# Advanced Methods for Point Cloud Processing and Simplification
## 1 Introduction
### Related Works  
A wide survey of 3D surface reconstruction methods is provided in reference [1]. The authors accurately and in a well-arranged way evaluated the state-of-the-art methods with their advantages and disadvantages. The survey includes a table of all methods, where the main features and the possibility of use are summarized. Valuable work is presented by the authors in reference [2]. The paper deals with the automatic reconstruction of the fully volumetric 3D building models from oriented point clouds. The proposed method can process the large complex real-world point clouds from the unfiltered form into the separated room model, including suppressing of undesired objects inside a room. Several methods for 3D modelling an indoor environment relying explicitly on the prior knowledge of scanner positions are in reference [3]. In many software products provided by a manufacturer of scanners, the scanning position is irretrievably lost. Therefore, the authors propose a method for the reconstruction of the original position of the scanners. The presented method can determine these positions also under very unfavorable conditions. The next research focuses on the estimation of surface geometries directly from a point cloud as in reference [4]. It introduces the 3D surface estimation method of the household objects inside the area. The authors developed a Global Principal Curvature Shape Descriptor (GPCSD) for the categorization of objects into groups. The main purpose is to improve the manipulation planning for a robotic hand. Researchers in reference [5] introduced a point cloud segmentation method for urban environment data. Their method is based on the robust normal estimation by the construction of an octree-based hierarchical representation for input data. The achieved results prove the concept of usability for the urban environment point clouds even if there is space for improving the detection of some regular objects with curved surfaces. The octree-based approach, with a combination of PCA analysis, is presented in reference [6]. Results are compared with other detection algorithms such as a 3D Kernel-based Hough Transform (3D-KHT) or the classical Random Sample Consensus (RANSAC). This method provides accurate results, robustness to noise, and the possibility to detect planes with small angle variations. The similar research in reference [7] focuses on the plane segmentation of building point clouds. The proposed Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method also respects the curvatures to provide the final fine segmentation. The DBSCAN clustering for surface segmentation is used also in reference [8]. A part, in which the results are presented, shows the desired surface segmentation, but only in a simple point cloud, which is not a real-world case. The surfaces can be extracted also from the RGBD images (see reference [9]). The proposed split and merge approach produces interesting visual results. The planes are detected from depth images by the Depth-driven Plane Detection (DPD) algorithm based on a region grooving (see reference [10]). The Hough transformation (HT) approach called D-KHT can be used for a plane segmentation from these images, as it is shown in reference [11]. Due to the properties of the HT, this method can detect the planes with discontinuities. The ground plane detection using the depth maps captured by a stereo camera is presented in reference [12]. The camera is moving, and the system deals with the elimination of their roll angle for the correct plane segmentation. The growing topical issue of deep learning in recent years is used for point cloud segmentation. One case of use is indoor boundary estimation (see reference [13]). The described method relies on the depth estimation and wall segmentation to generate the exterior point cloud. The deep supervised clustering helps to fit the wall planes to obtain the resulting boundary map.