====== Point Clouds Processing and Interpretation ======

==== Local Normal Vector Estimation ====
If the point cloud data represents an object as scanned from some device, usually the points lie on the surface of the object. So they are really 2D structurally speaking (local to each point), though they are in 3D.  Let's write a function to estimate the local normals to the point cloud, plus one more to visualize them. There are actually two ways to perform the [[ECE4580:Module_PCD:LocalNormals|local normal estimation]].  Let's try out both.

  - One is via principal component analysis, otherwise known as [[ECE4580:Module_PCD:LocalNormals#PCA|PCA]].
  - The other is using the [[ECE4580:Module_PCD:LocalNormals#SVD|SVD]] we know and love (by now).

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.


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