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The primary objective of this study is to tackle the enduring challenge of preserving feature integrity during the manipulation of geometric data in computer graphics. Our work aims to introduce and validate a variational sparse diffusion model that enhances the capability to maintain the definition of sharp features within meshes throughout complex processing tasks such as segmentation and repair.

We developed a variational sparse diffusion model that integrates a high-order L₁ regularization framework with Dirichlet boundary constraints, specifically designed to preserve edge definition. This model employs an innovative vertex updating strategy that optimizes the quality of mesh repairs. We leverage the augmented Lagrangian method to address the computational challenges inherent in this approach, enabling effective management of the trade-off between diffusion strength and feature preservation. Our methodology involves a detailed analysis of segmentation and repair processes, focusing on maintaining the acuity of features on triangulated surfaces.

Our findings indicate that the proposed variational sparse diffusion model significantly outperforms traditional smooth diffusion methods in preserving sharp features during mesh processing. The model ensures the delineation of clear boundaries in mesh segmentation and achieves high-fidelity restoration of deteriorated meshes in repair tasks. The innovative vertex updating strategy within the model contributes to enhanced mesh quality post-repair. Empirical evaluations demonstrate that our approach maintains the integrity of original, sharp features more effectively, especially in complex geometries with intricate detail.

The originality of this research lies in the novel application of a high-order L₁ regularization framework to the field of mesh processing, a method not conventionally applied in this context. The value of our work is in providing a robust solution to the problem of feature degradation during the mesh manipulation process. Our model’s unique vertex updating strategy and the use of the augmented Lagrangian method for optimization are distinctive contributions that enhance the state-of-the-art in geometry processing. The empirical success of our model in preserving features during mesh segmentation and repair presents an advancement in computer graphics, offering practical benefits to both academic research and industry applications.

It is a challenge to print a model with the size that is larger than the working volume of a three-dimensional (3D) printer. The purpose of this paper is to present a feasible approach to divide a large model into small printing parts to fit the volume of a printer and then assemble these parts into the final model.

The proposed approach is based on the skeletonization and the minima rule. The skeleton of a printing model is first extracted using the mesh contraction and the principal component analysis. The 3D model is then partitioned preliminarily into many smaller parts using the space sweep method and the minima rule. The preliminary partition is finally optimized using the greedy algorithm.

The skeleton of a 3D model can effectively represent a simplified version of the geometry of the 3D model. Using a model’s skeleton to partition the model is an efficient way. As it is generally desirable to have segmentations at concave creases and seams, the cutting position should be located in the concave region. The proposed approach can partition large models effectively to well retain the integrity of meaningful parts.

The proposed approach is new in the rapid prototyping field using the model skeletonization and the minima rule. Based on the authors’ knowledge, there is no method that concerns the integrity of meaningful parts for partitioning. The proposed method can achieve satisfactory results by the integrity of meaningful parts and assemblability for most 3D models.

Mesh Parameterization is central to reverse engineering, tool path planning, etc. This work synthesizes parameterizations with un-constrained borders, overall minimum angle plus area distortion. This study aims to present an assessment of the sensitivity of the minimized distortion with respect to weighed area and angle distortions.

A Mesh Parameterization which does not constrain borders is implemented by performing: isometry maps for each triangle to the plane Z = 0; an affine transform within the plane Z = 0 to glue the triangles back together; and a Levenberg–Marquardt minimization algorithm of a nonlinear F penalty function that modifies the parameters of the first two transformations to discourage triangle flips, angle or area distortions. F is a convex weighed combination of area distortion (weight: α with 0 ≤ α ≤ 1) and angle distortion (weight: 1 − α).

The present study parameterization algorithm has linear complexity [𝒪(n), n = number of mesh vertices]. The sensitivity analysis permits a fine-tuning of the weight parameter which achieves overall bijective parameterizations in the studied cases. No theoretical guarantee is given in this manuscript for the bijectivity. This algorithm has equal or superior performance compared with the ABF, LSCM and ARAP algorithms for the Ball, Cow and Gargoyle data sets. Additional correct results of this algorithm alone are presented for the Foot, Fandisk and Sliced-Glove data sets.

The devised free boundary nonlinear Mesh Parameterization method does not require a valid initial parameterization and produces locally bijective parameterizations in all of our tests. A formal sensitivity analysis shows that the resulting parameterization is more stable, i.e. the UV mapping changes very little when the algorithm tries to preserve angles than when it tries to preserve areas. The algorithm presented in this study belongs to the class that parameterizes meshes with holes. This study presents the results of a complexity analysis comparing the present study algorithm with 12 competing ones.

