Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Faculty of Construction and Environment | en_US |
| dc.contributor | Department of Land Surveying and Geo-Informatics | en_US |
| dc.contributor.advisor | Shi, Wenzhong John (LSGI) | - |
| dc.creator | Tian, Tianyang | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/10536 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | - |
| dc.rights | All rights reserved | en_US |
| dc.title | Analysis of visibility issues of points cloud data based on observation location | en_US |
| dcterms.abstract | In this paper, we consider the visibility problem of a point clouds scene. we focus on how to efficiently visualize a complex point clouds scene by removing the invisible or visible parts. In order to remove the points cloud, we need the appropriate data structure to store the data. At the same time, we need a search method suitable for this data structure to help us to quickly complete the analysis of the data. In this article, we use the kd-tree data structure to store the disorder and massive cloud data and use the nearest neighbor search method to establish the buffer to complete the analysis. For the specific method of analyzing point cloud visibility, we refer to the very common z-buffer algorithm in computer vision and make appropriate improvements to it. Considering the three-dimensional data of point cloud data, we also need to carry out projection transformation. The projection method mainly refers to the classical algorithm and center projection in photogrammetry. But the central projection still needs to be improved because its default viewing angle is the overlooking to observe the object, but in computer vision, our observation perspective may be in any direction. By combining the improved depth buffer with the central projection and setting the appropriate threshold, we can segment the visible and invisible parts of the point cloud. Finally, we use QT development platform, vs 2010 and pcl library. We put the algorithm on the computer to do the experiments and found some problems and also thought about the solution. | en_US |
| dcterms.extent | ix, 79 pages pages : color illustrations | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2017 | en_US |
| dcterms.educationalLevel | M.Sc. | en_US |
| dcterms.educationalLevel | All Master | en_US |
| dcterms.LCSH | Image processing -- Digital techniques | en_US |
| dcterms.LCSH | Remote sensing | en_US |
| dcterms.LCSH | Hong Kong Polytechnic University -- Dissertations | en_US |
| dcterms.accessRights | restricted access | en_US |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 991022385851203411.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 2.21 MB | Adobe PDF | View/Open |
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