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Hi everyone,

Is the python code developed for the 3DMASC paper available? I fail to find it. It would be very useful as a tutorial to getting started with 3DMASC, as well as for reproducibility and benchmarking for potential optimizations. The examples/Clouds_23_3dmasc_hands_on.ipynb in the Github repo does not include the full workflow (e.g. parameter file, feature optimization, ...) and the description at https://lidar-platform.readthedocs.io/e ... dmasc.html doesn't point to the parameter file in the paper.

Thanks in advance!
FG

The code you find in the lidar-platform library IS the one developed for the 3DMASC paper. If you look for a kind of all-inclusive script or notebook which would allow you to rebuild the results of the article I am not sure that it exists somewhere. But the article, the dataset (https://lidar.univ-rennes.fr/3dmasc-datasets), the library and the documentation (which is not perfect but not so bad ;)) should help you to progress. And yes, we should think of building a tutorial for the plugin.

I will ask one other contributor to the project for such a script/notebook, we never know.

Hi Paul,

Thanks for allocating the time/resources. Yes, I meant an all-inclusive sript/notebook illustrating the workflow step-by-step. I believe such material would ease the onboarding to 3DMASC and resolve many doubts/questions before hitting the forum.

In the meantime, could you please point me to where can I find the final parameter file used in the published article? The parameter file combined with the great dataset/library/documentation should allow me to keep going ;)

Thanks!
FG

Hello,
The jupyter notebook actually contains the complete workflow, including feature selection, and goes through each part step by step (feature computation, classification using different algorithms, prediction confidence analysis, SHAP analysis, feature selection).
We updated the notebook after your feedback and improved its documentation, I hope this newer version helps you get familiar with the method.
We might follow your suggestion in the future and publish a notebook to reproduce the paper's results. Until then, this notebook provides a generic tutorial to apply the method as described in the paper to any data. Combined with the code and the documentation, we think it provides a solid foundation for the advanced use of 3DMASC :)

Regarding the parameter files, we need to re-upload them, and that won't be possible before January... In the meantime, the documentation and the information in the paper—such as the final sets of features and scales—can serve as a starting point if you need to create a parameter file urgently.

Hi mletard,

Thank you the updates and considering publishing the notebook allowing the reproduction of the paper's results!
The parameter files will be a very nice xmas present :) I look forward to them!

FG