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From Wikipedia, the free encyclopedia

A point cloud is a set of vertices in a three-dimensional coordinate system. These vertices are usually defined by X, Y and Z coordinates.

Point clouds are most often created by 3D scanners. These devices measure in a automatic way a large number of points on the surface of an object, and, often, output a point cloud as a data file. The point cloud represents the set of points that device has measured.

As the result of 3D scanning process point clouds are used for many purposes, including to create 3D CAD models for manufactured parts, metrology/quality inspection, and a multitude of visualization, animation, rendering and mass customization applications.

While point clouds can be directly rendered and inspected [1], usually point clouds themselves are generally not directly usable in most 3D applications, and therefore are usually converted to polygon 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 trough Marching cubes algorithm [2].

One application in which point clouds are directly usable is industrial metrology or inspection. The point cloud of a manufactured part can be aligned to a CAD model (or even another point cloud), and compared to check for differences. These differences can be displayed as color maps that give a visual indicator of the deviation between the manufactured part and the CAD model. Geometric dimensions and tolerances can also be extracted directly from the point cloud.

Point clouds can also be used to represent volumetric data used for example in medical imaging. Using point clouds multi-sampling and data compression is achieved[3].

See also

  • 3D Scanning, the main source of point cloud data;
  • MeshLab, an open source tool for managing point clouds and converting them into 3D triangular meshes;


  1. ^ Rusinkiewicz, S. and Levoy, M. 2000. QSplat: a multiresolution point rendering system for large meshes. In Siggraph 2000. ACM , New York, NY, 343-352. DOI=
  2. ^ Meshing Point Clouds A short tutorial on how to build surfaces from point clouds
  3. ^ Sitek et al. "Tomographic Reconstruction Using an Adaptive Tetrahedral Mesh Defined by a Point Cloud" IEEE Trans. Med. Imag. 25 1172 (2006)


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