Iterative Closest Point Algorithm Research Articles

Clinical precision of Aoralscan 3 and Emerald S on the palatal and dentition areas: evaluation for forensic applications

ObjectivesTo compare the precision (repeatability and intermediate precision) of palatal and dentition scans taken with two different intraoral scanners. MethodsThe maxillary arch of 23 individuals was scanned three times using the Aoralscan 3 (Shining 3D) and Emerald S (Planmeca), resulting in 6 scans per individual. The scans were segmented in Meshmixer into palatal and dentition areas. Each replicate of an individual was compared within the specific scanner (repeatability) and to the corresponding replicate of the other scanner (intermediate precision). The scans were aligned using the iterative closest-point algorithm in the Zeiss Inspect software. The mean absolute distance between the aligned surfaces was calculated. Statistical comparisons were made using Friedman's two-way analysis of variance. Data are presented in median (quartile 1; quartile 3) form. ResultsNo significant difference in repeatability was found between Emerald S and Aoralscan 3 for the palate (26 µm [22,26] vs. 22 µm [18,26]) and for dentition (37 µm [31,44] vs. 38 µm [35,48]. Intermediate precision of the palate (32 µm [26,43]) and dentition (72 µm [63; 80]) was significantly lower than the repeatability of Emerald S (p < 0.05, p < 0.001) and of Aorlascan 3 (p < 0.001, p < 0.001). Both precision types of both scanners were significantly lower for the dentition than for the palate (p < 0.001). ConclusionThe precision of the dentition scan is lower than that of the palate. The precision of the Aoralscan 3 is similar to that of the Emerald S scanner. Clinical significanceNovel intraoral scanners could be used with high precision for palatal soft tissue scans, expanding their clinical utility. Aligning scans from two different IOSs still has high precision, facilitating the interchangeable use of intraoral scanners for orthodontic, prosthetic, and forensic examinations.

Impact of adjacent thoracic structures in negative left atrial remodelling in atrial fibrillation

Abstract Background Previous work has suggested that compression from external structures can promote negative atrial remodelling in atrial fibrillation (AF) [1]. This is mechanistically plausible, with many recognised risk factors in developing AF (age, hypertension, obesity, obstructive sleep apnoea, etc.) related through activation of the autonomic nervous system with an associated increase in atrial pressure and chronic elevation in wall stress [2,3]. Such negative remodelling contributes to the existence of arrhythmogenic atrial substrate [4]. This is highly significant when we consider catheter ablation of extrapulmonary vein targets. Purpose This study aims to investigate the relationship between abnormal conduction patterns indicative of potential AF drivers and their proximity to structures adjacent to the left atrium (LA). Methods Eight patients with paroxysmal or persistent AF, scheduled for an elective catheter ablation procedure using a charge density mapping (CDM) system [5] underwent pre-procedural cardiac MRI. 3D surface mesh reconstructions of individuals’ LA and adjacent structures (ascending aorta [AoA], descending aorta [AoD], and spine) were produced from 2D MRI slices using a proprietary graphical interface tool developed in MATLAB (Figure 1A &amp; B) [6]. MRI derived LA geometries were registered with their CDM counterparts by the Iterative Closest Point algorithm (Figure 1C); this enabled integration of MRI LA geometries into the CDM system. Subsequently, non-contact intrachamber voltage measurements from individuals’ procedures were used to derive cardiac activation directly onto MRI LA geometries. Abnormal conduction patterns representing possible AF drivers (focal firing, rotational propagation, and pivoting propagation) [5] were quantified as occurrences per second at each vertex of MRI LA geometries (Figure 1D). These conduction patterns could then be visualised and assessed in the context of adjacent structures (Figure 1E), with the Euclidean distance between each vertex of the MRI LA geometries and the closest point to their adjacent structures being calculated. Results Mean age was 65±9 years; 4 patients (50%) were male; AF was paroxysmal in 3 (37%) and persistent in 5 (63%); median time since AF diagnosis was 1 (1-3.8) years; ablation type was de novo in 5 (63%) and retreatment in 3 (38%). Frequencies of pivoting and rotational propagation were notably higher in regions of the LA closer to the AoA, and lower near the AoD and spine (Figure 2A &amp; B). Conversely, focal firing showed a less prominent trend, being more frequent in LA regions nearer the AoD and distant from the AoA (Figure 2C). Conclusions Compression from the ascending aorta may promote anterior LA remodelling in AF, leading to increased pivoting and rotational AF drivers. Clinically, this suggests a potential target for ablation therapy with an anterior seatbelt line connecting the right superior pulmonary vein to the mitral annulus.

