Editing Point cloud (section)

== Conversion to 3D surfaces ==
[[File:Extract Video Beit Ghazaleh Orthophoto Survey AG&P 2017.gif|thumb|An example of a 1.2 billion data point cloud render of [[Beit Ghazaleh]], a heritage site in danger in [[Aleppo]] (Syria)<ref>{{Citation|title=English: Image from a very high precision 3D laser scanner survey (1.2 billion data points) of Beit Ghazaleh -- a heritage site in danger in Aleppo Syria. This was a collaborative scientific work for the study, safeguarding and emergency consolidation of remains of the structure.|date=2017-11-02|url=https://commons.wikimedia.org/wiki/File:Extract_Video_Beit_Ghazaleh_Orthophoto_Survey_AG&P_2017.gif|access-date=2018-06-11}}</ref>]]

[[File:Synthesizing 3D Shapes via Modeling Multi-View Depth Maps and Silhouettes With Deep Generative Networks.png|thumb|Generating or reconstructing 3D shapes from single or multi-view [[depth map]]s or silhouettes and visualizing them in dense point clouds<ref name="3DVAE">{{Cite web|url=https://github.com/Amir-Arsalan/Synthesize3DviaDepthOrSil|title=Soltani, A. A., Huang, H., Wu, J., Kulkarni, T. D., & Tenenbaum, J. B. Synthesizing 3D Shapes via Modeling Multi-View Depth Maps and Silhouettes With Deep Generative Networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 1511-1519).|website=[[GitHub]]|date=27 January 2022}}</ref>]]

While point clouds can be directly rendered and inspected,<ref>Levoy, M. and Whitted, T., {{cite web|url=http://graphics.stanford.edu/papers/points|title=The use of points as a display primitive}}. Technical Report 85-022, Computer Science Department, University of North Carolina at Chapel Hill, January, 1985</ref><ref>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= http://doi.acm.org/10.1145/344779.344940</ref> point clouds are often converted to [[polygon mesh]] or [[triangle mesh]] models, [[non-uniform rational B-spline]] (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.<ref>[https://hal.inria.fr/hal-01348404v2/document 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.]</ref> Some approaches, like [[Delaunay triangulation]], [[alpha shape]]s, 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|volumetric]] [[distance field]] and reconstruct the [[implicit surface]] so defined through a [[marching cubes]] algorithm.<ref>[http://meshlabstuff.blogspot.com/2009/09/meshing-point-clouds.html Meshing Point Clouds] A short tutorial on how to build surfaces from point clouds</ref>

In [[geographic information system]]s, point clouds are one of the sources used to make [[digital elevation model]] of the terrain.<ref>[http://terrain.cs.duke.edu/pubs/lidar_interpolation.pdf From Point Cloud to Grid DEM: A Scalable Approach]</ref> They are also used to generate 3D models of urban environments.<ref>[http://www.isprs.org/proceedings/XXXVIII/part3/a/pdf/91_XXXVIII-part3A.pdf 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.]</ref> Drones are often used to collect a series of [[RGB color model|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.{{citation needed|date=March 2018}}

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.<ref>{{cite journal|last1=Sitek|display-authors=etal|year=2006|title=Tomographic Reconstruction Using an Adaptive Tetrahedral Mesh Defined by a Point Cloud|journal=IEEE Trans. Med. Imaging|volume=25|issue=9|pages=1172–9|doi=10.1109/TMI.2006.879319|pmid=16967802|s2cid=27545238|url=https://zenodo.org/record/1232253}}</ref>