An Unsupervised Outlier Detection Method For 3D Point Cloud Data

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Dey, Emon Kumar
Awrangjeb, Mohammad
Stantic, Bela
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2019
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Yokohama, Japan

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Abstract

This paper introduces an effective method for outlier detection from the point cloud data. Although, the state-of-the-art methods offer good results in removing outliers, in most of the cases inliers are also removed erroneously. This paper focuses on this issue using the information based on a relative location from a point to its neighbours and a robust z-score based on a statistical approach. Synthetic datasets for 3D building roofs have been created to evaluate the performance. When compared with the existing methods, the proposed method exhibits better performance, i.e., 19% more recall for inliers, 6% more precision for outliers and 10% more overall accuracy. In other words, it not only preserves the inliers, but also correctly removes the outliers with a better precision rate than the current state-of-the-arts methods.

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IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium

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© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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Artificial intelligence

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Dey, EK; Awrangjeb, M; Stantic, B, An Unsupervised Outlier Detection Method For 3D Point Cloud Data, IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, 2019