Performance evaluation of image co-registration methods in photoacoustic mesoscopy of the vasculature

Objective:The formation of functional vasculature in solid tumours enables delivery of oxygen and nutrients, and is vital for effective treatment with chemotherapeutic agents. Longitudinal characterisation of vascular networks can be enabled using mesoscopic photoacoustic imaging, but requires accurate image co-registration to precisely assess local changes across disease development or in response to therapy. Co-registration in photoacoustic imaging is challenging due to the complex nature of the generated signal, including the sparsity of data, artefacts related to the illumination/detection geometry, scan-to-scan technical variability, and biological variability, such as transient changes in perfusion. To better inform the choice of co-registration algorithms, we compared five open-source methods, in physiological and pathological tissues, with the aim of aligning evolving vascular networks in tumours imaged over growth at different time-points.Approach:Co-registration techniques were applied to 3D vascular images acquired with photoacoustic mesoscopy from murine ears and breast cancer patient-derived xenografts, at a fixed time-point and longitudinally. Images were pre-processed and segmented using an unsupervised generative adversarial network. To compare co-registration quality in different settings, pairs of fixed and moving intensity images and/or segmentations were fed into five methods split into the following categories: affine intensity-based using 1)mutual information (MI) or 2)normalised cross-correlation (NCC) as optimisation metrics, affine shape-based using 3)NCC applied to distance-transformed segmentations or 4)iterative closest point algorithm, and deformable weakly supervised deep learning-based using 5)LocalNet co-registration. Percent-changes in Dice coefficients, surface distances, MI, structural similarity index measure and target registration errors were evaluated.Main results:Co-registration using MI or NCC provided similar alignment performance, better than shape-based methods. LocalNet provided accurate co-registration of substructures by optimising subfield deformation throughout the volumes, outperforming other methods, especially in the longitudinal breast cancer xenograft dataset by minimising target registration errors.Significance:We showed the feasibility of co-registering repeatedly or longitudinally imaged vascular networks in photoacoustic mesoscopy, taking a step towards longitudinal quantitative characterisation of these complex structures. These tools open new outlooks for monitoring tumour angiogenesis at the meso-scale and for quantifying treatment-induced co-localised alterations in the vasculature.

Vibration Position Detection of Robot Arm Based on Feature Extraction of 3D Lidar.

In the process of construction, pouring and vibrating concrete on existing reinforced structures is a necessary process. This paper presents an automatic vibration position detecting method based on the feature extraction of 3D lidar point clouds. Compared with the image-based method, this method has better anti-interference performance to light with reduced computational consumption. First, lidar scans are used to capture multiple frames of local steel bar point clouds. Then, the clouds are stitched by Normal Distribution Transform (NDT) for preliminary matching and Iterative Closest Point (ICP) for fine-matching. The Graph-Based Optimization (g2o) method further refines the precision of the 3D registration. Afterwards, the 3D point clouds are projected into a 2D image. Finally, the locations of concrete vibration points and concrete casting points are discerned through point cloud and image processing technologies. Experiments demonstrate that the proposed automatic method outperforms ICP and NDT algorithms, reducing the mean square error (MSE) by 11.5% and 11.37%, respectively. The maximum discrepancies in identifying concrete vibration points and concrete casting points are 0.059 ± 0.031 m and 0.089 ± 0.0493 m, respectively, fulfilling the requirement for concrete vibration detection.

Accurate reconstruction of turbine blade point cloud and multiple point cloud registration based on structured light

In recent years, the industry has increasingly adopted vision-guided laser processing techniques to machine cooling holes in turbine blades, placing higher demands on the accuracy of visual measurement. Therefore, to meet the need for high-precision measurement of the three-dimensional shape of turbine blades in the context of laser processing of film cooling holes, this paper proposes a structured light-based method for precise reconstruction of turbine blade point clouds and multi-point cloud registration. Specifically, to improve the accuracy of point cloud reconstruction, an improved phase matching and reconstruction method based on epipolar constraints is proposed. To enhance the accuracy and stability of multi-point cloud registration, a weighted iterative closest point (ICP) method combined with a mark point registration strategy is proposed, optimizing the stability of the ICP algorithm and achieving high-quality multiple point cloud stitching. Experimental results validate the effectiveness and accuracy of the proposed method.

