Historical building point cloud segmentation combining hierarchical watershed transform and curvature analysis
| Autor: | Camila Kimi Cogima, Eloisa Dezen-Kempter, Pedro Victor Vieira de Paiva, Marco Antonio Garcia de Carvalho |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Ground truth
Watershed Laser scanning business.industry Computer science Point cloud Cloud computing 02 engineering and technology Mathematical morphology computer.software_genre 01 natural sciences Building information modeling Artificial Intelligence Region growing 0103 physical sciences Signal Processing 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Relevance (information retrieval) Computer Vision and Pattern Recognition Data mining 010306 general physics business computer Software |
| Zdroj: | Pattern Recognition Letters. 135:114-121 |
| ISSN: | 0167-8655 |
| DOI: | 10.1016/j.patrec.2020.04.010 |
| Popis: | Segmenting accurately point clouds is of great relevance in several fields of engineering and construction. Users are interested in properly dividing an point cloud into their components and then recognizing them. Point clouds representing historical buildings present an additional challenge because image details could be related to a cultural or architectural aspect. Therefore, the way the results are evaluated is also important. In this paper, we present a novel point cloud approach for segmenting historical building of different architectural styles and periods. In our approach, that works for organized and unorganized point clouds, we combine Hierarchical Watershed Transform and curvature analysis from region growing methods in order to obtain more suitable seeds. Experiments were conducted involving historical building acquired using drones and terrestrial laser scanner. The data was combined into a single point cloud. Finally, we evaluated our results qualitatively and quantitatively, by comparing them to a dataset containing the ground truth. The quantitative metrics demonstrate the effectiveness of our method when compared with state-of-the-art methods. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect