Selection of Relevant Geometric Features Using Filter-Based Algorithms for Point Cloud Semantic Segmentation

Muhammed Enes Atik*, Zaide Duran

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Semantic segmentation of mobile LiDAR point clouds is an essential task in many fields such as road network management, mapping, urban planning, and 3D High Definition (HD) city maps for autonomous vehicles. This study presents an approach to improve the evaluation metrics of deep-learning-based point cloud semantic segmentation using 3D geometric features and filter-based feature selection. Information gain (IG), Chi-square (Chi2), and ReliefF algorithms are used to select relevant features. RandLA-Net and Superpoint Grapgh (SPG), the current and effective deep learning networks, were preferred for applying semantic segmentation. RandLA-Net and SPG were fed by adding geometric features in addition to 3D coordinates (x, y, z) directly without any change in the structure of the point clouds. Experiments were carried out on three challenging mobile LiDAR datasets: Toronto3D, SZTAKI-CityMLS, and Paris. As a result of the study, it was demonstrated that the selection of relevant features improved accuracy in all datasets. For RandLA-Net, mean Intersection-over-Union (mIoU) was 70.1% with the features selected with Chi2 in the Toronto3D dataset, 84.1% mIoU was obtained with the features selected with the IG in the SZTAKI-CityMLS dataset, and 55.2% mIoU with the features selected with the IG and ReliefF in the Paris dataset. For SPG, 69.8% mIoU was obtained with Chi2 in the Toronto3D dataset, 77.5% mIoU was obtained with IG in SZTAKI-CityMLS, and 59.0% mIoU was obtained with IG and ReliefF in Paris.

Original languageEnglish
Article number3310
JournalElectronics (Switzerland)
Volume11
Issue number20
DOIs
Publication statusPublished - Oct 2022

Bibliographical note

Publisher Copyright:
© 2022 by the authors.

Funding

This research was funded by Istanbul Technical University Scientific Research Office (BAP) grant number MDK-2021-42992.

FundersFunder number
Istanbul Technical University Scientific Research Office
Bilimsel Araştırma Projeleri Birimi, İstanbul Teknik ÜniversitesiMDK-2021-42992

    Keywords

    • deep learning
    • feature selection
    • geometric features
    • mapping
    • point cloud
    • semantic segmentation

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