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Classification of objects in the LIDAR point clouds using Deep Neural Networks based on the PointNet model

Abstrakt

This work attempts to meet the challenges associated with the classification of LIDAR point clouds by means of deep learning. In addition to achieving high accuracy, the designed system should allow the classification of point clouds covering an area of several dozen square kilometers within a reasonable time interval. Therefore, it must be characterized by fast processing and efficient use of memory. Thus, the most popular approaches to the point cloud classification using neural networks are discussed. At the same time, their shortcomings are indicated. A developed model based on the PointNet architecture is presented and the way of preparing data for classification is shown. The model is tested on a cloud coming from the 3D Semantic Labeling competition, achieving a good result, confirmed by the high quality of the system, i.e. a high rate of categorization of objects.

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Accepted albo Published Version
Licencja
Copyright (2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd.)

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Informacje szczegółowe

Kategoria:
Publikacja w czasopiśmie
Typ:
artykuły w czasopismach
Opublikowano w:
IFAC-PapersOnLine nr 52, strony 416 - 421,
ISSN: 2405-8963
Język:
angielski
Rok wydania:
2019
Opis bibliograficzny:
Kowalczuk Z., Szymański K.: Classification of objects in the LIDAR point clouds using Deep Neural Networks based on the PointNet model// IFAC-PapersOnLine -Vol. 52,iss. 8 (2019), s.416-421
DOI:
Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.1016/j.ifacol.2019.08.099
Bibliografia: test
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Weryfikacja:
Politechnika Gdańska

wyświetlono 169 razy

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