To address the challenge of achieving high-quality 3D reconstruction with Neural Radiation Fields (NeRF) under the influence of occluding objects, a method based on the collaborative optimization of Structure-from-Motion (SfM) and the Segment Anything Model (SAM) was propose. Building upon the Scale-Invariant Feature Transform (SIFT) algorithm within the SfM reconstruction process, geometric inconsistencies in dynamic scenes were leveraged for feature point identification and matching. Unmatched feature points were treated as dynamic occluders, guiding the SAM model—capable of point-guided segmentation—to perform dynamic occluder segmentation and generate a static scene mask. Based on the segmentation results, mask-aware volumetric rendering was used to predict colors and a quadruple loss function was established: comprising reconstruction loss, structural consistency loss, adversarial loss, and self-supervised patching loss. These objectives were jointly optimized to constrain the color output in patched regions. After iterative training, consistent restoration of geometric structure and appearance in occluded areas across multiple viewpoints was achieved. The radiometric integrity was preserved while occlusions were removed. Validation on public dynamic scene datasets demonstrated that the mask-based volumetric rendering combined with joint optimization produced an average Peak Signal-to-Noise Ratio (PSNR) improvement of 5.24 dB over baseline models and mainstream occlusion removal methods, alongside a 35% reduction in Learned Perceptual Image Patch Similarity (LPIPS). This approach established a new paradigm for 3D reconstruction in complex dynamic environments.

Aeroengine blades are core components of engines; tiny surface defects can lead to serious safety accidents. Traditional vision detection technology is limited by motion blur, low dynamic range, background redundancy, and so forth. To address these challenges, a method of aeroengine blade defect detection based on Dynamic Vision Sensor (DVS) was proposed. Dynamic vision sensor produced data in an asynchronous event-stream format, and were therefore referred to as event camera, which exhibited the advantages of large dynamic range, high frame rate, and strong ability to capture small targets. Firstly, a defect detection platform based on DVS was built, and its imaging characteristics and advantages were explored. On this basis, the first Event-based Defect Detection Dataset of Aeroengine Blade (EDD-AB) dataset based on DVS was constructed, covering nearly 6 000 images of scratches, point marks and edge damage, with approximately 12 000 finely annotated target labels. The dataset was released as open source (link: https://github. com/NiBieZhouMei5520/EDD-AB.git). Furthermore, a multi-scale defect-detection algorithm based on asynchronous event-stream frame aggregation (AEAF-ABDD) was proposed: event streams were visualized through frame aggregation technology using a fixed time window; a Multi-Resolution Adaptive Feature Pyramid Network (MRAFPN) was developed to enhance multi-scale defect feature extraction capability; a lightweight SimAM attention mechanism was incorporated to strengthen focus on key regions; a star-convolution module (StarNet) was fused to improve the efficiency of high-dimensional nonlinear feature mapping, enabling accurate detection of multi-scale defects on complex curved workpieces. Experiments demonstrated that AEAF-ABDD achieved a mean Average Precision (mAP) of 97.7% on the EDD-AB dataset and a detection speed of 105 frames per second, substantially outperforming mainstream algorithms. An efficient solution for automated quality inspection of highly reflective curved workpieces was thereby provided, promoting the application of DVS in the field of industrial inspection.

Few-shot image generation has important application value in fields such as medical imaging and artistic creation. In recent years, significant research progress has been made in this task, with mainstream approaches typically relying on transferring generative models pretrained on large-scale source domain datasets to target domains to mitigate data-scarcity challenges. However, when substantial semantic gaps exist between source and target domains, direct transfer often introduced incompatible source-specific features, degrading image realism and style consistency. Although existing methods have removed redundant features via static pruning strategies, such as fixed-threshold filter pruning, they struggle to adapt to the dynamic evolution of features across different layers of deep networks, often resulting in the mistaken removal of general low-level features while retaining redundant high-level ones, thereby affecting the adaptation performance and generation quality of the model. To address this, a dynamic pruning method based on filter-importance estimation was proposed. Specifically, the method continuously tracked the changes in Fisher information of each layer’s filters during training to evaluate their importance for image generation quality. Based on the Fisher information, a cumulative importance weight-based adaptive pruning mechanism was constructed to dynamically determine the pruning ratio for each layer, enabling more precise removal of redundant or incompatible filters while preserving general structural semantic information. Experiments were conducted on several representative few-shot target domains, and results showed that the proposed method significantly outperformed existing approaches in terms of image quality (Frechet Inception Distance, FID) and image diversity (Intra-domain Learned Perceptual Image Patch Similarity, Intra-LPIPS). In target domains exhibiting significant semantic differences from the source domain, the proposed method achieved superior FID scores compared with the current state-of-the-art methods, demonstrating its stability and superiority for cross-domain few-shot image generation tasks.

