Comments (4)
From the README:
SuperPoint follows a different, restrictive license (this includes its pre-trained weights and its inference file)
In this repo, the Apache 2.0 license applies to files that do not include a different license notice in their header.
from lightglue.
Thanks for the clarification. So there are three licenses at play here.
- License 1: The Apache 2.0 License which applies to files in the repo, unless otherwise specified in the particular file.
- License 2: The actual license that applies to the superpoint code, which is mentioned in the README and points to an external repository, and which you have linked above.
- License 3: The license that appears in superpoint.py which is quite a bit more restrictive than the above license.
License 3 (superpoint.py) states
ANY REPRODUCTION, MODIFICATION, DISTRIBUTION,
PUBLIC PERFORMANCE, OR PUBLIC DISPLAY OF OR THROUGH USE OF THIS
SOURCE CODE WITHOUT THE EXPRESS WRITTEN CONSENT OF COMPANY IS
STRICTLY PROHIBITED
License 2 gives an academic license. Can you confirm that I should ignore License 3 and consider License 2 to be the one to apply to the superpoint.py file? In that case, would it be okay to copy-paste License 2 over License 3 in my local repository?
from lightglue.
The file superpoint.py
is copied verbatim from the original repo. I am not a lawyer so I am unable to comment on whether License 2 or 3 applies.
from lightglue.
I understand, thank you. To stay on the safe side, I will just delete that file from my local repo.
from lightglue.
Related Issues (20)
- How to get description from giving point(x,y) HOT 2
- The matching effect of similar images is too different HOT 5
- ERROR in the Installation HOT 1
- TSNE on descriptors outputted by SuperPoint?
- How to display the matching status of each pair of feature points HOT 2
- Retrain model LightGlue with ourdataset HOT 2
- While Reproducing the results HOT 1
- Questions about SIFT+LightGlue HOT 3
- spelling error in README HOT 1
- Compatibility Issue with GridSample and ONNX Opset 15 on NXP i.MX 93 NPU HOT 1
- How to do inference with trained model? HOT 1
- No keypoints with sift = error
- FlashAttention actually does not support attention mask HOT 3
- What's the difference between matches0 and matches1
- some problem that delopy on device, such as snapdragon snpe HOT 1
- Training with own dataset on both extractor and matcher HOT 2
- sift+lightglue HOT 5
- LightGlue-full-compile won't work with torch '2.2.1+cu121' HOT 3
- Error:_pickle.UnpicklingError: invalid load key, '\x0a'.
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from lightglue.