u/Downtown-Dot-3101

I’ve been looking into Bucket Robotics and their approach to defect detection. Instead of the usual "collect 10,000 images and label them" grind, they’re training models directly from CAD files.

They call them "part-native models", and the idea is that the AI learns the geometry and physics of the part itself, making it invariant to lighting or camera swaps.

My technical question:
How are they actually bridging the Sim-to-Real gap? Is this just extreme domain randomization?
Has anyone here tried a CAD-first pipeline? Does it actually hold up when the industry setup?

Is 'part-native' just a fancy rebrand for extreme domain randomization, or is there actually something new under the hood here?