A Point Cloud Registration Method Based on Point-to-Triangulation Estimation for Optical Window Free-Form Surfaces Testing by Coordinate Measuring Machine

Because of its high sensitivity to misalignment, precision grinding of free-form surfaces with micron accuracy requires accurate registration of the surface measurement point cloud. Registration of point clouds obtained with a coordinate measuring machine (CMM) is generally an iterative process of finding optimal coordinate transformation between the CMM frame and the model frame of the workpiece by minimizing the point-to-surface distances with probe radius compensation. For free-form surfaces, frequent calculation of point-to-surface distances consumes very much time, and a trade-off has to be made between the efficiency and the accuracy. This paper presents a method for fast registration of free-form surface point clouds based on the point-to-triangle distance which involves only Delaunay triangulation of a two-dimensional dataset, and the surface normal is quickly calculated from cross product. Probe radius compensation is realized by registering the probe center points with the offset surface. We prove that it is equivalent to registering the probe contact points with the nominal surface through theoretical analysis. The registration problem is then formulated as sequential linear least-square problems with properly defined ball constraints. To validate the method, numerical simulations are presented to show the accuracy of the point-to-triangle distance. The registration algorithm also shows excellent robustness against misalignment of tens of millimeters/degrees. Finally measurement, registration, and grinding of a free-form optical surface are experimentally demonstrated. The surface error obtained after registration is used for compensatory grinding which reduces it to micron level.

Coded light measurement is an important method of non-contact optical measurement. Because of the camera view limitations, it was hard to complete the measurement of the whole surface at a time. Then it needs registration of 3D point clouds. The registration of point clouds can reflect the 3D surface of the object to be measured, which can achieve the full range of requirements. According to the encoded optical 3D measurement system has been built in laboratory, exploring a method which is high accurate and high speed to reach a full range measurement to industrial parts. This paper describes the color target cube. According to the existing method of coordinate transformation (the Nearest Point Iterative Method), Evaluating the registration accuracy. The registration method based on auxiliary three-dimensional targets was been validated, which has its own rationality.

Freeform optical components (FOCs) can effectively simplify optical system configuration and improve optical preforms, they can be applied on imaging system, illumination system and phase compensation system. Scattered sagittal points reported by coordinate measuring machine (CMM) often exist uncertainty and makes contour error inaccurate. A new model which subtracts CMM date from enveloping surface calculated by theoretical equation includes tilt, rotation and shift. Experiments result shows this method introduced by this article could efficiently analysis scattered CMM data and PV (Peak-to-valley) less than 2 μm which matched accuracy of CMM (1.8+ L/300 μm) and accuracy of fabrication error of SPDT.

Terrestrial laser scanners have achieved great popularity in the last decade. Their easy on-site application and the possibility of flexible and high quality post processing added to their success in several fields such as architectural, archaeological, and heritage documentation. We present a method for handling the automatic registration of point clouds which are characterized by significant noise level, generally imperfect geometry and occlusions. Hereby we combine and extend already existing and established methods to facilitate the registration of point clouds without prior pre-processing. Our approach consists—similar to other methods—of three steps which are scan analysis, pair-wise registration, and global registration. To handle the abovementioned datasets we propose to use imperfect and subdivided features, and to implement Genetic Algorithms (GAs). At the same time our approach can be seen as extension to already known Genetic Algorithms used for the registration of point clouds. By implementing an adapted version of a Genetic Algorithm in the classical registration process between rough alignment and fine registration, we are able to maintain robustness and computational performance also when registering point clouds of bigger objects characterized by a notably increased number of points, a significant noise level, and occlusions. We show and discuss the successful application of the algorithm on a scene which does not consist of classical geometric primitives such as planes.

Abstract. The registration of ground laser point cloud and close-range image is the key content of high-precision 3D reconstruction of cultural relic object. In view of the requirement of high texture resolution in the field of cultural relic at present, The registration of point cloud and image data in object reconstruction will result in the problem of point cloud to multiple images. In the current commercial software, the two pairs of registration of the two kinds of data are realized by manually dividing point cloud data, manual matching point cloud and image data, manually selecting a two – dimensional point of the same name of the image and the point cloud, and the process not only greatly reduces the working efficiency, but also affects the precision of the registration of the two, and causes the problem of the color point cloud texture joint. In order to solve the above problems, this paper takes the whole object image as the intermediate data, and uses the matching technology to realize the automatic one-to-one correspondence between the point cloud and multiple images. The matching of point cloud center projection reflection intensity image and optical image is applied to realize the automatic matching of the same name feature points, and the Rodrigo matrix spatial similarity transformation model and weight selection iteration are used to realize the automatic registration of the two kinds of data with high accuracy. This method is expected to serve for the high precision and high efficiency automatic 3D reconstruction of cultural relic objects, which has certain scientific research value and practical significance.

