CADOps-Net: Jointly Learning CAD Operation Types and Steps from Boundary-Representations

• URL: http://arxiv.org/abs/2208.10555v1
• Date: Mon, 22 Aug 2022 19:12:20 GMT
• Title: CADOps-Net: Jointly Learning CAD Operation Types and Steps from Boundary-Representations
• Authors: Elona Dupont, Kseniya Cherenkova, Anis Kacem, Sk Aziz Ali, Ilya Arzhannikov, Gleb Gusev, Djamila Aouada
• Abstract summary: This paper proposes a new deep neural network, CADOps-Net, that jointly learns the CAD operation types and the decomposition into different CAD operation steps. Compared to existing datasets, the complexity and variety of CC3D-Ops models are closer to those used for industrial purposes.
• Score: 17.051792180335354
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 3D reverse engineering is a long sought-after, yet not completely achieved goal in the Computer-Aided Design (CAD) industry. The objective is to recover the construction history of a CAD model. Starting from a Boundary Representation (B-Rep) of a CAD model, this paper proposes a new deep neural network, CADOps-Net, that jointly learns the CAD operation types and the decomposition into different CAD operation steps. This joint learning allows to divide a B-Rep into parts that were created by various types of CAD operations at the same construction step; therefore providing relevant information for further recovery of the design history. Furthermore, we propose the novel CC3D-Ops dataset that includes over $37k$ CAD models annotated with CAD operation type labels and step labels. Compared to existing datasets, the complexity and variety of CC3D-Ops models are closer to those used for industrial purposes. Our experiments, conducted on the proposed CC3D-Ops and the publicly available Fusion360 datasets, demonstrate the competitive performance of CADOps-Net with respect to state-of-the-art, and confirm the importance of the joint learning of CAD operation types and steps.

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