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## Total Least Squares Registration of 3D Surfaces

#### Umut Aydar [1] , M. Orham Altan [2]

Co-registration of point clouds of partially scanned objects is the first step of the 3D modeling workflow. The aim of co-registration is to merge the overlapping point clouds by estimating the spatial transformation parameters. In computer vision and photogrammetry domain one of the most popular methods is the ICP (Iterative Closest Point) algorithm and its variants. There exist the 3D Least Squares (LS) matching methods as well (Gruen and Akca, 2005). The co-registration methods commonly use the least squares (LS) estimation method in which the unknown transformation parameters of the (floating) search surface is functionally related to the observation of the (fixed) template surface. Here, the stochastic properties of the search surfaces are usually omitted. This omission is expected to be minor and does not disturb the solution vector significantly. However, the a posteriori covariance matrix will be affected by the neglected uncertainty of the function values of the search surface. . This causes deterioration in the realistic precision estimates. In order to overcome this limitation, we propose a method where the stochastic properties of both the observations and the parameters are considered under an errors-in-variables (EIV) model. The experiments have been carried out using diverse laser scanning data sets and the results of EIV with the ICP and the conventional LS matching methods have been compared.

Laser scanning, Point Cloud, Registration, Matching
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Journal Section Research Articles Author: Umut AydarInstitution: Istanbul Technical University, Faculty of Civil Engineering, Department of Geomatics Engineering, IstanbulCountry: Turkey Author: M. Orham AltanInstitution: Istanbul Technical University, Faculty of Civil Engineering, Department of Geomatics Engineering, IstanbulCountry: Turkey Publication Date : August 3, 2015
 Bibtex @research article { ijegeo303539, journal = {International Journal of Environment and Geoinformatics}, issn = {}, eissn = {2148-9173}, address = {}, publisher = {Cem GAZİOĞLU}, year = {2015}, volume = {2}, pages = {27 - 38}, doi = {10.30897/ijegeo.303539}, title = {Total Least Squares Registration of 3D Surfaces}, key = {cite}, author = {Aydar, Umut and Altan, M. Orham} } APA Aydar, U , Altan, M . (2015). Total Least Squares Registration of 3D Surfaces. International Journal of Environment and Geoinformatics , 2 (2) , 27-38 . DOI: 10.30897/ijegeo.303539 MLA Aydar, U , Altan, M . "Total Least Squares Registration of 3D Surfaces". International Journal of Environment and Geoinformatics 2 (2015 ): 27-38 Chicago Aydar, U , Altan, M . "Total Least Squares Registration of 3D Surfaces". International Journal of Environment and Geoinformatics 2 (2015 ): 27-38 RIS TY - JOUR T1 - Total Least Squares Registration of 3D Surfaces AU - Umut Aydar , M. Orham Altan Y1 - 2015 PY - 2015 N1 - doi: 10.30897/ijegeo.303539 DO - 10.30897/ijegeo.303539 T2 - International Journal of Environment and Geoinformatics JF - Journal JO - JOR SP - 27 EP - 38 VL - 2 IS - 2 SN - -2148-9173 M3 - doi: 10.30897/ijegeo.303539 UR - https://doi.org/10.30897/ijegeo.303539 Y2 - 2015 ER - EndNote %0 International Journal of Environment and Geoinformatics Total Least Squares Registration of 3D Surfaces %A Umut Aydar , M. Orham Altan %T Total Least Squares Registration of 3D Surfaces %D 2015 %J International Journal of Environment and Geoinformatics %P -2148-9173 %V 2 %N 2 %R doi: 10.30897/ijegeo.303539 %U 10.30897/ijegeo.303539 ISNAD Aydar, Umut , Altan, M. Orham . "Total Least Squares Registration of 3D Surfaces". International Journal of Environment and Geoinformatics 2 / 2 (August 2015): 27-38 . https://doi.org/10.30897/ijegeo.303539 AMA Aydar U , Altan M . Total Least Squares Registration of 3D Surfaces. International Journal of Environment and Geoinformatics. 2015; 2(2): 27-38. Vancouver Aydar U , Altan M . Total Least Squares Registration of 3D Surfaces. International Journal of Environment and Geoinformatics. 2015; 2(2): 38-27.