Point cloud


A point cloud is a set of data points in space. Point clouds are generally produced by 3D scanners or by photogrammetry software, which measure many points on the external surfaces of objects around them. As the output of 3D scanning processes, point clouds are used for many purposes, including to create 3D CAD models for manufactured parts, for metrology and quality inspection, and for a multitude of visualization, animation, rendering and mass customization applications.

Alignment and registration

Point clouds are often aligned with 3D models or with other point clouds, a process known as point set registration.
For industrial metrology or inspection using industrial computed tomography, the point cloud of a manufactured part can be aligned to an existing model and compared to check for differences. Geometric dimensions and tolerances can also be extracted directly from the point cloud.

Conversion to 3D surfaces

While point clouds can be directly rendered and inspected, point clouds are often converted to polygon mesh or triangle mesh models, NURBS surface models, or CAD models through a process commonly referred to as surface reconstruction.
There are many techniques for converting a point cloud to a 3D surface. Some approaches, like Delaunay triangulation, alpha shapes, and ball pivoting, build a network of triangles over the existing vertices of the point cloud, while other approaches convert the point cloud into a volumetric distance field and reconstruct the implicit surface so defined through a marching cubes algorithm.
In geographic information systems, point clouds are one of the sources used to make digital elevation model of the terrain. They are also used to generate 3D models of urban environments.
Drones are often used to collect a series of RGB images which can be later processed on a Computer Vision Algorithm platform such as on AgiSoft Photoscan or Pix4D or DroneDeploy to create RGB point clouds from where distances and volumetric estimations can be made.
Point clouds can also be used to represent volumetric data, as is sometimes done in medical imaging. Using point clouds, multi-sampling and data compression can be achieved.