Search

The array interferometric synthetic aperture radar (Array InSAR) system resolves shadow issues by employing two scans in opposite directions, facilitating the acquisition of a comprehensive three-dimensional representation of the observed scene. The point clouds obtained from the two scans need to be transformed into the same coordinate system using registration techniques to create a more comprehensive visual representation. However, the two-point clouds lack corresponding points and exhibit distinct geometric distortions, thereby preventing direct registration. This paper analyzes the error characteristics of array InSAR point clouds and proposes a robust registration method for array InSAR point clouds in urban scenes. It represents the 3D information of the point clouds using images, with pixel positions corresponding to the azimuth and ground range directions. Pixel intensity denotes the average height of points within the pixel. The KAZE algorithm and enhanced matching approach are used to obtain the homonymous points of two images, subsequently determining the transformation relationship between them. Experimental results with actual data demonstrate that, for architectural elements within urban scenes, the relative angular differences of registered facades are below 0.5°. As for ground elements, the Root Mean Square Error (RMSE) after registration is less than 1.5 m, thus validating the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Within this study, we present a pioneering cross-platform point cloud registration (PCR) framework aimed at the automated alignment of UAV and terrestrial forest LiDAR point clouds. This framework leverages canopy profile skyline (CPS) descriptors and feature orientation information to support registration. Given the inherent irregular and natural distribution of point clouds derived from crown environments, conventional registration techniques that operate on geometric primitives such as points, lines, and planes are susceptible to inadequacies. In this article, we first analyze the high resolution and robustness of a skyline formed from successive tree canopy profiles as feature elements in forest data. Subsequently, we highlight the intriguing property that, whether they are UAV-based or terrestrial-based scanning data, the tree CPS obtained from the same location exhibits remarkably similar shapes and trends. Therefore, we propose a novel feature descriptor that encompasses M dimensions across 8 directions, with the aim of establishing reliable feature correspondences, subsequently leveraging directional constraints and employing one-shot estimation RANSAC to achieve automated UAV and terrestrial-based forest point cloud registration. Finally, the standard ICP algorithm combined with the constraint strategy based on tree trunk geometric morphological features will be utilized to refine the registration results. We conducted tests using five datasets with heterogeneous tree species and structures, and the results demonstrate that the proposed approach achieves SOTA performance.

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. [ABSTRACT FROM AUTHOR]

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.

The array interferometric synthetic aperture radar (Array InSAR) system resolves shadow issues by employing two scans in opposite directions, facilitating the acquisition of a comprehensive three-dimensional representation of the observed scene. The point clouds obtained from the two scans need to be transformed into the same coordinate system using registration techniques to create a more comprehensive visual representation. However, the two-point clouds lack corresponding points and exhibit distinct geometric distortions, thereby preventing direct registration. This paper analyzes the error characteristics of array InSAR point clouds and proposes a robust registration method for array InSAR point clouds in urban scenes. It represents the 3D information of the point clouds using images, with pixel positions corresponding to the azimuth and ground range directions. Pixel intensity denotes the average height of points within the pixel. The KAZE algorithm and enhanced matching approach are used to obtain the homonymous points of two images, subsequently determining the transformation relationship between them. Experimental results with actual data demonstrate that, for architectural elements within urban scenes, the relative angular differences of registered facades are below 0.5°. As for ground elements, the Root Mean Square Error (RMSE) after registration is less than 1.5 m, thus validating the superiority of the proposed method.

