Posted by Tycho87 3 days ago
in README:
Licenses - The sample code is released under Apple Sample Code License.
- The data is released under CC-by-NC-ND.
- The models are released under Apple ML Research Model Terms of Use.
Acknowledgements
- We use and acknowledge contributions from multiple open-source projects in ACKNOWLEDGEMENTS."
then having in github license button "Copyright (C) 2025 Apple Inc. All Rights Reserved."
in repo file LICENSE LICENSE_MODEL
why making it so confusing and elaborate? Its so useless to even use by 3rd party devs for making apps and releasing on their platform. So then just make it one license with the most strict restrictions you can make AGPL and/or CC-by-NC-ND .
> CC-BY-NC-ND is a type of Creative Commons license that allows others to use a work non-commercially, but they cannot modify it or create derivative works. This means the original work can be shared, but it must remain unchanged and cannot be used for commercial purposes.
Notwithstanding it's only applied to the data in this case, it sure looks like a useful license for code.
Because the Creative Commons folks themselves say it’s not because it doesn’t cover a number of software specific edge cases.
https://equinaut.surge.sh/?eqr=https://raw.githubusercontent...
Not quite right I think because the source image issn't 2x1 aspect ratio.
They can look really nice: both in the real world - https://equinaut.surge.sh/?eqr=https://upload.wikimedia.org/...
or
the virtual world: https://equinaut.surge.sh/?eqr=https://live.staticflickr.com...
It's possible it's some artifact of the processing resolution, but I think most people that have worked with NNs for AR input will be surprised that this is not considered disappointing.
Do you mean the accuracy of the classification or the precision of the lidar scans?
In my experience the lidar precision on the iPhones is decent but not great, so the texture mapping can look a bit off at times.
I'd love to have these bounding boxes on my scans though.
This is obviously an attempt at the general case to apply cubes to anything, but what is disappointing is the performance on boxy objects is lower than I've seen on private NNs used for AR and CV for years (ironically enough on iPads), using just rgb and no depth.
I half think the exercise here was to establish if transformers were the way to go for this, and on the strength of that the answer would be probably not.
Edit: Yes, looking at its other comment.
