A new Point Cloud Task (PCT) for smoothing noisy point cloud (LAS) data has been added. This PCT is a moving least square smoothing algorithm that reduces “noise” in high density, noisy point cloud data such as that produced by automotive grade LIDAR scanners (e.g. Velodyne, Quanergy).  This smoothing algorithm is notable in that it can be applied to unfiltered data (e.g. non-ground classified data).  While it causes a bit of smoothing of sharp edges, it will not destroy complex vertical objects such as trees.  This means that, in many cases, it can simply run across the entire input data set. For more background details and explanation on how the Smoothing, Point Cloud PCT works, see Data Smoothing in LP360. 

• Input Dataset - Select the data to use in the LAS Smoothing process
• Source Points - Select the points within the dataset to use in the processing
  • • Source Filter - Opens the Live View to allow filtering of the Source Points
• Las Output Location - Select an Output location
  • • Tip: Using the <> icon found on the right hand side of the  I/O Manager will add the LP360 Project Path Variable (<LP360_PROJECT_PATH>) which will automatic output into the currently open Project folder.
• Tiling Method
  • • Single File - Will generate only a single LAS FIle
    • Separately Process Source Files - When this option is used, the source files are processed independently of one another.  This means that, in overlap regions, the data from different flight lines (if it is in flight line structure) will be independently processed.  In general, this exacerbates any vertical shift between flight lines.  On the other hand, if this option is not checked, the data are processed as mixed tiles.  In this case, the separation between flight lines is reduced.  
    • Tiled Files

See example point clouds below:

Before Smoothing: 

After Smoothing: