Classification of objects in a point cloud using neural networks

• Autorzy: Daszuta Marcin , Napieralska-Juszczak Ewa
• Języki publikacji: EN

Abstrakty

EN

3-dimensional scans captured in shape of point clouds are widely used in many different areas. Every such area use different kinds of sensors to ac-quire point clouds and do the analysis of the data but each of those needs some preanalysis to be done. One of the most important is segmentation and classification of points into types of objects. Such information considerably widens possibilities of usage for further purposes. There are many classifiers and many features based on which labeling can be done. In this paper few most commonly used approaches were chosen to check the influence of neighboring points acquisition on classification process. Results proof signif-icant relation between those two steps of point cloud analysis. Visualization of analyzed point cloud also shown that precision of predictions not always comes with better visibility of certain types of objects. Additionally, color-less analysis of geometrical features seems to be promising way for further research.

Słowa kluczowe

EN

point cloud classification; voxel partitioning; k-nearest neighbors in range; KNNR; random forest

PL

klasyfikacja chmury punktów; partycjonowanie wokseli; algorytm k najbliższych sąsiadów; KNNR; las losowy

• Wydawca: Wydawnictwo Politechniki Łódzkiej
• Czasopismo: Journal of Applied Computer Science
• Rocznik: 2019
• Tom: Vol. 27, nr 2
• Strony: 7--16
• Opis fizyczny: Bibliogr. 10 poz.

Twórcy

autor

Daszuta Marcin

autor

Napieralska-Juszczak Ewa

• LSEE, Univ. Artois Technoparc Futura, 62400 Bethune, France

Bibliografia

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• [5] Landrieu, L. and Simonovsky, M., Large-Scale Point Cloud Semantic Segmentation with Superpoint Graphs, 06 2018.
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• [7] Weinmann, M., Jutzi, B., Mallet, C., and Weinmann, M., Geometric Features and Their Relevance for 3D Point Cloud Classification, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. IV1/W1, 06 2017.
• [8] Weinmann, M., Weinmann, M., Mallet, C., and Brédif, M., A Classification-Segmentation Framework for the Detection of Individual Trees in Dense MMS Point Cloud Data Acquired in Urban Areas, Remote Sensing, Vol. 9, 03 2017, pp. 277.
• [9] Li, M. and Sun, C., Refinement of LiDAR point clouds using a super voxel based approach, ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 143, 2018, pp. 213-221.
• [10] Weinmann, M., Jutzi, B., and Mallet, C., Feature relevance assessment for the semantic interpretation of 3D point cloud data, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 5, No. W2, 2013, pp. 1.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).