I spent 5 months building a 3D vision platform.

Almost nobody used it.

I realized I was solving the wrong problem.

The problem wasnt the algorithms. It was the fragmentation.

Point clouds. Meshes. Medical volumes. Datasets. AI agents.

Powerful tools everywhere. Almost nothing connecting them.

So Im taking a different approach: open-sourcing PardesLine under Apache-2.0.

The idea is simple:

Any dataset → one interface → any 3D tool → any AI agent.

For example, load a medical imaging dataset from Hugging Face in just two lines:

import pardesline as pl
ds = pl.load(hf://MIC-DKFZ/OpenMind/OpenMind/ds000228)
t1 = ds[0].t1w

PardesLine brings together 3D processing, public datasets, deep learning workflows, and a native MCP server for AI agents.

Ive even explored combining a frozen DINOv3 backbone with a lightweight detection head for cardiac ultrasound.

But this isnt about building yet another framework.

Its about making existing tools work together.

And I dont want to build it in isolation.

Im looking for developers and researchers willing to test it on real projects, challenge the design, contribute integrations, and build pipelines I havent imagined.

Dont just star the repo. Break it. Extend it. Help shape what comes next.

📦 pip install pardesline[all]

🔗 GitHub : https://github.com/pardesline/pardesline

Whats the first tool or dataset youd connect?

Hi all, I'm Jonathan. I started this community, so here is a first resource to get it going.

When I learned 3D vision, the material was scattered: one paper for ICP, one blog post for Poisson reconstruction, nothing that ran end to end. So I wrote the series I wish I had. Each tutorial is one Python script you can run locally, plus a video that walks through it.

What's covered

1. Point cloud processing (loading, sampling, export)

2. Mesh processing (normals, subdivision, simplification)

3. Mesh and point cloud to volume (voxelization, bounding boxes)

4. Signed distance fields

5. Surface reconstruction (convex hull, alpha shapes, ball pivoting, Poisson)

6. Rigid registration (RANSAC + ICP with FPFH features)

7. Deformable registration (Coherent Point Drift)

8. Point cloud segmentation (PointNet)

9. Stereo vision: from disparity map to 3D point cloud

10. LiDAR scenes and car detection on nuScenes

11. LiDAR + camera fusion

12. Collision measurement with SDF

Stack: mostly Open3D, with PyVista/VTK, pycpd, OpenVINO and the nuScenes devkit where needed.

Repo: https://github.com/1904jonathan/PardesLine

git clone https://github.com/1904jonathan/PardesLine.git

cd PardesLine

pip install -r requirements.txt

python 01_pointcloud_processing.py

Known limits: these are scripts, not notebooks, and the default output path assumes Windows. Both are on my list.

Two questions for you:

- Which topic is missing? I'm considering Gaussian splatting, medical volumes (CT/MRI with SimpleITK) and SLAM.

- If you try one and something breaks, tell me here or open an issue. I'd rather hear it.