Search Results (997)

Understanding vehicle emissions is essential for developing effective carbon reduction strategies in the transport sector. Conventional emission models often assume homogeneity and linearity, overlooking real-world asymmetries that arise from variations in vehicle design and powertrain configurations. This study explores how machine learning and explainable AI techniques can effectively capture both symmetric and asymmetric emission patterns across different vehicle types, thereby contributing to more sustainable transport planning. Addressing a key gap in the existing literature, the study poses the following question: how do structural and behavioral factors contribute to asymmetric emission responses in internal combustion engine vehicles compared to new energy vehicles? Utilizing a large-scale Spanish vehicle registration dataset, the analysis classifies vehicles by powertrain type and applies five supervised learning algorithms to predict CO₂ emissions. SHapley Additive exPlanations (SHAPs) are employed to identify nonlinear and threshold-based relationships between emissions and vehicle characteristics such as fuel consumption, weight, and height. Among the models tested, the Random Forest algorithm achieves the highest predictive accuracy. The findings reveal critical asymmetries in emission behavior, particularly among hybrid vehicles, which challenge the assumption of uniform policy applicability. This study provides both methodological innovation and practical insights for symmetry-aware emission modeling, offering support for more targeted eco-design and policy decisions that align with long-term sustainability goals. Full article

Over three decades of research has been undertaken on point cloud registration algorithms, resulting in mature theoretical frameworks and methodologies. However, among the numerous registration techniques used, the impact of point cloud scanning quality on registration outcomes has rarely been addressed. In most engineering and industrial measurement applications, the accuracy and density of LiDAR point clouds are highly dependent on laser scanners, leading to significant variability that critically affects registration quality. Key factors influencing point cloud accuracy include scanning distance, incidence angle, and the surface characteristics of the target. Notably, in short-range scanning scenarios, incidence angle emerges as the dominant error source. Building on this insight, this study systematically investigates the relationship between scanning incidence angles and point cloud quality. We propose an incident-angle-dependent weighting function for point cloud observations, and further develop an improved weighted Iterative Closest Point (ICP) registration algorithm. Experimental results demonstrate that the proposed method achieves approximately 30% higher registration accuracy compared to traditional ICP algorithms and a 10% improvement over Faro SCENE’s proprietary solution. Full article

The integration of artificial intelligence (AI) and advanced digital workflows is revolutionising full-arch implant dentistry, particularly for geriatric patients with edentulous and atrophic arches, for whom achieving both prosthetic passivity and optimal aesthetic outcomes is critical. This narrative review evaluates current challenges in intraoral scanning accuracy—such as scan distortion, angular deviation, and cross-arch misalignment—and presents how innovations like the Medit SmartX AI-guided workflow and the Scan Ladder system can significantly enhance precision in implant position registration. These technologies mitigate stitching errors by using real-time scan body recognition and auxiliary geometric references, yielding mean RMS trueness values as low as 11–13 µm, comparable to dedicated photogrammetry systems. AI-driven prosthetic design further aligns implant-supported restorations with facial symmetry and smile aesthetics, prioritising predictable midline and occlusal plane control. Early clinical data indicate that such tools can reduce prosthetic misfits to under 20 µm and lower complication rates related to passive fit, while shortening scan times by up to 30% compared to conventional workflows. This is especially valuable for elderly individuals who may not tolerate multiple lengthy adjustments. Additionally, emerging AI applications in design automation, scan validation, and patient-specific workflow adaptation continue to evolve, supporting more efficient and personalised digital prosthodontics. In summary, AI-enhanced scanning and prosthetic workflows do not merely meet functional demands but also elevate aesthetic standards in complex full-arch rehabilitations. The synergy of AI and digital dentistry presents a transformative opportunity to consistently deliver superior precision, passivity, and facial harmony for edentulous implant patients. Full article

LiDAR-based Simultaneous Localization and Mapping (SLAM) serves as a fundamental technology for autonomous navigation. However, in complex environments, LiDAR odometry often experience degraded localization accuracy and robustness. This paper proposes a computationally efficient enhancement strategy for LiDAR odometry, which improves system performance by reinforcing high-quality features throughout the optimization process. For non-ground features, the method employs statistical geometric analysis to identify stable points and incorporates a contribution-weighted optimization scheme to strengthen their impact in point-to-plane and point-to-line constraints. In parallel, for ground features, locally stable planar surfaces are fitted to replace discrete point correspondences, enabling more consistent point-to-plane constraint formulation during ground registration. Experimental results on the KITTI and M2DGR datasets demonstrated that the proposed method significantly improves localization accuracy and system robustness, while preserving real-time performance with minimal computational overhead. The performance gains were particularly notable in scenarios dominated by unstructured environments. Full article