Traditionally, control of manufactured threaded surfaces is carried out using gauges. However, in single-piece or small-scale production of parts with non-standard threads, the production of expensive special gauges becomes unprofitable. The trend of recent decades, associated with a reduction in serial production, has led to the increasingly widespread use of universal measuring equipment and, above all, coordinate measuring machines (CMM). To control metric or buttress cylindrical threads, the accuracy of which, according to the standard, is determined using the virtual pitch thread diameter, a number of methods have being developed to calculate this diameter using point clouds from CMMs. In the case of tapered threads, their accuracy must be determined by the position of their planes of handtight engagement. This position, namely: the distance from this plane to the end plane of the threaded part has to be within the limits specified by the tolerance. To calculate that distance, the presented paper proposes to use two contiuous ideal helical surfaces adjacent to the point cloud obtained from the CMM. These two surfaces together, like a ring gauge with a given virtual pitch thread diameter, define the desired plane. The calculations of the point cloud for the case of an inch tapered thread, performed using the proposed method, showed that the distance of the axial position of the plane corresponds to the values specified in the standard. At the same time, the has established that the accuracy of calculations significantly depends on the accuracy of determining the coordinates of the thread points. An acceptable accuracy calculation (0.2%) was obtained when determining the thread points coordinates with an accuracy of 5 µm, which is achievable using some of optical CMMs. The proposed approach can also be applied for the case of screwing an external taperedthreads with an internal cylindrical thread. Thus, the research results presented in this article have prospects and require further improvement, for example, in order to determine the axis of ideal screw surfaces adjacent to the point cloud of real threads

Partial point cloud registration is an essential and fundamental component of generating complete 3D shapes, aiming at converting partial scans into a unified coordinate system. However, existing point cloud registration methods suffer from inadequately rich local feature extraction and feature interaction. In addition, these methods still face challenges in modeling the global contextual information of point clouds sufficiently, which limits the improvement in registration effectiveness, especially for partial-to-partial point cloud registration with high noise. To overcome these issues, this paper proposes an enhanced PointNet and assignable weights transformer network (LFA-Net) for partial point cloud registration. The model achieves coarse-to-fine point cloud registration through three core modules. First, the Sufficient Local Feature Extraction Module (LFM) is constructed to extract various local feature information. Then, the Adequate Feature Aggregation Module (FAM) is designed to integrate the feature information from different point clouds. Finally, the Assignable Weights Transformer Module (ATM) is presented to stimulate the model’s global modeling ability during the registration process, enabling the selection of representative points for optimal point cloud registration. Extensive experiments conducted on ModelNet40 using partially overlapping point clouds illustrate the superior registration performance of LFA-Net compared with other state-of-the-art methods. Moreover, Numerous experiments on synthetic and real-word datasets further indicate that LFA-Net also has significant advantages in registering partial point clouds with noise and unseen categories, which demonstrates its excellent robustness and generalization ability for real-world practical application.

With the widespread application of large-scale 3D point cloud data in real-world scenarios, efficient and accurate point cloud registration has become a crucial challenge. We propose an end-to-end point cloud registration method based on the Transformer architecture. This method addresses the issues of low overlap and registration in large scenes, exhibiting strong algorithmic versatility and efficiency. We introduce a combination of dynamic position encoding and ternary angular position encoding within the Transformer, effectively enhancing the representation capability of point cloud data and algorithmic generality, thus better tackling point cloud registration challenges in large scenes. Additionally, to enhance the learning capacity of the attention mechanism, we employ an improved cross-attention mechanism that multiplies the softmax with adaptive weights, enabling the model to capture key information within the point cloud more accurately. In the decoding stage, we introduce a multi-scale feature fusion approach that fully exploits the multi-layer information in point cloud data, further improving registration accuracy and robustness. Through the fusion of multi-scale features, we effectively mitigate information loss and handle matching problems between point clouds of varying sizes. Experimental results demonstrate the excellence of our method in addressing low overlap and registration tasks in large scenes, validated across multiple datasets including 3DMatch, ModelNet, KITTI, and MVP-RG.

