preliminary result, the training application performs one or more operations to determine that the machine learning model has not been trained to perform a first task. The training application updates at least one parameter of a graph neural network included in the machine learning model based on the preliminary result to generate a modified machine learning model. The training application performs one or more operations to determine that the modified machine learning model has been trained to perform the first task.

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.

Inventor： Pradeep Kumar JAYARAMAN ,  Nishkrit DESAI ,  Joseph George LAMBOURNE ,  Nigel Jed Wesley MORRIS ,  Aditya SANGHI ,  Karl D. D. WILLIS

IPC: G06F30/12 ,  G06N7/01

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.

公开(公告)号：US20240028783A1

公开(公告)日：2024-01-25

申请号：US17894978

申请日：2022-08-24

Applicant: AUTODESK, INC.

Inventor： Rui WANG ,  David BENJAMIN ,  Pradeep Kumar JAYARAMAN ,  Nigel Jed Wesley MORRIS

IPC: G06F30/13

CPC classification number: G06F30/13

Abstract: A generative design system includes a solver and a modeling tool comprising a visual programming interface and a design workflow script. The visual programming interface enables the user to specify a design problem including design constraints comprising parameters associated with standard building components, such as beams and joints. After the design problem is specified by the user, the modeling tool executes the design workflow script to automatically perform a design workflow that generates a design solution for the design problem. The design workflow script controls the operations of the modeling tool and the solver to interact in a collaborative manner to execute the design workflow comprising an ordered sequence of operations. The design solution comprises a 3D model of a modular beam structure that can be easily fabricated using standard building components, such as standardized beams and joints.

公开(公告)号：US20220318637A1

公开(公告)日：2022-10-06

申请号：US17348338

申请日：2021-06-15

Applicant: AUTODESK, INC.

Inventor： Pradeep Kumar JAYARAMAN ,  Thomas Ryan DAVIES ,  Joseph George LAMBOURNE ,  Nigel Jed Wesley MORRIS ,  Aditya SANGHI ,  Hooman SHAYANI

IPC: G06N3/08 ,  G06N3/04 ,  G06F30/10 ,  G06F16/901

Abstract: In various embodiments, an inference application performs tasks associated with 3D CAD objects that are represented using B-reps. A UV-net representation of a 3D CAD object that is represented using a B-rep includes a set of 2D UV-grids and a graph. In operation, the inference application maps the set of 2D UV-grids to a set of node feature vectors via a trained neural network. Based on the node feature vectors and the graph, the inference application computes a final result via a trained graph neural network. Advantageously, the UV-net representation of the 3D CAD object enabled the trained neural network and the trained graph neural network to efficiently process the 3D CAD object.

公开(公告)号：US20220318466A1

公开(公告)日：2022-10-06

申请号：US17348295

申请日：2021-06-15

Applicant: AUTODESK, INC.

Inventor： Pradeep Kumar JAYARAMAN ,  Thomas Ryan DAVIES ,  Joseph George LAMBOURNE ,  Nigel Jed Wesley MORRIS ,  Aditya SANGHI ,  Hooman SHAYANI

IPC: G06F30/27 ,  G06N20/00 ,  G06T17/10 ,  G06F30/23

Abstract: In various embodiments, a parameter domain graph application generates UV-net representations of 3D CAD objects for machine learning models. In operation, the parameter domain graph application generates a graph based on a B-rep of a 3D CAD object. The parameter domain graph application discretizes a parameter domain of a parametric surface associated with the B-rep into a 2D grid. The parameter domain graph application computes at least one feature at a grid point included in the 2D grid based on the parametric surface to generate a 2D UV-grid. Based on the graph and the 2D UV-grid, the parameter domain graph application generates a UV-net representation of the 3D CAD object. Advantageously, generating UV-net representations of 3D CAD objects that are represented using B-reps enables the 3D CAD objects to be processed efficiently using neural networks.