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Designing an anatomical structure for a surgical procedure is not a simple task. It is especially true of the craniofacial area, which consists of bone tissues with very complex geometry. CAD modelers need appropriate knowledge and skills in medicine and technical sciences to fully use the currently available tools in related processes with the reconstruction of the craniofacial areas. The presented preliminary studies are based on four patients treated at the Department of Maxillofacial Surgery. The segmentation process of the mandible model was performed in the ITK SNAP software. The process of generating surface body models was performed in the Auto Surfacing module in Geomagic software using two different methods: organic and mechanical. Then compare both methods for the accuracy of generating a CAD model of the mandible based on a triangle mesh structure in the Focus Inspection and the GOM Inspect software.

Abstract This research paper tackles the problem of determining displacements of complex-shaped shell structures, measured periodically using laser scanning. Point clouds obtained during different measurement epochs can be compared with each other directly or they can be converted into continuous models in the form of a triangle mesh or smooth patches (spline functions). The accuracy of the direct comparison of point clouds depends on the scanning density, while the accuracy of comparing the point cloud to the model depends on approximation errors that are formed during its creation. Modelling using triangle meshes flattens the local structure of the object compared to the spline model. However, if the shell has edges in its structure, their exact representation by spline models is impossible due to the undulations of functions along them. Edges can also be distorted by the mesh model by their chamfering with transverse triangles. These types of surface modelling errors can lead to the generation of pseudo-deformation of the structure, which is difficult to distinguish from real deformation. In order to assess the possibility of correct determination of deformation using the above-mentioned methods, laser scanning of a complex shell structure in two epochs was performed. Then, modelling and comparison of the results of periodic measurements were carried out. As a result of the research, advantages and disadvantages of each method were identified. It was noticed that none of the methods made it possible to correctly represent all deformations while suppressing pseudo-deformation. However, the combination of their best qualities made it possible to determine the actual deformation of the structure.

Oblique photogrammetry models are indispensable for implementing digital twins of cities. Geographic information system researchers have proposed plenty of methods to load and visualize these city-scaled scenes. However, when the area viewed changes quickly in real-time rendering, current methods still require excessive GPU calculation and memory occupation. In this study, we propose a data organization method in which we merged all quadtrees and used a binary encoding method to encode nodes in a merged tree so that the parent–child relationship between the tree nodes could be calculated using rapid binary operations. After that, we developed a strategy to cancel the loading of redundant nodes based on the parent–child relationship, which helped to reduce the hard disk loading time and the amount of memory occupied in visualization. Moreover, we introduced a parameter to measure the area of the triangle mesh per pixel to achieve unified data scheduling under different production standards. We implemented our method based on Unreal Engine (UE), and three experiments were designed to illustrate the advantages of our methods in index acceleration, frame time, and memory reduction. The results show that our methods can significantly improve visualization fluency and reduce memory usage.

A decision support system (DSS) was developed that outputs suggestions for socket-rectification actions to the prosthetist, aiming at improving the fitness of transfemoral prosthetic socket design and reducing the time needed for the final socket design. For this purpose, the DSS employs a fuzzy-logic inference engine (IE) which combines a set of rectification rules with pressure measurements generated by sensors embedded in the socket, for deciding the rectification actions. The latter is then processed by an algorithm that receives, manipulates and modifies a 3D digital socket model as a triangle mesh formatted inside an STL file. The DSS results were validated and tested in an FEA simulation environment, by simulating and comparing the donning process among a good-fitting socket, a loose socket (poor-fit) and several rectified sockets produced by the proposed DSS. The simulation results indicate that volume reduction improves the pressure distribution over the stump. However, as the intensity of socket rectification increases, i.e., as volume reduction increases, high pressures appear in other parts of the socket which generate discomfort. Therefore, a trade-off is required between the amount of rectification and the balance of the pressure distributions experienced at the stump.

