Search Results (3,841)

3D reconstruction technology generates three-dimensional representations of real-world objects, scenes, or environments using sensor data such as 2D images, with extensive applications in robotics, autonomous vehicles, and virtual reality systems. Traditional 3D reconstruction techniques based on 2D images typically rely on RGB spectral information. With advances in sensor technology, additional spectral bands beyond RGB have been increasingly incorporated into 3D reconstruction workflows. Existing methods that integrate these expanded spectral data often suffer from expensive scheme prices, low accuracy, and poor geometric features. Three-dimensional reconstruction based on NeRF can effectively address the various issues in current multispectral 3D reconstruction methods, producing high-precision and high-quality reconstruction results. However, currently, NeRF and some improved models such as NeRFacto are trained on three-band data and cannot take into account the multi-band information. To address this problem, we propose Multispectral-NeRF—an enhanced neural architecture derived from NeRF that can effectively integrate multispectral information. Our technical contributions comprise threefold modifications: Expanding hidden layer dimensionality to accommodate 6-band spectral inputs; redesigning residual functions to optimize spectral discrepancy calculations between reconstructed and reference images; and adapting data compression modules to address the increased bit-depth requirements of multispectral imagery. Experimental results confirm that Multispectral-NeRF successfully processes multi-band spectral features while accurately preserving the original scenes’ spectral characteristics. Full article

Accurate extrinsic calibration between optical sensors, such as camera and LiDAR, is crucial for multimodal perception. Traditional methods based on specific calibration targets exhibit poor robustness in complex optical environments such as glare, reflections, or low light, and they rely on cumbersome manual operations. To address this, we propose a fully unsupervised, end-to-end calibration framework. Our approach adopts a dual-Transformer architecture: a Vision Transformer extracts semantic features from the image stream, while a Point Transformer captures the geometric structure of the 3D LiDAR point cloud. These cross-modal representations are aligned and fused through a neural network, and a regression algorithm is used to obtain the 6-DoF extrinsic transformation matrix. A multi-constraint loss function is designed to enhance structural consistency between modalities, thereby improving calibration stability and accuracy. On the KITTI benchmark, our method achieves a mean rotation error of 0.21° and a translation error of 3.31 cm; on a self-collected dataset, it attains an average reprojection error of 1.52 pixels. These results demonstrate a generalizable and robust solution for optical-sensor extrinsic calibration, enabling precise and self-sufficient perception in real-world applications. Full article

The Building Information Model (BIM) has been increasingly adopted for building maintenance and management. For existing buildings lacking prior digital models, a BIM is often generated from 3D scanned point clouds. In recent years, the automatic construction of simple BIMs comprising major structural elements, such as floors, walls, ceilings, and columns, has become feasible. However, the automated generation of detailed BIMs that incorporate building equipment, such as electrical installations and safety systems, remains a significant challenge, despite their essential role in facility maintenance. This process not only enriches the information content of the BIM but also provides a foundation for evaluating building safety and hazard levels, as well as for supporting evacuation planning and disaster-preparedness simulations. Such equipment is typically attached to ceilings or walls and is difficult to detect due to its small surface area and thin geometric profile. This paper proposes a method for detecting building equipment based on laser reflection intensity, with the objective of facilitating the automatic construction of detailed BIMs from point clouds acquired by mobile laser scanners (MLSs). The proposed approach first corrects the reflection intensity by eliminating the effects of distance and incidence angle using polynomial approximation, thereby normalizing the intensity values for surfaces composed of identical materials. Given that the corrected intensity approximately follows a normal distribution, outliers are extracted as candidate points for building equipment via thresholding. Subsequently, the point cloud is converted into a 2D image representation, and equipment regions are extracted using morphological operations and connected component labeling. Experiments conducted on point clouds of building ceilings and walls demonstrate that the proposed method achieves a high detection accuracy for various types of building equipment. Full article