The purpose of this paper is to present a new method for automated post machining process planning for a hybrid manufacturing process. The manufacturing process is expected to generate complex functional parts by taking advantage of free form surface creation from additive manufacturing and high-quality surface finishing from CNC milling.

The hybrid process starts with additive manufacturing to generate a near net shape part with pre-defined machining allowances on surfaces requiring high quality surface or tight tolerances, along with integrated fixture geometry. The next step is to conduct automated machining process planning to determine critical parameters such as setup angle, tool selection, depth, tool containment, and consequently, the NC code to machine the part.

This method is shown to be a feasible solution for rapidly creating functional parts. The tests have been conducted to validate the method developed in this paper.

This paper introduces a new automated post machining process planning method for integrating additive manufacturing with a rapid milling process.

This paper aims to automatically obtain the implant parameter from the CBCT images to improve the outcome of implant planning.

This paper proposes automatic simulated dental implant positioning on CBCT images, which can significantly improve the efficiency of implant planning. The authors introduce the fusion point calculation method for the missing tooth's long axis and root axis based on the dental arch line used to obtain the optimal fusion position. In addition, the authors proposed a semi-interactive visualization method of implant parameters that be automatically simulated by the authors' method. If the plan does not meet the doctor's requirements, the final implant plan can be fine-tuned to achieve the optimal effect.

A series of experimental results show that the method proposed in this paper greatly improves the feasibility and accuracy of the implant planning scheme, and the visualization method of planting parameters improves the planning efficiency and the friendliness of system use.

The proposed method can be applied to dental implant planning software to improve the communication efficiency between doctors, patients and technicians.

Rapid prototyping (RP) of large‐scale solid models requires the stereolithographic (STL) file to be precisely partitioned. Especially, the selection of cutting positions is critical for the fabrication and assembly of sub‐models. The purpose of this paper is to present an efficient curvature‐based partitioning for selecting the best‐fit loop and decomposing the large complex model into smaller and simpler sub‐models with similar‐shaped joints, which facilitate the final assembly.

The partition algorithm is benefited from curvature analysis of the model surface, including extracting the feature edges and constructing the feature loops. The efficiency enhancement is achieved by selecting the best‐fit loop and constructing the similar‐shape joints. The utility of the algorithm is demonstrated by the fabrication of large‐scale rapid prototypes.

By using the proposed curvature‐based partition algorithm, the reasonability and efficiency of STL model partition can be greatly improved, and the complexity of sub‐models has been reduced. It is found that the large‐scale model is efficiently partitioned and the sub‐models are precisely assembled using the proposed partitioning.

The curvature‐based partition algorithm is used in the RP field for the first time. Based on the curvature‐based partitioning, the reasonability and efficiency of large‐scale RP is addressed in this paper.

The purpose of this paper is to numerically study blood flow through a subject‐specific carotid artery with a moderately severe stenosis, also to thoroughly analyse the wall shear stress (WSS), oscillatory shear index (OSI) and WSS angular deviation (WSSAD). One of the important aspects of this study is the investigation on the influence of the extensions attached to the domain outlets.

The segmentation of the carotid artery is carried out using a deformable model based on a level set method. A geometric potential force (GPF) is employed to deform the level set to obtain the carotid artery geometry. The initial surface meshing is generated using an advanced marching cubes (MC) method, before improving the quality of the surface mesh via a number of mesh cosmetic steps. The volume mesh generation has two parts. In the first part, a quasi‐structured, boundary layer mesh is generated in the vicinity of the geometry walls. The second part of the meshing involves unstructured tetrahedral meshing of the inner part of the geometry. After the meshing stage, the flow boundary conditions are generated by numerically solving the Helmholtz equation in both space and time. Finally, the explicit characteristic‐based split (CBS) method is employed in a parallel environment to produce a detailed analysis of wall quantities.

In general, WSS is very high in the vicinity of the carotid artery apex and in the proximity of the stenosis. From the results obtained, it is clear that the influence of outlet domain extension is marginal. While the peak instantaneous WSS differs by a maximum of 5.7 per cent, the time‐averaged WSS difference due to extended domain is only 1.3 per cent. Two other derived parameters are also examined in the paper, the oscillating shear index and the WSSAD. Both these quantities also display minor or negligible differences due to domain extension.

