Comments (5)
Hi @rjanvier
I am one of the creators of cut pursuit. If you also need to port cut pursuit (this repo) to setuptools and need any help, do not hesitate.
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HI @loicland , thank you very much. I have just opened a PR on gitlab https://gitlab.com/1a7r0ch3/parallel-cut-pursuit/-/merge_requests/13.
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Hi Romain, thanks for your active work on this project, I really appreciate it.
Porting pgeof
to setuptools
would be awesome ! Ideally, if this project could simply be distributed as a pip
package, it would be great. Unfortunately, I do not have time (nor extensive setuptools
skills) to work on this at the moment. So if you feel like making improvements to the project to facilitate its distribution, I would gladly accept the help !
Besides, if you are actively using this library, I take this opportunity to mention that I had an extension in mind that could improve features expressivity: multi-scale features. This would require computing the eigenfeatures at several neighborhood radii. I could provide guidelines for doing so, if you wanted to implement this.
In any case, thanks for your contributions.
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Hi Damien,
Thanks a lot!
For now I will focus on easing the installation of pgeof and cut pursuit. I have a CloudCompare plugin for "SPT-Inference" in mind and I want to reduce python dependencies or the need of a c++ compiler to install them. I also plan to offer alternative to CUDA-only modules (FRNN could be replaced by scikit CKDTree / Nanoflann in a CPU only context). Multiscale features sound like a good idea, it reminds me https://inria.hal.science/hal-00713678v2/document somehow
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Hi Romain,
Glad to hear you are also interested in SPT ! Same as above, I would love to wrap it as a simple pip
package somehow but cannot dedicate time to this.
So, I trust that your skills for simplifying the dependencies, I agree that would help the installation.
Would you mind creating an issue on SPT for discussing SPT-specific stuff, when relevant ?
Regarding the multiscale features, you are right, I do not claim any novelty in this idea, it has a long history in literature. Just thought it would be an easy extension to pgeof
and would be curious to see how it could help downstream models like SPT for instance (at the cost of some extra hyperparameters).
Thanks for your help
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