Traditional acupuncture relies on the precise selection of acupuncture points to adjust Qi flow along meridians. Traditionally, acupuncture points are localized using cun (or body-relative cun) as a proportional measurement. However, locating specific points can be challenging, even for experienced practitioners. This study aims to enhance the accuracy and efficiency of acupuncture point localization by introducing an augmented reality (AR) navigation system utilizing AR glasses (Magic Leap One). The system employs a Six-Point Landmark-Based AR Registration method to overlay an acupuncture point model onto a patient’s head without the need for external markers. Methods included testing with traditional Chinese medicine students, measuring positional errors, and evaluating stability. Results demonstrated an average error of 5.01 ± 2.64 mm, which is well within the therapeutic range of 2 cun (about 5 cm), with minimal drift during stability tests. This AR system provides an accurate and intuitive tool for practitioners and learners, reducing variability in acupuncture point selection and offering promise for broader clinical applications. Full article

The integration of Building Information Modeling (BIM) and 3D Geographic Information System (3D GIS) models provides high-precision spatial data for digital twin watersheds. To tackle the challenges of large data volumes and rendering latency in integrated models, this study proposes a three-step framework that uses Industry Foundation Classes (IFCs) as the base model and Open Scene Graph Binary (OSGB) as the target model: (1) geometric optimization through an angular weighting (AW)-controlled Quadric Error Metrics (QEM) algorithm; (2) Level of Detail (LOD) hierarchical mapping to establish associations between the IFC and OSGB models, and redesign scene paging logic; (3) coordinate registration by converting the IFC model’s local coordinate system to the global coordinate system and achieving spatial alignment via the seven-parameter method. Applied to the Santun River Basin digital twin project, experiments with 10 water gate models show that the AW-QEM algorithm reduces average loading time by 15% compared to traditional QEM, while maintaining 97% geometric accuracy, demonstrating the method’s efficiency in balancing precision and rendering performance. Full article

Background: Inadequate presurgical planning is a key contributor to suboptimal outcomes in orthognathic surgery. This study aims to assess the accuracy of a digital surgical planning workflow conducted prior to any orthodontic intervention. Methods: Digital planning was performed for 26 patients before orthodontic treatment (T0) and compared to the actual preoperative planning (T1). Digitized plaster casts were merged with CBCT data and converted to orthodontic setups to create a 3D virtual head model. After voxel-based registration of T0 and T1, dental arches were virtually osteotomized and repositioned according to planned outcomes. These T0 segments were then aligned with T1 planning using bony landmarks of the maxilla. Anatomical landmarks were used to construct virtual triangles on maxillary and mandibular segments, enabling assessment of positional and orientational differences. Transformations between T0 and T1 were translated into clinically meaningful metrics. Results: Significant differences were found between T0 and T1 at the dental level. T1 exhibited a greater clockwise rotation of the dental maxilla (mean: 2.85°) and a leftward translation of the mandibular dental arch (mean: 1.19 mm). In SARME cases, the bony mandible showed larger anti-clockwise roll differences. Pitch variations were also more pronounced in maxillary extraction cases, with both the dental maxilla and bony mandible demonstrating increased clockwise rotations. Conclusions: The proposed orthognathic surgical planning workflow shows potential for simulating mandibular outcomes but lacks dental-level accuracy, especially in maxillary anterior torque. While mandibular bony outcome predictions align reasonably with pretreatment planning, notable discrepancies exceed clinically acceptable thresholds. Current accuracy limits routine use; further refinement and validation in larger, homogeneous patient groups are needed to enhance clinical reliability and applicability. Full article

Medical image registration is an indispensable preprocessing step to align medical images to a common coordinate system before in-depth analysis. The registration precision is critical to the following analysis. In addition to representative image features, the initial pose settings and multiple poses in images will significantly affect the registration precision, which is largely neglected in state-of-the-art works. To address this, the paper proposes a dual-pose medical image registration algorithm based on improved differential evolution. More specifically, the proposed algorithm defines a composite similarity measurement based on contour points and utilizes this measurement to calculate the similarity between frontal–lateral positional DRR (Digitally Reconstructed Radiograph) images and X-ray images. In order to ensure the accuracy of the registration algorithm in particular dimensions, the algorithm implements a dual-pose registration strategy. A PDE (Phased Differential Evolution) algorithm is proposed for iterative optimization, enhancing the optimization algorithm’s ability to globally search in low-dimensional space, aiding in the discovery of global optimal solutions. Extensive experimental results demonstrate that the proposed algorithm provides more accurate similarity metrics compared to conventional registration algorithms; the dual-pose registration strategy largely reduces errors in specific dimensions, resulting in reductions of 67.04% and 71.84%, respectively, in rotation and translation errors. Additionally, the algorithm is more suitable for clinical applications due to its lower complexity. Full article

