A new approach for removing point cloud outliers using box plot

Hafsa Benallal; Youssef Mourchid; Ilyass Abouelaziz; Ayman Alfalou; Hamid Tairi; Jamal Riffi; Mohammed El Hassouni

doi:10.1117/12.2618842

27 May 2022 A new approach for removing point cloud outliers using box plot

Hafsa Benallal, Youssef Mourchid, Ilyass Abouelaziz, Ayman Alfalou, Hamid Tairi, Jamal Riffi, Mohammed El Hassouni

Proceedings Volume 12101, Pattern Recognition and Tracking XXXIII; 1210108 (2022) https://doi.org/10.1117/12.2618842
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

Cleaning a point cloud building is challenging issue, it is crucial for a better representation of the scan-to-BIM 3D model. During the scan, the point cloud is in generally influenced by several factors. The scanner can provide false data due to reflections on reflective surfaces like mirrors, windows, etc. The false points can form a whole bunch of disturbing data which is not easy to detect. In this work, we use a statistical method called box plot to clean the data from false points. This method is a developed method of reading histograms. We test the proposed method on private database containing four point cloud buildings specifically designed for building information modeling (BIM) application. The experimental results are satisfying and our method detect most of the false points in the database.

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Citation Download Citation

Hafsa Benallal, Youssef Mourchid, Ilyass Abouelaziz, Ayman Alfalou, Hamid Tairi, Jamal Riffi, and Mohammed El Hassouni "A new approach for removing point cloud outliers using box plot", Proc. SPIE 12101, Pattern Recognition and Tracking XXXIII, 1210108 (27 May 2022); https://doi.org/10.1117/12.2618842

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