The point clouds registration is a key step in data processing for the 3D laser scanner to obtain complete information of the object surface, and there are many algorithms. In order to overcome the disadvantages of slow calculation speed and low accuracy of existing point clouds registration algorithms, a fast point clouds registration algorithm based on the improved voxel filter and ISS-USC feature is proposed. Firstly, the improved voxel filter is used for down-sampling to reduce the size of the original point clouds data. Secondly, the intrinsic shape signature (ISS) feature point detection algorithm is used to extra feature points from the down-sampled point clouds data, and then the unique shape context (USC) descriptor is calculated to describe the extracted feature points. Next, the improved random sampling consensus (RANSAC) algorithm is used for coarse registration to obtain the initial position. Finally, the iterative closest point (ICP) algorithm based on KD tree is used for fine registration, which realizes the transform from the point clouds scanned by the 3D laser scanner at different angles to the same coordinate system. Through comparing with other algorithms and the registration experiment of the VGA connector for monitor, the experimental results verify the effectiveness and feasibility of the proposed algorithm, and it has fastest registration speed while maintaining high registration accuracy. [ABSTRACT FROM AUTHOR]

The main solution for large-scale point clouds registration is to first obtain a set of matched 3D keypoint pairs and then accomplish the point cloud registration task based on these matched keypoint pairs. However, at present, many methods study the feature descriptors in the point clouds registration task, but few methods discuss the 3D keypoints detection issues. The commonly used 3D keypoints detection strategy is the voxel-grid-based downsampling method, and the detected 3D keypoints are usually with a relatively huge amount and also with no explicit geometrical properties, which finally leads to a low inlier ratio. In this study, we rethink the 3D keypoints detection problem for large-scale point clouds with deep learning. Specifically, we discuss four kinds of 3D keypoints detection methods based on the joint keypoint detection and description learning framework D3Feat, and carry out extensive analyses on both the indoor large-scale point clouds dataset 3DMatch and the outdoor large-scale point clouds dataset KITTI Odometry. Experimental results demonstrate that the Multi-layer Perceptron (MLP) based method achieves the best inlier ratios under the different numbers of extracted 3D keypoints on both the indoor and outdoor large-scale point clouds. Further, we test these four kinds of keypoints detection methods under the application of large-scale point clouds registration, and the MLP-based method also achieves the state-of-the-art registration performance.

Registration of partially overlapping, featureless three-dimensional (3D) point sets with noise is a difficult problem in the applications of large-scale metrology (LSM). Existing approaches use the sparse iterative closest points (SICP) method applying the l p norm to decrease the influence of outliers during registration. However, we reveal in this study that sparse point-to-point becomes easily trapped into local minima in featureless point clouds registration, and the error landscape of sparse point-to-plane is too shallow to restrain the sliding due to the lack of constraints in the large flat areas. Also, point clouds sampled from the large flat areas cause the low-rank matrix of linear equation in estimating the transformation matrix. Hence, we propose using the point-to-point l p distance constraints to restrain the sliding along large flat areas. We further define a weighted enhanced l p distance (WELD) error metric to slacken the constraints and escape from the local minima. Moreover, WELD can improve stability with the full-rank linear equation in estimating the transformation matrix. To verify the capability of escaping from the local minima and restraining the sliding, we choose SICP and two other algorithms to be compared with our method in simulated and actual point clouds. The comparisons show that our method successfully can escape from the local minima and restrain the sliding to handle outliers and noisy featureless point clouds effectively. The source code is available at https://github.com/Timbersaw-wangzw/WES-ICP. [ABSTRACT FROM AUTHOR]

Array interferometric synthetic aperture radar (Array InSAR) has a 3-D resolution capability and solves the layover problem in interferometric SAR (InSAR) by arranging multiple antennas in the cross-orbit direction. Airborne Array InSAR point clouds are obtained from two scans for complete building information in urban areas, resulting in very low overlapping point cloud. The existing methods are difficult to extract the identical features for the registration of Array InSAR point clouds. To this end, a robust registration approach Array InSAR point clouds in urban areas is proposed in this study. The main contribution of this article is raising the theoretically optimal transformation for achieving point cloud registration, considering the constraint from parallel facades of a certain building. Point density estimation is adopted to retain building facade points for initial registration. The facade pairs of a specific building are then matched and divided into two categories by judging whether one contains the concave–convex features or not, for performing rotation rectification and fine shift fixation, respectively. Experimental results of both simulated and real data validate the feasibility and reliability of our approach. For the simulated data, the results reach an average rotation error of about 0.01° and an average translation error of less than 0.8 m. For the real data, two evaluation criteria are designed for the lack of reference data. The results reach an average of 0.4° of the defined angle difference and less 0.8-m distance difference from the source facades center to the normal extension of the target facades. [ABSTRACT FROM AUTHOR]

