# Mates2Motion: Learning How Mechanical CAD Assemblies Work

arXiv: https://arxiv.org/abs/2208.01779 · HTML: https://mlsanity.com/paper/2208.01779/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 15 references that are in the map.
- First posted on arXiv 2 Aug 2022.

## Builds on (most cited first, 11 of 11)

- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [ABC: A Big CAD Model Dataset For Geometric Deep Learning](https://mlsanity.com/paper/1812.06216/) (2018)
- [Shape2Motion: Joint Analysis of Motion Parts and Attributes from 3D Shapes](https://mlsanity.com/paper/1903.03911/) (2019)
- [BRepNet: A topological message passing system for solid models](https://mlsanity.com/paper/2104.00706/) (2021)
- [JoinABLe: Learning Bottom-up Assembly of Parametric CAD Joints](https://mlsanity.com/paper/2111.12772/) (2021)
- [SketchGraphs: A Large-Scale Dataset for Modeling Relational Geometry in Computer-Aided Design](https://mlsanity.com/paper/2007.08506/) (2020)
- [Fusion 360 Gallery: A Dataset and Environment for Programmatic CAD Construction from Human Design Sequences](https://mlsanity.com/paper/2010.02392/) (2020)
- [RPM-Net: Recurrent Prediction of Motion and Parts from Point Cloud](https://mlsanity.com/paper/2006.14865/) (2020)
- [AutoMate: A Dataset and Learning Approach for Automatic Mating of CAD Assemblies](https://mlsanity.com/paper/2105.12238/) (2021)
- [Unsupervised Kinematic Motion Detection for Part-segmented 3D Shape Collections](https://mlsanity.com/paper/2206.08497/) (2022)
- [UVStyle-Net: Unsupervised Few-shot Learning of 3D Style Similarity Measure for B-Reps](https://mlsanity.com/paper/2105.02961/) (2021)

## Built on by (most cited first, 2 of 2)

- [OmniCAD: A Large-Scale Benchmark for 3D Spatial Reasoning in Robotics Assemblies](https://mlsanity.com/paper/2608.22637/) (2026)
- [Three-Phase Evaluation of AI-Assisted Software Development Life Cycle](https://mlsanity.com/paper/2607.05125/) (2026)

## 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.