<p>So, I would like to do capture on 3d pointclouds of 3d models the planes/vectors where for each object we have the highest variance (i.e. for object represented by 2000 3d points I would like to get vectors or planes along which we have the most variance of points), and I assume PCA can be used to show that per object. Did anyone attempt to do something like that and what kind of other variance capturing methods would you suggest? Do you have any particular python library I can use for this, besides the sklearn one?</p>

CC BY-SA 4.0
2020-06-13T00:27:04.683Z
Are you after surface reconstruction? In that case to use PCA I suggest looking at Hoppe paper here
ali
CC BY-SA 4.0
2020-06-13T00:27:10.320Z
hhoppe.com/recon.pdf
ali
<3d>
Principal component analysis on 3d points and other ways of capturing variance
GreatDuke
# Principal component analysis on 3d points and other ways of capturing variance

(Asked by: username_0 on 2020-06-12)

So, I would like to do capture on 3d pointclouds of 3d models the planes/vectors where for each object we have the highest variance (i.e. for object represented by 2000 3d points I would like to get vectors or planes along which we have the most variance of points), and I assume PCA can be used to show that per object. Did anyone attempt to do something like that and what kind of other variance capturing methods would you suggest? Do you have any particular python library I can use for this, besides the sklearn one?

**Tags:** 3d

> **Comments:**
> **username_1**: Are you after surface reconstruction? In that case to use PCA I suggest looking at Hoppe paper here  
> **username_1**: hhoppe.com/recon.pdf  

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