The development of the co-ordinate measuring machine (CMM) dramatically improved metrology in manufacturing environments. As modern manufacturing techniques have developed to supply products with ever decreasing dimensions, which depend on more accurate geometric specifications of micro-scale parts, developers of CMMs have responded accordingly. Initially, micro-scale probes for CMMs were developed and then, following further demand for micro-scale measurements, dedicated CMM platforms (micro-CMMs) were designed. The effectiveness of these micro-CMMs depends entirely on their ease of use and the access afforded by the probes to high aspect ratio features on micro-scale parts. The National Physical Laboratory (NPL) has purchased a commercial micro-CMM and is using it to perform traceable measurements with the aim to better understand the limits of this technology. Initial work, both at NPL and around the world, has suggested that current micro-CMM platforms are capable of higher measurement accuracy than is achieved with existing probes. To address this imbalance, NPL has developed a micro-scale probe that promises to realise the full potential of micro-CMMs. This paper presents the current research being undertaken on the commercial micro-CMM at NPL along with the recent advances in probe development.

Point cloud registration is of great significance to the reconstruction of high‐precision 3D city models. There are some challenges when aligning aerial and close‐range photogrammetric point clouds, such as huge view differences caused by the different sights of the sensors, massive noisy points due to the error in dense matching, and scale uncertainty since there are no control points for absolute orientation. To achieve complementary advantages of aerial and close‐range point clouds, in this paper, a robust cross‐source point clouds registration method is proposed using image visual context features and scale consistency. First, a cross‐view image matching method based on image visual context features is proposed to obtain corresponding points. Second, to overcome the challenges of noisy points and scale differences, an outlier filtering method is designed based on scale consistency. Finally, the dual quaternions model considering the scale factor is introduced to solve the spatial transformation model. To analyze the feasibility of this method qualitatively and quantitatively, experiments are conducted using the public scene dataset of Dortmund, Germany, and the scene dataset of Zhengzhou City, China. Three kinds of cross‐source point clouds registration experiments are conducted in this paper, including aerial and close‐range point clouds registration in Dortmund, and aerial and ground point clouds registrations in both Dortmund and Zhengzhou. The chamfer distances of the three sets of experiments are 4.48 m, 5.97 m and 4.78 m, respectively. The ablation study shows that the outlier filtering method and the dual quaternions model improve the accuracy by at least 14% and 25%, respectively. The experiments demonstrate that the method accomplishes the cross‐source point clouds registration in large‐scale scenes accurately and efficiently, providing a solid foundation for subsequent fine 3D reconstruction.

The registration of 3-D point clouds is an important procedure during the terrestrial laser scanning data processing. Recently, due to their high flexibility and the powerful mathematical model, a large amount of least-squares-based (LSs-based) methods are proposed and widely applied to estimate the transformation parameters of 3-D point clouds registration. In these LSs-based methods some based on the generalized Gauss–Markov model do not correct the influence of random errors on source 3-D point clouds. Although there are other methods based on the errors-in-variables (EIV) model, they are inapplicable for transformation problems with large rotation angles and arbitrary scale ratio. In addition, the gross errors are usually ignored in previous studies on 3-D point clouds registration, which, however, exists commonly and could distort the registration severely. Aiming to avoid the influence of gross errors and extend its application, an advanced outlier detected total least-squares (OD-TLS) method is proposed in this paper. Based on the generalized EIV model OD-TLS performs a seven-parameter 3-D similarity transformation with large rotation angles and arbitrary scale ratio. The random errors of both source and target 3-D point clouds are considered. Furthermore, outliers are detected and removed automatically by combining the data snooping method with total least-squares (TLS) estimation. In order to indicate the benefits of OD-TLS, comparative experiments with the LS3D and weighted total least squares (WTLS) on synthetic and real-world scanned 3-D point clouds were performed. The experimental results show OD-TLS not only enhances the registration accuracy but also increases its robustness.

