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Three-dimensional reconstruction plays a vital role in assisting doctors and surgeons in diagnosing the healing progress of bone defects. Common three-dimensional reconstruction methods include surface and volume rendering. As the focus is on the shape of the bone, this study omits the volume rendering methods. Many improvements have been made to surface rendering methods like Marching Cubes and Marching Tetrahedra, but not many on working towards real-time or near real-time surface rendering for large medical images and studying the effects of different parameter settings for the improvements. Hence, this study attempts near real-time surface rendering for large medical images. Different parameter values are experimented on to study their effect on reconstruction accuracy, reconstruction and rendering time, and the number of vertices and faces. The proposed improvement involving three-dimensional data smoothing with convolution kernel Gaussian size 5 and mesh simplification reduction factor of 0.1 is the best parameter value combination for achieving a good balance between high reconstruction accuracy, low total execution time, and a low number of vertices and faces. It has successfully increased reconstruction accuracy by 0.0235%, decreased the total execution time by 69.81%, and decreased the number of vertices and faces by 86.57% and 86.61%, respectively.

Achieving lightweight representations of building mesh models with accurate geometry and fine structural details is a key challenge in urban 3D modelling. Most existing mesh simplification methods focus on minimizing geometric error while neglecting the specific characteristics of building models in terms of geometric structure and semantic hierarchy, thus leading to structural degradation and semantic inconsistencies. To address this issue, this paper proposes a structure–semantic dual-constrained edge-collapse decimation method for simplifying dense building mesh models reconstructed from point clouds. Our core innovation lies in the joint enforcement of geometric structural constraints and building semantic constraints to effectively preserve both geometric structural features and component-level semantic structures of the models. By incorporating these two constraints, we adaptively assign higher collapse penalties to key structural edges and semantic boundaries, achieving lightweight building model simplification while maintaining fine-level structural details even under high compression ratios. Our method is extensively validated on several datasets of varying scales and complexities, including single-building models from Sketchfab, the large-scale urban datasets SUM and STPLS3D, and the ArCH cultural heritage dataset. Experimental results demonstrate that our method achieves superior or comparable performance compared to the existing methods across all the test datasets, consistently achieving lower or on-par geometric errors measured by RMSE and MAE. Furthermore, our simplified results can be semantically organized and stored under the CityGML paradigm, which provides a unified data support for sharing, semantic retrieval, downstream analysis, and other applications of lightweight building models.

Recent advancements in 3D data acquisition and processing have enabled high-fidelity urban modeling. Yet, production of structured 3D models in standards like CityGML remain complex, resource-intensive, and difficult to automate. This paper introduces a low-cost alternative that we call “structured mesh model” designed to cover many applications of structured 3D models at a lower cost. It relies on integrating geometric simplification with segmentation alignment to produce a lightweight, unified mesh representation. Using an edge-collapse algorithm, our method combines geometry from an existing mesh with labeled point cloud data to create a continuous mesh with edges aligned to segmentation boundaries, preserving both geometric fidelity and semantic clarity. The resulting structured mesh efficiently reduces memory requirements while maintaining accuracy, offering a practical solution for simulations and urban analyses that require structured 3D data.

Mesh simplification is an effective way to solve the contradiction between 3D models and limited transmission bandwidth and smooth model rendering. The existing mesh simplification algorithms usually have problems of texture distortion, deformation of different degrees, and no texture simplification. In this paper, a model simplification algorithm suitable for 3D reconstruction is proposed by taking full advantage of the recovered 3D scene structure and calibrated images. First, the reference 3D model scene is constructed on the basis of the original mesh; second, the images are collected on the basis of the reference 3D model scene; then, the mesh and texture are simplified by using the reference image set combined with the QEM algorithm. Lastly, the 3D model data of a town in Tengzhou are used for experimental verification. The results show that the algorithm proposed in this paper basically has no texture distortion and deformation problems in texture simplification and can effectively reduce the amount of texture data, with good feasibility.

To achieve timeliness and accuracy when conducting a performance analysis of a complex product assembly process, we consider the performance analysis of an array antenna assembly process, and investigate the measurability of the assembly process’ performance. In this study, a digital twin modeling method is proposed for the assembly performance analysis of an array antenna. This method integrates finite elements, mesh model simplification, and surrogate models. To achieve real-time prediction and rapid visualization of the assembly performance of the assembly process, a mesh simplification algorithm for a finite element mesh model based on edge collapse is proposed and a Gaussian process regression surrogate model for product assembly performance is constructed to complete the construction of a digital twin model for assembly performance analysis. Finally, the proposed method is verified by an example of the assembly performance analysis of a typical array antenna. Results show that the digital twin model proposed achieves a high response speed and fidelity in the analysis of assembly performance and provides a valuable reference to application of digital twin technology in product assembly processes.

