MPCR-Net: Multiple Partial Point Clouds Registration Network Using a Global Template

With advancements in photoelectric technology and computer image processing technology, the visual measurement method based on point clouds is gradually being applied to the 3D measurement of large workpieces. Point cloud registration is a key step in 3D measurement, and its registration accuracy directly affects the accuracy of 3D measurements. In this study, we designed a novel MPCR-Net for multiple partial point cloud registration networks. First, an ideal point cloud was extracted from the CAD model of the workpiece and used as the global template. Next, a deep neural network was used to search for the corresponding point groups between each partial point cloud and the global template point cloud. Then, the rigid body transformation matrix was learned according to these correspondence point groups to realize the registration of each partial point cloud. Finally, the iterative closest point algorithm was used to optimize the registration results to obtain the final point cloud model of the workpiece. We conducted point cloud registration experiments on untrained models and actual workpieces, and by comparing them with existing point cloud registration methods, we verified that the MPCR-Net could improve the accuracy and robustness of the 3D point cloud registration.

EVALUATION OF 3D BUILDING MODEL USING TERRESTRIAL LASER SCANNING AND DRONE PHOTOGRAMMETRY

The objective of our study is the evaluation of the 3D modeling of buildings and the extraction of structural elements from point clouds obtained using two acquisition techniques (drone and terrestrial laser scanner), as well as the evaluation of the usefulness of their integration. The drone shooting mission was carried using the DJI Phantom 3 Professional and the Sony EXMOR 1/2.3" CMOS RGB camera. For the TLS scanning mission, 9 scanning stations were performed using the FARO Focus S350 laser scanner.To allow the fusion of the two point clouds obtained from drone imagery and TLS, an alignment step is applied. This step was performed using the Iterative Closest Point algorithm. Segmentation was performed using the adapted RANSAC algorithm on point clouds obtained from the drone mission and the TLS mission as well as on the merged point cloud in order to extract structural elements of the building such as windows, doors and stairs. Analysis of the results emphasizes the importance of TLS and drone in 3D modeling. TLS gave better results than the drone in extracting structural elements. This work confirms the importance of complementarity between these two technologies to produce detailed, complete and precise 3D models.

A Novel Digital Technique for Measuring the Accuracy of an Indirect Bonding Technique Using Fixed Buccal Multibracket Appliances

The aim of this study was to analyze the accuracy and predictability of the indirect bonding technique of fixed buccal multibracket appliances using a customized iterative closest point algorithm. Materials and Methods: A total of 340 fixed buccal multibracket appliances were virtually planned and bonded on 34 experimental anatomically based acrylic resin models by using orthodontic templates designed and manufactured to indirectly bond the fixed buccal multibracket appliances. Afterwards, the models were submitted to a three-dimensional impression technique by an intraoral scanner, and the standard tessellation language digital files from the virtual planning and the digital impression were aligned, segmented, and realigned using morphometric software. Linear positioning deviations (mm) of the fixed buccal multibracket appliances were quantified at mesio-distal, bucco-lingual/palatal, and gingival/occlusal (vertical) planes, and angular deviations (°) were also recorded by analyzing the torque, tip, and rotation using a customized iterative closest point algorithm, the script for which allowed for an accuracy measurement procedure by comparing the tessellation network positioning of both standard tessellation language digital files. Results: The mean mesio-distal deviation was −0.065 ± 0.081 mm, the mean bucco-lingual/palatal deviation was 0.129 ± 0.06 m, the mean vertical deviation was −0.094 ± 0.147 mm, the mean torque deviation was −0.826 ± 1.721°, the mean tip deviation was −0.271 ± 0.920°, and the mean rotation deviation was −0.707 ± 0.648°. Conclusion: The indirect bonding technique provides accurate and predictable positioning of fixed buccal multibracket appliances.

