CloudCompare forum

Without data, quite hard for me to answer your questions, sorry. 12 hours seems quite long for 11500 points but without any information on your machine, impossible to say something. May take more time on a raspberry pi. ;-) Your images are not displayed. In the classify step, you should give points ...

To train your classifier, 1000 points of each class in the training cloud (your PCX), are enough. How many points do you have in the training cloud? As a PC1 cloud, one generally uses a full density point cloud as features are computed in each point of PCX using the points of PC1 (seeing the feature...

What you are trying to do is not clear, please reformulate your objectives.
Please provide a subset of your data if you want some help (CloudCompare .bin file with point clouds explicitly named and their association with PCX and PC1).

Hi, In the parameter file related to the article, PC1 and PC2 are HD data which are used to compute features in the location specified by PCX. PC1, PC2, PCX and CTX are just labels, you can use the names you want (except CORE and TEST which have specific roles). What is important is i) the line core...

I finally posted the parameter file here https://lidar.univ-rennes.fr/3dmasc-datasets

Enjoy!

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-r...

Can you retry with the very last version of CloudCompare? I think it has been merged on November 09.

Again, I never saw such an error and we did use 3DMASC on large point clouds.
If you send me your data, your parameter file and the exact syntax of your command, I can launch the command on my side to test it with my Python.

So the command can compute the features. Does it close this post?

Cloud you provide your parameter file and your data, or at least a sample of it? I never saw such an error. If you have strategies to try, I would say that you should try them to gain some experience. In terms of classification, practice is a good thing. Begin with a small subset of your data before...