جستجوی مقالات مرتبط با کلیدواژه "point cloud" در نشریات گروه "جغرافیا"

Reconstruction of 3D models and their use in photogrammetry and remote sensing has been considered as the most important and challenging topics in recent years. With the development of laser scanner technology and obtaining spatial data of the environment and objects, the use of this technology has increased nowadays. This technology extracts points from the external surfaces of the environment or objects in high volume, in a short time, which is called point cloud.
Due to laser scanners’ easy placement, point clouds are usually taken from different angles, so they define in the different coordinate systems, which must be unified to give a complete 3d view of the object. The process is considered as “registration”.
For this purpose, first, the corresponding pairs of points in each point cloud must be determined and then they must be matched correctly.after all a three-dimensional model is created.
Finding the best pair of corresponding points in the Point clouds as well as estimating the optimal error metric and the displacement between pairs of corresponding points is one of the most important and challenging steps of three-dimensional reconstruction.
Three-dimensional descriptors are one of the most suitable tools for determining the corresponding pairs of points in Point cloud. These descriptors create a set of information for every single point to determine the corresponding points in each Point cloud. Defining a three-dimensional descriptor whose computation complexity is low but its descriptive is high, can help to find the correct pair of points for 3d registration and modeling.

The main purpose of the present study is to define a strong three-dimensional descriptor to find the best corresponding pair of points to reconstruct the three-dimensional model.
The descriptor proposed in this study consists of two single local three-dimensional descriptors based on the spatial and geometric properties of the Point cloud, which combine to form a strong descriptor to determine corresponding points in the Point cloud.
Laser scanners extract a large volume of points from surfaces in a short period of time, which due to the reflection of laser beams, Point cloud may contain noise and mistakes. In the process of analyzing and using the data, these mistakes cause problems and should be removed in the pre-processing phase. To define the desired descriptor, in the pre-processing phase the Point cloud gets ready to extract the required properties.
The Statistical removal filter method is used to remove the noise and the voxel grid filter method is used to improve the speed of future preprocessing.
Each point in the neighborhood of Query Point provides a lot of that can be used to create the desired descriptor.
In the present study, by determining the appropriate neighborhood radius and Nearest Neighbor Search (NNS) method, using the k-dimensional tree, correct and efficient neighborhoods are determined for each point.
In the first step, a spatial descriptor is formed for each point. This descriptor is defined in the form of a histogram based on two distances for the point in its neighborhood. In the second step, the angles of the normal vectors of the Point cloud in different states are used to create a descriptor based on geometric information. In this research, two features called and have been used, which for each descriptor is formed in the form of a histogram. Then the spatial descriptor is combined with each of the descriptors based on the geometric feature and forms two desired descriptors.
To ensure the accuracy of the matching process based on the proposed descriptor, by assigning a suitable threshold for the basis of the distance between the Query point and its neighborhood, with the corresponding point of the Query point and its neighborhood in the second Point Cloud, incorrect correspondences are detected and removed. Next, the remained correct corresponding pairs of points are used to reconstruct the three-dimensional model.

In this research, two sets of Point cloud have been used to evaluate the proposed process. These two data sets are obtained in such a way that in the first data set the perspective and angle of view and in the second data set the position and arrangement of objects are changed.
By forming descriptors based on spatial and geometric features in different neighborhood radii and then forming a proposed combination descriptor based on what has been mentioned, it can be considered that combining the geometric descriptors with spatial descriptors, in cases where The two datasets have less relative overlap or more relative rotation than each other, in contrast to the position shift, leading to improved descriptor performance and increased matching accuracy.
Considering the results obtained from the comparison of the proposed descriptors, it can be said that because of the existence of two different radii in each part of descriptors based on spatial and geometric relations in the proposed descriptors, it turns out that the required descriptor is high quality.
On the other hand, the properties used in these descriptors are also resistant to changing the position of objects and have high efficiency in mentioned category. Also, the process of identifying and eliminating incorrect correspondences improves the matching process and increases the matching percentage of similar points up to 25% in the study data set.

