Efficient training of semantic point cloud segmentation via active learning

Y. Lin*, G. Vosselman, Y. Cao, M. Y. Yang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

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Abstract

With the development of LiDAR and photogrammetric techniques, more and more point clouds are available with high density and in large areas. Point cloud interpretation is an important step before many real applications like 3D city modelling. Many supervised machine learning techniques have been adapted to semantic point cloud segmentation, aiming to automatically label point clouds. Current deep learning methods have shown their potentials to produce high accuracy in semantic point cloud segmentation tasks. However, these supervised methods require a large amount of labelled data for proper model performance and good generalization. In practice, manual labelling of point clouds is very expensive and time-consuming. Active learning can iteratively select unlabelled samples for manual annotation based on current statistical models and then update the labelled data pool for next model training. In order to effectively label point clouds, we proposed a segment based active learning strategy to assess the informativeness of samples. Here, the proposed strategy uses 40% of the whole training dataset to achieve a mean IoU of 75.2% which is 99.1% of the accuracy in mIoU obtained from the model trained on the full dataset, while the baseline method using same amount of data only reaches 69.6% in mIoU corresponding to 90.9% of the accuracy in mIoU obtained from the model trained on the full dataset.
Original languageEnglish
Title of host publicationISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Subtitle of host publicationXXIV ISPRS Congress
EditorsN. Paparoditis, C. Mallet, F. Lafarge, F. Remondino, I. Toschi, T. Fuse
PublisherInternational Society for Photogrammetry and Remote Sensing (ISPRS)
Pages243-250
Number of pages8
VolumeV-2-2020
DOIs
Publication statusPublished - 3 Aug 2020
EventXXIVth ISPRS Congress 2020 - Nice-Acropolis Congress and Exhibition Centre, Nice, France
Duration: 4 Jul 202010 Jul 2020
Conference number: 24
http://www.isprs2020-nice.com

Publication series

NameISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
PublisherCopernicus
ISSN (Print)2194-9042

Conference

ConferenceXXIVth ISPRS Congress 2020
Abbreviated titleISPRS 2020
CountryFrance
CityNice
Period4/07/2010/07/20
Internet address

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    Lin, Y., Vosselman, G., Cao, Y., & Yang, M. Y. (2020). Efficient training of semantic point cloud segmentation via active learning. In N. Paparoditis, C. Mallet, F. Lafarge, F. Remondino, I. Toschi, & T. Fuse (Eds.), ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences: XXIV ISPRS Congress (Vol. V-2-2020, pp. 243-250). (ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences). International Society for Photogrammetry and Remote Sensing (ISPRS). https://doi.org/10.5194/isprs-annals-V-2-2020-243-2020