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AI Learns to Reverse Engineer CAD Code from 3D Point Clouds

Luxembourg, December 19, 2024 – Researchers at the University of Luxembourg have developed a groundbreaking AI system capable of reverse engineering CAD (Computer-Aided Design) code directly from 3D point clouds. Their work, detailed in a new paper titled “CAD-Recode: Reverse Engineering CAD Code from Point Clouds,” represents a significant leap forward in automated CAD design and demonstrates the power of large language models (LLMs) in tackling complex geometric problems.

Traditional CAD modeling is a laborious process requiring specialized expertise. Designers painstakingly create parametric sketches and apply a sequence of operations (like extrusion or revolution) to build a 3D model. Reverse engineering aims to automate this, reconstructing the design process from a 3D scan, typically a point cloud. Existing methods, however, often rely on hand-crafted representations of CAD sequences and small, specialized datasets, limiting their accuracy and generalizability.

CAD-Recode tackles this challenge through three key innovations:

  1. Code-Based Representation: Instead of using custom representations, the researchers represent CAD sequences as executable Python code using the CadQuery library. This approach allows for greater flexibility, interpretability, and integration with existing CAD software. For instance, instead of representing a simple extrusion as a series of numerical parameters, it’s represented as a concise and easily understandable python function call.

  2. LLM Decoder: The core of CAD-Recode is a pre-trained LLM, fine-tuned to translate point cloud data into this Python code. Leveraging the LLM’s inherent understanding of code syntax and structure significantly simplifies the learning process. Imagine teaching a human to reverse engineer CAD; giving them the code directly would be much easier than teaching them a custom, abstract representation. The LLM acts similarly, learning to “read” point clouds and “write” correct Python CAD code.

  3. Large-Scale Synthetic Dataset: To train the LLM effectively, the researchers created a synthetic dataset of one million diverse CAD sequences, also represented as Python CadQuery code. This massive dataset provides the training data needed for robust and generalizable performance. Existing real-world datasets are often limited in size and diversity, hindering the development of accurate and broadly applicable reverse engineering systems.

Superior Performance: CAD-Recode significantly outperforms existing state-of-the-art methods across multiple benchmark datasets (DeepCAD, Fusion360, and CC3D). On the DeepCAD dataset, it achieves a 10x lower mean Chamfer distance (a measure of geometric accuracy), demonstrating a remarkable improvement in the fidelity of the reconstructed CAD models. Moreover, it requires substantially fewer input points, making it more efficient.

Interpretability and Editability: Because the output is interpretable Python code, the model’s predictions can be directly used and modified by existing CAD software. Furthermore, the researchers demonstrate that an off-the-shelf LLM (GPT-4) can be used to answer CAD-specific questions about the generated models directly from the code, enabling a new level of interaction and editing.

CAD-Recode represents a breakthrough in automated CAD reverse engineering. Its code-based representation, LLM-based approach, and large-scale synthetic dataset pave the way for more efficient, accurate, and user-friendly CAD design tools, potentially revolutionizing various industries that rely on CAD modeling. The authors plan to make their synthetic dataset publicly available, further accelerating research in this field.