A Hybrid Model Method for Accurate Surface Deformation and Incision Based on FEM and PBD

In some simulations like virtual surgery, an accurate surface deformation method is needed. Many deformation methods focus on the whole model swing and twist. Few methods focus on surface deformation. For the surface deformation method, two necessary characteristics are needed: the accuracy and real-time performance. Some traditional methods, such as position-based dynamics (PBD) and mass-spring method (MSM), focus more on the real-time performance. Others like the finite element method (FEM) focus more on the accuracy. To balance these two characteristics, we propose a hybrid mesh deformation method for accurate surface deformation based on FEM and PBD. Firstly, we construct a hybrid mesh, which is composed of a coarse volume mesh and a fine surface mesh. Secondly, we implement FEM on coarse volume mesh and PBD on fine surface mesh, and the deformation of fine surface mesh is constrained by the displacement of the coarse volume mesh. Thirdly, we introduced a small incision process for our proposed method. Finally, we implemented our method on a simple deformation simulation and a small incision simulation. The result shows an accurate surface deformation performance by implementing our method. The incision effect shows the compatibility of our proposed method. In conclusion, our proposed method acquires a better trade-off between accuracy and real-time performance.

Abnormal Bone Morphology in Charcot-Marie-Tooth Disease

Background: Although long suspected, it has yet to be shown whether the foot and ankle deformities of Charcot-Marie-Tooth disease (CMT) are generally associated with abnormalities in osseous shape. Computed tomography (CT) was used to quantify morphologic differences of the calcaneus, talus, and navicular in CMT compared with healthy controls. Methods: Weightbearing CT scans of 21 patients (27 feet) with CMT were compared to those of 20 healthy controls. Calcaneal measurements included radius of curvature, sagittal posterior tuberosity-posterior facet angle, and tuberosity coronal rotation. Talar measurements included axial and sagittal body-neck declination angle, and coronal talar head rotation. Surface-mesh model analysis of the hindfoot was performed comparing the average of the CMT cohort to the controls using a CT analysis software (Disior Bonelogic 2.0). Means were compared with a t test ( P < .05). Results: CMT patients had significantly less talar sagittal declination vs controls (17.8 vs 25.1 degrees; P < .05). Similarly, CMT patients had less talar head coronal rotation vs controls (30.8 vs 42.5 degrees; P < .001). The calcaneal radius of curvature in CMT patients was significantly smaller than controls (822.8 vs 2143.5 mm; P < .05). CMT sagittal posterior tuberosity–posterior facet angle was also significantly different from that of controls (60.3 vs 67.9 degrees respectively; P < .001). Surface-mesh model analysis demonstrated the largest differences in morphology at the navicular tuberosity, medial talar head, sustentaculum tali, and anterior process of the calcaneus. Conclusion: This is the first study to quantify the morphologic differences in hindfoot osteology seen in CMT patients. Patients identified with osseous changes of the calcaneus, especially a smaller axial radius of curvature, may benefit from a 3-dimensional osteotomy for correction.

Response Surface Mesh with the Outer Input Method

Artificial intelligence in general and optimization tasks applied to the design of very efficient structures rely on response surfaces to forecast the output of functions, and are vital part of these methodologies. Yet they have important limitations, since greater precisions require greater data sets, thus, training or updating larger response surfaces become computationally expensive or unfeasible. This has been an important bottle neck limitation to achieve more promising results, rendering many optimization and AI tasks with a low performance.To solve this challenge, a new methodology created to segment response surfaces is hereby presented. Differently than other similar methodologies, this algorithm named outer input method has a very simple and robust operation, generating a mesh of near isopopulated partitions of inputs which share similitude. The great advantage it offers is that it can be applied to any data set with any type of distribution, such as random, Cartesian, or clustered, for domains with any number of coordinates, significantly simplifying any metamodel with a mesh ensemble.This study demonstrates how one of the most known and precise metamodel denominated Kriging, yet with expensive computation costs, can be significantly simplified with a response surface mesh, increasing training speed up to 567 times, while using a quad-core parallel processing. Since individual mesh elements can be parallelized or updated individually, its faster operational speed has its speed increased.

