[GIS] Thinning large LiDAR point cloud

large datasetslaslidarpoint cloudsoftware-recommendations

I have a large point cloud (LAS file) and I am trying to thin this using a complex technique. I know it is possible to thin a point cloud using simple techniques such as extracting every n_th point. However the point cloud I am working with is more dense in some areas and less dense in other areas. I would like to be able to thin the cloud so that the denser areas are thinned more heavily than the less dense areas. To do this manually one could create a grid and select certain number of points within each cell. (ie one at each corner and one in the center) However this point cloud is much too large for this task. I am trying to figure out if there are any existing solutions to this problem such as a complex selection query, other automated process, or existing software application that could be used.

Best Answer

It sounds like you want either a voxel-based thinning or maybe a Poisson-based one. PDAL can do either. See PDAL's tutorial on the topic at https://pdal.io/tutorial/sampling/index.html

As far as the size of the file, please define "large". Just about any technique except simple rank decimation (remove n-th points) is going to want to have access to the entire file in memory. Other options would require a more sophisticated two-stage index+sampling technique to avoid that requirement.

Related Solutions

[GIS] Converting LiDAR point cloud to 3D mesh

Be aware that .las and .obj are not the same type of data, in which the first is composed by points, and the second is a type of surface.

So, even processing the point cloud to output a surface it is needed that the points in the .las file make sense to what is being generated. For example, triangulating first returns (for DSM) or last returns, or returns from class 2 (for DTM).

With that being said, it is possible with LAStools' las2tin. See some examples:

Triangulates only first returns and stores the resulting TIN in OBJ (for DSM).

las2tin -i lidar.las -first_only -o tin.obj

The same, but triangulates only returns classified as ground (class 2, for DTM).

las2tin -i lidar.las -o tin.obj -keep_class 2

R LiDAR – How to Filter Above Ground LiDAR Points Using Catalog in lidR

In your case the filter you are using is a simple one: Classification != 2. And you don't need the ground points at all. You are better to use a streaming filter and a streamed processing.

ctg <- catalog("/...2015TestGroup")
opt_chunk_size(ctg) <- 0
opt_chunk_buffer(ctg) <- 0
opt_output_files(ctg) <- ".../Outputs/2015nonground/{XLEFT}_{YBOTTOM}_2015"
opt_filter(ctg) <- "-drop_class 2"
new_ctg <- catalog_retile(ctg)

catalog_retile is a streamed function. It means that not a single point is loaded at the R level. Each point is read, filtered and written immediately without calling any R code and without loading any memory. It is thus way faster than using the R function lasfilter.

That being said, you are better to use las2las from LAStools for this kind of job. It does exactly the same actually but is more efficient and simple than calling more or less equivalent code from R.

Best Answer

Related Solutions

[GIS] Converting LiDAR point cloud to 3D mesh

R LiDAR – How to Filter Above Ground LiDAR Points Using Catalog in lidR

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