Scanning Point Cloud Data Research Articles

Feasibility Study of Using Mobile Laser Scanning Point Cloud Data for GNSS Line of Sight Analysis

The positioning accuracy with good GNSS observation can easily reach centimetre level, supported by advanced GNSS technologies. However, it is still a challenge to offer a robust GNSS based positioning solution in a GNSS degraded area. The concept of GNSS shadow matching has been proposed to enhance the GNSS based position accuracy in city canyons, where the nearby high buildings block parts of the GNSS radio frequency (RF) signals. However, the results rely on the accuracy of the utilized ready-made 3D city model. In this paper, we investigate a solution to generate a GNSS shadow mask with mobile laser scanning (MLS) cloud data. The solution includes removal of noise points, determining the object which only attenuated the RF signal and extraction of the highest obstruction point, and eventually angle calculation for the GNSS shadow mask generation. By analysing the data with the proposed methodology, it is concluded that the MLS point cloud data can be used to extract the GNSS shadow mask after several steps of processing to filter out the hanging objects and the plantings without generating the accurate 3D model, which depicts the boundary of GNSS signal coverage more precisely in city canyon environments compared to traditional 3D models.

Cloud-based Design and Additive Manufacturing of Custom Orthoses

This paper presents a cyber design and additive manufacturing (CDAM) system at the University of Michigan Orthotics and Prosthetics Center. This CDAM system aims at shortening the delivery and improving the fit and comfort of custom orthoses and prostheses. The ankle foot orthosis (AFO), a custom made brace that supports muscles and provides control and protection for feet, is selected as an example to demonstrate the CDAM system. Traditionally, custom orthoses are fabricated using plaster molding which requires multiple patient visits and extensive labor and fabrication time. Four key components: 1) the digital scanning of foot and leg geometry using a stand with transparent foot plate and ergonomic procedure for 3D optical scanning, 2) a cloud-based design software that enables clinicians to access scanned point cloud data on the geometry of patients’ feet as well as create 3D models for AFOs based on patients’ needs, 3) a cloud-based manufacturing software that generates tool paths and process parameters for additive manufacturing to fabricate the AFO, 4) the evaluation using inertia measurement unit (IMU) for measurement of the AFO motion for gait analysis. The feasibility of CDAM system for one-day delivery of AFOs to patients has been demonstrated.

Performance evaluation of automated 3D image-scan-based reverse engineering in architectural MEP pipe object modeling

This study aims to analyze the productivity of automatic 3D reverse engineering for architectural mechanical, electrical and plumbing (MEP) pipe objects modeling. Recently, 3D image scan-based reverse engineering is emerging technology in facility management, operation domain. However, because a large amount of scanned point cloud data is so complex and difficult, it requires manual task additionally. Manual-based reverse engineering occurs low-productivity, data loss and error. We suggest the process of automatic 3D reverse engineering work for modeling pipeline which occupies the largest part of MEP phase. Furthermore, this study analyzes the productivity effect related to the proposed method. The results show that the productivity is improved from 118 to 162% (average work effectiveness: 141%).

Distance field guided $$L_1$$ L 1 -median skeleton extraction

We introduce a distance field guided \(L_1\)-median method to extract topologically clean 1D curve skeleton from the point cloud model. We first voxelize the input point cloud, and compute the distance field for the point cloud. Then with the distance field, we extract the initial skeleton of the model using a multi-scale parameter controlled thinning method. Finally, we incorporate the initial skeleton into the \(L_1\)-median optimization, and develop a distance field guided \(L_1\)-median to effectively extract the complete skeleton from the point cloud. Our method exhibits the advantages of both the distance field based skeleton extraction methods and the \(L_1\)-median skeleton extraction methods. Our skeleton extraction system is robust and effective, and can be applied to the raw scanned point cloud data.