Plant disease recognition is a critical technology for ensuring food security and advancing precision agriculture. However, challenges such as class imbalance, heterogeneous image quality, and limited model interpretability remain unresolved. In this study, we propose a Synergistic Dual-Augmentation and Class-Aware Hybrid (SDA-CAH) model designed to achieve robust and interpretable recognition of plant diseases. Our approach introduces two innovative augmentation strategies: (1) an optimized MixUp method that dynamically integrates class-specific features to enhance the representation of minority classes; and (2) a customized augmentation pipeline that combines geometric transformations with photometric perturbations to strengthen the model’s resilience against image variability. To address class imbalance, we further design a class-aware hybrid sampling mechanism that incorporates weighted random sampling, effectively improving the learning of underrepresented categories and optimizing feature distribution. Additionally, a Grad-CAM–based visualization module is integrated to explicitly localize lesion regions, thereby enhancing the transparency and trustworthiness of the predictions. We evaluate SDA-CAH on the PlantVillage dataset using a pretrained EfficientNet-B0 as the backbone network. Systematic experiments demonstrate that our model achieves 99.95% accuracy, 99.89% F1-score, and 99.89% recall, outperforming several strong baselines, including an optimized Xception (99.42% accuracy, 99.39% F1-score, 99.41% recall), standard EfficientNet-B0 (99.35%, 99.32%, 99.33%), and MobileNetV2 (95.77%, 94.52%, 94.77%). For practical deployment, we developed a web-based diagnostic system that integrates automated recognition with treatment recommendations, offering user-friendly access for farmers. Experimental evaluations indicate that SDA-CAH outperforms existing approaches in predictive accuracy and simultaneously defines a new paradigm for interpretable and scalable plant disease recognition, paving the way for next-generation precision agriculture. Full article

The aerodynamic performance of racing motorcycles plays a crucial role in improving speed, stability, and rider control under dynamic conditions. While most existing studies focus on front-mounted winglets and fairing extensions, the aerodynamic role of rear fairing appendages remains comparatively unexplored despite their potential influence on drag, downforce distribution, and wake behaviour. In this work, three alternative rear winglet configurations were parametrically designed in Siemens NX and systematically evaluated within a validated CFD framework based on Simcenter STAR-CCM+, with the aim of assessing how geometric variations influence aerodynamic performance and achieve a favourable trade-off between reduced aerodynamic resistance and enhanced rear downforce. The numerical setup employed has been previously validated against wind-tunnel measurements in similar aerodynamic applications, ensuring the reliability and accuracy of the predicted flow fields. A Design Space Exploration (DSE) was performed through an automated multi-software workflow, enabling systematic variation in key geometric parameters and real-time assessment of their aerodynamic effects. The study revealed distinct influences of the different configurations on drag and lift coefficients, as well as on wake structure and flow detachment, highlighting the critical aerodynamic mechanisms governing rear stability and flow closure. Through iterative design and simulation, the workflow identified the most effective configuration, achieving a balance between reduced aerodynamic resistance and increased downforce, both essential for competitive racing performance. The results demonstrate the potential of integrating parametric modelling, automated CFD simulation, and DSE optimization in the aerodynamic design phase. This methodology not only offers new insights into the scarcely studied rear aerodynamic region of racing motorcycles but also establishes a replicable framework for future developments involving advanced optimization algorithms, experimental validation, and wake-interaction analyses between leading and trailing riders. Full article

The objective of the article is the quantitative assessment of the reliability index for a specific type of structure—trusses with node snap-through. The trends in contemporary geometric and structural design of architectural forms of rod domes are evolving towards increasing diameters and reducing rise. Therefore, it is justified to assess the safety of this type of structure. The Hasofer–Lind reliability index (β) was adopted as the reliability measure. In the reliability analysis, the FORM method was applied using the implicit form of the random variables function (combining external reliability software with the noncommercial finite-element method program) and using explicit forms of limit-state functions (neural networks were used and own original finite-element method module). In addition, the classical Monte Carlo method and the hybrid Monte Carlo method (combining with a neural network) were used. For dome loads in the range of 73–100%, the reliability index β can be estimated with reasonable accuracy (error) compared to standard methods. The obtained approximation functions allow for easy determination of the percentage of the maximum load that ensures safe operation. In addition, they allow us to indicate at what load level the reliability index reaches the standard level (at least β = 1.5 for the serviceability limit state). Full article

Accurate remote sensing scene classification is essential for applications such as environmental monitoring and disaster management. In real-world scenarios, particularly during emergency response and disaster relief operations, acquiring nadir-view satellite images is often infeasible due to cloud cover, satellite scheduling constraints, or dynamic scene conditions. Instead, off-nadir images are frequently captured and can provide enhanced spatial understanding through angular perspectives. However, remote sensing scene classification has primarily relied on nadir-view satellite or airborne imagery, leaving off-nadir perspectives largely unexplored. This study addresses this gap by introducing Off-nadir-Scene10, the first controlled and comprehensive benchmark dataset specifically designed for off-nadir satellite image scene classification. The Off-nadir-Scene10 dataset contains 5200 images across 10 common scene categories captured at 26 different off-nadir angles. All images were collected under controlled single-day conditions, ensuring that viewing geometry was the sole variable and effectively minimizing confounding factors such as illumination, atmospheric conditions, seasonal changes, and sensor characteristics. To effectively leverage abundant nadir imagery for advancing off-nadir scene classification, we propose an angle-aware active domain adaptation method that incorporates geometric considerations into sample selection and model adaptation processes. The method strategically selects informative off-nadir samples while transferring discriminative knowledge from nadir to off-nadir domains. The experimental results show that the method achieves consistent accuracy improvements across three different training ratios: 20%, 50%, and 80%. The comprehensive angular impact analysis reveals that models trained on larger off-nadir angles generalize better to smaller angles than vice versa, indicating that exposure to stronger geometric distortions promotes the learning of view-invariant features. This asymmetric transferability primarily stems from geometric perspective effects, as temporal, atmospheric, and sensor-related variations were rigorously minimized through controlled single-day image acquisition. Category-specific analysis demonstrates that angle-sensitive classes, such as sparse residential areas, benefit significantly from off-nadir viewing observations. This study provides a controlled foundation and practical guidance for developing robust, geometry-aware off-nadir scene classification systems. Full article

