From 2D to 3D in a Single Click: My TripoSR Reimplementation Journey

Ever wanted to turn a simple image into a full-blown 3D model? I did too — and thanks to TripoSR by Stability AI, I managed to do exactly that using just a Google Colab notebook.

In this blog, I’ll walk you through how I reimplemented TripoSR in Colab using a PyImageSearch tutorial as a base, customized it, debugged some pain points, and pushed the entire pipeline to GitHub. This is perfect for AI beginners, 3D devs, or anyone curious about building vision pipelines with powerful open-source tools.

What is TripoSR?

TripoSR is a cutting-edge 3D reconstruction model released by Stability AI. It takes in a single 2D image and outputs a 3D mesh of the object with no extra training or fine-tuning needed.

It uses a transformer-based architecture and is trained to generalize across categories — think of it like Stable Diffusion, but for generating geometry.

📝 You can read the full paper here: TripoSR: Ultra-Fast 3D Reconstruction from a Single Image

What My Notebook Does

Here’s what I implemented step-by-step:

1. Cloned the TripoSR repo
2. Installed all requirements and fixed a few Mac-specific issues
3. Uploaded a custom image via Colab’s file picker
4. Removed the background for better object isolation
5. Resized + cleaned the image for input to the model
6. Loaded the model from Hugging Face
7. Ran inference to generate a 3D scene code
8. Rendered 30 viewpoints into a turntable .mp4 video
9. Exported the final .obj mesh to download and use elsewhere

Project Repo

Check out the full notebook here 👇

🔗 GitHub - TripoSR Colab Reimplementation

What’s Next?

This notebook was my MVP — but I’ve got plans to:

• 🔄 Turn this into a web app using FastAPI + React
• 🐳 Dockerize it for local and cloud deployment
• 🖼️ Add a gallery of demo images and results

Final Thoughts

Reimplementing TripoSR helped me:

• Understand how 2D → 3D inference works
• Deal with real-world issues like dependencies and rendering
• Build a pipeline that’s clean, reproducible, and shareable

This is just step one in my AI engineer journey — and I’m hyped to keep building.

If you wanna try it yourself or contribute, feel free to fork the repo or hit me up on GitHub.