It has been perceived that domain extension is essential to avoid wrong application of boundary conditions. The results obtained, however, conclusively show that the outlet domain extension has only a moderate influence on WSS. Thus, outlet extension to the domains may not be essential for arterial blood flows. It is also observed that the dramatic values of peak WSS obtained near the stenosis is the result of high resolution mesh along with boundary layers used in this study. Both the outcomes represent the originality of this paper.

The purpose of this paper is to find automatic post‐processing scheme to give textures and motion data to three dimensional (3D) body scan data.

Semi‐implicit particle‐based method was applied to post‐processing of 3D body scan data. The template avatar mesh was draped onto the target scan data and the texture/motion data were transferred to regenerated body. Automatic body feature detection was used to correlate the template body with the target body.

Using semi‐implicit particle method, there are advantages in both computational stability and accuracy. The calculation is done in a few minutes and even data with many holes could be used.

There are several researches for body feature detection and scan body regeneration but this paper aims for fully automatic method which needs no human intervention. The semi‐implicit particle method, which is popularly used for cloth simulation, is applied to body data regeneration. The conventional 3D body scan data, which had no colors and motions can be given textures and motions with this approach. And even the face can be freely interchanged with the use of external face generation software.

The visible time window (VTW) calculation of satellites to ground targets is significant for Earth observation satellites' operation management and control. With the improvement of satellite maneuvering capability and the complexity of on-orbit observation tasks, the traditional VTW calculation methods can no longer meet the demands of satellite operation management and control due to a large amount of calculation and low efficiency. The purpose of this study is to propose a fast VTW calculation method based on map segmentation named map segmentation method (MSM), to improve the calculation efficiency, and further solve this problem.

The main feature of the MSM method is to segment the map and subsatellite trajectories and traverse the subsatellite points within a specific range around the target, significantly reducing the search space and the amount of computation and improving computational efficiency.

Numerical simulations for two satellite orbits are implemented to verify the feasibility of the proposed VTW calculation method, and the traditional traversal method (TM) is also performed for comparative analysis. The results show that the proposed method can obtain the same VTW, using less calculation time than the TM. The computational efficiency is significantly improved, especially for many tasks. The calculation time of observing 500 targets is saved by more than 70%, indicating a broad application prospect.

This paper proposes an original VTW calculation method based on map segmentation to improve the calculation efficiency. The simulation scenarios are designed to verify the accuracy and effectiveness of the proposed method, and the observation targets are randomly distributed on the map. For comparative analysis, the TM is also performed under the same simulation conditions.

This paper aims to achieve automatically surface segmentation for painting different kinds of aircraft efficiently considering the demands of painting robot.

This project creatively proposed one method that accepts point cloud, outputs several blocks, each of which can be handled by ABB IRB 5500 in one station. Parallel PointNet (PPN) is proposed in this paper for better handling six dimensional aircraft data including every point normal. Through semantic segmentation of PPN, each surface has its own identity information indicating which part this surface belongs to. Then clustering considering constraints is applied to complete surface segmentation with identity information. To guarantee segmentation paintable and improve painting efficiency, different dexterous workspaces of IRB 5500 corresponding to different postures have been analyzed carefully.

The experiments confirm the effectiveness of the proposed surface segmentation method for painting different types of aircraft by IRB 5500. For semantic segmentation on aircraft data with point normal, PPN has higher precision than PointNet. In addition, the whole algorithm can efficiently segment one complex aircraft into qualified blocks, each of which has its own identity information, can be painted by IRB 5500 in one station and has fewer edges with other blocks.

As the provided experiments indicate, the proposed method can segment one aircraft into qualified blocks automatically, which highly improves the efficiency in aircraft painting compared with traditional approaches. Moreover, the proposed method is able to provide identity information of each block, which is necessary for application of different paint parameters and different paint materials. In addition, final segmentation results by the proposed method behaves better than k-means cluster on variance of normal vector distance.

Inspired by semantic segmentation of 3 D point cloud, some improvements based on PointNet have been proposed for better handling segmentation of 6 D point cloud. By introducing normal vectors, semantic segmentation could be accomplished precisely for close points with opposite normal, such as wing upper and lower surfaces. Combining deep learning skills with traditional methods, the proposed method is proved to behave much better for surface segmentation task in aircraft painting.