Inspired by the concept of antennas in electromagnetics, this study proposes a novel acoustic metamaterial using Archimedean spiral structures. Unlike traditional resonant absorption structures, the present structure does not rely on resonant cavities but consists of multiple channels bent according to specific geometric parameters. The absorption mechanism is attributed to the combination of Fabry–Pérot (FP) resonance and viscous loss effects at waveguide boundaries. A theoretical model based on the transfer matrix method has been established and validated through numerical methods. Furthermore, the present study investigated the relationship between absorption performance and geometric parameters through theoretical analysis and numerical simulations, achieving efficient absorption across a wide frequency range and at low frequencies by adjusting these parameters. Additionally, samples have been fabricated using additive manufacturing techniques and experimental validation confirmed the accuracy of the theoretical and numerical simulations. The structure designed in this paper is expected to be applied to the engineering field with the need of broadband sound absorption. Full article

Accurate semantic interpretation of 3D point clouds is essential for the digital transformation of architecture, engineering, and construction (AEC). Building Information Modeling (BIM) depends on both geometric precision and semantic consistency, yet raw scans are typically noisy, redundant, and computationally expensive to process. This work presents an Information Bottleneck (IB) formulation that regularizes latent features to preserve only task-relevant information, yielding compact and interpretable representations within point-based neural networks. Our method, named IB-PC (Information Bottleneck for Point Clouds), introduces an Information Bottleneck (IB) layer as an auxiliary regularization task alongside supervised prediction, encouraging information compression and improving model robustness. Evaluations are conducted on two representative benchmarks, Semantic3D (outdoor) and TUM RGB-D (indoor) across, five criteria: (i) segmentation accuracy, (ii) calibration, (iii) robustness to noise and occlusion, (iv) computational efficiency, and (v) structural fidelity of architectural elements. The IB-regularized model consistently improves mean Intersection over Union (mIoU), overall accuracy, and macro F1, while reducing Expected Calibration Error (ECE) and Negative Log-Likelihood (NLL). The model remains stable under noise, occlusion, and varying point densities, and yields more consistent segmentation of architectural components such as walls, floors, and columns. These improvements are achieved with roughly 30% fewer FLOPs and reduced memory consumption, demonstrating the method’s efficiency and suitability for large-scale scan-to-BIM and digital twin applications. Full article

Accurate kinematic parameter identification is essential for space manipulators to attain millimeter-level positioning accuracy and robust motion control. This study develops a universal strategy for comprehensive parameter identification by establishing a generalized geometric error model using Denavit–Hartenberg (DH) parameterization. For robotic calibration, the Fibonacci spiral sampling technique optimizes pose selection, ensuring end-effector poses fully cover the manipulator’s workspace to enhance identification convergence. By combining the local convergence capability of the Levenberg–Marquardt (LM) algorithm with the global search characteristics of Particle Swarm Optimization (PSO), we propose a novel hybrid PSO-LM optimization algorithm, achieving synergistic enhancement of global exploration and local refinement. An experimental platform using a laser tracker as the metrology reference was constructed, with a 6-degree-of-freedom (6-DOF) space manipulator selected as a validation case. Experimental results demonstrate that the proposed method significantly reduces the average positioning error from 10.87 mm to 0.47 mm, achieving a 95.7% improvement in relative accuracy. These findings validate that the parameter identification approach can precisely determine the actual geometric parameters of space manipulators, providing critical technical support for high-precision on-orbit operations. Full article