Deep-Learning-Based Classification for DTM Extraction from ALS Point Cloud

Airborne laser scanning (ALS) point cloud data are suitable for digital terrain model (DTM) extraction given its high accuracy in elevation. Existing filtering algorithms that eliminate non-ground points mostly depend on terrain feature assumptions or representations; these assumptions result in errors when the scene is complex. This paper proposes a new method for ground point extraction based on deep learning using deep convolutional neural networks (CNN). For every point with spatial context, the neighboring points within a window are extracted and transformed into an image. Then, the classification of a point can be treated as the classification of an image; the point-to-image transformation is carefully crafted by considering the height information in the neighborhood area. After being trained on approximately 17 million labeled ALS points, the deep CNN model can learn how a human operator recognizes a point as a ground point or not. The model performs better than typical existing algorithms in terms of error rate, indicating the significant potential of deep-learning-based methods in feature extraction from a point cloud.

포인트 클라우드를 이용한 파이프라인 연결 자동 모델링에 관한 연구

Manual 3D pipeline modeling from LiDAR scanned point cloud data is laborious and time-consuming process. This paper presents a method to extract the pipe, elbow and branch information which is essential to the automatic modeling of the pipeline connection. The pipe geometry is estimated from the point cloud data through the Hough transform and the elbow position is calculated by the medial axis intersection for assembling the nearest pair of pipes. The branch is also created for a pair of pipe segments by estimating the virtual points on one pipe segment and checking for any feasible intersection with the other pipe’s endpoint within the pre-defined range of distance. As a result of the automatic modeling, a complete 3D pipeline model is generated by connecting the extracted information of pipes, elbows and branches.

Analysis of Efficient Visualization and Interactive Editing of 3D Point Cloud Data

In this study, an efficient technique for immersive visualization of point cloud data is described and analyzed, and a manual extraction method is proposed. With the advancement of reconstruction technologies, laser scanned point cloud data has gained importance in robotics, 3D model reconstruction, and other computer fields. The point cloud data from the laser scanner has been widely used to reconstruct into a realistic 3D model. The 3D visualization of an unprocessed point cloud data from the laser scanner is often necessary to perform visual analysis, interactive selection, and editing operations in 3D. For the interactive visualization of point cloud data, OpenGL point primitives are used to accelerate for proficient rendering. The manual editing operations are performed using octree-based neighbors within a radius search algorithm. Finally, the implementation and performance comparison between an octree and kd-tree radius search method are evaluated using real-world example datasets.

Does Tree Architectural Complexity Influence the Accuracy of Wood Volume Estimates of Single Young Trees by Terrestrial Laser Scanning?

Accurate estimates of the wood volume or biomass of individual trees have gained considerable importance in recent years. The accuracy of wood volume estimation by terrestrial laser scanning (TLS) point cloud data may differ between individual trees due to species-specific differences in tree architecture. We selected three common and ecologically important central European deciduous tree species, which differ considerably in tree architectural complexity in early ontogenetic stages: Acer pseudoplatanus (simple), Sorbus aucuparia (intermediate) and Betula pendula (complex). We scanned six single young trees for each species (18 trees in total) under optimal scan conditions (single tree stand, leafless state, four scanning positions, high resolution). TLS-based volume estimates were derived for the total tree as well as for the two compartments; trunk and branches, using a voxel-based bounding box method. These estimates were compared with highly accurate xyolmetric (water displacement) volume measurements. Coefficients of determination between xylometric measurements and bounding box estimates were very high for total trees (R2adj = 0.99), trunks (R2adj = 0.99), and high for branches (R2adj = 0.78). The accuracy of estimations for total tree and trunk volume was highly similar among the three tree species. In contrast, significant differences were found for branch volume estimates: the accuracy was very high for Sorbus aucuparia, intermediate for Betula pendula, and low for Acer pseudoplatanus. A stepwise multiple regression showed that the accuracy of branch volume estimates was negatively related to the number of the first-order branches within diameter sizes of D ≤ 5 mm and crown surface area (R2adj = 0.61). We conclude that the accuracy in total tree and trunk volume estimates was not affected by the studied types of tree architectural complexity. The impact of the structural variability of branches and occlusion by branches was, thus, not as high as expected. In contrast, the accuracy of branch volume estimates was strongly influenced by tree architectural complexity, though not in a simple way. Because underestimations originated from different sources, the accuracy of branch volume estimates cannot be directly derived from the degree of architectural complexity. These results imply that the voxel-based bounding box method provides highly accurate total tree and trunk volume estimates, whereas further research is needed to improve branch volume estimation for young trees of different architectural types.

