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A point cloud is a discrete set of data points in space. The points may represent a 3D shape or object. Each point position has its set of Cartesian coordinates (X, Y, Z). Points may contain data other than position such as RGB colors, normals, Timestamp and others. 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 computer-aided design (CAD) or geographic information systems (GIS) models for manufactured parts, for metrology and quality inspection, and for a multitude of visualizing, animating, rendering, and mass customization applications.
Point clouds are often aligned with 3D models or with other point clouds, a process termed point set registration.
The Iterative closest point (ICP) algorithm can be used to align two point clouds that have an overlap between them, and are separated by a rigid transform. Point clouds with elastic transforms can also be aligned by using a non-rigid variant of the ICP (NICP). With advancements in machine learning in recent years, point cloud registration may also be done using end-to-end neural networks.
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.
There are many techniques for converting a point cloud to a 3D surface. Berger, M., Tagliasacchi, A., Seversky, L. M., Alliez, P., Guennebaud, G., Levine, J. A., Sharf, A. and Silva, C. T. (2016), A Survey of Surface Reconstruction from Point Clouds. Computer Graphics Forum. Some approaches, like Delaunay triangulation, , 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 voxel distance field and reconstruct the implicit surface so defined through a marching cubes algorithm. Meshing Point Clouds A short tutorial on how to build surfaces from point clouds
In geographic information systems, point clouds are one of the sources used to make digital elevation model of the terrain. From Point Cloud to Grid DEM: A Scalable Approach They are also used to generate 3D models of urban environments. K. Hammoudi, F. Dornaika, B. Soheilian, N. Paparoditis. Extracting Wire-frame Models of Street Facades from 3D Point Clouds and the Corresponding Cadastral Map. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences (IAPRS), vol. 38, part 3A, pp. 91–96, Saint-Mandé, France, 1–3 September 2010. 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, Pix4D, DroneDeploy or Hammer Missions 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.
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