Mates2Motion: Learning How Mechanical CAD Assemblies Work

• URL: http://arxiv.org/abs/2208.01779v2
• Date: Thu, 4 May 2023 22:39:40 GMT
• Title: Mates2Motion: Learning How Mechanical CAD Assemblies Work
• Authors: James Noeckel, Benjamin T. Jones, Karl Willis, Brian Curless, Adriana Schulz
• Abstract summary: We train our model using a large dataset of real-world mechanical assemblies consisting of CAD parts and mates joining them together. We present methods for re-defining these mates to make them better reflect the motion of the assembly, as well as narrowing down the possible axes of motion.
• Score: 7.987370879817241
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: We describe our work on inferring the degrees of freedom between mated parts in mechanical assemblies using deep learning on CAD representations. We train our model using a large dataset of real-world mechanical assemblies consisting of CAD parts and mates joining them together. We present methods for re-defining these mates to make them better reflect the motion of the assembly, as well as narrowing down the possible axes of motion. We also conduct a user study to create a motion-annotated test set with more reliable labels.

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