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--- ece4580:module_pcd:normals [2017/04/08 17:42] – created pvela
+++ ece4580:module_pcd:normals [2024/08/20 21:38] (current) – external edit 127.0.0.1
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 For the sample point cloud file given, plot the normals. Do not really plot them all, but rather sub-sample the points array and only compute then plot the normals for those points. The Matlab example linked to in the discussion page for this problem (above link for //local normal estimation//) shows how to perform that.
-==== Week #4: Clustering by Normal Structure ====
-The connected component clustering does not do too well when different objects are actually connected, say because they are touching, or one is resting on the other.  Usually, however, the objects might have differing local normals at the interface where they are touching.  Let's add normal agreement to the connected component clustering to see how the normals can be used to differentiate parts of the scene.
-Perform a region growing clustering strategy using the connected components for the region expansion candidate, and using the angular difference between normals to assess membership to the same cluster.
-/*Read the slides on [[http://www.connellybarnes.com/work/class/2013/shape/04_normal.pptx|normal vector estimation]].
-For Matlab folks, an exemplar implementation of normal vector estimation can be found [[https://vision.princeton.edu/pvt/points2normals.m|here]]. Try to implement your own estimator.
-For PCL folks, you may refer to the [[http://pointclouds.org/documentation/tutorials/normal_estimation.php|sample code]]. Try to implement your own estimator.
-*/
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