公开(公告)号：US20250077874A1

公开(公告)日：2025-03-06

申请号：US18457854

申请日：2023-08-29

Applicant: Autodesk, Inc.

Inventor： Kaveh Hassani ,  Hyunmin Cheong ,  Adam Noble Arnold ,  Kamal Rahimi Malekshan

IPC: G06N3/088 ,  G06F18/2321 ,  G06F30/12

Abstract: A method and system provide the ability to utilize three-dimensional (3D) models to perform a predictive task. Multiple 3D models, consisting of non-Euclidean data, are obtained. Each 3D model is translated into a relational graph with nodes and edges. Each relational graph is processed using a graph neural network (GNN) that computes a node representation per node. The node representations are aggregated into a structural representation of the 3D model. Multiple different views of the 3D model are captured and passed through a convolutional neural network (CNN) to compute a view representation of each view. The view representations are aggregated into a single visual representation. The GNN and CNN are trained using a multiview contrastive training objective to maximize agreement between the structural representation and the single visual representation to form final learned representations. The final learned representation is utilized to perform the predictive task.