Irregularly spaced 3D point clustering / segmentation

Greetings fellow R users,

I would really enjoy (and eagerly anticipate) any discussions on ideas for
handling a LIDAR (laser) data set of a New England forest.  The LIDAR
dataset is essentially xyz coordinates that form an irregularly spaced 3D
data cloud of points.  I have brought the data in as SpatialPointsDataFrame,
SpatialPixelsDataFrame, SpatialGridDataFrame, marked Point Pattern Process
objects, matrices etc.  I can view the interpolated surface with ?interp in
library(akima) as well as 3D points and surfaces in library(rgl).

So, importing the LIDAR data and viewing it or exporting it so that ImageJ
can handle it is not the issue.  

The LIDAR data set essentially produces a set of "mounds" from the elevation
data recorded in the z variable.  Each "mound" represents a tree in the
forest.  I am hoping to get some ideas on ways to cluster this data set so
that I can isolate each mound for further analysis and segmentation.  One
possibility that I have looked into with Matlab software is
"marker-controlled watershed segmentation".  This essentially inverts the
interpolated surface and "fills" the inverted image with "water" starting at
the local minimas until the water starts to spill over into the next
watershed at which point it builds a "dam" between local valleys.  This is a
function in Matlab and I haven't been able to see the code to bring it to R.

Any ideas on this method or suggestions for better methods to isolate
"mounds" in 3D space?  Template matching, perhaps??

Thanks in advance and thanks to all the innovative producers and users of
the R domain!!  
Andrew