An Improved Algorithm of Precise Point Cloud Registration

Basing on the application of digital protection of cultural sites, this paper presented a precise algorithm for the multi-resolution point cloud based on sequence iterative. Firstly, based on the synchronous scanning image, a rough registration of the adjacent point cloud can be obtained in an interactive way. Secondly, based on a normal-based algorithm of registration sphere center, the points of sphere surface are filtered from all scanned point cloud data according to the normal data points and the characteristic of viewpoint, and then the center of registration sphere can be calculated accurately and the initial registration of the adjacent point cloud can be obtained by setting Matching Registration Labels mode as the constraint condition. Finally, based on the 3D edge points of the adjacent point cloud from mahalanobis distance calculations, some overlapping images can be eliminated by the way of constantly optimizing the transition matrix in the iteration process. The engineering practice of the Small Wild Goose Pagoda in Tang Dynasty and the Ancient Tomb in Han Dynasty proves that the method is reliable and easy to design and implement and can effectively restrain the accumulative errors of sequence registrations.

Reconstructing DEM using TLS point cloud data and NURBS surface

Underground coal mining inevitably results in land surface subsidence. Acquiring information on land surface subsidence is important in the detection of surface change. However, conventional data acquisition techniques cannot always retrieve information on whole subsidence area. This study focuses on the reconstruction of a digital elevation model (DEM) with terrestrial laser scanning (TLS) point cloud data. Firstly, the methodology of the DEM with terrestrial 3-dimensional laser scanning is introduced. Then, a DEM modeling approach that involves the application of curved non-uniform rational B-splines (NURBS) surface is put forward. Finally, the performance of the DEM modeling approach with different surface inverse methods is demonstrated. The results indicate that the DEM based on the point cloud data and curved NURBS surface can achieve satisfactory accuracy. In addition, the performance of the hyperbolic paraboloid appears to be better than that of the elliptic paraboloid. The reconstructed DEM is continuous and can easily be integrated into other programs. Such features are of great importance in monitoring dynamic ground surface subsidence.

AUTOMATIC GEO-REFERENCING MOBILE LASER SCANNING DATA TO UAV IMAGES

Abstract. In this paper, a framework for adjusting mobile laser scanning point cloud data to improve the accuracy is proposed by integrating high resolution UAV images and MLS. First, aerial triangulated images with a few high accuracy ground control points are taken as control information. Then, a hierarchical strategy is proposed for robust pairwise registration of feature points between point cloud and images, so as to find the deviation of the point cloud. In the next step, a shape-preserving piecewise cubic interpolating method is employed to fit the time dependent error model of the trajectory. Finally, experiments are given to prove the effectiveness of proposed framework.

Subway Tunnel Cross-Section Surveying Based on Ground 3D Laser Scanning Data

Afterthe construction of subway main structure, in order to realize route adjustmentof alignment and gradient, it needs to survey the cross-section of subwaytunnel. Compared with conventional measuring methods, 3D laser scanning has thecharacteristics of non-contact measurement and can collect space 3D point clouddata with high density, this can improve the working efficiency for the subwaycross-section surveying. Based on the Leica Scanstation 2 scanners this paperanalyzed the 3D laser scanning point cloud data collection procedures and dataprocessing, expounded the subway cross-section surveying method based on pointcloud data; analyzed the feasibility of 3D laser scanning technology in theapplication of tunnel cross-section surveying based on the field validationdata. The results show that the cross-section measured by this method can meetthe technical requirements of route adjustment of alignment and gradient.

