Classification of 3D data

The use of 3D data (point clouds, polygonal models, etc.) is nowadays widely diffused for various applications and in different fields, from the documentation of cultural heritage to autonomous driving, from urban planning to semantic 3D modeling. Nevertheless, to provide useful 3D data, it is necessary to associate some semantic information that can help operators to better understand and interpret the data. Our research activity aims at carrying out an optimal, repeatable and reliable segmentation and classification procedure to manage various types of 3D survey data and associate them with heterogeneous information and attributes to characterize and describe the surveyed scene. The developed methods are based on supervised / data-driven machine/deep learning methods and are tested on various scenarios, from heritage objects to architectures or urban environments, providing for 3D classified data.

See report on FBK Magazine "Giving a meaning to 3D points".

Integrative AI

Grilli, E., Daniele, A., Bassier, M., Remondino, F., Serafini, L., 2023: Knowledge Enhanced Neural Networks for Point Cloud Semantic Segmentation. Remote Sensing, 15(10):2590

Cultural Heritage - Related publications:

G. Mazzacca, E. Grilli, G. P. Cirigliano, F. Remondino, S. Campana, 2022: Seeing among foliage with lidar and machine learning: towards a transferable archaeological pipeline. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-2/W1-2022, 365–372
Grilli, E., Poux, F., and Remondino, F., 2021: Unsupervised object-based clustering in support of supervised point-based 3D point cloud classification. ISPRS Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2021, 471–478
Matrone, F., Grilli, E., Martini, M., Paolanti, M., Pierdicca, R., Remondino, F., 2020: Comparing Machine and Deep Learning methods for large 3D heritage semantic segmentation. MDPI International Journal of Geo-Information, 9, 535
Teruggi, S., Grilli, E., Russo, M., Fassi, F., Remondino, F., 2020: A hierarchical machine learning approach for multi-level and multi-resolution 3D point cloud classification. MDPI Remote Sensing, 12(16), 2598
E. Grilli, F. Remondino, 2020: Machine learning generalisation across different 3D architectural. ISPRS International Journal of Geo-Information, 9(6), 379
E. Grilli, F., Remondino, 2019: Classification of 3D Digital Heritage. MDPI Remote Sensing, Vol. 11(7), 847; https://doi.org/10.3390/rs11070847
E. Grilli, E. Özdemir and F. Remondino, 2019: Application of machine and deep learning strategis for the classification of heritage point clouds. ISPRS Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., Vol. XLII-4/W18, pp. 447–454
E. Özdemir and F. Remondino, 2019: Classification of aerial point clouds with deep learning. ISPRS;Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., Vol. XLII-2/W13, pp. 103-110. ISPRS Geospatial Week 2019, Enschede (NL)
E. Grilli, F. Remondino, 2019: Classification of 3D Digital Heritage. MDPI Remote Sensing, Vol. 11(7), 847; https://doi.org/10.3390/rs11070847
Grilli, E., Dininno, M., Marsicano, L., Petrucci, G., Remondino, F., 2018: Supervised segmentation of 3D cultural heritage. IEEE Proc. of Digital Heritage 2018 3rd International Congress & Expo, San Francisco (USA), in press
E. Grilli, D. Dininno, G. Petrucci, F. Remondino, 2018: From 2D to 3D supervised segmentation and classification for cultural heritage applications. ISPRS Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., Vol. XLII-2, pp. 399-406
Grilli, E., Menna, F., Remondino, F., 2017: A review of point clouds segmentation and classification algorithms. ISPRS Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., Vol. XLII-2-W3, pp. 339-344

Urban environments - Related publications:

Bayrak, O. C., Remondino, F., and Uzar, M., 2023: A new dataset and methodology for urban-scale 3D point cloud classification. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1/W3-2023, 1–8
Oezdemir, E., Remondino, F., Golkar, A., 2021: An Efficient and General Framework for Aerial Point Cloud Classification in Urban Scenarios. Remote Sens. 2021, 13, 1985
Oezdemir, E., Remondino, F., Golkar, A., 2019: Aerial point cloud classification with with deep learning and machine learning algorithms. ISPRS Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., Vol. XLII-4/W18, pp. 843-849
Oezdemir, E., Remondino, F., 2018: Segmentation of 3D photogrammetric point cloud for 3D building modeling. ISPRS Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., Vol. XLII-4/W10, pp. 135-142

Funding: private companies, internal, AI4CH project

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