# Advanced Methods for Point Cloud Processing and Simplification
## 9 Conclusions  
In this paper, we presented several methods for point cloud processing from basic preprocessing, such as removing outlier points, initial rotation angle estimation against base coordinate axis to the point cloud simplification, and global space parameter estimation. The main assumption for its success depended on planar surface presence in analyzed data, which is highly probable and almost every time fulfilled in an indoor space. The results of these methods are summarized in the following three points:  
* Outliers and rotation--The simple point concentration histogram offers to remove outliers. This improved the result of the final point cloud composition by the registration process. Moreover, the thoroughly selected processing of histograms in the two axes allows the relatively fast finding of the initial point cloud orientation against the third coordinate axis. This is mainly important for the decreasing processing time. Only three passes of the scanning algorithms allow finding of all planar surfaces in each coordinate axis as documented in the tables of results. * Holes removing--The composed point cloud from several scans may lack of points in some part of the presented surfaces. The filling of holes in a level image representing a planar surface makes it possible to reconstruct the missing points. The filled holes allow the estimation of the real surface area. It makes even it possible to estimate the volume of a space when its height is known in the case of the floor or ceiling. Figure 23: Point cloud segmentation by the level image: (**a**) Input point cloud; (**b**) Input level image; (**c**) Point cloud of the segmented planar surface. Figure 24: Segmentation of different planar surfaces: (**a**) Illustration of two segmented planar surfaces with different area in Y-axis; (**b**) First level segmentation in X-axis; (**c**) Resultant point cloud. * Simplification of objects--The proposed approach showed the successful planar surface detection in the desired coordinate axes. It offers alternative point cloud processing and extends the possibilities of the present methods. The scanning algorithm for the planar surface detection allows the description of these planar surfaces by the statistical parameters. The standard deviation of the detected planar surfaces is small, which indicates their precise detection, as was shown in the capture results. We can see that thanks to this, it is easy to possibly detect the parallel walls. The presentation of planar surfaces by the level images offers to estimate important space properties as the area and perimeter. The image processing methods allow easy determination of these parameters against the difficult mathematical solution for the irregular shapes. All the detected properties make it possible to classify each planar surface. The results also show the ability of the algorithm for the descriptive point decreasing. All the planar surfaces presented only by their vertices showed a 99% decrease in points almost in all cases. Moreover, all features of the proposed processing pipeline are usable not only for point clouds, but they are valid for the detection and analysis of different levels for any 3D data in general. Future research will focus on object detection and tracking. The planar surfaces representation allows removal of the scanned space boundary and extracts only the desired objects. The analysis of the level of image shape can be used to categorize objects into groups. The extension of the level image content in more depth can offer the categorization of 3D objects. The next possible way is to create the simulator based on the measured data, which will create the training spaces with some targets, and this can be used for neural networks training. [PERSON] and [PERSON] helped with the work on the signal processing and made the revision of the text integrity. [PERSON] developed the main concept of the level scanning algorithm and level image. He is the writer of the original draft.