Automatic posing of a meshed human model using point clouds

We introduce a markerless approach to deform a quality human body template mesh from its original pose to a different pose specified by a point cloud. The point cloud may be noisy, incomplete, or even captured from a different person. In this approach, we first build coarse correspondences between the template mesh and the point cloud through a squeezed spectral embedding technique that exploits human body extremities. Based on these correspondences, we define the goal of non-rigid registration using an elastic energy functional and apply a discrete gradient flow to reduce the difference between a coarse control mesh and the point cloud. The deformed template mesh can then be obtained from the deformation of the control mesh using mean value coordinates afterwards. Our experiments show (see the supplementary video) that the approach is capable of equipping a mesh with the pose of a scanned point cloud data even if it is incomplete and noisy.

3D Restructuring and Splicing Technology of Shape Space Frame

The 3D Shape Measuring Technology for physical objects has wide application in fields covering automatic detection, physical object profile modelling, automatic production, computer aided design/manufacturing and quality control, etc. Through the matching relations of several feature points between the partial 3D scanning point cloud data and the space frame, the transformation relations between different coordinate systems can be inferred. As for the inferring of the transformation parameters R and T, the SVD matrix decomposition algorithm has been used to acquire a better precision solution. According to the transformation relation, after registering the partial 3D scanning point cloud data and the space frame, through splicing the partial scanning point cloud data and the space frame data, the detection across the whole field on large-area objects can be realized, which will effectively increase the precision of three-dimensional measuring of large-area objects.

Measured Scan Data Reduction Combination Algorithm Based on Scan Line

The 3D laser scanning technology is a hot spot in developed measuring in recent years. In the surface reconstruction of reverse engineering, the 3D laser scanning point cloud data is too large, and is not conducive to the computation, storage and surface reconstruction. After understanding the research status of the point cloud data processing streamline method at home and abroad, and through the analysis of minimum distance algorithm and the angle-chord height combined code method applicable to engineering characteristics, at the same time, the combination algorithm, which is based on the minimum distance algorithm and the angle-chord height combined code method, is proposed to simplify the point cloud data. The scanning point cloud is simplified by using matlab line by line.

Modeling outlier formation in scanning reflective surfaces using a laser stripe scanner

Scanning reflective surfaces using 3D laser scanners is a challenging task since reflective surfaces of complex geometry promote the formation of unwanted data outliers. These outliers are characterized with large measurement errors, which significantly deteriorate the quality of the scanned point cloud data. This paper experimentally investigates the formation of outliers in relation to scanning reflective surfaces using a commercial laser stripe scanner. Two outlier formation models are developed: mixed reflection and multi-path reflection. The undesirable specular reflections in both mixed reflection and multi-path reflection scanning situations cause multiple peaks in the image sensing arrays. The false image peaks are recorded and will eventually be observed as outliers because they are not part of the scanned object surface geometry. A series of scanning experiments have been conducted and the results confirm the validity of the developed outlier formation models. Potential applications of the developed models such as scan path evaluation and outlier filter design are also discussed.

Integration of Image Data for Refining Building Boundaries Derived from Point Clouds

Abstract. Geometrically and topologically correct 3D building models are required to satisfy with new demands such as 3D cadastre, map updating, and decision making. More attention on building reconstruction has been paid using Airborne Laser Scanning (ALS) point cloud data. The planimetric accuracy of roof outlines, including step-edges is questionable in building models derived from only point clouds. This paper presents a new approach for the detection of accurate building boundaries by merging point clouds acquired by ALS and aerial photographs. It comprises two major parts: reconstruction of initial roof models from point clouds only, and refinement of their boundaries. A shortest closed circle (graph) analysis method is employed to generate building models in the first step. Having the advantages of high reliability, this method provides reconstruction without prior knowledge of primitive building types even when complex height jumps and various types of building roof are available. The accurate position of boundaries of the initial models is determined by the integration of the edges extracted from aerial photographs. In this process, scene constraints defined based on the initial roof models are introduced as the initial roof models are representing explicit unambiguous geometries about the scene. Experiments were conducted using the ISPRS benchmark test data. Based on test results, we show that the proposed approach can reconstruct 3D building models with higher geometrical (planimetry and vertical) and topological accuracy.

