PointNet-based modeling of systematic distance deviations for improved TLS accuracy

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Original languageEnglish
Pages (from-to)613-628
Number of pages16
JournalJournal of Applied Geodesy
Volume18
Issue number4
Early online date19 Jun 2024
Publication statusPublished - 1 Oct 2024

Abstract

Terrestrial laser scanners (TLSs) have become indispensable for acquiring highly detailed and accurate 3D representations of the physical world. However, the acquired data is subject to systematic deviations in distance measurements due to external influences, such as distance and incidence angle. This research introduces a calibration approach by applying a deep learning model based on PointNet to predict and correct these systematic distance deviations, incorporating not only the XYZ coordinates but also additional features like intensity, incidence angle, and distances within a local neighbourhood radius of 5 cm. By predicting and subsequently correcting systematic distance deviations, the quality of TLS point clouds can be improved. Hence, our model is designed to complement and build upon the foundation of prior internal TLS calibration. A data set collected under controlled environmental conditions, containing various objects of different materials, served as the basis for training and validation the PointNet based model. In addition our analysis showcase the model's capability to accurately model systematic distance deviations, outperforming existing methods like gradient boosting trees by capturing the spatial relationships and dependencies within the data more effectively. By defining test data sets, excluded from the training process, we underscore the ongoing effectiveness of our model's distance measurement calibration, showcasing its ability to improve the accuracy of the TLS point cloud.

Keywords

    calibration, deep learning, PointNet, systematic distance deviation, terrestrial laser scanning

ASJC Scopus subject areas

Cite this

PointNet-based modeling of systematic distance deviations for improved TLS accuracy. / Hartmann, Jan; Ernst, Dominik; Neumann, Ingo et al.
In: Journal of Applied Geodesy, Vol. 18, No. 4, 01.10.2024, p. 613-628.

Research output: Contribution to journalReview articleResearchpeer review

Hartmann J, Ernst D, Neumann I, Alkhatib H. PointNet-based modeling of systematic distance deviations for improved TLS accuracy. Journal of Applied Geodesy. 2024 Oct 1;18(4):613-628. Epub 2024 Jun 19. doi: 10.1515/jag-2023-0097
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abstract = "Terrestrial laser scanners (TLSs) have become indispensable for acquiring highly detailed and accurate 3D representations of the physical world. However, the acquired data is subject to systematic deviations in distance measurements due to external influences, such as distance and incidence angle. This research introduces a calibration approach by applying a deep learning model based on PointNet to predict and correct these systematic distance deviations, incorporating not only the XYZ coordinates but also additional features like intensity, incidence angle, and distances within a local neighbourhood radius of 5 cm. By predicting and subsequently correcting systematic distance deviations, the quality of TLS point clouds can be improved. Hence, our model is designed to complement and build upon the foundation of prior internal TLS calibration. A data set collected under controlled environmental conditions, containing various objects of different materials, served as the basis for training and validation the PointNet based model. In addition our analysis showcase the model's capability to accurately model systematic distance deviations, outperforming existing methods like gradient boosting trees by capturing the spatial relationships and dependencies within the data more effectively. By defining test data sets, excluded from the training process, we underscore the ongoing effectiveness of our model's distance measurement calibration, showcasing its ability to improve the accuracy of the TLS point cloud.",
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T1 - PointNet-based modeling of systematic distance deviations for improved TLS accuracy

AU - Hartmann, Jan

AU - Ernst, Dominik

AU - Neumann, Ingo

AU - Alkhatib, Hamza

N1 - Publisher Copyright: © 2024 Walter de Gruyter GmbH, Berlin/Boston 2024.

PY - 2024/10/1

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N2 - Terrestrial laser scanners (TLSs) have become indispensable for acquiring highly detailed and accurate 3D representations of the physical world. However, the acquired data is subject to systematic deviations in distance measurements due to external influences, such as distance and incidence angle. This research introduces a calibration approach by applying a deep learning model based on PointNet to predict and correct these systematic distance deviations, incorporating not only the XYZ coordinates but also additional features like intensity, incidence angle, and distances within a local neighbourhood radius of 5 cm. By predicting and subsequently correcting systematic distance deviations, the quality of TLS point clouds can be improved. Hence, our model is designed to complement and build upon the foundation of prior internal TLS calibration. A data set collected under controlled environmental conditions, containing various objects of different materials, served as the basis for training and validation the PointNet based model. In addition our analysis showcase the model's capability to accurately model systematic distance deviations, outperforming existing methods like gradient boosting trees by capturing the spatial relationships and dependencies within the data more effectively. By defining test data sets, excluded from the training process, we underscore the ongoing effectiveness of our model's distance measurement calibration, showcasing its ability to improve the accuracy of the TLS point cloud.

AB - Terrestrial laser scanners (TLSs) have become indispensable for acquiring highly detailed and accurate 3D representations of the physical world. However, the acquired data is subject to systematic deviations in distance measurements due to external influences, such as distance and incidence angle. This research introduces a calibration approach by applying a deep learning model based on PointNet to predict and correct these systematic distance deviations, incorporating not only the XYZ coordinates but also additional features like intensity, incidence angle, and distances within a local neighbourhood radius of 5 cm. By predicting and subsequently correcting systematic distance deviations, the quality of TLS point clouds can be improved. Hence, our model is designed to complement and build upon the foundation of prior internal TLS calibration. A data set collected under controlled environmental conditions, containing various objects of different materials, served as the basis for training and validation the PointNet based model. In addition our analysis showcase the model's capability to accurately model systematic distance deviations, outperforming existing methods like gradient boosting trees by capturing the spatial relationships and dependencies within the data more effectively. By defining test data sets, excluded from the training process, we underscore the ongoing effectiveness of our model's distance measurement calibration, showcasing its ability to improve the accuracy of the TLS point cloud.

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