Joint registration of point clouds obtained from multiple views is a key step of reconstruction for blades. However, due to the structural and surface characteristics of blades, some views do not meet the overlap constraints of registration, which results in significant initial errors of pose estimation. Thus, we propose a novel approach to recover the accuracy of poses estimation. The proposed method is robust to overlap extent of views through a stochastic framework. The approach formulates a variable-parameters graph optimization problem. Then a simulated annealing algorithm is used to solve the global optimal parameters. The candidate parameters in the simulated annealing process are obtained through the improved unscented Kalman filter, which reduces the initial errors and enhances the information matrices. The acceptance of the candidate parameters is determined by the optimization problem constrained by joint point correspondences and closed-loop consistency. And the parameters that can improve the registration accuracy are selected. We test our algorithm with simulated synthetic data and real data obtained by the robot measurement system. We compare the proposed algorithm with several state-of-the-art algorithms. The experimental results show that in the presence of significant initial errors, our method can estimate the poses more accurately and obtain better blade reconstructions. [ABSTRACT FROM AUTHOR]

The concept of obtaining uniform and high-precision 3D models of large and complex engineering structures based on terrestrial laser scanning surveys highly depends on the accuracy of registering a series of adjacent scans. If it is not possible to place the required number of reference points in the observation scene, and there are no identifiable structural elements that could serve as such reference points, the issue can be solved by using the virtual reference points. The virtual reference points are not marked, but their position can be re-created in the post-processing of the scans on the basis of the geometrical features of the object. In the case of an industrial chimney, these can be the structural axis points determined at arbitrarily selected altitudes. In the solution presented herein, the coordinates of the reference points were independently determined twice: in the geodetic coordinate system using traditional method, in the local system of the scanner using the bisector of the chord of the circle, and by approximation of the circular arc shape of the chimney shell. Both procedures are preceded by the filtration of outliers. The concept of virtual reference points was verified by the measurement of the verticality of the axis and the structural geometry of a steel industrial chimney, which was accessible only on the foundation level.

The point clouds registration is a key step in data processing for the 3D laser scanner to obtain complete information of the object surface, and there are many algorithms. In order to overcome the disadvantages of slow calculation speed and low accuracy of existing point clouds registration algorithms, a fast point clouds registration algorithm based on the improved voxel filter and ISS-USC feature is proposed. Firstly, the improved voxel filter is used for down-sampling to reduce the size of the original point clouds data. Secondly, the intrinsic shape signature (ISS) feature point detection algorithm is used to extra feature points from the down-sampled point clouds data, and then the unique shape context (USC) descriptor is calculated to describe the extracted feature points. Next, the improved random sampling consensus (RANSAC) algorithm is used for coarse registration to obtain the initial position. Finally, the iterative closest point (ICP) algorithm based on KD tree is used for fine registration, which realizes the transform from the point clouds scanned by the 3D laser scanner at different angles to the same coordinate system. Through comparing with other algorithms and the registration experiment of the VGA connector for monitor, the experimental results verify the effectiveness and feasibility of the proposed algorithm, and it has fastest registration speed while maintaining high registration accuracy.