The results of comparing the set of Point Cloud studied using the proposed descriptor indicate that this descriptor is more efficient in cases where two data sets rotate relative to each other, compared to cases where the location of the data pair has changed relative to each other. And the accuracy of the comparison obtained from the proposed method, in this case, increases compared to other data pair placement modes.

On true orthophotos, there are some distortions on the structural edges of buildings, which is due to defects in these areas in the point cloud used in the digital surface model. This problem is greater for orthophotos that have been made from UAV images in urban areas because of their lower altitude. Before interpolation of the point cloud and preparation of the digital surface model and then preparation of orthophotos of it, it is necessary to complete the point cloud in areas with defects. Some studies have shown that adding edge points has the effect of decreasing the distortion of true orthophotos. In this study, a new method for completing point clouds using a trained deep learning network is proposed, which includes steps: 1) Preparation and normalization of point cloud data, 2) completion of the point cloud by learned networks; 3) reversion of the completed point cloud to real-world coordinates and, 4) integration with the existing original point cloud and preparation of the digital surface model and generation of true orthophotos.

In this study, the imaging of the Yazd region was done with a Phantom 4 drone equipped with a DJI camera. The SfM algorithm has been used to calibrate the camera, estimate the internal and external camera parameters, and produce images without distortion and low-density point clouds, and SGM has been used to produce dense point clouds. In the proposed method, the trained SnowflakeNet network is used to complete the incomplete roof points of the building. Assuming that the points on the roof of each building are predetermined, without noise, and have incomplete edges, these point clouds were introduced as inputs to the network to complete. Points related to edge points were extracted for each roof and added to the existing point cloud after increasing the density and returning to the actual coordinates. The final point cloud was used in the preparation of digital models to produce irregular and then regular surfaces and in the preparation of true orthophotos using camera parameters and undistorted images. One of the images with buildings marked as numbers 1 to 4 was selected to perform tests and prepare orthophotos.

The lack of structural edge points on any roof, which is the distance between severe height differences between levels, causes the greatest amount of distortion on the edge of the roof and around it. Adding these points with edge line recognition and reconstruction algorithms to the point cloud improves the resulting digital surface model. Since the quality and accuracy of the digital elevation model directly affects the resulting orthophoto, using a more accurate digital elevation model improves these images. In the proposed method, these point clouds are complemented by the deep learning method, and quantitative and qualitative comparisons show better results in reducing distortion in most of the buildings tested. The reasons for the superiority of the proposed method over previous methods include determining and calculating a more complete and integrated form of the roof of each building instead of multiple line segments and considering the outermost edges of the buildings.

In this study, a new method was introduced to improve the quality of true orthophoto edges by using a deep learning network to complete the point cloud, which was tested on several building images and compared with the results of previous methods. In this study, in addition to the fact that, for the first time, a deep learning network was used to improve point clouds to produce orthophotos, Compared to the previous method, the amount of distortion on the selected edge of four buildings has been significantly reduced and the success of the results with the latest proposed method of true orthophoto enhancement indicates an improvement of about 62% and 55% in the distortion decreasing of the structural edges and maintaining their coordinate accuracy. Despite the reduction of distortion on the selected structural edge using the proposed method, this value is increasing in curved areas as well as the corners of the roofs due to the type of network training and network output error. However, this can be reduced by improving the structure of the deep learning network and increasing the training data to a variety of roof modes with curved walls.

