They overcomplicate by using 3-4 different (sub) license in one project:
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 .
brookst 4 hours ago [-]
They could have transformed it from insane to sublime by slapping a highly restrictive license on the readme itself. Seriously missed opportunity.
guipsp 2 hours ago [-]
It complicated, but it's not overcomplicated. CC is not adequate for code and I belive that none of the code is GPL so your suggestion regarding AGPL is strange.
generalizations 22 minutes ago [-]
Why isn't CC-by-NC-ND adequate for code? Kinda makes sense IMO and the summary looks useful?
> 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.
desertmonad 17 hours ago [-]
Looks promising but the license, Attribution-NonCommercial-NoDerivatives is pretty limiting..
huxley 15 hours ago [-]
That’s just for the data, isn’t it, the code is Apple Sample Code License which I seem to recall is an MIT type license
pzo 6 hours ago [-]
"models are released under Apple ML Research Model Terms of Use."
callumprentice 11 hours ago [-]
I keep meaning to get back to my suite of equirectangular image functions - viewers, editors, authoring etc. and this reminded me to resurrect the Viewer.
Surprised this isn’t in coreML. Seems useful for the Vision Pro or something
hokumguru 11 hours ago [-]
Might see it at WWDC this year?
fidotron 16 hours ago [-]
The accuracy of the results don't seem that great. For example, looking at the pictures on the wall in their sample, or the beams in the ceiling.
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.
ellisv 2 hours ago [-]
> The accuracy of the results don't seem that great. For example, looking at the pictures on the wall in their sample, or the beams in the ceiling.
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.
fidotron 15 minutes ago [-]
I mean the accuracy with which it's locating the bounds. What is extra curious is it obviously supports rotated cubes, yet it often doesn't use them when it should, leading to overstating the bounds, as if it's over enthusiastically trying to put things aligned to some inferred axis.
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.
Svip 9 hours ago [-]
Will it work on a picture of a Power Mac G4 Cube[0]? Whenever I see "cube" and "apple" together (which, in fairness, is rare), I think of the Cube.
https://threejs.org/examples/webgl_loader_usdz.html
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.
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.
[0] https://en.wikipedia.org/wiki/Power_Mac_G4_Cube
Edit: Yes, looking at its other comment.