Comments (2)
I've thought about this a bit, and it sounds like a great addition for e.g. the Nuth & Kääb implementation.
For ICP, however, I don't see a way of providing weights to neither opencv nor PDAL.
We could perhaps provide a weights=
argument as an optional keyword argument, but then warn if this is not used (which it presumably won't ever with ICP)
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My little finger is telling me that the most robust co-registration method will indeed be a weighted co-registration, possibly with Bayesian snow-cover drift estimation :)
I have a MSc student starting on this subject in 2/3 weeks so I will work on implementing this
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Related Issues (20)
- Switch to a different JupyterHub host following MyBinder server reduction? HOT 3
- Start GitHub discussions for user questions HOT 1
- import error (Add warning on use of `pip` in README and documentation) HOT 3
- `xdem.coreg.ICP` fails very underscriptively when not enough points are available HOT 1
- `xdem.coreg.ICP.fit_pts` fails for unknown reason with a regular dataset HOT 4
- Setup a PR template to ensure `setup.py` changes are properly reported
- Add automated approach to validate that requirement files are consistent
- Use `pytest.fixture` to provide consistent test data across all test modules?
- Argument `subsample` does not work for a `CoregPipeline` HOT 4
- Remove pinning of SciPy<1.11.1 once 1.11.4 released
- Decide on author attribution across GeoUtils and xDEM HOT 2
- Improve `Coreg` docstrings
- Way forward for co-registration module HOT 3
- Provide default downsampling logic in `Coreg` pre-processing for performance
- Unhandled numba exception HOT 3
- Plots of advanced variogram example seem wrong
- Coregistration: Tilt and VerticalShift pipelines in 0.0.16 HOT 2
- module 'xdem.coreg' has no attribute 'VerticalShift' HOT 5
- Unforce `pydata-sphinx-theme=0.13` once `sphinx-book-theme=1.1` released
- ICP slow for large DEMs no matter `subsample` HOT 1
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