DP-MVS: Detail Preserving Multi-View Surface Reconstruction of Large-Scale Scenes

This paper presents an accurate and robust dense 3D reconstruction system for detail preserving surface modeling of large-scale scenes from multi-view images, which we named DP-MVS. Our system performs high-quality large-scale dense reconstruction, which preserves geometric details for thin structures, especially for linear objects. Our framework begins with a sparse reconstruction carried out by an incremental Structure-from-Motion. Based on the reconstructed sparse map, a novel detail preserving PatchMatch approach is applied for depth estimation of each image view. The estimated depth maps of multiple views are then fused to a dense point cloud in a memory-efficient way, followed by a detail-aware surface meshing method to extract the final surface mesh of the captured scene. Experiments on ETH3D benchmark show that the proposed method outperforms other state-of-the-art methods on F1-score, with the running time more than 4 times faster. More experiments on large-scale photo collections demonstrate the effectiveness of the proposed framework for large-scale scene reconstruction in terms of accuracy, completeness, memory saving, and time efficiency.

Multi-Shape Free-Form Deformation Framework for Efficient Data Transmission in AR-Based Medical Training Simulators

Augmented reality medical training simulators can provide a realistic and immersive experience by overlapping the virtual scene on to the real world. Latency in augmented reality (AR) medical training simulators is an important issue as it can lead to motion sickness for users. This paper proposes a framework that can achieve real-time rendering of the 3D scene aligned to the real world using a head-mounted display (HMD). Model deformation in the 3D scene is categorised into local deformation derived from user interaction and global deformation determined by the simulation scenario. Target shapes are predefined by a simulation scenario, and control points are placed to embed the predefined shapes. Free-form deformation (FFD) is applied to multiple shapes to efficiently transfer the simulated model to the HMD. Global deformation is computed by blending a mapping matrix of each FFD with an assigned weighting value. The local and global deformation are then transferred through the control points updated from a deformed surface mesh and its corresponding weighting value. The proposed framework is verified in terms of latency caused by data transmission and the accuracy of a transmitted surface mesh in a vaginal examination (VE) training simulation. The average latency is reduced to 7 ms, less than the latency causing motion sickness in virtual reality simulations. The maximum relative error is less than 3%. Our framework allows seamless rendering of a virtual scene to the real world with substantially reduced latency and without the need for an external tracking system.

Response Surface Mesh with the Outer Input Method

Artificial intelligence in general and optimization tasks applied to the design of aerospace, space,and automotive structures, rely on response surfaces to forecast the output of functions, and are vital part of these methodologies. Yet they have important limitations, since greater precisions require greater data sets, thus, training or updating larger response surfaces become computationally expensive, sometimes unfeasible. This has been a bottle neck limitation to achieve more promising results, rendering many AI related task with a low efficiency.To solve this challenge, a new methodology created to segment response surfaces is hereby presented. Differently than other similar methodologies, the novel algorithm here presented named outer input method, has a very simple and robust operation. With only one operational parameter, maximum element size, it efficiently generates a near isopopulated mesh for any data set with any type of distribution, such as random, Cartesian, or clustered, for domains with any number of coordinates.Thus, it is possible to simplify the response surfaces by generating an ensemble of response surfaces, here denominated response surface mesh. This study demonstrates how a metamodel denominated Kriging, trained with a large data set, can be simplified with a response surface mesh, significantly reducing its often expensive computation costs> experiments here presented achieved an speed increase up to 180 times, while using a dual core parallel processingcomputer. This methodology can be applied to any metamodel, and metamodel elements can be easily parallelized and updated individually. Thus, its already faster training operation has its speed increased.

Response Surface Mesh with the Outer Input Method

Artificial intelligence in general and optimization tasks applied to the design of aerospace, space,and automotive structures, rely on response surfaces to forecast the output of functions, and are vital part of these methodologies. Yet they have important limitations, since greater precisions require greater data sets, thus, training or updating larger response surfaces become computationally expensive, sometimes unfeasible. This has been a bottle neck limitation to achieve more promising results, rendering many AI related task with a low efficiency.To solve this challenge, a new methodology created to segment response surfaces is hereby presented. Differently than other similar methodologies, the novel algorithm here presented named outer input method, has a very simple and robust operation. With only one operational parameter, maximum element size, it efficiently generates a near isopopulated mesh for any data set with any type of distribution, such as random, Cartesian, or clustered, for domains with any number of coordinates.Thus, it is possible to simplify the response surfaces by generating an ensemble of response surfaces, here denominated response surface mesh. This study demonstrates how a metamodel denominated Kriging, trained with a large data set, can be simplified with a response surface mesh, significantly reducing its often expensive computation costs> experiments here presented achieved an speed increase up to 180 times, while using a dual core parallel processingcomputer. This methodology can be applied to any metamodel, and metamodel elements can be easily parallelized and updated individually. Thus, its already faster training operation has its speed increased.