A methodology for smoothing of point cloud data based on anisotropic heat conduction theory
| Autor: | Juntong Xi, Xue-Chang Zhang, Jun-Qi Yan |
|---|---|
| Rok vydání: | 2006 |
| Předmět: |
Reverse engineering
Mathematical optimization Field (physics) Mechanical Engineering Point cloud Boundary (topology) computer.software_genre Thermal conduction Industrial and Manufacturing Engineering Computer Science Applications Control and Systems Engineering Feature (computer vision) Adiabatic process Algorithm computer Software Smoothing Mathematics |
| Zdroj: | The International Journal of Advanced Manufacturing Technology. 30:70-75 |
| ISSN: | 1433-3015 0268-3768 |
| DOI: | 10.1007/s00170-005-0050-9 |
| Popis: | It is necessary to smooth point cloud data in reverse engineering or the inspection of free-form surfaces because noisy points will have a negative influence on the post-processing of this data. The big problem in smoothing point cloud data is how to solve the dilemma between removing noisy points and keeping feature boundary information, whilst controlling the diffusiveness of noisy points. In this paper, the theory of anisotropic heat conduction is adopted to establish a mathematical model of point cloud data smoothing. The point cloud data can be considered as a temperature field with an adiabatic boundary. So the heat is only conducted inside the temperature filed and has no effect on the outer side. For point cloud data, it means that the smoothing is only on the local area, which makes a good balance between deleting noisy points and keeping feature boundary information. The method has been implemented by using two cases for practical application, and the result proves its efficiency. |
| Databáze: | OpenAIRE |
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