Robust statistical approaches for local planar surface fitting in 3D laser scanning data

This paper proposes robust methods for local planar surface fitting in 3D laser scanning data. Searching through the literature revealed that many authors frequently used Least Squares (LS) and Principal Component Analysis (PCA) for point cloud processing without any treatment of outliers. It is known that LS and PCA are sensitive to outliers and can give inconsistent and misleading estimates. RANdom SAmple Consensus (RANSAC) is one of the most well-known robust methods used for model fitting when noise and/or outliers are present. We concentrate on the recently introduced Deterministic Minimum Covariance Determinant estimator and robust PCA, and propose two variants of statistically robust algorithms for fitting planar surfaces to 3D laser scanning point cloud data. The performance of the proposed robust methods is demonstrated by qualitative and quantitative analysis through several synthetic and mobile laser scanning 3D data sets for different applications. Using simulated data, and comparisons with LS, PCA, RANSAC, variants of RANSAC and other robust statistical methods, we demonstrate that the new algorithms are significantly more efficient, faster, and produce more accurate fits and robust local statistics (e.g. surface normals), necessary for many point cloud processing tasks. Consider one example data set used consisting of 100 points with 20% outliers representing a plane. The proposed methods called DetRD-PCA and DetRPCA, produce bias angles (angle between the fitted planes with and without outliers) of 0.20° and 0.24° respectively, whereas LS, PCA and RANSAC produce worse bias angles of 52.49°, 39.55° and 0.79° respectively. In terms of speed, DetRD-PCA takes 0.033s on average for fitting a plane, which is approximately 6.5, 25.4 and 25.8 times faster than RANSAC, and two other robust statistical methods, respectively. The estimated robust surface normals and curvatures from the new methods have been used for plane fitting, sharp feature preservation and segmentation in 3D point clouds obtained from laser scanners. The results are significantly better and more efficiently computed than those obtained by existing methods.

Efficient point cloud data processing in shipbuilding: Reformative component extraction method and registration method

Abstract To survive in the current shipbuilding industry, it is of vital importance for shipyards to have the ship components' accuracy evaluated efficiently during most of the manufacturing steps. Evaluating components' accuracy by comparing each component's point cloud data scanned by laser scanners and the ship's design data formatted in CAD cannot be processed efficiently when (1) extract components from point cloud data include irregular obstacles endogenously, or when (2) registration of the two data sets have no clear direction setting. This paper presents reformative point cloud data processing methods to solve these problems. K-d tree construction of the point cloud data fastens a neighbor searching of each point. Region growing method performed on the neighbor points of the seed point extracts the continuous part of the component, while curved surface fitting and B-spline curved line fitting at the edge of the continuous part recognize the neighbor domains of the same component divided by obstacles' shadows. The ICP (Iterative Closest Point) algorithm conducts a registration of the two sets of data after the proper registration's direction is decided by principal component analysis. By experiments conducted at the shipyard, 200 curved shell plates are extracted from the scanned point cloud data, and registrations are conducted between them and the designed CAD data using the proposed methods for an accuracy evaluation. Results show that the methods proposed in this paper support the accuracy evaluation targeted point cloud data processing efficiently in practice.

Technology of Mobile Phone 3D Model Reconstruction Based on the Geomagic Studio

Studied the modeling strategy and application features of rapid surfacing reconstruction system. The 3D digital model reconstruction of real mobile phone as an example, using non-contact optical 3D scanning point cloud data access to mobile phone, get the mobile phone CAD model through data processing by point stage, polygon stage and shape stage, and the various stages of the process and the target are discussed in this paper.