title: Normalization (Feature scaling) of Point Cloud Dataset
body: <p>I have point cloud data set where single data is represented by N * 3 where N is number of points. Similarly I have "M" number of points clouds in Dataset. The range of these point clouds varies largely. Some have very large values (e.g., in term of 10^6 for all N points) while some has very small values (e.g., in term of 10^1 for all N points). I want to normalize each point cloud. How should i do that?</p>
  
  <p><strong>Q1.</strong> Should I normalize(min-max) each point cloud (single point cloud N*3) individually along x, y, z dimension by choosing min and max from this point cloud only. In this scenario, for all "M" point clouds we have different min-max. The same is done for output  point cloud. Please view the image for more understanding <a href="https://i.stack.imgur.com/tKauw.jpg" rel="nofollow noreferrer">https://i.stack.imgur.com/tKauw.jpg</a></p>
  
  <p><strong>Q2.</strong> Or Should I normalize (min-max) all point clouds along x, y, z dimension by choosing min and max (from M * N *3 in x, y, z columns) from this entire data set only. In this scenario, for all "m" point clouds we have same min-max. Please view the image for more understanding  <a href="https://i.stack.imgur.com/0HAhn.jpg" rel="nofollow noreferrer">https://i.stack.imgur.com/0HAhn.jpg</a></p>