How to densify point feature class in ArcGIS?

I have a 3D (x,y,z) single point feature class which points are equally spaced, like this:

As the distance between each points are more than one meter, I was wondering if there's a possibility to automatically identify data by adding new points like this (maybe also in arcpy):

• You question is not related to lidar or point-cloud but to raster processing right ? – JRR Jan 10 at 14:57
• sorry, thats true- I just edited the title – feinheitsbrei Jan 10 at 15:10
• For limited number of points. Create tin, extract time edges, create mid points, split edges by points. Create both ends points, delete spatial duplicate. Add z information. – FelixIP Jan 11 at 0:01

2 Answers

You can use Spatial Analyst extension Interpolate Tools (IDW) to create a Raster from the input point features. Then convert back to points if that is your needed format. Make sure to specify environment setting or specify cellSize to desired resolution (ie 1 = 1m) if you are in projected coordinate system

``````import arcpy
from arcpy.sa import *

arcpy.CheckOutExtension("Spatial")

inFeats = "C:\mydata.gdb\3dpoints"
cellsize= 1
zField = "zfld"

ras = Idw(inFeats, zField, cellsize)

outfeats = "C:\mydata.gdb\3dpoints_1m"
arcpy.RasterToPoint_conversion(ras, Outfeats,"Value")
``````

If you have Geostatistical Analyst you can use the Inverse Distance Weighting tool to fill in your gaps.

I think IDW is the most appropriate for your use-case of the various interpolation tools available, though they do require Spatial Analyst license.

It will output a raster. Then, you can convert back to points at your desired resolution, then use Extract Values to Points to join back your z values. See: Assigning Raster Value to Point Data

• But this outputs a raster right? Do I have to use raster to point after that? – feinheitsbrei Jan 10 at 15:03
• And raster to point conversions deletes my z-values – feinheitsbrei Jan 10 at 15:14
• You should be able to use Extract Values to Points after the interpolation. I will add this step to the answer... – mikeLdub Jan 10 at 15:28
• At it's simplest level, if you have evenly-spaced points representing elevation I think IDW would simultaneously work effectively and be easiest to describe in a methods section (preventing the need to outline the statistics involved in kriging, for example). I could be convinced otherwise which is why I provided the link to all the interpolation tools. – mikeLdub Jan 10 at 18:08