Comments (2)
sarlinpe
commented on May 23, 2024
- Each model of LightGlue is trained for a given type of descriptors only. Using LightGlue with your own descriptors requires retraining it. We do not provide the training code but will do so soon.
- The models that we released are trained on static scenes (MegaDepth) and are thus likely to reject matches between dynamic objects, even if they move only slightly. You might need to retrain with your won data for optimal performance.
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bartoszptak
commented on May 23, 2024
Thanks for your answer!
- So, did you train your own descriptors of SuperPoint?
- I know that LighGlue was trained on static scenes, but I thought some of the reparametrisation was possible to adapt the algorithm.
I can't wait for the training code! Good job!
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Related Issues (20)
- can I use this algorithm to get the similarity score of two pictures? HOT 1
- 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
- Illegal License HOT 4
- inference_for_LightGlue
- Bad matches running on GPU (related to non_blocking parameter) HOT 1
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