[Numpy-discussion] OT: A Way to Approximate and Compress a 3DSurface

Nadav Horesh nadavh@visionsense....
Wed Nov 21 03:43:24 CST 2007

Wouldn't a random or regular subsampling of the set will do the job?
For data interpolation: 2D-Delaunay triangulation based method (I think you can find one in the scipy cookbook).


-----Original Message-----
From: numpy-discussion-bounces@scipy.org on behalf of Geoffrey Zhu
Sent: Tue 20-Nov-07 19:50
To: Discussion of Numerical Python
Subject: [Numpy-discussion] OT: A Way to Approximate and Compress a 3DSurface
Hi Everyone,

This is off topic for this mailing list but I don't know where else to ask.

I have N tabulated data points { (x_i, y_i, z_i) } that describes a 3D
surface. The surface is pretty "smooth." However, the number of data
points is too large to be stored and manipulated efficiently. To make
it easier to deal with, I am looking for an easy method to compress
and approximate the data. Maybe the approximation can be described by
far fewer number of coefficients.

If you can give me some hints about possible numpy or non-numpy
solutions or let me know where is better to ask this kind of question,
I would really appreciate it.

Many thanks,
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