Point Cloud Autocad Crack 19
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Abstract:The mobile laser scanning (MLS) technique has attracted considerable attention for providing high-density, high-accuracy, unstructured, three-dimensional (3D) geo-referenced point-cloud coverage of the road environment. Recently, there has been an increasing number of applications of MLS in the detection and extraction of urban objects. This paper presents a systematic review of existing MLS related literature. This paper consists of three parts. Part 1 presents a brief overview of the state-of-the-art commercial MLS systems. Part 2 provides a detailed analysis of on-road and off-road information inventory methods, including the detection and extraction of on-road objects (e.g., road surface, road markings, driving lines, and road crack) and off-road objects (e.g., pole-like objects and power lines). Part 3 presents a refined integrated analysis of challenges and future trends. Our review shows that MLS technology is well proven in urban object detection and extraction, since the improvement of hardware and software accelerate the efficiency and accuracy of data collection and processing. When compared to other review papers focusing on MLS applications, we review the state-of-the-art road object detection and extraction methods using MLS data and discuss their performance and applicability. The main contribution of this review demonstrates that the MLS systems are suitable for supporting road asset inventory, ITS-related applications, high-definition maps, and other highly accurate localization services.Keywords: mobile laser scanning (MLS); point cloud; road surface; road marking; driving line; road crack; traffic sign; street light; tree; power line; deep learning
This study aimed at evaluating the feasibility of combined images and LiDAR data for façade features detection and measurement. In particular the 3D representation of the crack propagation and geometrical formation. The approach acquires LiDAR data and 2D images independently, the images collected at optimal position and time for capturing the surface details. The transformation of 3D LiDAR point clouds to 2D structured depth images enables the implementation of existing computer vision algorithms developed for 2D color images. The depth map will be produced for every 2D image, resulting in an additional D-channel to the color (RGB) image channels. The algorithm is implemented on experimental data collected from the Treasury of Petra Ancient city in Jordan.
In general, 3D point segmentation approaches in the literatures are mainly categorized as region-growing, model-based, edge detection, and image-based approaches. The selection of suitable methods of segmentation relies on the type of image and applications [12]. Region growing is most common because it has a high-level understanding of the image component. In practice, two issues relate to the region-growing technique: the first issue is seed selection; the seed point is the reference for expanding the regions, their selection is very critical for segmentation success. The second is the selection of similarity criteria, where a fixed formula is required to contain the neighboring growth pixels and the suitable constraints to prevent the growth process [13]. Habib and Lin [14] introduced a region-growing method that uses the kd-tree data structure to distinguish point and pole-like surface features in point cloud. The segmentation method of Dimitrov and Golparvar-Fard [15] used a multi scale scheme to distinguish the features; curvatures are estimated at each 3D point followed by seed selection and regional growth processes. Che and Olsen [16] proposed multi-scale technique; the Normal Variation Analysis procedure is used in the first step to identify edge points, to achieve better segmentation performance, the points are then clustered on a smooth surface using a region-growing process. Vo et al. [17] suggested cloud point segmentation using adaptive growing Octree area. The algorithm has two steps based on the coarse to fine process method. First the major segments are extracted using octree-based voxelized representation. Then the results passed through refinement process. The challenges will appear in case of 3D complex scene which has irregular sampling point and different object types. Although the outcome of region growing techniques is reasonable, they have a common problem with over-segmentation, complexity and expensive computation. More challenges are the choice of the initial seed points and the number of object surfaces [18].
Comparatively to the region growing method in the literature, a few works present edge detection techniques in 3D point clouds, Lin et al. [22] developed strategy of LiDAR segmentation applied directly on the acquired point clouds. The point clouds are first split into facets by sorting the local k-means into carefully selected seeds. The extracted facets provide sufficient information to determine the linear characteristics of the local planar region. Huang and Brenner [23] used the curvature values for extracting the borders in the irregular mesh, for complex shape objects, multi level scheme is proposed to enhance the results. Many other algorithms in edge detection converted the LiDAR point clouds into depth image representation in order to structure the point cloud. The pixels in the image denotes the depth values of the object from the LiDAR, the values are stored in the pixels as a real number values. Generally, depth image edge detection techniques have three basic primitive edge types, step, crease, and roof edges. Step edges are corresponding to in-depth discontinuities, crease edges are congruent with normal surface discontinuities, where the roof edges are characterized by the discontinuity of curvature. Miyazaki et al. [24] proposed a line based approach to extract planar regions from an anisotropic distribution points. The approach splits the cloud input point into scanning lines, and then the algorithm selects segments that best represent the point sequence of each scanning line. Generally, most of the edge work algorithms are constrained to images of high quality. Others are complex with numerous parameters and cannot guarantee closed boundaries [25].
Although a large number of surface points and triangles are identified in the 3D model created by the laser scanner, outlines such as edges and cracks are lost beyond the resolution of the available laser information. Modeling these features can further reduce data sizes, allowing for advanced analysis of simplified models instead of bulkier point clouds. While many current cloud segmentation approaches have been shown to effectively segment TLS data, complex real-scene implementations still have significant shortcomings and challenges. Existing methods of segmentation require curvature and normal estimation before data analysis and grouping. Despite a number of solutions to adaptive neighborhood description, curvatures approximation on edges or rough surfaces such as the historic building can still be unreliable [40]. In order to clarify this problem in our data, we apply a mean curvature segmentation algorithm proposed by Alshawabkeh et al. [41]. The proposed algorithm efficiently estimate the mean curvature value at each sampled pixel using convolution distinct sizes of windows running across the image in only one direction. The algorithm classifies the edge points based on selected threshold values of the mean curvature. Using multiple-scale masks allows for reliability in estimating curvature values in the presence of noise problems, particularly in real scene environments. The findings of the experiment are shown in Fig. 4. Various mask sizes and threshold values are used, but the small surface features are still missing, the clear edges are only detected.
Because of the different laser and image data acquisition position, depth images will contain occlusion information where an object or parts of it are not visible due to another object closer to the camera blocking the view. The concept of the problem of occlusion is shown in Fig. 6. Adequate concept of visibility is used in our approach to filter the hidden LiDAR points. The algorithm suggests that the visible point is closest to the center of the image perspective, while the other overlapping points are considered occluded points in the current view of the image. The algorithm compares the estimated depth values for each LiDAR point to the available depth buffer values. The final grid will only process and project the point clouds that could be visible in the camera field.
In other hand, the existing methods that use color information during the data processing, depends on a camera fixed from the same viewpoint of the 3D system in order to capture color and depth images simultaneously [26, 31,32,33]. These photographic images may suffer from scene-wide variations in lighting, such as shadows that are common throughout the outdoor scene. Furthermore, the scanner location and distance from the scene may not be sufficient for the camera to capture the require fine surface information. The proposed method in this study solves such challenges by collecting the point clouds and images separately, allowing optimal time and position for the high visual quality of the scene features. The results provide satisfactory 3D contour points that represent the location of the facade edges and linear features as depicted in Fig. 10. The 3D edge features are accurately mapped into the corresponding 2D images. Figure 11 shows the flexibility mapping of the 3D extracted features into different 2D images and the 3D meshed through reverse central projective transformation. The results allow better data understanding and weathering forms quantification. Automatic detection of the continuous extent of material displacements with digital measurements will reduce cost of field inspections and increase safety. In addition, the method significantly reduces the amount of information that can be displayed and interact fluently with the obtained 3D model from 3.3 million to 148 thousand. 2b1af7f3a8