Analysis of healthy glenohumeral arthrokinematics using four-dimensional computed tomography throughout internal rotation and forward elevation

BackgroundThe glenohumeral joint is the most mobile joint in the human body and can translate in addition to rotating in its socket. Currently, it is not well established in literature how much the healthy humeral head translates, and how that changes overtime as people age. The objective of this study is to quantify glenohumeral joint proximity and translation in healthy patients and determine if there are any age, position, or direction related differences. MethodsThirty-one participants were recruited for this study and split into two cohorts: young (≤ 37 years old) and old (≥45 years old). Four-dimensional computed tomography scans were taken as these participants completed internal rotation to the back (IR) and forward elevation (FE). 3D bone models of the humerus and scapula were created using 3D Slicer. An inter-bone distance algorithm, and an Iterative Closest Point algorithm were used to determine glenohumeral joint proximity and translation, respectively. ResultsThis study found that older participants displayed significantly closer joint proximity (63% of glenoid surface was within 4mm of humeral head) during the middle of IR, compared to younger participants (52% of glenoid surface within 4mm of humeral head). Additionally, younger participants had significantly more translation in the Superior/Inferior direction (16% of glenoid height) compared to the Anterior/Posterior direction (10% of glenoid width) throughout IR. ConclusionThis study demonstrates the significance of translational movements within the glenohumeral joint throughout IR and FE, which will aid implant manufacturers in designing implants that will allow for more normalized glenohumeral translations.

Accuracy evaluation of dental CBCT and scanned model registration method based on pulp horn mapping surface: an in vitro proof-of-concept

Background and aim3D fusion model of cone-beam computed tomography (CBCT) and oral scanned data can be used for the accurate design of root canal access and guide plates in root canal therapy (RCT). However, the pose accuracy of the dental pulp and crown in data registration has not been investigated, which affects the precise implementation of clinical planning goals. We aimed to establish a novel registration method based on pulp horn mapping surface (PHMSR), to evaluate the accuracy of PHMSR versus traditional methods for crown-pulp registration of CBCT and oral scan data.Materials and methodsThis vitro study collected 8 groups of oral scanned and CBCT data in which the left mandibular teeth were not missing, No. 35 and No. 36 teeth were selected as the target teeth. The CBCT and scanned model were processed to generate equivalent point clouds. For the PHMSR method, the similarity between the feature directions of the pulp horn and the surface normal vectors of the crown were used to determine the mapping points in the CBCT point cloud that have a great influence on the pulp pose. The small surface with adjustable parameters is reconstructed near the mapping point of the crown, and the new matching point pairs between the point and the mapping surface are searched. The sparse iterative closest point (ICP) algorithm is used to solve the new matching point pairs. Then, in the C + + programming environment with a point cloud library (PCL), the PHMSR, the traditional sparse ICP, ICP, and coherent point drift (CPD) algorithms are used to register the point clouds under two different initial deviations. The root square mean error (RSME) of the crown, crown-pulp orientation deviation (CPOD), and position deviation (CPPD) were calculated to evaluate the registration accuracy. The significance between the groups was tested by a two-tailed paired t-test (p < 0.05).ResultsThe crown RSME values of the sparse ICP method (0.257), the ICP method (0.217), and the CPD method (0.209) were not significantly different from the PHMSR method (0.250). The CPOD and CPPD values of the sparse ICP method (4.089 and 0.133), the ICP method (1.787 and 0.700), and the CPD method (1.665 and 0.718) than for the PHMSR method, which suggests that the accuracy of crown-pulp registration is higher with the PHMSR method.ConclusionCompared with the traditional method, the PHMSR method has a smaller crown-pulp registration accuracy and a clinically acceptable deviation range, these results support the use of PHMSR method instead of the traditional method for clinical planning of root canal therapy.