The challenges of point cloud registration in intelligent vehicle driving lie in the large scale, complex distribution, high noise, and strong sparsity of lidar point cloud data. This paper proposes an efficient registration algorithm for large-scale outdoor road scenes by selecting the continuous distribution of key area laser point clouds as the registration point cloud. The algorithm extracts feature descriptions of the key point cloud and introduces local geometric features of the point cloud to complete rough and fine registration under constraints of key point clouds and point cloud features. The algorithm is verified through extensive experiments under multiple scenarios, with an average registration time of 0.5831 s and an average accuracy of 0.06996 m, showing significant improvement compared to other algorithms. The algorithm is also validated through real-vehicle experiments, demonstrating strong versatility, reliability, and efficiency. This research has the potential to improve environment perception capabilities of autonomous vehicles by solving the point cloud registration problem in large outdoor scenes.

Analysis of footwear under the Harmonized Tariff Schedule of the United States (HTSUS) is partly based on identifying the boundary (parting line) between the external surface area upper (ESAU) and the sample's sole. Often, that boundary is obscured. We establish the parting line as the curved intersection between the sample outer surface and its insole surface. The outer surface is determined by discrete point cloud coordinates obtained using a laser scanner. The insole surface is defined by point cloud data, obtained using a touch probe device-a coordinate measuring machine (CMM). Because these point cloud data sets do not overlap spatially, a polynomial surface is fitted to the insole data and extended to intersect a mesh fitted to the outer surface point cloud. This line of intersection defines the ESAU boundary, permitting further fractional area calculations to proceed. The defined parting line location is sensitive to the polynomial used to fit experimental data. Extrapolation to the intersection with the ESAU can heighten this sensitivity. We discuss a methodology for transforming these data into a common reference frame. Three scenarios are considered: measurement error in point cloud coordinates, from fitting a polynomial surface to a point cloud then extrapolating beyond the data set, and error from reference frame transformation. These error sources can influence calculated surface areas. We describe experiments to assess error magnitude, the sensitivity of calculated results on these errors, and minimizing error impact on calculated quantities. Ultimately, we must ensure that statistical error from these procedures is minimized and within acceptance criteria.

During the process of the point cloud registration, the problem that the accuracy is not high is due to the unknown relative position of the multi-view point clouds and the diversity of the target structure. This paper proposes a point cloud registration method based on extracting overlapping regions to solve it. First of all, according to the characteristics of the target geometric structure, each point cloud is divided into blocks and the ESF multi-dimensional shape descriptors of each point cloud block are constructed, the region with the greatest similarity between the descriptors, namely the overlapping region between the point clouds. Then our algorithm uses the Super Four Point Fast Robust Matching (Super4PCS) algorithm to execute a coarse registration of point clouds in overlapping regions, the initial positions of the point clouds after the coarse registration are obtained according to the consistency constraints, and finally uses the iterative closest point algorithm (ICP) to precisely register the overlapping region and obtain the final point cloud model. Compared with traditional Super4PCS algorithm, the experimental results show that the method proposed in this paper effectively improves the accuracy and accelerates the process of point cloud registration.

Scanning electron microscopy (SEM) has an important application in the petroleum field, which is often used to analyze the microstructure of reservoir rocks, etc. Most of these analyses are based on two-dimensional images. In fact, SEM can carry out micro-nano scale three-dimensional measurement, and three-dimensional models can provide more accurate information than two-dimensional images. Among the commonly used SEM 3D reconstruction methods, parallax depth mapping is the most commonly used method. Multiple SEM images can be obtained by continuously tilting the sample table at a certain Angle, and multiple point clouds can be generated according to the parallax depth mapping method, and a more complete point clouds recovery can be achieved by combining the point clouds registration. However, the root mean square error of the point clouds generated by this method is relatively large and unstable after participating in point clouds registration. Therefore, this paper proposes a new method for generating point clouds. Firstly, the sample stage is rotated by a certain angle to obtain two SEM images. This operation makes the rotation matrix a known quantity. Then, based on the imaging model, an equation system is constructed to estimate the unknown translation parameters, and finally, triangulation is used to obtain the point clouds. The method proposed in this paper was tested on a publicly available 3D SEM image set, and the results showed that compared to the disparity depth mapping method, the point clouds generated by our method showed a significant reduction in root mean square error and relative rotation error in point clouds registration.