Objectives: The aim of this study was to evaluate the effectiveness of spectrophotometers for objective tooth color measurement, particularly in bleaching procedures enhanced by digital positioning templates. Methods: Tooth color registration was conducted using both subjective methods with shade guides and objective methods with spectrophotometers. Spectrophotometers were chosen for their ability to provide objective, quantifiable, and reproducible results, crucial for monitoring color modifications accurately. Digital workflows were implemented to enhance the registration process further. These workflows included providing a precise positioning matrix for spectrophotometer sensors and optimizing working models to ensure high-quality therapeutic splints. Results: The use of spectrophotometers demonstrated superior performance in registering tooth color objectively compared to subjective shade guides. Digital workflows significantly improved the precision and efficiency of spectrophotometer measurements through a digital matrix, enhancing the quality of therapeutic splints obtained. Conclusions: Spectrophotometers are recommended for objective and precise tooth color registration, particularly in bleaching procedures. Integrating a digital positioning matrix enhances measurement accuracy and reliability, supporting effective monitoring and treatment outcomes. Full article

Objectives: Facial soft tissue imaging is crucial in orthodontic treatment planning, and the structured light scanning technology found in the latest iPhone models constitutes a promising method. Currently, studies which evaluate the accuracy of smartphone-based three-dimensional (3D) facial scanners are scarce. This study compares smartphone scans with cone-beam computed tomography (CBCT) images. Materials and Methods: Three-dimensional images of 23 screened patients were captured with the camera of an iPhone 13 Pro Max and processed with the Scandy Pro application; CBCT scans were also taken as a standard of care. After establishing unique image pairs of the same patient, linear and angular measurements were compared between the images to assess the scanner’s two-dimensional trueness. Following the co-registration of the virtual models, a heat map was generated, and root mean square (RMS) deviations were calculated for quantitative assessment of 3D trueness. Precision was determined by comparing consecutive 3D facial scans of five participants, while intraobserver reliability was assessed by repeating measurements on five subjects after a two-week interval. Results: This study found no significant difference in soft tissue measurements between smartphone and CBCT images (p > 0.05). The mean absolute difference was 1.43 mm for the linear and 3.16° for the angular measurements. The mean RMS value was 1.47 mm. Intraobserver reliability and scanner precision were assessed, and the Intraclass Correlation Coefficients were found to be excellent. Conclusions: Smartphone facial scanners offer an accurate and reliable alternative to stereophotogrammetry systems, though clinicians should exercise caution when examining the lateral sections of those images due to inherent inaccuracies. Full article

Image registration constitutes the fundamental basis for the joint interpretation of synthetic aperture radar (SAR) and optical images. However, robust image registration remains challenging due to significant regional heterogeneity in remote sensing scenes (e.g., co-existing urban and marine areas within a single image). To overcome this challenge, this article proposes a novel optical–SAR image registration method named Gradient and Standard Deviation Feature Weighted Fusion (GDWF). First, a Block-local standard deviation (Block-LSD) operator is proposed to extract block-based feature points with regional adaptability. Subsequently, a dual-modal feature description is developed, constructing both gradient-based descriptors and local standard deviation (LSD) descriptors for the neighborhoods surrounding the detected feature points. To further enhance matching robustness, a confidence-weighted feature fusion strategy is proposed. By establishing a reliability evaluation model for similarity measurement maps, the contribution weights of gradient features and LSD features are dynamically optimized, ensuring adaptive performance under varying conditions. To verify the effectiveness of the method, different optical and SAR datasets are used to compare it with the currently advanced algorithms MOGF, CFOG, and FED-HOPC. The experimental results demonstrate that the proposed GDWF algorithm achieves the best performance in terms of registration accuracy and robustness among all compared methods, effectively handling optical–SAR image pairs with significant regional heterogeneity. Full article

Point clouds acquired by mobile laser scanners (MLS) are widely used for generating as-built building information models (BIM), particularly in indoor construction environments and existing buildings. While MLS offers fast and efficient scanning through SLAM technology, its accuracy and precision remains lower than that of terrestrial laser scanners (TLS). This study investigates the potential to improve MLS-based as-built BIM accuracy by analyzing and utilizing error distribution patterns inherent in MLS point clouds. Based on the assumption that each MLS device exhibits consistent and unique error distribution patterns, an experiment was conducted using three MLS devices and TLS-derived reference data. The analysis employed iterative closest point (ICP) registration and cloud-to-mesh (C2M) distance measurements on mock-ups with closed shapes. The results revealed that error patterns were stable across scans and could be leveraged as correction factors. In other words, the results indicate that when using MLS for as-built BIM generation, robust fitting methods have limitations in obtaining realistic object dimensions, as they do not account for the unique error patterns present in MLS point clouds. The proposed method provides a simple and repeatable approach for enhancing MLS accuracy, contributing to improved dimensional reliability in MLS-driven BIM applications. Full article

