-1

RSME calculates how close the predicted value is compared to the actual value, but in a point cloud, there are 2 things that I am confused about:

  1. How do we know which point corresponds to which point, to be subtracted from?
  2. Point clouds are 3-dimensional since it has xyz values, but how do people turn those 3 values to one RSME value?
artguy_
  • 7
  • 2

1 Answers1

0

First of all, it's RMSE, not RSME. It stands for Root Mean Square Error:

https://en.wikipedia.org/wiki/Root-mean-square_deviation

With 3D coordinates you can compare component wise, or however else you choose to define a distance measure. Then you plug this into the RMSE formula. Essentially this means comparing an expected value to your observed value.

As for the point correspondence - this depends on the algorithm of choice. Probably one of the most famous examples is ICP:

https://de.wikipedia.org/wiki/Iterative_Closest_Point_Algorithm

In a nutshell for every point of one cloud, the closest point of the other cloud is determined. Then an error measure is calculated and lastly points are transformed. This is done an arbitrary number of times, depending on the desired precision.

Since I strongly suspect that you are indeed looking for ICP, here is the description as to how they are put together:

https://en.wikipedia.org/wiki/Iterative_closest_point

Other than that you will have to do some reading yourself.

Koenigsberg
  • 1,726
  • 1
  • 10
  • 22
  • thank you for your answer, actually I am working with CPD currently trying to figure out how the accuracy quantification returns 1 value instead of 3 vales. https://au.mathworks.com/help/vision/ref/pcregistercpd.html#d122e127037 here is an example of built in rsme in cpd from matlab – artguy_ Sep 28 '20 at 23:04