Efficient level-of-detail (LOD) management is crucial for handling large-scale 3D meshes in BIM, GIS, and digital twin applications. In practice, both individual models and complex multi-mesh scenes require multi-resolution representations. Yet two practical issues persist: (i) simplification rates are often fixed a priori, lacking principled guidance and yielding suboptimal fidelity–cost trade-offs; and (ii) after a scene-level target is set, workflows commonly impose a uniform rate on all models, which is ill-suited to heterogeneous geometry and produces uneven visual quality. This paper presents an automatic approach that constructs a cumulative edge collapse loss curve using a QEM (Quadric Error Metrics)-based process. Shape analysis of this curve defines four representative LOD targets, and an automated procedure then determines their corresponding simplification rates. The method is first developed for individual meshes and then extended to multi-mesh scenes, assigning model-specific rates that satisfy a prescribed scene-level reduction while maintaining visual consistency. Experiments on complex engineering datasets show higher fidelity than uniform-rate baselines, especially at high reductions. The approach provides a practical, automated framework for object- and scene-level LOD generation.

Three-dimensional building models, as core data in Building Information Modeling, are extensively applied in various fields such as architectural design and urban planning. However, the geometric complexity and the vast amount of data involved pose significant challenges for storage, computation, and efficient application. Therefore, ensuring the geometric accuracy and semantic integrity of models while reducing data redundancy has become a critical issue in current research. To address this problem, this paper proposes a lightweighting method for 3D building models based on semantic constraints. The method combines face merging and face movement graphical simplification algorithms to simplify models from LOD3 to LOD1 in a layer-by-layer manner. In the transition from LOD3 to LOD2, a face merging algorithm is employed to analyze the semantic consistency of adjacent faces, ensuring that only semantically consistent faces are merged, thus generating an LOD2 model with semantic integrity. In the transition from LOD2 to LOD1, a concave-convex face movement and merging algorithm is utilized to reduce redundant data while maintaining geometric similarity and optimizing the model's topological structure, ultimately producing a lightweight LOD1 model. Experimental results demonstrate that the proposed method significantly reduces the model's storage requirements while effectively preserving its semantic information.

Simplification of Computer-Aided-Design models plays an important role in reducing the complex features of man-made objects produced according to engineering needs. This paper proposes an efficient feature preserving method to simplify CAD models by extracting sharp edges and decomposing the original mesh model into low varying curvature sub-regions which are representable through their boundary curves. To extract the sharp edges and important boundaries of the model, the maximum curvature value and the minimum curvature direction are taken into account. We analyze the anisotropic features of the mesh to specify robust features on the mesh. An enhanced segmentation algorithm is used to decompose the mesh into sub-sections with smooth boundary curves that are suitable for our simplification framework. Once the input mesh is segmented into new sub-sections, the common boundary space curves between two different segments are determined and decimated by down sampling the vertices on the boundary curve. The sorted list of selected sample points is the only data used to represent our simplified model. After interpolating the decimated vertices, we fit a surface to the reconstructed boundary curve. Surface fitting is completed in three steps. First, the interpolated vertices of an enclosed curve are projected onto a plane. Second, the finite element mesh generation technique is employed to triangulate inside of each segment. Finally, the surface fitting to the space boundary curves is accomplished using interpolation technique for the vertices of the triangulated planer mesh inside the segment. Experimental results demonstrate that the proposed method can efficiently simplify a CAD model with complex geometric features and complicated shapes. Several comparisons have been conducted to establish the superiority of the presented algorithm over other state-of-the-art methods.

Existing contour-based building-reconstruction methods face the challenge of producing low-poly results. In this study, we introduce a novel iterative contour-based method to reconstruct low-poly meshes with only essential details from mesh soups. Our method focuses on two primary targets that determine the quality of the results: reduce the total number of contours, and generate compact surfaces between contours. Specifically, we implemented an iterative pipeline to gradually extract vital contours by loss and topological variance, and potential redundant contours will be removed in a post-processing procedure. Based on these vital contours, we extracted the planar primitives of buildings as references for contour refinement to obtain compact contours. The connection relationships between these contours are recovered for surface generation by a contour graph, which is constructed using multiple bipartite graphs. Then, a low-poly mesh can be generated from the contour graph using our contour-interpolation algorithm based on polyline splitting. The experiments demonstrated that our method produced satisfactory results and outperformed the previous methods.

Recently, the learning method is gradually penetrating into the field of 3D saliency, but the ground truth annotation is too insufficient to directly train a 3D saliency network. Here, we propose a novel attention-embedding strategy for 3D saliency estimation by directly applying the attention embedding scheme to 3D mesh. With this method, the network is trained in a weakly supervised manner, requiring no saliency annotations but generalizing well on different categories of objects, such as animals, furniture, cars and people. Experimental results show that our approach is comparable with existing state-of-the-art methods. We also apply saliency results to mesh simplification. Evaluations on simplified models show that the visually significant parts can be retained during saliency-aware simplification.