Efficient geological point cloud registration method combining dimension reduction and feature points

In geological scene registration with laser-scanned point cloud data, traditional algorithms often face reduced precision and efficiency due to extensive data volume and scope, which increase complexity and computational demands. This study introduces, to our knowledge, a novel registration method to address these limitations. Through dimension reduction that integrates height and curvature data, this approach converts point clouds into images, streamlining feature extraction. Log-variance enhancement mitigates information loss from dimensionality reduction, aiding in coarse registration. Further, incorporating weighted distances of feature points into the Iterative Closest Point (ICP) algorithm improves precision in point matching. Experiments indicate an average threefold increase in initial registration efficiency compared to traditional coarse registration algorithms, with improvements in accuracy. The optimized ICP algorithm achieves 50% and 15% accuracy improvements across various datasets, enhancing large-scale geological point cloud data registration.

An Improved Large Planar Point Cloud Registration Algorithm

The traditional Iterative Closest Point (ICP) algorithm often suffers from low computational accuracy and efficiency in certain scenarios. It is highly sensitive to the initial pose, has a poor ability to resist interference, and frequently becomes trapped in local optima. Extracting feature points accurately from partially overlapping points with weak three-dimensional features, such as smooth planes or surfaces with low curvature, is challenging using only the traditional ICP algorithm for registration. This research introduces a “First Rough then Precise” registration strategy. Initially, the target position is extracted in complex environments using an improved clustering method, which simultaneously reduces the impact of environmental factors and noise on registration accuracy. Subsequently, an improved method for calculating normal vectors is applied to the Fast Point Feature Histogram (FPFH) to extract feature points, providing data for the Sample Consistency Initial Algorithm (SAC-IA). Lastly, an improved ICP algorithm, which has strong anti-interference capabilities for partially overlapping point clouds, is utilized to merge such point clouds. In the experimental section, we validate the feasibility and precision of the proposed algorithm by comparing its registration outcomes with those of various algorithms, using both standard point cloud dataset models and actual point clouds obtained from camera captures.

Four-Dimensional Digital Monitoring and Registering of Historical Architecture for the Preservation of Cultural Heritage

Preserving cultural heritage through monitoring, registering, and analyzing damage in historical architectural structures presents significant financial and logistical burdens. Developed approaches for monitoring and registering 4D (4-dimensional)-scanned range and raster images of damaged objects were investigated in a case study of historical Baron Palace in Egypt. In the methodology, we first prepared and observed the damaged historical models. The damaged historical models were scanned using a laser scanner at a predetermined date and time. Simultaneously, digital images of the models were captured (by a calibrated digital camera) and stored on a researcher’s tablet device. By observing and comparing the scanned models with the digital images, geometric defects and their extent are identified. Then, the observed data components were detected on the map. Then, damaged statue materials were investigated using system of energy dispersive (SEM; scanning electron microscope, Gemini Zeiss-Ultra 55) and XRF (X-ray fluorescence) spectroscopic analysis to identify the statue’s marble elements, and the results indicate that SEM-EDX and XRF analyses accurately identify major and minor compositions of the damaged statue. Then, the damaged models were registered in two stages. In the registration stages, the corresponding points were determined automatically by detecting the closest points in the clouds and ICP (iterative closest point) algorithm in RiSCAN. The point clouds (of the Palace and damaged statues) gave very detailed resolutions and more realistic images in RiSCAN, but it is a costly program. Finally, the accuracies of the registration tasks were assessed; the standard deviations are within acceptable limits and tend to increase irregularly as the number of polydata observations used in the registration calculations increase.

Application of Micro-Plane Projection Moving Least Squares and Joint Iterative Closest Point Algorithms in Spacecraft Pose Estimation

Application of Micro-Plane Projection Moving Least Squares and Joint Iterative Closest Point Algorithms in Spacecraft Pose Estimation

Multi-GPU 3D k-nearest neighbors computation with application to ICP, point cloud smoothing and normals computation

The k-Nearest Neighbors algorithm is a fundamental algorithm that finds applications in many fields like Machine Learning, Computer Graphics, Computer Vision, and others. The algorithm determines the closest points (d-dimensional) of a reference set R according to a query set of points Q under a specific metric (Euclidean, Mahalanobis, Manhattan, etc.). This work focuses on the utilization of multiple Graphical Processing Units for the acceleration of the k-Nearest Neighbors algorithm with large or very large sets of 3D points. With the proposed approach the space of the reference set is divided into a 3D grid which is used to facilitate the search for the nearest neighbors. The search in the grid is performed in a multiresolution manner starting from a high-resolution grid and ending up in a coarse one, thus accounting for point clouds that may have non-uniform sampling and/or outliers. Three important algorithms in reverse engineering are revisited and new multi-GPU versions are proposed based on the introduced KNN algorithm. More specifically, the new multi-GPU approach is applied to the Iterative Closest Point algorithm, to the point cloud smoothing, and to the point cloud normal vectors computation and orientation problem. A series of tests and experiments have been conducted and discussed in the paper showing the merits of the proposed multi-GPU approach.