Point clouds registration is an important step for laser scanner data processing, and there have been numerous methods. However, the existing methods often suffer from low accuracy and low speed when registering large point clouds. To meet this challenge, an improved iterative closest point (ICP) algorithm combining random sample consensus (RANSAC) algorithm, intrinsic shape signatures (ISS), and 3D shape context (3DSC) is proposed. The proposed method firstly uses voxel grid filter for down-sampling. Next, the feature points are extracted by the ISS algorithm and described by the 3DSC. Afterwards, the ISS-3DSC features are used for rough registration with the RANSAC algorithm. Finally, the ICP algorithm is used for accurate registration. The experimental results show that the proposed algorithm has faster registration speed than the compared algorithms, while maintaining high registration accuracy. [ABSTRACT FROM AUTHOR]

This article is focused on a challenging topic emerging from the registration of point clouds, specifically the registration of dynamic objects with low overlapping ratio. This problem is especially difficult when the static scanner is installed on a floating platform, and the objects it scans are also floating. These issues make most of the automatic registration methods and software solutions invalid. To solve this problem, explicit exploration of the static region is necessary for both the coarse and fine registration steps. Fortunately, determining the corresponding regions can be eased by the intuitive realization that in urban environments, natural objects neither present straight boundaries nor stack vertically. This intuition has guided the authors to develop a robust approach for the detection of static regions using planar structures. Then, silhouettes of the objects are extracted from the planar structures, which assist in the determination of an SE(2) transformation in the horizontal direction by a novel line matching method. The silhouettes also enable identification of the correspondences of planes in the step of fine registration using a variant of the iterative closest point method. Experimental evaluations using point clouds of cargo ships with different sizes and shapes reveal the robustness and efficiency of the proposed method, which gives 100% success and reasonable accuracy in rapid time, suitable for an online system. In addition, the proposed method is evaluated systematically with regard to several practical situations caused by the floating platform, and it demonstrates good robustness to limited scanning time and noise. [ABSTRACT FROM AUTHOR]

Intra-operative ultrasound (US) is a popular imaging modality for its non-radiative and real-time advantages. However, it is still challenging to perform an interventional procedure under two-dimensional (2-D) US image guidance. Accordingly, the trend has been to perform three-dimensional (3-D) US image guidance by equipping the US probe with a spatial position tracking device, which requires accurate probe calibration for determining the spatial position between the B-scan image and the tracked probe. In this report, we propose a novel probe spatial calibration method by developing a calibration phantom combined with the tracking stylus. The calibration phantom is custom-designed to simplify the alignment between the stylus tip and the B-scan image plane. The spatial position of the stylus tip is tracked in real time, and its 2-D image pixel location is extracted and collected simultaneously. Gaussian distribution is used to model the spatial position of the stylus tip and the iterative closest point-based optimization algorithm is used to estimate the spatial transformation that matches these two point sets. Once the probe is calibrated, its trajectory and the B-scan image are collected and used for the volume reconstruction in our freehand 3-D US imaging system. Experimental results demonstrate that the probe calibration approach results in less than 1-mm mean point reconstruction accuracy. It requires less than 5 min for an inexperienced user to complete the probe calibration procedure with minimal training. The mockup test shows that the 3-D images are geometrically correct with 0.28°-angle accuracy and 0.40-mm distance accuracy. [ABSTRACT FROM AUTHOR]

Point clouds registration is an important step for laser scanner data processing, and there have been numerous methods. However, the existing methods often suffer from low accuracy and low speed when registering large point clouds. To meet this challenge, an improved iterative closest point (ICP) algorithm combining random sample consensus (RANSAC) algorithm, intrinsic shape signatures (ISS), and 3D shape context (3DSC) is proposed. The proposed method firstly uses voxel grid filter for down-sampling. Next, the feature points are extracted by the ISS algorithm and described by the 3DSC. Afterwards, the ISS-3DSC features are used for rough registration with the RANSAC algorithm. Finally, the ICP algorithm is used for accurate registration. The experimental results show that the proposed algorithm has faster registration speed than the compared algorithms, while maintaining high registration accuracy.

