A Review of Data Structure and Filtering in Handling 3D Big Point Cloud Data for Building Preservation

3D digital documentation for buildings has become a necessary tool in preserving them. Heritage buildings are exposed from various kind of threats such as human negligence, natural disaster and weather changes. The fundamental in 3D digital documentation which is the 3D point cloud data has captures great attention and has widely used in many fields due to the availability of laser scanners. The use of laser scanning in engineering surveys is gaining attention due to its advantage of producing high accuracy data. In most situations, it also able to scan the entire required site, thus offers a good potential technique for large-scale applications like for heritage buildings preservation. The data, which consists of high density of points, can be delivered in a short time. However, this causes a massive amount of data generated and hence, it becomes very difficult to be managed. Due to this issue, there are critical needs to have a good method in managing 3D point cloud data to maintain features and visualization of buildings, specially the old and aged ones. This paper will review developed methods in handling these data, concentrating on two specific processes, which are data structure and data filtering. The 3D point cloud data is having a unique representation, thus researchers are no longer concentrating on the usual concepts of data registration, meshing and reconstruction to handle it, but data structure and data filtering are preferred. In data structure, mathematical methods incorporating geometric and topological techniques can be used for studying finite set of points. As most of the data captured contains noises and outliers, filtering is also important and can be treated as one of the processes that can be adapted in handling 3D point cloud data. The implementation of various solutions within these areas are presented in this paper and will be analyzed by emphasizing their contributions. Then, results will be studied to explain the effectiveness of the methods used in handling big point data. Finally, some future work for 3D point cloud handling will be highlighted to conclude this critical review focusing in building data for its preservation.