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Point Cloud vs. Mesh Features for Building Interior Classification
| Content Provider | MDPI |
|---|---|
| Author | Bassier, Maarten Vergauwen, Maarten Poux, Florent |
| Copyright Year | 2020 |
| Description | Interpreting 3D point cloud data of the interior and exterior of buildings is essential for automated navigation, interaction and 3D reconstruction. However, the direct exploitation of the geometry is challenging due to inherent obstacles such as noise, occlusions, sparsity or variance in the density. Alternatively, 3D mesh geometries derived from point clouds benefit from preprocessing routines that can surmount these obstacles and potentially result in more refined geometry and topology descriptions. In this article, we provide a rigorous comparison of both geometries for scene interpretation. We present an empirical study on the suitability of both geometries for the feature extraction and classification. More specifically, we study the impact for the retrieval of structural building components in a realistic environment which is a major endeavor in Building Information Modeling (BIM) reconstruction. The study runs on segment-based structuration of both geometries and shows that both achieve recognition rates over 75% F1 score when suitable features are used. |
| Starting Page | 2224 |
| e-ISSN | 20724292 |
| DOI | 10.3390/rs12142224 |
| Journal | Remote Sensing |
| Issue Number | 14 |
| Volume Number | 12 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2020-07-11 |
| Access Restriction | Open |
| Subject Keyword | Remote Sensing Feature Extraction Unsupervised Segmentation Classification Machine Learning Bim Point Clouds Mesh |
| Content Type | Text |
| Resource Type | Article |
