3D POINT CLOUD DATA DENOISING: A COMPARATIVE STUDY

Abstract

The most basic illustration of an object's surface is a point cloud. This type of data is nowadays used in a variety of applications that are affecting the advancement of technology. Sensor technology being the mean for this data acquisition is a highly advanced and robust technology that allowes object to “sense” their surroundings and provide real-time information. However, the acquisition methods are imperfect and the raw data produced by these sensors is presented as disorganized point clouds suffering from noise thus representing the geometry of 3D data in an unstructured manner. Working directly with a clear representation is crucial and significant because of this source of data's rising popularity and wide range of uses. To address this issue several methods have been proposed throughout the years resulting in effective denoising approaches. In this thesis, we are going to analyze some of the denoising methods and provide an objective and subjective evaluation based on their performance.