MPCR-Net: Multiple Partial Point Clouds Registration Network Using a Global Template

With the advancement of photoelectric technology and computer image processing technology, the visual measurement method based on point clouds is gradually applied to the 3D measurement of large workpieces. Point cloud registration is a key step in 3D measurement, and its registration accuracy directly affects the accuracy of 3D measurements. In this study, we designed a novel MPCR-Net for multiple partial point cloud registration networks. First, an ideal point cloud was extracted from the CAD model of the workpiece and was used as the global template. Next, a deep neural network was used to search for the corresponding point groups between each partial point cloud and the global template point cloud. Then, the rigid body transformation matrix was learned according to these correspondence point groups to realize the registration of each partial point cloud. Finally, the iterative closest point algorithm was used to optimize the registration results to obtain a final point cloud model of the workpiece. We conducted point cloud registration experiments on untrained models and actual workpieces, and by comparing them with existing point cloud registration methods, we verified that the MPCR-Net could improve the accuracy and robustness of the 3D point cloud registration.

Point Cloud Approach For Modelling The Lost Volume of The Fillaboa Bridge Cutwater

The digitisation of heritage is being rapidly realised in many parts of the world thanks to LiDAR technology. In addition to the simple digital preservation of heritage, 3D acquisition makes it possible to monitor the structural condition and assess possible damage. This paper presents a method for modelling the lost volume of a heritage bridge. The selected case study is the Fillaboa bridge, in Salvaterra de Miño, Spain, which has two cutwaters with the same cutting angle, one of which is damaged and has a stone loss. The bridge was acquired with a Terrestrial Laser Scanner. The method consists of the following processes. First, the walls of the whole cutwater are segmented and aligned by Iterative Closest Point algorithm over the damaged cutwater. Second, the distance between the two point clouds is calculated and the damaged area is delimited in both point clouds. And third, the alpha shape algorithm is applied to model the point cloud of the damaged area to a polygon. By searching for the optimal alpha radius, the polygon that best fits the damaged volume is generated. The proposed method also allows digital reconstruction of the damaged area, although it is sensitive to acquisition problems, which require manual interventions in the processing. The accuracy of the method is mainly dependent on the acquired point cloud registration (with an RMS error of 60mm) and the ICP registration error (31mm). Its use is limited to the existence of two geometries that allow superposition: one in good condition and one damaged to compare.

Real-time limb tracking in single depth images based on circle matching and line fitting

Modern lower limb prostheses neither measure nor incorporate healthy residual leg information for intent recognition or device control. In order to increase robustness and reduce misclassification of devices like these, we propose a vision-based solution for real-time 3D human contralateral limb tracking (CoLiTrack). An inertial measurement unit and a depth camera are placed on the side of the prosthesis. The system is capable of estimating the shank axis of the healthy leg. Initially, the 3D input is transformed into a stabilized coordinate system. By splitting the subsequent shank estimation problem into two less computationally intensive steps, the computation time is significantly reduced: First, an iterative closest point algorithm is applied to fit circular models against 2D projections. Second, the random sample consensus method is used to determine the final shank axis. In our study, three experiments were conducted to validate the static, the dynamic and the real-world performance of our CoLiTrack approach. The shank angle can be tracked at 20 Hz for one sixth of the entire human gait cycle with an angle estimation error below $$2.8\pm 2.1^{\circ }$$ 2.8 ± 2 . 1 ∘ . Our promising results demonstrate the robustness of the novel CoLiTrack approach to make “next-generation prostheses” more user-friendly, functional and safe.

An efficient LiDAR-based localization method for self-driving cars in dynamic environments

Real-time localization is an important mission for self-driving cars and it is difficult to achieve precise pose information in dynamic environments. In this paper, a novel localization method is proposed to estimate the pose of self-driving cars using a 3D-LiDAR sensor. First, the multi-frame curb features and laser intensity features are extracted. Meanwhile, based on the high-precision curb map generated offline, obstacles on road are detected using region segmentation methods and their features are removed. Furthermore, a map-matching method is proposed to match the features to the map, a robust iterative closest point algorithm is utilized to deal with curb features along with a probability search method dealing with intensity features. Finally, two separate Kalman filters are used to fuse the low-cost global positioning systems and map-matching results. Both offline and online experiments are carried out in dynamic environments and the results demonstrate the accuracy and robustness of the proposed method.