Poster 354: Tibiofemoral Bone Shape Features Are Associated with Re-Tear after Anterior Cruciate Ligament Reconstruction

Introduction: Knee bony morphology is linked to anterior cruciate ligament (ACL) injury, ACL graft failure, and osteoarthritis (OA) risk, and employing tools such as statistical shape modeling could identify individuals prone to these joint conditions. Re-tear after ACL reconstruction remains a common and devastating complication, and therefore, recognizing patients at high risk of graft failure before primary ACL reconstruction is crucial for potential intervention to reduce failure rates. Magnetic resonance imaging (MRI) is most commonly obtained in the setting of possible ACL injury and before ACL reconstruction surgery, yet currently available MRI-based bone shape assessment methods require high-resolution sequences or manual segmentation, limiting feasibility of large-scale analysis. The development of automated tools for bone shape extraction from clinical MRI could allow for three-dimensional statistical shape modeling across large datasets. We established an automated pipeline for bone segmentation from proton density (PD)-weighted MRI scans, aiming to extract knee bony morphology from preoperative clinical MRIs in patients with ACL injuries. We hypothesized that our segmentation pipeline could uncover specific tibiofemoral joint bone shape features associated with ACL re-tear after reconstruction. Objective: To identify bone shape features prior to initial primary ACL reconstruction in patients with eventual ACL graft re-tear compared to those with a known, intact ACL graft. Methods: Patients who retrospectively experienced a subsequent re-tear after reconstruction within three years of initial injury were compared to those without a re-tear within three years (20 without subsequent re-tear, 22 with subsequent re-tear for distal femur; 22 without subsequent re-tear, 19 with subsequent re-tear for proximal tibia). The control group had MRI scans at 3 years after reconstruction that confirmed an intact ACL graft. An automatic femur and tibia segmentation framework was developed by training two deep convolutional neural networks using clinical, two-dimensional PD-weighted knee MRI sequences (3.5 mm slice thickness) from patients with ACL tears. Using the segmentation pipeline, bone was automatically segmented from preoperative clinical PD-weighted knee MRI sequence (3 mm slice thickness), three-dimensional triangulated meshes were generated, scaled to a reference, and aligned and matched to an atlas using Iterative Closest Point algorithm. Modes of shape variance were extracted from principal component analysis (PCA). The effect of each mode is modeled by three-dimensional surfaces for each specific shape feature. Modes were considered statistically significant if the mean principal component score between groups was p&lt;0.05 using Students T-test. Results: Statistical shape modeling of the femoral condyles using 15 PCA modes reveals significant differences (p &lt; 0.05) in Modes 1 and 4 at the initial injury in patients who subsequently experienced ACL re-tear after reconstruction compared to those without re-tear. Femur Mode 1 is characterized by a narrower intercondylar notch width, a wider medial condylar width, and an increased ratio of femoral condylar offset in patients with ACL re-tear. Femur Mode 4 illustrates a more prominent trochlear sulcus and increased surface area along the lateral femoral condyle relative to the medial condyle in patients with ACL re-tear. The initial 15 PCA modes of the distal femur collectively account for 75.04% of the normalized accumulated total variance. Statistical shape modeling of the tibial plateau with 15 PCA modes reveals that Mode 7 is significantly different (p &lt; 0.05) at the initial injury in patients who experienced subsequent ACL re-tear after reconstruction compared to those without re-tear. Tibia Mode 7 indicates diminished ACL facet prominence, a squared lateral tibial plateau, and a broader, flatter tibial spine in patients with subsequent ACL re-tear. The first 15 PCA modes of the proximal tibia collectively explain 73.60% of the normalized accumulated total variance. Conclusions: Using an automatic bone segmentation pipeline on preoperative clinical MRIs, we identify specific shape features in the tibiofemoral joint associated with ACL re-tear after reconstruction. Patients with subsequent ACL re-tear exhibit a variety of distinct three-dimensional morphology differences, including reduced intercondylar notch width, wider medial condylar width, increased lateral tibial plateau convexity, and broader, lateralized tibial spine, uncovered using statistical shape modeling. Statistical shape modeling identifies three-dimensional morphology patterns, thus capturing differences otherwise unaccounted for by traditional two-dimensional measurements. This approach offers predictive potential for patients at risk of ACL re-tear, improving treatment decisions for those with high-risk morphologic features.