The Digital Terrain Model (DTM) is a statistical presentation of the earth surface using some points with predefined 3D coordinates. Extracting the DTM as an important product of photogrammetry and remote sensing that is the basis of many practical projects, has always been considered for experts. LiDAR is a powerful equipment that can provide 3D point cloud with high accuracy from the earth. Nowadays, advances in technology make the generating 3D point cloud from the digital aerial images by dense matching feasible. These point clouds represents an approximate Digital Surface Model (DSM) of the earth. The DSM contains both terrain points and off-terrain points, but the DTM contains only the terrain points. In other words, the DTM presents a bare earth without any natural and artificial objects. Generating the DTM using the DSM is a challenging topic in photogrammetry and remote sensing. In this paper an algorithm with two independent approaches is proposed. Before beginning the process, the irregular point clouds was gridded, interpolate and convert to the image by specifying a point interval.
The first proposed approach was a progressive morphological filter that detect the off-terrain points from the DSM. This approachused the simple morphological filter in a specific procedure with four steps. In the first step, a minimal surface was generated by identifying the points which have minimum elevation in predefine scan windows. The structural element of the morphological filters should be determined. As it is a progressive filter, a vector that contains the structural elements sizes was determined in the second step. In the third step, a morphological opening was applied on the point cloud with a structural element accordance with the produced vector in step1. For each window size in the vector, an elevation threshold was calculated by multiplying the interval distance and supplied slope parameter. Then, the difference between initial surface and the result of applying the morphological opening was computed. The points with difference value more than the calculated elevation threshold was selected as off-terrain points.
In the second approach an iterative procedure was designed which was based on morphological filters. The geodesic dilation was a combination of traditional morphological filter. Although the morphological filters operated based on the image and structural element, geodesic dilation operated with two images including the mask and the marker. In geodesic dilation of size one the marker image was dilated by an elementary isotropic structural element and the resulting image was forced to remain below the mask image. In other words, the mask image acts as a limitation for the dilated marker image. Image reconstructing using geodesic dilation on an image was done by performing some successive geodesic dilations on the image. The results of geodesic dilation was depending on the elevation value. If this value was low, only the building ridge line was extracted andoff-ground. Moreover, if the elevation value was high, some of the bare earth was cut as off-terrain, wrongly. Hence, an iterative procedure was proposed to make the extracting the most of the object possible. In this way, the probability of error was reduced. In each loop, the elevation value was increased and some objects was extracted using geodesic dilation. Among the extracted parcels in each loop, the parcels which have local range variation more than a threshold were selected and the others were removed. The local range variation for each point was computed by specifying a window and calculating the difference between maximum and minimum elevation value in that window. This procedure was repeated till analyzing all of the elevation values.
Finally, the results of detecting the off terrain points using the both of approach were accumulated to acquire the final class of off-terrain points. Then this points were removed and the cubic interpolation was employed in order to retrieval the elevation of the losses points and generate the DTM.
In order to analyze the performance of the proposed algorithm, 7 test area was used. The point cloud of area 1, 2 and 3 were produced using dense matching of digital aerial images (Ultracam) by National GeographyOrganization of Iran. The point spacing of these areas is about 0.5 meter. The point cloud of area 4, 5, 6 and 7 were captured using LiDAR by International Society for Photogrammetry and Remote Sensing. The point spacing of these areas were 3, 1, 2.5 and 3 meter, respectively. The data set covered the most of the features such as steep slopes, mixture of vegetation and building, bridge underpass, road and building with various roof shape. Evaluating the performance of proposed algorithm represented the 4.85% error for extracting the off-terrain points and 0.68 meter error for generated DTM in all test areas, averagely. The evaluation results clarify the ability of proposed practical algorithm in generating the DTM using the DSM

There exist a number of factors that affect the quality laser scanner. In other words, the accuracy of a terrestrial scanner is limited extensively by systematic errors and thus must be calibrated. Indeed, calibration is a prerequisite for obtaining 3D precise and reliable data from point clouds. Until now, several models have been proposed to improve the accuracy of laser scanner data, most of which include both physical empirical parameters which are produced by observing point residuals, As a result, these models are just usable solely for those observations. The authors of have previously developed a new general parametric model based on the internal structure of laser scanner which can be used for a variety of TLS instruments. Due to of the importance of stability of parameters in a model, stability of them and the correlation between them needs to be investigated precisely, a task which is addressed thoroughly in this paper through a number of practical experiments. The results show that this model with a relative stability can improve the accuracy of TLS data.