Mates2Motion: Learning How Mechanical CAD Assemblies Work
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 11 of its 15 references that are in the map. First posted on arXiv 2 Aug 2022.
Builds on
11 of 15 references.
References · 11
- 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
- 2018ABC: A Big CAD Model Dataset For Geometric Deep Learningcited by 339
- 2019Shape2Motion: Joint Analysis of Motion Parts and Attributes from 3D Shapescited by 117
- 2021BRepNet: A topological message passing system for solid modelscited by 92
- 2021JoinABLe: Learning Bottom-up Assembly of Parametric CAD Jointscited by 77
- 2020SketchGraphs: A Large-Scale Dataset for Modeling Relational Geometry in Computer-Aided Designcited by 72
- 2020Fusion 360 Gallery: A Dataset and Environment for Programmatic CAD Construction from Human Design Sequencescited by 36
- 2020RPM-Net: Recurrent Prediction of Motion and Parts from Point Cloudcited by 26
- 2021AutoMate: A Dataset and Learning Approach for Automatic Mating of CAD Assembliescited by 13
- 2022Unsupervised Kinematic Motion Detection for Part-segmented 3D Shape Collectionscited by 13
- 2021UVStyle-Net: Unsupervised Few-shot Learning of 3D Style Similarity Measure for B-Repscited by 3
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Citing papers per year: 2026: 2.
2 papers.