3D deformation analysis for earth dam monitoring based on terrestrial laser scanning (TLS) and the iterative closest point (ICP) algorithm

Introduction: The conventional 3-D point cloud-based deformation analysis methods, such as the shortest distance (SD), cloud-to-cloud (C2C), and multiscale model-to-model cloud comparison (M3C2), essentially regard the closest distance between two periods of point cloud data as the deformation, rather than the true position of the same point in 3-D space before and after deformation.Methods: This paper proposes a method based on the ICP algorithm to calculate the differences between the chunked multi-period point clouds to recognizes the 3-D deformations.Results and discussion: The results show that the obtained results are very close to the GNSS data but with a much larger spatial monitoring range. The accuracy is higher than that of the SD method. Moreover, we analyze the statistical relationship between the point cloud block size and the deformation vector error and determine the optimal block size. The aim of this article is to optimize the deformation analysis method and improve its accuracy to provide techniques and ideas for the wider surface deformation monitoring research field. For instance, combining this method with data from contact methods constructs a 3D overall deformation model of the mountain, enabling real-time monitoring and early warning of debris flows.

Automation of three-dimensional inspection using the iterative closest point algorithm: application to a gas turbine blade

Automation of three-dimensional inspection using the iterative closest point algorithm: application to a gas turbine blade

Determination of Accuracy and Usability of a SLAM Scanner GeoSLAM Zeb Horizon: A Bridge Structure Case Study

The presented paper focuses on testing the performance of a SLAM scanner Zeb Horizon by GeoSLAM for the creation of a digital model of a bridge construction. A cloud acquired using a static scanner Leica ScanStation P40 served as a reference. Clouds from both scanners were registered into the same coordinate system using a Trimble S9 HP total station. SLAM scanner acquisition was performed independently in two passes. The data acquired using the SLAM scanner suffered from relatively high noise. Denoising using the MLS (Moving Least Squares) method was performed to reduce noise. An overall comparison of the point clouds was performed on both the original and MLS-smoothed data. In addition, the ICP (Iterative Closest Point) algorithm was also used to evaluate local accuracy. The RMSDs of MLS-denoised data were approximately 0.02 m for both GeoSLAM passes. Subsequently, a more detailed analysis was performed, calculating RMSDs for several profiles of the construction. This analysis revealed that the deviations of SLAM data from the reference data did not exceed 0.03 m in any direction (longitudinal, transverse, elevation) which is, considering the length of the bridge of 133 m, a very good result. These results demonstrate a high applicability of the tested scanner for many applications, such as the creation of digital twins.

A Point Cloud Registration Algorithm Based on Weighting Strategy for 3D Indoor Spaces

Point cloud registration is a technology that aligns point cloud data from different viewpoints by computing coordinate transformations to integrate them into a specified coordinate system. Many cutting-edge fields, including autonomous driving, industrial automation, and augmented reality, require the registration of point cloud data generated by millimeter-wave radar for map reconstruction and path planning in 3D environments. This paper proposes a novel point cloud registration algorithm based on a weighting strategy to enhance the accuracy and efficiency of point cloud registration in 3D environments. This method combines a statistical weighting strategy with a point cloud registration algorithm, which can improve registration accuracy while also increasing computational efficiency. First, in 3D indoor spaces, we apply PointNet to the semantic segmentation of the point cloud. We then propose an objective weighting strategy to assign different weights to the segmented parts of the point cloud. The Iterative Closest Point (ICP) algorithm uses these weights as reference values to register the entire 3D indoor space’s point cloud. We also show a new way to perform nonlinear calculations that yield exact closed-form answers for the ICP algorithm in generalized 3D measurements. We test the proposed algorithm’s accuracy and efficiency by registering point clouds on public datasets of 3D indoor spaces. The results show that it works better in both qualitative and quantitative assessments.

