Semantic segmentation of multimodal point clouds from the railway context

P. Dibari; M. Nitti; R. Maglietta; G. Castellano; G. Dimauro; V. Renò

doi:10.1117/12.2593839

20 June 2021 Semantic segmentation of multimodal point clouds from the railway context

P. Dibari, M. Nitti, R. Maglietta, G. Castellano, G. Dimauro, V. Renò

Author Affiliations +

Proceedings Volume 11785, Multimodal Sensing and Artificial Intelligence: Technologies and Applications II; 117850S (2021) https://doi.org/10.1117/12.2593839
Event: SPIE Optical Metrology, 2021, Online Only

Abstract

In this study we analyzed deep learning methods for point clouds semantic segmentation. We compared PointNet and PointNet++ on data with different characteristics, coming from distinct domains, in order to understand their behavior. Finally, we exploited the so gained knowledge to improve the performance of the models on railway data. In particular, we properly updated the training protocol and altered the PointNet++ architecture, in order to perform transfer learning by leveraging the models previously trained in the first experiments. Results on both state-of-the-art datasets and on a custom dataset specifically acquired for this scope demonstrate that transfer learning can effectively boost the performance of the models in terms of prediction accuracy and convergence rate in the railway context.

Conference Presentation

Citation Download Citation

P. Dibari, M. Nitti, R. Maglietta, G. Castellano, G. Dimauro, and V. Renò "Semantic segmentation of multimodal point clouds from the railway context", Proc. SPIE 11785, Multimodal Sensing and Artificial Intelligence: Technologies and Applications II, 117850S (20 June 2021); https://doi.org/10.1117/12.2593839

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