I wonder if someone had an opportunity to extract points of interest from randomly scattered laser data available as 3d point cloud?
I have plotted it successfully using scatter plot. But I want to take measurements on curvature. For instance, for a 3d point cloud data for 'Human Arm', I wish to be able to measure distance between finger tips and elbow. When posing to measure biceps size, I need to measure angle between elbow and shoulder. I have read we could create mathematical model for one axis in terms of other two axis i.e.
Z = a + bx + cy + dx2 ....
But model might not define angles, slopes, height that I want to analyse on Arm.
Random scatteredness of data provides limited options to use standard slope equations to achieve these measurements. Data is scattered everywhere on arrays.
I have managed to achieve some measurements but it was after fully sorting the 3 million point array ( for each axis - x,y,z) and then just treating whole sets of data as arrays and using standard arrays functions i.e. max/min, mean etc.
I am not sure if I am heading in the right direction with taking measurements from 3d point cloud data. So I wanted to find how experts here at LAVA would do. Any ideas?