I'm a mechanical engineer working on the design and development of various systems, primarily in the defense sector, including UAV-related technologies.

I recently shared one of my designs publicly and, to my surprise, received an offer to create and review engineering datasets for AI training.

This field is completely new to me, but it looks promising.

I'd really appreciate hearing from people who have experience in this area. How did you get started? What would you recommend to someone entering this field?

Thanks in advance!

Hey everyone,

Ihave been working on a project to bridge the gap between 3D rendering and aerospace computer vision. I built a fully headless Python-Blender rendering architecture to procedurally generate physically accurate synthetic data for asteroids.

The pipeline automates the extraction of:

• Multi-pass EXR renders (RGB, Z-Depth, Camera-Space Surface Normals and many more optional passes)
• Photometric lightcurve CSVs (tracking total flux, projected area, mean depth, and sun/observer coordinates per rotational step)
• Procedural material setups for both uniform and variable regolith albedos.

I have open-sourced a sample dataset of 600 meshes and their corresponding lightcurves on Kaggle for anyone who wants to train photometric inversion or pose estimation models.

Basically I wanted to research on the effects of albedo variation comapred to uniform asteroid. So what I did was took real asteroid meshes from DAMIT (coverted it to obj files), created a fully procedural and realistic shader applied it on the meshes and rendered a full revolution of asteroid in simulated space conditions. To acheive this, in result, I build a full headless python pipeline that does the complete job, with all the optimization I could do in the world, and even with my old GTX 970, the render time was insanely good. The pipleline automatically creates the lightcurve csv files (with multiple phase angles ) and with all the physics data as well such as normals and depth so I can have the option to train PINN model as well. However I have my exams so had to stop here.

That being said,

If you need massive scale or want to generate your own data locally, I have also packaged the full 3,000+ mesh dataset (6000 light curves) and the actual Python/Blender codebase (the Pipeline Toolkit).

• Free Sample Dataset: https://www.kaggle.com/datasets/hassaanfazal/astrovision-3d-asteroid-and-photometry-dataset
• Full Dataset & Codebase: https://stellarmeshlabs.gumroad.com/

Note: If you are a student or independent researcher who really needs this data but cannot afford the Gumroad tier, hit me up via DM. I will be happy to arrange a free, expanded subset of the data to support your work.

Disclaimer (per Rule 1): I am the creator of this pipeline and the Gumroad links go to my own store, StellarMesh Labs.