This project shows how to implement Superpoint point extraction and description with ggml using pretrained model weights.
The feature is no thirdparty dependencies needed~
If you want to integrate superpoint to your project and refuse to use thirdparty libs like libtorch, tensorrt, etc, superpoint.cpp is an option!
TODO:
- image has to been preprocessed to size of 480,640
- image loading is complex and dirty
- acceleration ...
Download the model weights:(optional)
$ wget https://github.com/magicleap/SuperPointPretrainedNetwork/blob/master/superpoint_v1.pth
compile the project and generate the executable file
$ mkdir build
$ cd build
$ cmake ..
$ make
$ mv bin/superpoint ../examples/superpoint
Convert the weights to GGUF format (optional): since the superpoint.gguf is uploaded to the folder superpoint, this step could be skipped.
$ cd /examples/superpoint
$ ./convert-pth-ggml.py
inference
$ ./superpoint -i dog_color.jpg
feature extration
matching performance
https://github.com/ggerganov/ggml
https://github.com/magicleap/SuperPointPretrainedNetwork https://github.com/adityamwagh/SuperSLAM