Total Least Squares Registration of 3D Surfaces
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 me...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IJEGEO
2015-08-01
|
| Series: | International Journal of Environment and Geoinformatics |
| Subjects: | |
| Online Access: | http://dergipark.gov.tr/download/article-file/290414 |
| _version_ | 1797922848291946496 |
|---|---|
| author | Umut Aydar M. Orhan Altan |
| author_facet | Umut Aydar M. Orhan Altan |
| author_sort | Umut Aydar |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-10T14:38:43Z |
| format | Article |
| id | doaj.art-e2c7939a14494d90b8e965f291e312ca |
| institution | Directory Open Access Journal |
| issn | 2148-9173 2148-9173 |
| language | English |
| last_indexed | 2024-04-10T14:38:43Z |
| publishDate | 2015-08-01 |
| publisher | IJEGEO |
| record_format | Article |
| series | International Journal of Environment and Geoinformatics |
| spelling | doaj.art-e2c7939a14494d90b8e965f291e312ca2023-02-15T16:08:25ZengIJEGEOInternational Journal of Environment and Geoinformatics2148-91732148-91732015-08-0122273810.30897/ijegeo.303539Total Least Squares Registration of 3D SurfacesUmut Aydar0 M. Orhan Altan1Istanbul Technical University, Faculty of Civil Engineering, Department of Geomatics Engineering, ISTANBUL-TRIstanbul Technical University, Faculty of Civil Engineering, Department of Geomatics Engineering, ISTANBUL-TRCo-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.http://dergipark.gov.tr/download/article-file/290414Laser scanningPoint CloudRegistrationMatchingTotal Least Squares |
| spellingShingle | Umut Aydar M. Orhan Altan Total Least Squares Registration of 3D Surfaces International Journal of Environment and Geoinformatics Laser scanning Point Cloud Registration Matching Total Least Squares |
| title | Total Least Squares Registration of 3D Surfaces |
| title_full | Total Least Squares Registration of 3D Surfaces |
| title_fullStr | Total Least Squares Registration of 3D Surfaces |
| title_full_unstemmed | Total Least Squares Registration of 3D Surfaces |
| title_short | Total Least Squares Registration of 3D Surfaces |
| title_sort | total least squares registration of 3d surfaces |
| topic | Laser scanning Point Cloud Registration Matching Total Least Squares |
| url | http://dergipark.gov.tr/download/article-file/290414 |
| work_keys_str_mv | AT umutaydar totalleastsquaresregistrationof3dsurfaces AT morhanaltan totalleastsquaresregistrationof3dsurfaces |