Temporal Shape Changes of Pedicle Screw-rod Constructs After Lumbar Interbody Fusion.

Retrospective multicenter study. To examine the shape change of screw-rod constructs over time after short-segment lumbar interbody fusion and to clarify its relationship to clinical characteristics. No study has focused on the shape change of screw-rod constructs after short-segment fusion and its clinical implications. One hundred eight patients who had single-level lumbar interbody fusion with pedicle screws and cages were enrolled. Three-dimensional (3D) images of screw-rod constructs were generated from baseline CT on the day after surgery and follow-up CT and were superposed on the right and left side, respectively, using the iterative closest point algorithm. The shape change was quantitatively assessed by computing the median distance between the 3D images, which was defined as the shape change value. Among the 5 time-course categories of follow-up CT (≤1, 2-3, 4-6, 7-12, and ≥13 months), the shape change values were compared. The relationships between the shape change values and clinical characteristics, such as age, CT-derived vertebral bone mineral density, screw and rod materials, and postoperative interbody fusion status, cage subsidence, and screw loosening, were evaluated. A total of 237 follow-up CTs were included (≤1 [34 scans], 2-3 [33 scans], 4-6 [80 scans], 7-12 [48 scans], and ≥13 months [42 scans]) because many patients underwent multiple follow-up CTs. There were significant differences in shape change values among the time-course categories ( P <0.001 in Kruskal-Wallis test). Most shape changes occurred within 6 months postoperatively, with no significant changes observed at 7 months or more. There were no significant relationships between the shape change values and each clinical characteristic. The temporal shape changes of screw-rod constructs following short-segment lumbar interbody fusion progressed up to 6 months after surgery but not significantly thereafter.

Automatic image alignment and stitching for ultrasound-based robotic inspection of complex geometry components

Robotic inspection of complex geometry components using immersion ultrasonic techniques is relevant for many ultrasonic Non-Destructive Testing (NDT) applications in different industries. To image large component, ultrasonic probe is manipulated around the component immersed in water using a robotic guided system to create a tiled image of the entire component. Due to the mechanical vibrations of the high-speed robotic arm, the probe position coordinates provided by the robotic system are not precise enough to ensure an accurate reconstruction (stitching) of a composite image from all individual images. In this work, we propose a stitching method specifically designed to create a single 2D image of the surface of an inspected component from a stack of Total Focusing Method (TFM) images. This stitching method uses Iterative Closest Point (ICP) registration to estimate a 2D rigid transformation between two consecutive 2D images, by maximizing the overlap between the surface geometries extracted from each image. The capabilities of this imaging technique are illustrated by various simulated and experimental results carried out in a water tank. Significant improvements in surface image quality, leading to accurate surface reconstruction, are shown for vertical vibrations with displacements of more than two operating wavelengths and for rotational vibrations with deviation angles of less than 1°. The results also show that the resolving power of the ICP algorithm decreases for strong rotational vibrations.

Semantic Segmentation Method for Road Intersection Point Clouds Based on Lightweight LiDAR

Lightweight LiDAR, characterized by its ease of use and cost-effectiveness, offers advantages in road intersection information acquisition. This study used lightweight LiDAR to collect 3D point cloud data from an urban road intersection and propose a semantic segmentation model based on the improved RandLA-Net. Initially, raw data from multiple positions and perspectives were obtained, and complete road intersection point clouds were stitched together using the iterative closest point algorithm for sequential registration. Subsequently, a semantic segmentation method for point clouds based on the improved RandLA-Net was proposed. This method included a spatial information encoding module based on feature similarities and a feature enhancement module based on multi-pooling fusion. This model optimized the feature aggregation capabilities during downsampling with the weighted cross-entropy loss function applied to reduce the impact of input sample scale imbalances. In comparisons of the improved RandLA-Net with PointNet++ and RandLA-Net on the same dataset, our method showed improved segmentation accuracy for various categories. The overall prediction accuracy on two road intersection point cloud test sets was 87.68% and 89.61%, with average F1 scores of 82.76% and 80.61%, respectively. Most notably, the prediction accuracy for road surface areas reached 94.48% and 94.79%. The results show that our model can enrich the spatial feature expression of input data and enhance semantic segmentation performance in road intersection scenarios.