Real-time simulation and visualisation of cloth using edge-based adaptive meshes

Real-time rendering and the animation of realistic virtual environments and characters has progressed at a great pace, following advances in computer graphics hardware in the last decade. The role of cloth simulation is becoming ever more important in the quest to improve the realism of virtual environments. The real-time simulation of cloth and clothing is important for many applications such as virtual reality, crowd simulation, games and software for online clothes shopping. A large number of polygons are necessary to depict the highly exible nature of cloth with wrinkling and frequent changes in its curvature. In combination with the physical calculations which model the deformations, the effort required to simulate cloth in detail is very computationally expensive resulting in much diffculty for its realistic simulation at interactive frame rates. Real-time cloth simulations can lack quality and realism compared to their offline counterparts, since coarse meshes must often be employed for performance reasons. The focus of this thesis is to develop techniques to allow the real-time simulation of realistic cloth and clothing. Adaptive meshes have previously been developed to act as a bridge between low and high polygon meshes, aiming to adaptively exploit variations in the shape of the cloth. The mesh complexity is dynamically increased or refined to balance quality against computational cost during a simulation. A limitation of many approaches is they do not often consider the decimation or coarsening of previously refined areas, or otherwise are not fast enough for real-time applications. A novel edge-based adaptive mesh is developed for the fast incremental refinement and coarsening of a triangular mesh. A mass-spring network is integrated into the mesh permitting the real-time adaptive simulation of cloth, and techniques are developed for the simulation of clothing on an animated character

Shape Animation with Combined Captured and Simulated Dynamics

We present a novel volumetric animation generation framework to create new types of animations from raw 3D surface or point cloud sequence of captured real performances. The framework considers as input time incoherent 3D observations of a moving shape, and is thus particularly suitable for the output of performance capture platforms. In our system, a suitable virtual representation of the actor is built from real captures that allows seamless combination and simulation with virtual external forces and objects, in which the original captured actor can be reshaped, disassembled or reassembled from user-specified virtual physics. Instead of using the dominant surface-based geometric representation of the capture, which is less suitable for volumetric effects, our pipeline exploits Centroidal Voronoi tessellation decompositions as unified volumetric representation of the real captured actor, which we show can be used seamlessly as a building block for all processing stages, from capture and tracking to virtual physic simulation. The representation makes no human specific assumption and can be used to capture and re-simulate the actor with props or other moving scenery elements. We demonstrate the potential of this pipeline for virtual reanimation of a real captured event with various unprecedented volumetric visual effects, such as volumetric distortion, erosion, morphing, gravity pull, or collisions

HIGH QUALITY HUMAN 3D BODY MODELING, TRACKING AND APPLICATION

Geometric reconstruction of dynamic objects is a fundamental task of computer vision and graphics, and modeling human body of high fidelity is considered to be a core of this problem. Traditional human shape and motion capture techniques require an array of surrounding cameras or subjects wear reflective markers, resulting in a limitation of working space and portability. In this dissertation, a complete process is designed from geometric modeling detailed 3D human full body and capturing shape dynamics over time using a flexible setup to guiding clothes/person re-targeting with such data-driven models. As the mechanical movement of human body can be considered as an articulate motion, which is easy to guide the skin animation but has difficulties in the reverse process to find parameters from images without manual intervention, we present a novel parametric model, GMM-BlendSCAPE, jointly taking both linear skinning model and the prior art of BlendSCAPE (Blend Shape Completion and Animation for PEople) into consideration and develop a Gaussian Mixture Model (GMM) to infer both body shape and pose from incomplete observations. We show the increased accuracy of joints and skin surface estimation using our model compared to the skeleton based motion tracking. To model the detailed body, we start with capturing high-quality partial 3D scans by using a single-view commercial depth camera. Based on GMM-BlendSCAPE, we can then reconstruct multiple complete static models of large pose difference via our novel non-rigid registration algorithm. With vertex correspondences established, these models can be further converted into a personalized drivable template and used for robust pose tracking in a similar GMM framework. Moreover, we design a general purpose real-time non-rigid deformation algorithm to accelerate this registration. Last but not least, we demonstrate a novel virtual clothes try-on application based on our personalized model utilizing both image and depth cues to synthesize and re-target clothes for single-view videos of different people

