[UofT] Print Anything: Building the Pipeline from Natural Language to 3D Object

What if you could 3D print something simply by describing it in plain English, just as you ask ChatGPT a question? That is the idea behind our project.

Our system fine-tunes a Qwen 3 model with LoRA in Google Colab to generate ImplicitCAD code from natural language prompts. The generated code is then compiled into STL files, which can be used for 3D printing.

ImplicitCAD is a code-based CAD tool, similar to OpenSCAD. Many people in the 3D printing community like it because script-based design allows precise and reproducible measurements. However, the downside is that users need to know how to write code.

To address this, we built a two-phase training pipeline. In the first phase, the model learns ImplicitCAD syntax using real examples collected and filtered from the Thingiverse dataset on HuggingFace. This helps the model understand how 3D geometry is written in code. In the second phase, we introduce logical and spatial reasoning problems so the model can go beyond simple pattern matching.

In addition to the model, we also built a web-based preview environment and a Dockerized version of ImplicitCAD. This removes the usual edit-compile-wait workflow that makes these tools difficult to use. The project is meant to be a starting point that future teams can build on and extend.