When 3D scanning objects, the objective is usually to obtain a continuous surface. However, most surface scanning methods, such as structured light scanning, yield a point cloud. Obtaining a continuous surface from a point cloud requires a subsequent surface reconstruction step, which is directly affected by any error from the computation of the point cloud. In this work, we propose a one-step approach in which we compute the surface directly from structured light images. Our method minimizes the least-squares error between photographs and renderings of a triangle mesh, where the vertex positions of the mesh are the parameters of the minimization problem. To ensure fast iterations during optimization, we use differentiable rendering, which computes images and gradients in a single pass. We present simulation experiments demonstrating that our method for computing a triangle mesh has several advantages over approaches that rely on an intermediate point cloud. Our method can produce accurate reconstructions when initializing the optimization from a sphere. We also show that our method is good at reconstructing sharp edges and that it is robust with respect to image noise. In addition, our method can improve the output from other reconstruction algorithms if we use these for initialization.

In the context of shape processing, the estimation of the medial axis is relevant for the simplification and re-parameterization of 3D bodies. The currently used methods are based on (1) General fields, (2) Geometric methods and (3) voxel-based thinning. They present shortcomings such as (1) overrepresentation and non-smoothness of the medial axis due to high frequency nodes and (2) biased-skeletons due to skewed thinning. To partially overcome these limitations, this article presents a non-deterministic algorithm for the estimation of the 1D skeleton of triangular B-Reps or voxel-based body representations. Our method articulates (1) a novel randomized thinning algorithm that avoids possible skewings in the final skeletonization, (2) spectral-based segmentation that eliminates short dead-end branches, and (3) a maximal excursion method for reduction of high frequencies. The test results show that the randomized order in the removal of the instantaneous skin of the solid region eliminates bias of the skeleton, thus respecting features of the initial solid. An Alpha Shape-based inversion of the skeleton encoding results in triangular boundary Representations of the original body, which present reasonable quality for fast non-minute scenes. Future work is needed to (a) tune the spectral filtering of high frequencies off the basic skeleton and (b) extend the algorithm to solid regions whose skeletons mix 1D and 2D entities.

AbstractTo date, slicing algorithms for additive manufacturing is the most effective for favourable triangular mesh topologies; worst-case models, where a large percentage of triangles intersect each slice plane, take significantly longer to slice than a like-for-like file. In larger files, this results in a significant slicing duration, when models are both worst cases and contain more than 100,000 triangles. The research presented here introduces a slicing algorithm which can slice worst-case large models effectively. A new algorithm is implemented utilising an efficient contour construction method, with further adaptations, which make the algorithm suitable for all model topologies. Edge matching, which is an advanced sorting method, decreases the number of sorts per edge from n total number of intersections to two, alongside additional micro-optimisations that deliver the enhanced efficient contour construction algorithm. The algorithm was able to slice a worst-case model of 2.5 million triangles in the 1025s. Maximum improvement was measured as 9400% over the standard efficient contour construction method. Improvements were also observed in all parts in excess of 1000 triangles. The slicing algorithm presented offers novel methods that address the failings of other algorithms described in literature to slice worst-case models effectively.

On-site manual inspection of metro tunnel leakages has been faced with the problems of low efficiency and poor accuracy. An automated, high-precision, and robust water leakage inspection method is vital to improve the manual approach. Existing approaches cannot provide the leakage location due to the lack of spatial information. Therefore, an integrated deep learning method of water leakage inspection using tunnel lining point cloud data from mobile laser scanning is presented in this paper. It is composed of three parts as follows: (1) establishment of the water leakage dataset using the acquired point clouds of tunnel linings; (2) automated leakage detection via a mask-region-based convolutional neural network; and (3) visualization and quantitative evaluation of the water leakage in 3D space via a novel triangle mesh method. The testing result reveals that the proposed method achieves automated detection and evaluation of tunnel lining water leakages in 3D space, which provides the inspectors with an intuitive overall 3D view of the detected water leakages and the leakage information (area, location, lining segments, etc.).