To address the issues of high computational cost and complex network models in point cloud classification and segmentation methods, a point cloud classification and segmentation algorithm based on lightweight networks and weighted Random Forest (RF) was proposed. The algorithm achieved efficient classification and segmentation in a hierarchical manner. Firstly, to address the issues of multiple layers and complex computation in traditional neural networks, a lightweight neural network was constructed to extract point cloud features such as global shape, inter-regional relationships, curvature, normal vector, and color, thereby achieving rapid rough classification and segmentation of point clouds. Then, to address data imbalance, an adaptive classification and segmentation strategy was designed. By introducing a weighted RF and combining inconsistency-measurement screening with dynamic-weighting optimization mechanisms, fine classification and segmentation of point clouds were achieved. The algorithm conducted classification experiments on the ModelNet40 dataset and segmentation experiments on the Semantic3D dataset and outdoor-scene point-cloud data. The results showed that compared with Local Geo-Transformer, PointNeXt, and FastPointNet++, classification and segmentation accuracy increased by approximately 1.9%, 1.6%, and 1.7%, respectively, while classification and segmentation time was reduced by approximately 40%, 30%, and 20%, respectively. Thus, the proposed point-cloud classification and segmentation algorithm based on lightweight networks and weighted RF can effectively reduce the training time of the model and improve the efficiency of classification and segmentation while maintaining high accuracy, making it an effective point cloud classification and segmentation algorithm.

Currently the mainstream enveloping box methods are widely used in 3D scene rendering, ray tracing, and collision detection tasks; however, these methods suffer from the problems of low space utilization and insufficient fitting accuracy in fitting complex geometries, which are difficult to ensure strict conservatism and still have room for improvement in reducing false detection rates. To address these issues, a conservative bounding-box construction method combining implicit geometric coding and Lipschitz constraints was proposed. Implicit geometric coding mapped the input coordinates to a high-dimensional space via position coding, thus capturing local and global geometric information and improving bounding-box adaptability. A trainable Lipschitz-constrained linear layer was introduced to dynamically adjust Lipschitz constants control gradient changes, and Lipschitz regularization loss was combined with dynamically weighted cross-entropy loss to reduce the FP rate while optimizing the boundary fitting. The experimental results demonstrated that the method can achieve a false-negative rate of 0 on multiple 3D models and reduce the false-detection rate by up to 3.1% compared to the benchmark method, and improve the single-ray query method by 1.7 ms, providing a highly efficient and robust solution for high-precision conservative bounding box fitting.

Given that the impact of rough surfaces on assembly accuracy had been insufficiently considered in the existing assembly accuracy prediction for complex products, leading to inaccurate precision prediction and limited practical assembly applicability, an assembly-accuracy prediction method considering rough surfaces was proposed. Firstly, an assembly-accuracy information model was constructed to express mating feature, geometric tolerance, and roughness information. Based on the model, an assembly-precision knowledge graph was constructed. Secondly, a geometric-tolerance representation model was established based on the Small-Displacement Torsor (SDT) theory; a simulation method for rough surfaces of plane and cylindrical parts as well as a determination method of SDT expressions were studied. Thirdly, the error-propagation path of the assembly was determined according to the assembly sequence, and a pose-relationship graph for the assembly was constructed. Then, the assembly-precision prediction was achieved using a Jacobian-torsor model. Finally, the feasibility of the method was verified using the crank-connecting-rod mechanism of a specific construction-machine model as an example. The simulation results demonstrated that the method could achieve accurate assembly-precision prediction and provided valuable guidance for practical assembly operations.

To address the challenges in integrating Digital-Twin (DT) technology with large-scale generative models in industrial design, a CAD-LDT digital-twin modeling framework based on generative foundation models was proposed. The framework adopted a triadic architecture consisting of a physical-entity module, an intelligent generation module, and a virtual-entity module, and innovatively incorporated multi-modal data fusion mechanisms and domain-knowledge constraints to enable autonomous generation of parameterized CAD models from physical-entity descriptions. Utilizing LLaVA-7B and LLaMA-7B as backbone models, the framework employed LoRA-based lightweight adapters to achieve cross-modal alignment between visual and textual features, and introduced a constraint encoder that transformed geometric tolerances and physical rules into structured JSON objects. To enhance the mathematical consistency of spatial transformations, Lie-group algorithms were adopted for the optimization of rigid-body transformations, while a geometric-weight binning strategy was proposed to discretize complex assembly relationships. Moreover, a spatiotemporal-decoupled generation strategy was designed to jointly optimize spatial layout and assembly sequencing. Experimental results on the DeepCAD dataset indicated that the proposed framework achieved an Intersection- over-Union (IoU) of 83.6%, a constraint satisfaction rate of 91.3%, and a 26.5% improvement in generation efficiency, significantly outperforming existing baseline models. Further ablation studies confirmed the critical contributions of multi-modal fusion, constraint encoding mechanisms, and Lie-group optimization to modeling performance, providing a novel DT modeling paradigm for intelligent manufacturing with demonstrated value in parametric design and assembly process optimization.

