Nuvole di punti semantiche

Fabio Remondino; Emre Ozdemir; Eleonora Grilli

doi:10.48258/geo.v23i2.1628

Nuvole di punti semantiche

Fabio Remondino, Emre Ozdemir, Eleonora Grilli

Abstract

Point clouds, generated by photogrammetry or laser scanning, mainly contain geometric information. This makes them not very useful for different applications.
Artificial Intelligence methods have opened up a new area of research and development, providing automatic solutions for segmentation and classification purposes.

Parole chiave

nuvole di punti; fotogrammetria; laser scanner; semantica; classificazione; Intelligenza Artificiale

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Riferimenti bibliografici

Grilli, E., Menna, F., Remondino, F., 2017. A review of point clouds segmentation and classification algorithms.

ISPRS International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 42.

Grilli, E., Remondino, F., 2019. Classification of 3D Digital Heritage. MDPI Remote Sensing, Vol. 11(7), 847; https://doi.org/10.3390/rs11070847

Hackel, T., Wegner, J.D., Schindler, K., 2016. Fast semantic segmentation of 3D point clouds with strongly varying density. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. III(3), pp. 177-184.

Ozdemir, E., Remondino, F., 2018. Segmentation of 3D photogrammetric point cloud for 3D building modeling. ISPRS International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XLII-4/W10, pp. 135-142

Ozdemir, E., Remondino, F., 2019. Classification of aerial point clouds with deep learning. ISPRS International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences. Proceedings

Geospatial Week 2019, in press. Weinmann, M., Weinmann, M., 2017. Geospatial Computer Vision based on multi-modal data - How

valuable is shape information for the extraction of semantic information? Remote Sensing, Vol. 10(1).

DOI: https://doi.org/10.48258/geo.v23i2.1628