CloudCompare forum

Hello all,

I am new to CloudCompare. I have obtained a histogram for volume density but I am unable to interpret the plot. I would also like to know the unit of radius from which the nearest neighbors are calculated.

I have attached the sample Histogram. It would be a great help if I can get an explanation of the histogram variable on x and y axis. Thank you!

Abscissa: various "Volume density" values (regularly sampled between the minimum and maximum values found in the cloud)
Ordinate: the number of points that have the corresponding density value (or something close)

And the radius is expressed in the same units as the cloud. CloudCompare doesn't manage units (because it's not indicated in the input file). But it keeps the coordinates/values as they were stored in the original file, and you should normally know them, or be able to deduce them.

Thank you Daniel for your quick response. I am confused with the definition of point density which states that "the number of coordinates collected per unit area". I found that the surface density in the cloudcompare software fits the definition of point cloud density since my goal is also to find the number of points in unit area. Is my understanding correct?

Also I have a query regarding the Align tool. Can you please guide me to the research work referred to implement the tool since it somehow performs differently compared to ICP.

the number of coordinates collected per unit area

Where did you find this definition? (it looks like a typo ;).

Bascially, when you define a 'radius', we count the points that fall inside the corresponding sphere. So you either derive from this:
- the volume density, which is the number of points divided by the sphere volume
- or the surface density, which is the number of points divided by the disc volume (assuming the surface is locally more or less flat)

And yes, it generally makes more sense to use the surface density.

Last, the Align tool is not iterative contrarily to ICP. It just registers the set of points you've selected in one shot. And that algorithm is actually the same as the one used by ICP for a single iteration (see "Closed-form solution of absolute orientation using unit quaternions" by B. Horn).

Thank you very much Daniel. I really appreciate your efforts to explain it. I just have one last query regarding the interpretation of histogram. I tried really hard to interpret the volume density histogram but somehow failed to do so. Can you please explain me few data points which can help me interpret the histogram.

Thanks a lot :-)

Sorry, it's my turn not to understand what information you are looking for :D

Hello,

I am not able to interpret the histogram completely. Can you help me by giving an example with 1 or 2 data points from the above attached figure?

Thank you :)