Open source point cloud classification

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sudo_ki
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Open source point cloud classification

Post by sudo_ki »

does anyone know of any os projects similar to 'https://pointly.ai/'?
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Re: Open source point cloud classification

Post by sudo_ki »

http://www.cloudcompare.org/doc/wiki/in ... O_(plugin)

I found this neat plugin. i'm going to give this a go for now.
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Re: Open source point cloud classification

Post by smacl »

sudo_ki wrote: Sun Aug 23, 2020 11:08 am does anyone know of any os projects similar to 'https://pointly.ai/'?
I'd recommend having a look at Florent Poux's work on segmentation, see http://pointcloudproject.com/the-future ... rspective/ It has a big advantage of not requiring training data so potentially a very efficient workflow and precursor to other automated feature extraction techniques. Not sure if he has any publicly available tools in the open source domain as yet but I'd guess they're coming. The issue with training a neural net against terrestrial scanned point cloud data is that there are a lot of inconsistencies in how a given object appears based on distance from the scanner and point density. I'd be interested in hearing how you get on with Canupo.
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Re: Open source point cloud classification

Post by sudo_ki »

nice rea.

I'm tempted to read his thesis. https://orbi.uliege.be/handle/2268/235520

I can' see anything on github for now, but let's wait and see.

There are so many interesting things happening right now, I can't wait to see more algorithms in the reality capture space.

Will let you know how i get on. My main goal is to find something i can use to batch process and remove vegetation.
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Re: Open source point cloud classification

Post by smacl »

sudo_ki wrote: Tue Aug 25, 2020 1:30 pm nice rea.

I'm tempted to read his thesis. https://orbi.uliege.be/handle/2268/235520

I can' see anything on github for now, but let's wait and see.

There are so many interesting things happening right now, I can't wait to see more algorithms in the reality capture space.

Will let you know how i get on. My main goal is to find something i can use to batch process and remove vegetation.
You're welcome to a demo license to SCC which is pretty good at bare earth extraction and subsequent object heighting, but is neither free nor command line. I'd also have a look at LAStools which can be batch driven and so far as I know is free for non-commercial use.

It is certainly an interesting area, you could spend years just working through all through the research material with new stuff arriving all the time.
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Re: Open source point cloud classification

Post by sudo_ki »

You're welcome to a demo license to SCC which is pretty good at bare earth extraction and subsequent object heighting, but is neither free nor command line.
Thank you. https://www.youtube.com/watch?v=miyEQj36Ew0 this looks really good! :)
could spend years just working through all through the research material
XD sometimes i feel like i do more reading than work
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Re: Open source point cloud classification

Post by dhirota »

smacl wrote: Mon Aug 24, 2020 10:18 am
sudo_ki wrote: Sun Aug 23, 2020 11:08 am does anyone know of any os projects similar to 'https://pointly.ai/'?
I'd recommend having a look at Florent Poux's work on segmentation, see http://pointcloudproject.com/the-future ... rspective/ It has a big advantage of not requiring training data so potentially a very efficient workflow and precursor to other automated feature extraction techniques. Not sure if he has any publicly available tools in the open source domain as yet but I'd guess they're coming. The issue with training a neural net against terrestrial scanned point cloud data is that there are a lot of inconsistencies in how a given object appears based on distance from the scanner and point density. I'd be interested in hearing how you get on with Canupo.
Shane

I do not have much time, but here are some examples of the CC plugin Canupo running on my Ryzen 3990X. I can tell you that this example of running 128 threads does makes a difference in time vs. Dual Xeon at 16 threads. Tweeking the resampling also makes a significant difference in time, the larger minimum distance the faster compute time since there are fewer core points to deal with. This looks like significant manual labor to get the configuration files in a decent shape. It would be handy if there are meter based files available.

1.Screenshot from 2020-08-25 10-32-05.jpg

2.Screenshot from 2020-08-25 09-39-30.jpg

3.VEGE-TIDAL-Screenshot from 2020-08-25 10-39-27.jpg

4.GRAVEL-Screenshot from 2020-08-25 09-45-31.jpg

5.BEDROCK-Screenshot from 2020-08-25 09-57-45.jpg

6.VEGE-SUPER-Screenshot from 2020-08-25 11-41-19.jpg


The Otira files are available from the CC forum if you look hard.


7.COMP-TIME-08-25-2020 12-20-34 PM.jpg
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Dennis Hirota, PhD, PE, LPLS
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Re: Open source point cloud classification

Post by Poux »

smacl wrote: Mon Aug 24, 2020 10:18 am
sudo_ki wrote: Sun Aug 23, 2020 11:08 am does anyone know of any os projects similar to 'https://pointly.ai/'?
I'd recommend having a look at Florent Poux's work on segmentation, see http://pointcloudproject.com/the-future ... rspective/ It has a big advantage of not requiring training data so potentially a very efficient workflow and precursor to other automated feature extraction techniques. Not sure if he has any publicly available tools in the open source domain as yet but I'd guess they're coming. The issue with training a neural net against terrestrial scanned point cloud data is that there are a lot of inconsistencies in how a given object appears based on distance from the scanner and point density. I'd be interested in hearing how you get on with Canupo.
Thanks for the share Shane!

Indeed, they are coming soon :). In the meantime, all the theoretical background is open access (https://scholar.google.com/citations?us ... AAAJ&hl=en), thus replicating should not be hard for those with the skills to do so.

What is currently super robust is the unsupervised segmentation part (getting nice clusters), and the classification really depends on the model at hand (if you have a lot of training data, I recommend SPG or RF - if you have little to no training data I recommend going through self-learning, see:
https://www.linkedin.com/posts/florent- ... 77888-X4Fc)

The expectation should be that it recognizes rightly 90% of elements in your scene, and find 90% of them. Tested for indoor point clouds with 13 classes, and currently in test for a wide range of applications. For those interested, you can PM me via linkedin and I can put your data (.las file) to the test.

1. Example segmentation with noisy point cloud
client1.png
2. Example segmentation with mass-market sensor (Matterport)
area3-l1.png
3. Classification results
rendering_10.jpeg
Cheers and Stay safe,

Adj. Prof. Dr.-Ing Florent Poux
Point Cloud Lab
Jack Dangermond Award Press Release
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Florent POUX
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sudo_ki
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Re: Open source point cloud classification

Post by sudo_ki »

thanks for the further replies :)
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