I was looking to use hloc for following usecase which is not uncommon in vision-based localization:
A 'map creator' creates a 'ground-truth map' of a place using a sequence of images.
New users use this map to localize their own new images (without altering the data and features in the ground-truth map).
However, I think the examples provided don't directly address this usecase, as they are mostly written to evaluate hloc on datasets. As a result, the reference images and query images are tightly coupled. For example, in pipeline_Aachen.ipynb, the features for both the reference and query images are extracted together. Localizing query images wihtout updating the ground-truth data (eg. features db) requires changing the code.
While the notebook demo.ipynb achieves something to this effect, it uses exhaustive matching which is impractical for most usecases.
I have added pipeline_custom.ipynb that demonstrates how to use hloc for one's own data (with some very small changes to the hloc APIs).
If this is useful, I can add more documentation on how to generate one's own data (eg. generating colmap SfM model) if needed.
I was looking to use hloc for following usecase which is not uncommon in vision-based localization:
However, I think the examples provided don't directly address this usecase, as they are mostly written to evaluate hloc on datasets. As a result, the reference images and query images are tightly coupled. For example, in
pipeline_Aachen.ipynb, the features for both the reference and query images are extracted together. Localizing query images wihtout updating the ground-truth data (eg. features db) requires changing the code. While the notebookdemo.ipynbachieves something to this effect, it uses exhaustive matching which is impractical for most usecases. I have addedpipeline_custom.ipynbthat demonstrates how to use hloc for one's own data (with some very small changes to the hloc APIs).If this is useful, I can add more documentation on how to generate one's own data (eg. generating colmap SfM model) if needed.