Fitting Geometric Shapes to Fuzzy Point Cloud Data
This article describes procedures and thoughts regarding the reconstruction of geometry-given data and its uncertainty. The data are considered as a continuous fuzzy point cloud, instead of a discrete point cloud. Shape fitting is commonly performed by minimizing the discrete Euclidean distance; how...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Journal of Imaging |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2313-433X/11/1/7 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832588316434759680 |
|---|---|
| author | Vincent B. Verhoeven Pasi Raumonen Markku Åkerblom |
| author_facet | Vincent B. Verhoeven Pasi Raumonen Markku Åkerblom |
| author_sort | Vincent B. Verhoeven |
| collection | DOAJ |
| description | This article describes procedures and thoughts regarding the reconstruction of geometry-given data and its uncertainty. The data are considered as a continuous fuzzy point cloud, instead of a discrete point cloud. Shape fitting is commonly performed by minimizing the discrete Euclidean distance; however, we propose the novel approach of using the expected Mahalanobis distance. The primary benefit is that it takes both the different magnitude and orientation of uncertainty for each data point into account. We illustrate the approach with laser scanning data of a cylinder and compare its performance with that of the conventional least squares method with and without random sample consensus (RANSAC). Our proposed method fits the geometry more accurately, albeit generally with greater uncertainty, and shows promise for geometry reconstruction with laser-scanned data. |
| format | Article |
| id | doaj-art-f5c70421a3cf4d60a8ee88ac7ecfcd5c |
| institution | Kabale University |
| issn | 2313-433X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj-art-f5c70421a3cf4d60a8ee88ac7ecfcd5c2025-01-24T13:36:14ZengMDPI AGJournal of Imaging2313-433X2025-01-01111710.3390/jimaging11010007Fitting Geometric Shapes to Fuzzy Point Cloud DataVincent B. Verhoeven0Pasi Raumonen1Markku Åkerblom2Faculty of Information Technology and Communication Sciences, Mathematics Research Centre, Tampere University, Korkeakoulunkatu 1, 33720 Tampere, FinlandFaculty of Information Technology and Communication Sciences, Mathematics Research Centre, Tampere University, Korkeakoulunkatu 1, 33720 Tampere, FinlandFaculty of Information Technology and Communication Sciences, Mathematics Research Centre, Tampere University, Korkeakoulunkatu 1, 33720 Tampere, FinlandThis article describes procedures and thoughts regarding the reconstruction of geometry-given data and its uncertainty. The data are considered as a continuous fuzzy point cloud, instead of a discrete point cloud. Shape fitting is commonly performed by minimizing the discrete Euclidean distance; however, we propose the novel approach of using the expected Mahalanobis distance. The primary benefit is that it takes both the different magnitude and orientation of uncertainty for each data point into account. We illustrate the approach with laser scanning data of a cylinder and compare its performance with that of the conventional least squares method with and without random sample consensus (RANSAC). Our proposed method fits the geometry more accurately, albeit generally with greater uncertainty, and shows promise for geometry reconstruction with laser-scanned data.https://www.mdpi.com/2313-433X/11/1/7uncertainty quantificationgeometry reconstructionlaser scanningpoint cloud |
| spellingShingle | Vincent B. Verhoeven Pasi Raumonen Markku Åkerblom Fitting Geometric Shapes to Fuzzy Point Cloud Data Journal of Imaging uncertainty quantification geometry reconstruction laser scanning point cloud |
| title | Fitting Geometric Shapes to Fuzzy Point Cloud Data |
| title_full | Fitting Geometric Shapes to Fuzzy Point Cloud Data |
| title_fullStr | Fitting Geometric Shapes to Fuzzy Point Cloud Data |
| title_full_unstemmed | Fitting Geometric Shapes to Fuzzy Point Cloud Data |
| title_short | Fitting Geometric Shapes to Fuzzy Point Cloud Data |
| title_sort | fitting geometric shapes to fuzzy point cloud data |
| topic | uncertainty quantification geometry reconstruction laser scanning point cloud |
| url | https://www.mdpi.com/2313-433X/11/1/7 |
| work_keys_str_mv | AT vincentbverhoeven fittinggeometricshapestofuzzypointclouddata AT pasiraumonen fittinggeometricshapestofuzzypointclouddata AT markkuakerblom fittinggeometricshapestofuzzypointclouddata |