Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/100739
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Land Surveying and Geo-Informatics | - |
| dc.creator | Ahmed, W | en_US |
| dc.creator | Shi, W | en_US |
| dc.creator | Xu, W | en_US |
| dc.date.accessioned | 2023-08-11T03:13:07Z | - |
| dc.date.available | 2023-08-11T03:13:07Z | - |
| dc.identifier.isbn | 978-1-7281-0247-4 (Electronic) | en_US |
| dc.identifier.isbn | 978-1-7281-0246-7 (USB) | en_US |
| dc.identifier.isbn | 978-1-7281-0248-1 (Print on Demand(PoD)) | en_US |
| dc.identifier.uri | http://hdl.handle.net/10397/100739 | - |
| dc.description | 2018 IEEE International Conference on Image Processing, Applications and Systems (IPAS), 12-14 December 2018, Sophia Antipolis, France. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | IEEE | en_US |
| dc.rights | © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en_US |
| dc.rights | The following publication W. AHMED, W. Shi and W. XU, "Modeling Complex Building Structure (LoD2) Using Image-Based Point Cloud," 2018 IEEE International Conference on Image Processing, Applications and Systems (IPAS), Sophia Antipolis, France, 2018, pp. 110-114 is available at https://doi.org/10.1109/IPAS.2018.8708864. | en_US |
| dc.subject | LoD2 | en_US |
| dc.subject | Model driven | en_US |
| dc.subject | Outdoor modeling | en_US |
| dc.subject | RANSAC | en_US |
| dc.subject | UAV | en_US |
| dc.title | Modeling complex building structure (LoD2) using image-based point cloud | en_US |
| dc.type | Conference Paper | en_US |
| dc.identifier.spage | 110 | en_US |
| dc.identifier.epage | 114 | en_US |
| dc.identifier.doi | 10.1109/IPAS.2018.8708864 | en_US |
| dcterms.abstract | A method designed to reconstruct outdoor 3D building models automatically from a point cloud is presented in this paper. The proposed approach starts with building detection using spectral and spatial data from the UAV point cloud to remove non-building features. RANSAC, modified convex hull, and line growing algorithms are used to extract main roof planes and their boundaries. Roof planes are adjusted to each other using geometrical constraints, the height of each plane is estimated and a 3D model for the whole structure is constructed with LoD2. The key contribution of this approach is using a hybrid approach of model-driven with statistical analysis for modeling complex structures from a noisy point cloud. The reconstructed model shows that the workflow is sufficient to describe the whole building structure in the required LoD. | - |
| dcterms.accessRights | open access | en_US |
| dcterms.bibliographicCitation | 2018 IEEE International Conference on Image Processing, Applications and Systems (IPAS), Sophia Antipolis, France, 12-14 December 2018, p. 110-114 | en_US |
| dcterms.issued | 2018 | - |
| dc.identifier.scopus | 2-s2.0-85066330317 | - |
| dc.relation.conference | International Image Processing, Applications and Systems Conference [IPAS] | - |
| dc.description.validate | 202305 bckw | - |
| dc.description.oa | Accepted Manuscript | en_US |
| dc.identifier.FolderNumber | LSGI-0287 | - |
| dc.description.fundingSource | RGC | en_US |
| dc.description.pubStatus | Published | en_US |
| dc.identifier.OPUS | 28990400 | - |
| dc.description.oaCategory | Green (AAM) | en_US |
| Appears in Collections: | Conference Paper | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Ahmed_Modeling_Complex_Building.pdf | Pre-Published version | 4.79 MB | Adobe PDF | View/Open |
Page views
112
Citations as of Apr 14, 2025
Downloads
120
Citations as of Apr 14, 2025
SCOPUSTM
Citations
3
Citations as of Dec 19, 2025
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.



