Working with Point Clouds

While there are a few ways to work with Point Clouds, NExT Lab is familiar with Cloud Compare for the usual workflow of post-processing & cleaning, aligning, meshing and more.
For some processes like cleaning, you could use Rhino, or you can do most of the workflow inside Photogrammetry software available to the MSD; Agisoft Metashape or Reality Capture.
The following videos are edited from recordings of ABPL90422 - SI_LAB, some of the terms will be subject-specific but should still be demonstrative to the workflow as a whole.

Cloud Compare

Introduction, Basics & Navigation

[LMB] to rotate, [RMB] to pan, [Scroll] to zoom. Use the left-menu to assist in navigation with various views and options. Use the magnifying glass to focus on a selected point cloud.
Files are organised in a folder-like structure - [RMB] to create empty folders to keep your workspace organise. CloudCompare does not have an undo feature, the trade-off being it is a fast and efficient for how large of a dataset it can handle.
Each file has its own properties, adjust its colour-mode, display point-size and any attached scalar data.
To save your workspace, select the entire structure and save as a .bin.


Use the segmentation or section tools to split up your point cloud data. Segmentation is mainly used for cleaning your dataset. You may have to go through various segmentations to find a clean camera angle to remove data.


Render by taking high-res captures of the viewport, or animate by tweening viewports.


Use surface reconstruction to re-create meshes. Ensure the Density option is toggled to be use the Scalar Field to extract the final mesh.


Use the transformation tools to roughly place two point clouds in space. The alignment algorithm (ICP), works best on datasets that overlap a lot, so you may have to extract out sections to work with.
Run ICP, organise carefully:
  • Reference data | YELLOW - this will not move
  • Data to align | RED - this will move
Use the transformation matrix provided in the result to transform the rest of your dataset.