Multiband fusion (MF) technology can generate an ultra-wideband echo (UWBE) from multiple sub-band echoes (SBEs), thereby improving radar range resolution and enhancing target recognition capabilities. However, current MF methods generally do not account for the incoherence introduced by fluctuations in the scattering characteristics of scattering centers (SCs) across different frequency bands. This oversight can lead to degraded fusion performance. To address this limitation, a novel MF method that explicitly considers the fluctuation of SC characteristics between sub-bands is proposed in this paper. Firstly, a theoretical analysis of the additional incoherent phase term introduced by these fluctuations is conducted, which demonstrates its impact on fusion accuracy. Based on this analysis, scattering centers are extracted from SBEs based on the geometrical theory of diffraction (GTD) model, and then categorized into two distinct types: intrinsic scattering centers (ISCs) and unique scattering centers (USCs). Subsequently, a new incoherent phase estimation and compensation method is proposed, leveraging this categorization to effectively mitigate the inter-sub-band incoherence. The two types of SCs are then processed through either fusion or super-resolution to generate individual UWBEs, which are finally combined to form the final UWBE. The effectiveness of the proposed method is validated using both simulated electromagnetic scattering data and static measured data. Numerical results demonstrate that the proposed method achieves significantly greater fusion accuracy compared to traditional MF approaches, confirming the practical benefits of incorporating SC fluctuation modeling into the fusion process. Full article

In this paper, an integrated geometric and thermal-induced errors prediction approach for active error compensation in machine tools is proposed and evaluated. The proposed approach is based on a hybrid of physical and neural network predictive modeling to drive an adaptive position controller for real-time error compensation including geometric and thermal-induced errors. Error components are formulated as a three-dimensional error field in the time-space domain. This approach involves four key steps for its development and implementation: (i) simplified experimental procedure combining a multicomponent laser interferometer measurement system and sixteen thermal sensors for error components measurement, (ii) artificial neural network-based predictive modeling of both position-dependent and position-independent error components, (iii) tridimensional volumetric error mapping using rigid body kinematics, and finally (iv) implementation of the real-time error compensation. Assessed on a turning center, the proposed approach conducts a significant improvement of the machine accuracy. The maximum error is reduced from 30 µm to less than 3 µm under thermally varying conditions. Full article

To address the challenges of limited degrees of freedom (DOF) and mutual coupling effects in sparse array-based Direction-of-arrival (DOA) estimation, this paper proposes a novel array configuration termed the Golden ratio Inspired Coprime Array (GICA). This design integrates the golden ratio ($\varphi \approx 1.618$) into the geometric arrangement of three hierarchically structured subarrays to achieve enhanced difference coarray properties. Theoretical analysis demonstrates that the proposed configuration, through strategic sensor placement and virtual domain processing, significantly increases the achievable DOF. Comprehensive simulations show that this design exhibits competitive estimation performance, achieving reduced root mean square error (RMSE) across most signal-to-noise ratio (SNR) regimes and snapshot conditions when compared with contemporary coprime array configurations. Additionally, quantitative mutual coupling analysis reveals that the proposed structure achieves superior electromagnetic compatibility, demonstrating the lowest coupling leakage coefficient among tested configurations. Experimental validation under varying coupling strengths shows that this array design maintains stable estimation performance with minimal degradation. These results confirm the proposed configuration as an effective sparse array solution that simultaneously enhances DOA estimation accuracy and mutual coupling robustness. Full article

Existing focal length calibration methods rely on predefined calibration fields or control point networks, which are unsuitable for real-time applications with variable zoom in industrial and agricultural environments. This paper proposes a method based on global scanning principles and geometric constraints, eliminating control points and using symmetric features. A spatial weighting strategy optimizes redundant measurements by integrating optical distortion and the spatial distribution of measured points, enhancing accuracy. Experimental results show that the method achieves micron-level calibration precision, significantly improving visual measurement system accuracy under complex zoom conditions. Full article

Efficient locomotion in autonomous driving and robotics requires clearer visualization and more precise map. This paper presents a high accuracy online mapping including weight matching LiDAR-IMU-GNSS odometry and an object-level highly dynamic point cloud filtering method based on a pseudo-occupancy grid. The odometry integrates IMU pre-integration, ground point segmentation through progressive morphological filtering (PMF), motion compensation, and weight feature point matching. Weight feature point matching enhances alignment accuracy by combining geometric and reflectance intensity similarities. By computing the pseudo-occupancy ratio between the current frame and prior local submaps, the grid probability values are updated to identify the distribution of dynamic grids. Object-level point cloud cluster segmentation is obtained using the curved voxel clustering method, eventually leading to filtering out the object-level highly dynamic point clouds during the online mapping process. Compared to the LIO-SAM and FAST-LIO2 frameworks, the proposed odometry demonstrates superior accuracy in the KITTI, UrbanLoco, and Newer College (NCD) datasets. Meantime, the proposed highly dynamic point cloud filtering algorithm exhibits better detection precision than the performance of Removert and ERASOR. Furthermore, the high-accuracy online mapping is built from a real-time dataset with the comprehensive filtering of driving vehicles, cyclists, and pedestrians. This research contributes to the field of high-accuracy online mapping, especially in filtering highly dynamic objects in an advanced way. Full article