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Thanks for the answer!

I guess my lack of knowledge in the field shines through. Continuous learning is apparently under active research at the moment, and this blog post about it [0] is less than two months old, so your intuition was right.

If I were to guess the secret sauce I'd say that a mirror might be involved. Is depth information not worth the trouble for these kinds of classification problems?

[0] https://deepmind.com/blog/enabling-continual-learning-in-neu...




> If I were to guess the secret sauce I'd say that a mirror might be involved.

You might be right there :)

> Is depth information not worth the trouble for these kinds of classification problems?

Yes, it would be, but there's much more to it than that. Also keep in mind that there are parts that are almost transparent and that no matter what background color you come up with there will be a bunch of lego parts that match it.


Would it help to get multiple images from the pieces on the belt, each illuminated from a different direction by some kind of strobe? Then the shadows could be incorporated in detecting some shape information. Might even help with translucent pieces.

Colored strobes may also help separating out different color pieces, although I expect that would be overkill.


checkerboard background?


Tried that one too, both b/w and purple/w... in the end a transparent background with a bunch of lights behind it works best.




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