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.
KEYWORDS: Clouds, Filtering (signal processing), 3D modeling, Optical filters, Data modeling, Laser scanners, Signal processing, 3D image processing, Process modeling, Error analysis
Cleaning data is one of the most important tasks in data science and machine learning. It solves many problems in datasets, such as time complexity, added noise, and so on. In a huge datasets, outliers are extreme values that deviate from an overall pattern on a sample. Usually, they indicate variability in measurements or experimental errors. Depending on whether the entity is numeric or categorical, we can use different techniques to study its distribution to detect outliers. Like histogram, box plot and z-score, etc. This work aims to develop a modelbased method to detect undesirable points in a 3D point cloud representing a building. Our proposed method relies on the Z-score concept for filtering outliers which is well known in statistics as the standard score. The idea behind the use of this concept is to help to understand if the data value is above or below average and at what distance. More specifically, the Z-score indicates how many standard deviations away a data point is from the mean.
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