S-NeRF++: Autonomous Driving Simulation via Neural Reconstruction and Generation

Autonomous driving simulation system plays a crucial role in enhancing self-driving data and simulating complex and rare traffic scenarios, ensuring navigation safety. However, traditional simulation systems, which often heavily rely on manual modeling and 2D image editing, struggled with scaling to extensive scenes and generating realistic simulation data. In this study, we present S-NeRF++, an innovative autonomous driving simulation system based on neural reconstruction. Trained on widely-used self-driving datasets such as nuScenes and Waymo, S-NeRF++ can generate a large number of realistic street scenes and foreground objects with high rendering quality as well as offering considerable flexibility in manipulation and simulation. Specifically, S-NeRF++ is an enhanced neural radiance field for synthesizing large-scale scenes and moving vehicles, with improved scene parameterization and camera pose learning. The system effectively utilizes noisy and sparse LiDAR data to refine training and address depth outliers, ensuring high quality reconstruction and novel-view rendering. It also provides a diverse foreground asset bank through reconstructing and generating different foreground vehicles to support comprehensive scenario creation. Moreover, we have developed an advanced foreground-background fusion pipeline that skillfully integrates illumination and shadow effects, further enhancing the realism of our simulations. With the high-quality simulated data provided by our S-NeRF++, we found the perception methods enjoy performance boost on several autonomous driving downstream tasks, which further demonstrate the effectiveness of our proposed simulator

ICASE/LaRC Workshop on Adaptive Grid Methods

Solution-adaptive grid techniques are essential to the attainment of practical, user friendly, computational fluid dynamics (CFD) applications. In this three-day workshop, experts gathered together to describe state-of-the-art methods in solution-adaptive grid refinement, analysis, and implementation; to assess the current practice; and to discuss future needs and directions for research. This was accomplished through a series of invited and contributed papers. The workshop focused on a set of two-dimensional test cases designed by the organizers to aid in assessing the current state of development of adaptive grid technology. In addition, a panel of experts from universities, industry, and government research laboratories discussed their views of needs and future directions in this field

Cfd Analysis Of Helicopter Rotor-fuselage Flow Interaction In Hovering And Forward Flight Conditions