Accurate feature matching between optical and synthetic aperture radar (SAR) images remains a significant challenge in remote sensing due to substantial modality discrepancies in texture, intensity, and geometric structure. In this study, we proposed an attention-context-aware deep learning framework (ACAMatch) for robust and efficient optical–SAR image registration. The proposed method integrates a structure-enhanced feature extractor, RS2FNet, which combines dual-stage Res2Net modules with a bi-level routing attention mechanism to capture multi-scale local textures and global structural semantics. A context-aware matching module refines correspondences through self- and cross-attention, coupled with a confidence-driven early-exit pruning strategy to reduce computational cost while maintaining accuracy. Additionally, a match-aware multi-task loss function jointly enforces spatial consistency, affine invariance, and structural coherence for end-to-end optimization. Experiments on public datasets (SEN1-2 and WHU-OPT-SAR) and a self-collected Gaofen (GF) dataset demonstrated that ACAMatch significantly outperformed existing state-of-the-art methods in terms of the number of correct matches, matching accuracy, and inference speed, especially under challenging conditions such as resolution differences and severe structural distortions. These results indicate the effectiveness and generalizability of the proposed approach for multimodal image registration, making ACAMatch a promising solution for remote sensing applications such as change detection and multi-sensor data fusion. Full article

To overcome the limitations in the perception performance of individual robots and homogeneous robot teams, this paper presents a distributed multi-robot collaborative SLAM method based on air–ground cross-domain cooperation. By integrating environmental perception data from UAV and UGV teams across air and ground domains, this method enables more efficient, robust, and globally consistent autonomous positioning and mapping. First, to address the challenge of significant differences in the field of view between UAVs and UGVs, which complicates achieving a unified environmental understanding, this paper proposes an iterative registration method based on semantic and geometric features assistance. This method calculates the correspondence probability of the air–ground loop closure keyframes using these features and iteratively computes the rotation angle and translation vector to determine the coordinate transformation matrix. The resulting matrix provides strong initialization for back-end optimization, which helps to significantly reduce global pose estimation errors. Next, to overcome the convergence difficulties and high computational complexity of large-scale distributed back-end nonlinear pose graph optimization, this paper introduces a multi-level partitioning majorization–minimization DPGO method incorporating loss kernel optimization. This method constructs a multi-level, balanced pose subgraph based on the coupling degree of robot nodes. Then, it uses the minimization substitution function of non-trivial loss kernel optimization to gradually converge the distributed pose graph optimization problem to a first-order critical point, thereby significantly improving global pose estimation accuracy. Finally, experimental results on benchmark SLAM datasets and the GRACO dataset demonstrate that the proposed method effectively integrates environmental feature information from air–ground cross-domain UAV and UGV teams, achieving high-precision global pose estimation and map construction. Full article

Background/Objectives: This study evaluates whether ultrawide-field optical coherence tomography angiography (UWF-OCTA) can guide navigated laser therapy for non-perfused areas (NPAs) in branch retinal vein occlusion (BRVO). It further explores whether the laser spots can be accurately placed according to plan, considering that the retina is three-dimensional (3D), while UWF-OCTA provides two-dimensional (2D) images. Methods: UWF-OCTA images from three devices—VG200, Xephilio OCT-S1, and Bmizar—guided the treatments. These images were superimposed onto NAVILAS^® system images to guide NPA treatments. Pre-treatment planning was strategically designed to avoid normal and collateral vessels, with immediate post-laser OCTA and en face images assessing the efficacy of the laser spots in avoiding these vessels as planned. The accuracy of navigated laser therapy was further analyzed by comparing the intended laser locations with the actual spots. Results: All montaged OCTA images from the three devices were seamlessly integrated into the navigated laser system without registration errors. All patients received treatments targeting the NPAs as planned. However, not all collateral or normal vessels were successfully avoided by the laser spots. A further analysis revealed that the actual locations of the laser spots deviated slightly from the planned locations, particularly in the mid-periphery areas. Conclusions: UWF-OCTA-guided navigated laser photocoagulation is feasible and precise for treating NPAs in BRVO. Nonetheless, minor deviations between planned and actual locations were observed. This discrepancy, particularly important when treating diseases of the macular area, should be carefully considered when employing OCTA-guided navigated laser photocoagulation. Full article