Abstract Three-dimensional (3D) model is a major form of cultural heritage documentation. In most cases, the properties of digital artefacts (e.g. readability, coverage) are affected by the acquisition procedure (e.g. device, workflow, conditions) and the characteristics of the physical artefact (e.g. shape, size and materials). In this paper, we study how to combine two acquisition techniques to acquire detailed 3D models of large physical objects. Specifically, we combine two laser scanning instruments: terrestrial laser scanning (TLS) and structured light scanner (SLS). TLS provides millimeter-scale resolution with large field of view, while SLS provides sub-millimeter resolution for limited field of view. This paper focuses on the registration of SLS and TLS point clouds, a critical step, which aims at aligning the acquired point clouds in a common frame. Existing registration systems mostly rely on manual post-processing or marker-based alignment. Manual registration is however time consuming and tedious, while markers increase the complexity of scanning and are not always acceptable in cultural site documentation. Therefore, we propose an automated markerless registration and fusion pipeline for point clouds. Firstly, we replace the marker-based coarse alignment by an automated registration of SLS and TLS point clouds; secondly, we refine the alignment of SLS point clouds on TLS data using the Iterative Corresponding Point algorithm; finally, we seamless stitch the SLS and TLS point clouds by globally regularizing the registration error for the all the point clouds at once. Our experiments show the efficiency of the proposed approach on two real-world cases, involving detailed point clouds correctly aligned without requiring markers or manual tuning. This paper provides an operational process reference for automated markerless registration of multi-source point clouds. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Image & Graphics is the property of Editorial Office of Journal of Image & Graphics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The high precision terrestrial LiDAR point clouds registration is the key to ensure 3D topological reconstruction of spatial objects. The reference LiDAR point clouds and the LiDAR point clouds to be registered have the issue of different position, attitude and scale. A registration method based on line cluster is proposed. Firstly, line cluster of the reference LiDAR point clouds and the LiDAR point clouds to be registered is respectively established according to the topological relation of lines. Then conjugate lines of point clouds are represented in Plücke coordinate. Through the spiral zooming motion of line cluster of the LiDAR point clouds to be registered， finally the scale of the two LiDAR point clouds is the same and the conjugate Plücke lines coincide with each other. The collinearity equations based on line cluster can solve the scale factor and the relative position and attitude simultaneously with high precision. The experimental results show that，the spiral and scaling of the line cluster enhances the geometric constraints of the registration equation，improves the ability of anti-noise， and realizes the high precision of terrestrial LiDAR point clouds registration. [ABSTRACT FROM AUTHOR]

Thee-dimensional (3-D) laser active imaging technology is widely used in military and civilian applications for its advantages of high energy density, coherence and directionality. 3-D pose estimation using laser array imaging system involves finding the rotation and translation between image point clouds and model point clouds. Owing to limitations such as detector technology and other factors, it is difficult to achieve high-resolution 3-D laser imaging systems. This article introduces a laser array imaging simulation method, and the pose estimation process is simulated. In the simulation, different range and spatial resolutions of the imaging system are discussed to analyse the influence of these imaging system factors on the accuracy of 3-D pose estimation. The results show that for a target size of 4 x 4 x 6 m³, the accuracy of orientation estimation could be 0.10° when the ranging resolution of the system is 0.35 m. For a ranging resolution of 1.50 m and spatial resolution of 0.15 m, the accuracy of orientation estimation could be 0.50°. The simulation results further show that the impact of the range resolution on the accuracy of pose estimation is greater than that of the spatial resolution. We can, therefore, reduce the spatial resolution and increase the range resolution of the imaging system to improve the accuracy of the pose estimation for certain special cases. The results of this analysis could be used in the design of systems with different needs, which makes the design more efficient in terms of the use of resources. The simulation method and analysis can be used in other applications, such as point clouds registration and lidar target recognition based on point clouds-model matching. [ABSTRACT FROM AUTHOR]

