3D point clouds can be generated from images using photogrammetric methods and dense matching algorithms, or with Light Detection And Ranging (LiDAR), also referred to as Laser Scanning. Both approaches deliver measurements with a tremendous degree of detail. Point clouds are used in many fields of technology and application areas: construction, quality evaluation and assurance, environmental monitoring, agriculture and forestry, to name but a few. Objects described by point clouds can be as small as a few millimeters, or as large as whole cities, including buildings, roads, trees and cars.

Point cloud processing services is a process that targets on images modification captured with a 3D laser scanner. The laser scanner is a measuring instrument mounted on a tripod. It is used to survey existing buildings or at a new construction site. The device rotates 360 degrees around the axis, emits millions of laser beams per minute and catches their reflections. A surveyor moves the scanner through a job site and stops several times to make the measurements. These are called substations. No blind zones should be left. As a result, once the field operations are complete, the raw data – point cloud – is taken to the office. Here is where BIM Machine steps in the game.

Next to the coordinate information, they may also include colour, mapped to every single 3D point, thus giving a very realistic presentation.

Point clouds are often the basis for highly accurate 3D models, which are then used for measurements and calculations directly in or on the object, e.g. distances, diameters, curvatures or cubatures. They are therefore a great source of information in 3D feature and object recognition, as well as in deformation analysis of 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.