# Advanced Methods for Point Cloud Processing and Simplification
## 1 Introduction
### Related Works
In a different research work, the deep learning method called PCPNet is used to estimate local 3D shape properties in point clouds (see reference [14]). The main purpose is to classify the objects and shapes or do semantic labeling. The presented results show good estimation ability for the normals and the curvatures, even in the noisy point clouds. The deep learning approach is likewise used on automatic building detection and segmentation from the aerial images or the point clouds (see reference [15]). The main focus lies in improving and preparing high-quality training data, which allows better segmentation of the detected objects. The presented results show the high detection accuracy (higher than 90%) with the RGB-lidar and the fused RGB-lidar data of the urban scenes. The next research (shown in reference [16]) presents the new topology-based global 3D point cloud descriptor called Signature of Topologically Persistent Points (STPP). The topological grouping improves the detection robustness and increases the resistance against a noise. The complementary information also improves deep learning performance. The different topological method TopoLAB described in reference [17], that no longer uses neural networks, focuses on a pipeline to recover the broken topology of planar primitives during the reconstruction of complex building models. Due to the scanning difficulties and a variable point cloud density, some parts of a model can be missing. The proposed method allows the recovery of these parts, including to visualize the different levels of the details. With respect to the safety and health of the persons, the laser scanning plays an important role in exploring abandoned or dangerous places like various mines (see reference [18]). Similar to this use is the scanning of rock masses (see reference [19]). The proposed method for the planar surface extraction is able to deal with rough and complex surfaces. For the Mobile Laser Scanning (MLS), the new method Planes detection and Segmentation is proposed based on the Planarity values of Scan profile groups (PSPS) (see reference [20]). The presented results prove the quality of this method in comparison with other state-of-the-art algorithms for these types of spare point clouds, for example, the segmented planar surfaces stand out by their compactness. The 3D point cloud processing is important not only to get physical shapes, but also it can be used to quantitatively characterize the height, width, and volume of shrub plants. Authors in reference [21] analyze point clouds of different blueberry genotype groups to improve the mechanical harvesting process. For comparison of different shrubs, the correlation is used. In the study described in reference [22], the researchers focus on the filtering method of leaves based on the manifold distance and the normal estimation. The scanned leaves contain the outliers and the noise. The precise estimation of the shape and area of a leaf is important to obtain the tree growth index. Moreover, also the ground plane segmentation plays an important role in the analysis of asparagus harvesting (see reference [23]). The asparagus stems are scanned by a modern time-of-flight camera, which ranks as the fastest scanning device. The proposed Hyun method outperformed the classical RANSAC method in a scene with high clutter. Similar to the previous works is a parametrization of the forest stand described in reference [24]. The researchers propose to use, instead of leaf area index, other space characteristics such as the stand denseness, canopy density, crown porosity, and others. In the practical part, they discovered a high correlation index between each other. The geometric characterization of vines from 3D point clouds is important in the agronomy sector (see reference [25]). The convex hull method is used for calculating the volume of these plants. The recent research in reference [26] dealsalso with this method in the aim to find the shortest path in 2D complexes with non-positive curvatures.