• An optical-markers-assisted registration (OMAR) is proposed for panoramic 3D shape measurement. • A markers design strategy is proposed to generate the desired optical markers according to different requirements. • A markers-guided algorithm is correspondingly designed to make full use of the optical markers to recover the 360 ∘ 3D shape. • Experiments demonstrate that our OMAR can achieve flexible, non-destructive, high-efficiency and high-accuracy panoramic 3D shape measurement. [Display omitted] Panoramic 3D shape measurement for larger-scale and complex objects inevitably requires multi-view point clouds registration. However, traditional registration methods often use expensive devices or inflexible physical markers. In this paper, a novel optical-markers-assisted registration (OMAR) method is proposed, which substitutes traditional physical markers with optical markers for the registration. Specifically, a markers design strategy is first proposed to generate the desired optical markers according to different requirements. The designed markers can be projected by using a low-cost projector or a laser with grating. The camera captures these projected markers from the object surface, and the 3D sensor collaboratively acquires the corresponding 3D point clouds. Finally, a markers-guided algorithm is specially designed to suit optical markers, which can make full use of the optical markers and recover the 360-degree 3D shape. The provided experimental results demonstrate both the accuracy and efficiency of the proposed OMAR in measuring large-scale, complex and texture-less objects. The proposed OMAR also enables flexible and non-destructive registration without losing accuracy compared with traditional registration methods. [ABSTRACT FROM AUTHOR]

In this paper, we present an automatic registration method based on planar primitive groups for building point clouds. This method distinguishes planar features with similar structures found in urban scenes, and reduces feature matching search complexity . In the method, planar patches with similar normal vectors are defined as a planar primitive group. We extract planes from point clouds as planar primitives. Using a threshold, we cluster the planar primitives with the similar normal vectors into groups. Finally, we match the planar primitives in groups, and calculate transformation parameters with an extended quaternion method. Experimental results show that this method is effective for automatic registration of building point clouds. [ABSTRACT FROM AUTHOR]

Point cloud registration is the important part in the large-scale city reconstruction. Considering the ground plane and buildings, which are the main objects in the city, always consist of several planes, we propose a new method that registering large-scale point clouds together through matched planes. For extracting the planes exactly from the point cloud, this paper presents a novel co-clustering method that clusters data points and hypotheses simultaneously. The traditional methods only cluster the data points, so they will fail to extract some planes. After knowing the planes, we use the random sample method to match the planes. The transformation matrix between the point clouds is calculated from the matched planes. Experimental results not only show that the co-clustering method can extract the planar structure more accurate and robust, but also demonstrate the effectiveness of the novel registration method for the city point clouds. Since the process in our method is only performed to the sparse version of the point clouds, it reduces the amount and complexity of calculation, which makes it to be suitable for the large-scale city reconstruction. [ABSTRACT FROM AUTHOR]

Point clouds registration is a fundamental step of many point clouds processing pipelines; however, most algorithms are tested on data that are collected ad-hoc and not shared with the research community. These data often cover only a very limited set of use cases; therefore, the results cannot be generalized. Public datasets proposed until now, taken individually, cover only a few kinds of environment and mostly a single sensor. For these reasons, we developed a benchmark, for localization and mapping applications, using multiple publicly available datasets. In this way, we are able to cover many kinds of environment and many kinds of sensor that can produce point clouds. Furthermore, the ground truth has been thoroughly inspected and evaluated to ensure its quality. For some of the datasets, the accuracy of the ground truth measuring system was not reported by the original authors, therefore we estimated it with our own novel method, based on an iterative registration algorithm. Along with the data, we provide a broad set of registration problems, chosen to cover different types of initial misalignment, various degrees of overlap, and different kinds of registration problems. Lastly, we propose a metric to measure the performances of registration algorithms: it combines the commonly used rotation and translation errors together, to allow an objective comparison of the alignments. This work aims at encouraging authors to use a public and shared benchmark, instead of data collected ad-hoc, to ensure objectivity and repeatability, two fundamental characteristics in any scientific field. [ABSTRACT FROM AUTHOR]