Tez (Doktora) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2016Thesis (Ph.D.) -- İstanbul Technical University, Institute of Science and Technology, 2016Askı ve ileri uçuş durumunda zorlu rotor-gövde akış etkileşim problemini incelemek için zamana bağlı sıkıştırılabilir akış analizleri gerçekleştirilmiştir. Sistemi oluşturan herbir bileşenin akış yapısı üzerindeki etkilerini irdelemek için izole gövde ve izole rotor konfigürasyonları ele alınmıştır. Daha sonra, bileşenlerin birbirlerine olan etkilerini incelemek amacıyla sistemin tamamı analize tabi tutulmuştur. İzole gövde analizleri RANS tabanlı daimi hesaplamalara dayanmaktadır. Rotor palalarını içeren durumlar için ise URANS çözümleri gerçekleştirilmiştir. Akışın türbülanslı doğasını modellemek için daha güvenilir sonuç ürettiği analizler ile tespit edilmiş olan Realizable k-ε türbülans modeli kullanılmıştır. Zamana bağlı rotor analizleri üç farklı ilerleme oranı için gerçekleştirilmiştir. Hava yükleri nedeniyle palada gözlemlenen dinamik hareketler azimut açısı ile periyodik bir şekilde değişim gösterirken, aynı zamanda ilerleme oranına bağlı olarak da değişim göstermektedir. Palanın tanımlı hareketleri, mevcut kod yetenekleri ile temsil edilememektedir. Fakat, bu dinamik hareketler ticari HAD yazılımı içerisine kullanıcı tarafından yazılan bir kod vasıtasıyla simülasyon modeline dahil edilebilmektedir. Bilhassa ileri uçuş şartlarında daha belirgin olan çırpma ve yunuslama hareketlerini modellemek için birinci mertebe Fourier serilerinden yararlanılarak bir UDF kodu yazılmıştır. Hesaplama hacmi düzensiz yapıda olup karma elemanlardan oluşmaktadır. Dinamik çözüm ağı yaklaşımlarında sıklıkla görülen problemler çözüm ağı deformasyonu ve çözüm ağı oluşturma yöntemlerinin kullanıldığı dinamik ağlar ile aşılmıştır. Mevcut sayısal çalışmanın doğruluğu deneyler ve diğer sayısal çalışmaların sonuçları ile karşılaştırılarak ortaya konmuştur. Benzer başarılı sonuçlar, daha az sayıda çözüm ağı kullanılarak elde edilmiştir. Bu nedenle, mevcut yöntem hesaplama süresinde azalma sağlamakta ve makul hesaplama kaynağı kullanımını mümkün kılmaktadır.Unsteady compressible flow analyses are carried out to investigate the challenging helicopter rotor–fuselage interaction problem in hover and forward flight conditions. First, the isolated fuselage and the isolated rotor configurations are analyzed to examine the individual effects of each component on the flow field. Then, the rotor-fuselage interaction problem is considered. The isolated fuselage analyses are based on the steady RANS computations. URANS simulations are carried out for the cases with rotor blades. The Realizable k-ε turbulence model is found to perform best for the predictions. The time-dependent rotor analyses are simulated at three different advance ratios. The blade dynamic motions excited by the air loads, which vary periodically in the azimuth direction and also differ based on the advance ratio, have been prescribed by a UDF code embedded into the solver, since these motions cannot be directly represented with the existing commercial code capabilities. Azimuthal variations of the flap and pitch motions of the blades are prescribed a priori as a first order Fourier series through User Defined Function feature of the code. The computational domain was modeled by unstructured hybrid mesh elements. Commonly seen dynamic mesh problems are alleviated by appropriately formed dynamic grids using the spring based smoothing and cell re-meshing methods. The accuracy of the present numerical predictions has been demonstrated by the comparison of obtained results with the experiments and other numerical results available in the open literature. The present single grid methodology has given similar successful results with much lower number of grid elements, thus resulting in much shorter computing times, using modest computational power.DoktoraPh.D

Repair of metallic components using hybrid manufacturing

Many high-performance metal parts users extend the service of these damaged parts by employing repair technology. Hybrid manufacturing, which includes additive manufacturing (AM) and subtractive manufacturing, provides greater build capability, better accuracy, and surface finish for component repair. However, most repair processes still rely on manual operations, which are not satisfactory in terms of time, cost, reliability, and accuracy. This dissertation aims to improve the application of hybrid manufacturing for repairing metallic components by addressing the following three research topics. The first research topic is to investigate and develop an efficient best-fit and shape adaption algorithm for automating 3D models\u27 the alignment and defect reconstruction. A multi-feature fitting algorithm and cross-section comparison method are developed. The second research topic is to develop a smooth toolpath generation method for laser metal deposition to improve the deposition quality for metallic component fabrication and repair. Smooth connections or transitions in toolpath planning are achieved to provide a constant feedrate and controllable deposition idle time for each single deposition pass. The third research topic is to develop an automated repair process could efficiently obtain the spatial information of a worn component for defect detection, alignment, and 3D scanning with the integration of stereo vision and laser displacement sensor. This dissertation investigated and developed key technologies to improve the efficiency, repair quality, precision, and automation for the repair of metallic components using hybrid manufacturing. Moreover, the research results of this dissertation can benefit a wide range of industries, such as additive manufacturing, manufacturing and measurement automation, and part inspection --Abstract, page iv

Grid related issues for static and dynamic geometry problems using systems of overset structured grids

Grid related issues of the Chimera overset grid method are discussed in the context of a method of solution and analysis of unsteady three-dimensional viscous flows. The state of maturity of the various pieces of support software required to use the approach is considered. Current limitations of the approach are identified