A new approach for removing point cloud outliers using box plot
Conférence : Communications avec actes dans un congrès international
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 a 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.