The traditional development approach for complex forming equipment typically relies on Document-Based Systems Engineering (DBSE), which often leads to issues such as protracted development cycles due to inadequate requirement analysis, incomplete requirement coverage caused by textual ambiguity, and equipment development delays lagging behind technological iterations. These shortcomings frequently result in final designs that fail to meet target performance metrics and require inefficient, repetitive modifications. Therefore, in the conceptual design stage of complex forming equipment, and drawing on the U.S. Department of Defense Architecture Framework (DoDAF) combined with Model-Based Systems Engineering (MBSE), an MBSE-based conceptual-design method for complex forming equipment was proposed. This method utilized five viewpoints, including panoramic viewpoint, capability viewpoint, operational viewpoint, systems viewpoint, and standards viewpoint, as entry points for the conceptual design of complex forming equipment. Through multi-perspective analysis, the method performed top-level requirements acquisition, requirements refinement analysis, functional analysis, and system modeling across four design levels. Eleven types of models were established using the Systems Modeling Language (SysML), enabling digital and procedural expression in the conceptual design stage of complex forming equipment. Finally, superplastic-forming equipment was used as a representative example to demonstrate the application of this design method. The application of the method addressed the shortcomings of traditional design approaches and demonstrated that the method provided effective guidance for the forward development of complex forming equipment.

With the widespread application of Building Information Modeling (BIM) technology in the field of engineering design, the Revit platform-driven 3D forward design has developed into a relatively mature solution for superstructure. However, as a critical structural component, isolated foundations still face challenges such as low efficiency and poor information integrity in 3D modeling and drawing generation. Moreover, due to the closed data interfaces of mainstream structural analysis software, it is extremely challenging to directly extract reinforcement information from fundamental database files and synchronize to the properties of Revit elements. To address this limitation, a new parametric modeling algorithm for isolated foundations based on AutoCAD layer and text recognition was proposed, utilizing the Revit Application Programming Interface (API) and the Model-View-ViewModel (MVVM) design pattern. By importing foundation layout and reinforcement annotation drawings generated by structural analysis software (e.g., YJK) into Revit, the algorithm calculated the center coordinates and planar dimensions of foundations using layer-recognition methods. Then, a matching logic was established between foundations and reinforcement annotations according to their relative positions. Subsequently, text recognition was employed to extract the corresponding annotations’ numbering, height, and reinforcement information, which were then automatically written into the family properties of the model. Additionally, the extraction of model attributes and geometric face references enabled automatic annotation of foundation dimensions and reinforcement. Finally, the proposed method was applied to the parametric modeling and drafting of isolated foundations for a self- built cold storage factory and was compared against conventional methods. The results demonstrated that this algorithm significantly improved the efficiency of three-dimensional modeling and annotation drawing for isolated foundations, while exhibiting excellent compatibility with calculation files exported from PKPM software.

Among all safety accidents in construction scenarios, collision accidents are regarded as one of the most common types of injury. To effectively prevent and monitor the occurrence of collision accidents, the computer graphics analysis technology has been used to assist collision detection and analysis; however, limitations remain in balancing the real-time performance with high precision of detection. To address this, a collision-detection method based on dynamic voxelization was proposed. This method integrated the generation of dynamic spatial voxel tree with the dynamic spherical voxelization calculation of resources to construct a collision detection and analysis mechanism. The core ideas are as follows: ① Based on the crowding-degree threshold, the space was recursively divided to generate a dynamic voxel tree, effectively filtering out non-collision risk areas. ② The side length of voxel units were dynamically calculated according to the relative distance between resources and resource volume, realizing the adaptive adjustment of voxel granularity. ③ Spherical voxels were used instead of traditional cubic voxels to avoid the computational burden of non-axis-aligned detection. ④ A hollowing-out procedure was introduced to eliminate internal invalid voxels, further optimizing detection efficiency. This method can accurately capture resource interactions in complex dynamic construction environments, significantly improving detection accuracy and optimizing computational efficiency. Experimental results showed that compared with traditional methods, the proposed method significantly improved the detection accuracy, with precision and accuracy reaching 94.64% and 96.67%, respectively. In terms of collision detection time, it was more efficient than most existing methods, with a calculation speed increase of at least about 11.36%. At the same time, the study analyzed the impact of key parameters such as voxel-tree depth, root-node size, and voxel side length on performance, and analyzed the consumption of CPU resources and memory resources by the method in scenarios of different scales. The consumption was within an acceptable range, verifying the applicability of the method in construction scenarios. The method provided an effective new idea of information processing for enhancing the intelligent level of construction safety management.