Complete Point Cloud Research Articles

Earth-Rock Dam Deformation Monitoring Based on Three-Dimensional Laser Scanning Technology

Three-dimensional laser scanning can obtain the complete geometry information of the dam quickly and accurately, and then achieve the overall deformation monitoring, which breaks the traditional limitation of single point monitoring and local monitoring. In this paper, the complete point cloud data of the dam was obtained by three-dimensional laser scanning technology, and the surface model of the earth-rock dam was constructed by three-dimensional spherical projection surface dam construction algorithm. Then the accurate three-dimensional surface model of the earth-rock dam was acquired, and the first phase of the model as reference model,with different periods of point cloud data collected were analyzed with the reference model to obtain information about the dam deformation.

Towards fully automatic reliable 3D acquisition: From designing imaging network to a complete and accurate point cloud

This paper describes a novel system for accurate 3D digitization of complex objects. Its main novelties can be seen in the new approach, which brings together different systems and tools in a unique platform capable of automatically generating an accurate and complete model for an object of interest. This is performed through generating an approximate model of the object, designing a stereo imaging network for the object with this model and capturing the images at the designed postures through exploiting an inverse kinematics method for a non-standard six degree of freedom robot. The images are then used for accurate and dense 3D reconstruction using photogrammetric multi-view stereo method in two modes, including resolving scale with baseline and with control points. The results confirm the feasibility of using Particle Swarm Optimization in solving inverse kinematics for this non-standard robot. The system provides this opportunity to test the effect of incidence angle on imaging network design and shows that the matching algorithms work effectively for incidence angle of 10°. The accuracy of the final point cloud generated with the system was tested in two modes through a comparison with a dataset generated with a close range 3D colour laser scanner.

大規模点群からの生産設備の形状再構成 (第1報)

The recent progress of laser scanners makes it possible to capture dense point-clouds of large-scale facilities. Large-scale facilities are mainly composed of many primitive surfaces, such as planes, cylinders, cones, spheres, and tori. However, it is not easy to reliably extract these surfaces from point-clouds because it is actually difficult to capture complete point-clouds of surfaces. In this paper, we discuss how to reconstruct pipe structures from insufficiently extracted surfaces. In our method, we first extract planes and cylinders, which can be reliably extracted, and detect the relationship among detected surfaces on a depth image. Then we estimate shapes and locations of various pipe structures using the relationships among surfaces. Finally we verify estimated shapes based on visibility tests and eliminate inconsistent structures. In our experiments, our method could sufficiently reconstruct pipe structures using only a single scanning data.

High-precision vision-based mobile augmented reality system for context-aware architectural, engineering, construction and facility management (AEC/FM) applications

Abstract Background Many context-aware techniques have been proposed to deliver cyber-information, such as project specifications or drawings, to on-site users by intelligently interpreting their environment. However, these techniques primarily rely on RF-based location tracking technologies (e.g., GPS or WLAN), which typically do not provide sufficient precision in congested construction sites or require additional hardware and custom mobile devices. Method This paper presents a new vision-based mobile augmented reality system that allows field personnel to query and access 3D cyber-information on-site by using photographs taken from standard mobile devices. The system does not require any location tracking modules, external hardware attachments, and/or optical fiducial markers for localizing a user’s position. Rather, the user’s location and orientation are purely derived by comparing images from the user’s mobile device to a 3D point cloud model generated from a set of pre-collected site photographs. Results The experimental results show that 1) the underlying 3D reconstruction module of the system generates complete 3D point cloud models of target scene, and is up to 35 times faster than other state-of-the-art Structure-from-Motion (SfM) algorithms, 2) the localization time takes at most few seconds in actual construction site. Conclusion The localization speed and empirical accuracy of the system provides the ability to use the system on real-world construction sites. Using an actual construction case study, the perceived benefits and limitations of the proposed method for on-site context-aware applications are discussed in detail.

Generation of photorealistic 3D image using optical digitizer

A technique to generate a photorealistic three-dimensional (3D) image and color-textured model using a dedicated optical digitizer is presented. The proposed technique is started with the range and texture image acquisition from different viewpoints, followed by the registration and integration of multiple range images to get a complete and nonredundant point cloud that represents a real-life object. The accuracy of the range image and the precision of correspondence between the range image and texture image are guaranteed by sensor system calibration. Based on the point cloud, a geometric model is established by considering the connectivity of adjacent range image points. In order to enhance the photorealistic effect, we suggest a texture blending technique that utilizes a composite-weight strategy to blend the texture images within the overlapped region. This technique allows more efficient removal of the artifacts existing in the registered texture image, leading to a 3D image with photorealistic quality and color-texture modeling. Experimental results are also presented to testify to the validity of the proposed method.

Registration of Point Clouds Based on Differential Geometry of Surface’s Feature

A method for accurate registration on point clouds is presented in the paper. Manual alignment or the use of landmarks is avoided in the process of multi-view point clouds. Firstly, the differential geometric information is extracted from the point clouds. The extended Gaussian sphere and combination features are used to define the corresponding points of crude alignment. Secondly, the optimal algorithm，the point-to-point Iterated Closest Point, is applied to the accurate registration on point clouds. Thus, the complete point cloud can be obtained in the method.

Application of 3D Optical Measuring System in Auto Shock Absorber’s Quality Inspection

Auto shock absorbers, with helical surfaces together with multiple punching procedures needed in manufacturing processes, make the measurement and inspection difficult. TieNiu 3DOMS is adopted to acquire their surface data, the digital point cloud data of one single vision within 10s, automatically registering complete point cloud by rotary platform, so that the efficiency of process inspection and final quality control of the work pieces of complicated surfaces are improved. Experiments show that the surface inspection precision is better than 0.05mm, and one single surface inspection within minutes.