xuelunshen / gim Goto Github PK

Thank you all for the amazing work!
As you said in #3 : “The code for SfM is based on hloc. In detail, we have imitated the LoFTR in hloc to implement the SfM for DKM” . To implement GIM, hloc need to add a new interface in hloc/extractors/ or hloc/matchers/, could you release the code for that?

congraduation, thanks for your work. could you please release the superglue model?

python demo.py
/home/yuanweizhong/anaconda3/envs/gim/lib/python3.8/site-packages/torchvision/io/image.py:11: UserWarning: Failed to load image Python extension: libc10_cuda.so: cannot open shared object file: No such file or directory
warn(f"Failed to load image Python extension: {e}")
Traceback (most recent call last):
File "demo.py", line 316, in
state_dict = torch.load(checkpoints_path, map_location='cpu')
File "/home/yuanweizhong/anaconda3/envs/gim/lib/python3.8/site-packages/torch/serialization.py", line 608, in load
return _legacy_load(opened_file, map_location, pickle_module, **pickle_load_args)
File "/home/yuanweizhong/anaconda3/envs/gim/lib/python3.8/site-packages/torch/serialization.py", line 777, in _legacy_load
magic_number = pickle_module.load(f, **pickle_load_arg

Hi, great work!

I'm wondering if you have a timeline for the release your proposed benchmark.
Personally I think this is more important than releasing the training code.

I've personally had some issues even downloading the related training sets (e.g. GL3D seems to be down now?).

Maybe authors of those datasets could allow you to bundle only the data used for the benchmark into an easy download (perhaps with some LICENSE restrictions)?

congraduation, thanks for your work. could you please release the loftr model?

congraduation, thanks for your work. Great job！
I would like to inquire about the possibility of optimizing the inference speed and GPU memory usage of the model. Based on my testing, inference on two images takes approximately 3.5 seconds on a 3090, with a maximum memory usage of 17GB.

Hi guys, thanks for your nice work! It seems that the server on the hugging face is not OK and we cannot try the online demo anymore. Is there any method to fix this?

EfficientLoFTR is much faster than LoFTR.
https://zju3dv.github.io/efficientloftr/files/EfficientLoFTR.pdf

Do we have plans to retrain EfficientLoFTR with GIM?