Feature output missmatch between CC & 3DMASC

[PablerasBCN]

Hi,

So, I was trying again to train a classifier and left it overnight for in the morning to find a somewhat meh ressult.

So I was checking the computed feature scalar fields when I noticed the looked quite different from the onces I did compute manually in order to guess the correct scale for the classifier.

I've put together a PDF where the differences between several features can be seen, ignore the small scale differences as I've tested this not to be the root of the issue.

https://drive.google.com/drive/folders/ ... sp=sharing

In the folder there is also the .bin with the point cloud if anyone whants to check in person.

So, does 3DMASC not use CC built in feature compute and have it's own? if so, could it be made to use CC's so we can manually do tests and decide what scales fit best, since to me, CC's feature do a better job.

Cheers!

EDIT: The "Classification" SF displays wrong data, as I originally renamed OriginalCloudIndex into Classification but something whent wrong, hence the classification of 3D Masc can´t be correct due to this critical issue. Still, the mismatch on how Feature SF look remains, Yet, having the classification unproperly labelled, I can´t tell if this is a critical issue or not.

[paul.leroy]

I did some testing on your data. I have the answer:
=> With CloudCompare, you define a RADIUS for the computation of a feature.
=> With 3DMASC, a scale is a DIAMETER. Use 0.871588 with 3DMASC instead of 0.435794 with CC to get the same results.

[PablerasBCN]

awesome catch! I'll check that out. !!