Surface Mesh Generation based on Imprinting of S-T Edge Patches

AbstractOne of the most robust and widely used algorithms for all-hexahedral meshes is the sweeping algorithm. However, for multi- sweeping, the most difficult problems are the surface matching and interval assignment for edges on the source and target surfaces. In this paper, a new method to generate surface meshes by imprinting edge patches between the source and target surfaces is proposed. The edge patch imprinting is based on a cage-based morphing of edge patches on the different sweeping layers where deformed and undeformed cages are extracted by propagating edge patches on the linking surfaces. The imprinting results in that the source or target surfaces will be partitioned with the imprinted edge patches. After partitioning, every new source surface should be matched to a new specific target surface where surface mesh projection from one-to-one sweeping based on harmonic mapping[19] can be applied. In addition, 3D edge patches are projected onto 2D computational domains where every sweeping level is planar in order to increase the robustness of imprinting. Finally, the algorithm time complexity is discussed and examples are provided to verify the robustness of our proposed algorithm

Evolvability-guided Optimization of Linear Deformation Setups for Evolutionary Design Optimization

Richter A. Evolvability-guided Optimization of Linear Deformation Setups for Evolutionary Design Optimization. Bielefeld: Universität Bielefeld; 2019.Andreas Richter gratefully acknowledges the financial support from Honda Research Institute Europe (HRI-EU).This thesis targets efficient solutions for optimal representation setups for evolutionary design optimization problems. The representation maps the abstract parameters of an optimizer to a meaningful variation of the design model, e.g., the shape of a car. Thereby, it determines the convergence speed to and the quality of the final result. Thus, engineers are eager to employ well-tuned representations to achieve high-quality design solutions. But, setting up optimal representations is a cumbersome process because the setup procedure requires detailed knowledge about the objective functions, e.g., a fluid dynamics simulation, and the parameters of the employed representation itself. Thus, we target efficient routines to set up representations automatically to support engineers from their tedious, partly manual work. Inspired by the concept of evolvability, we present novel quality criteria for the evaluation of linear deformations commonly applied as representations. We define and analyze the criteria variability, regularity, and improvement potential which measure the expected quality and convergence speed of an evolutionary design optimization process based on the linear deformation setup. Moreover, we target the efficient optimization of deformation setups with respect to these three criteria. In dynamic design optimization scenarios a suitable compromise between exploration and exploitation is crucial for efficient solutions. We discuss the construction of optimal compromises for these dynamic scenarios with our criteria because they characterize exploration and exploitation. As a result an engineer can initialize and adjust the deformation setup for improved convergence speed of a design process and for enhanced quality of the design solutions with our methods

The soft-tissue restraints of the knee and its balancing capacity in total knee arthroplasty procedures

Total knee arthroplasty is a successful surgical treatment for patients with severe knee joint arthrosis. However, restoring soft-tissue function is a major challenge. Depending on the positioning of the prosthesis, the implantation procedure and the pathology of the patient, it is necessary to adjust the soft-tissue structures of the joint in order to restore the function of the knee. The assessment and adaptation of the soft-tissue envelope is a subjective process that is strongly dependent on the surgeon. This dissertation addresses these challenges and seeks quantitative guidelines for softtissue management based on a meta-analysis of the laxity of the natural knee joint. A further aim of the present study was to clarify in the scope of in-vitro investigations to what extent the loosening and removal of individual structures alters joint laxity and how far the joint can be balanced by targeted resection of soft-tissue structures. In addition, in-silico investigations within the scope of this thesis form the basis for a numerical tool to better understand the function of the ligaments and to better plan soft-tissue balancing preoperatively in the future. The investigations of the natural laxity of the knee jointin different flexion angles and loading directions by utilizing a meta-analysis show a strong dependency of the joint laxity on the flexion angle. Furthermore, the results show a distinct asymmetry of joint laxity when comparing translations in opposite directions within a certain degree of freedom. The data collected provide the surgeon with quantitative target parameters for natural soft-tissue balancing in knee arthroplasty procedures. The in-vitro investigations on 19 human knee specimens show that the restoration of soft-tissue function of the knee after arthroplasty cannot be achieved by kinematic alignment alone. The use of a bicruciate-retaining knee arthroplasty is the only way to keep the anterior and posterior stability of the joint in balance. To correct varus deformities, balancing of the medial collateral ligament appears to be a safe method. Correction of valgus laxity can be achieved by partially or completely resecting the lateral collateral ligament, however this increases the risk of instability in joint flexion. Within the scope of this work, subject-specific multi-body simulation models could be developed with which the laxity of the knee joint can be predicted, especially for low flexion angles. The presented procedure for the approximation of the ligament attachment sites represents a time-saving alternative to the segmentation of the attachments in MRI images.Deutsche Forschungsgemeinschaft/Sachbeihilfe/HU 873/7-1/E

Improving 3D Reconstruction using Deep Learning Priors

Modeling the 3D geometry of shapes and the environment around us has many practical applications in mapping, navigation, virtual/ augmented reality, and autonomous robots. In general, the acquisition of 3D models relies on using passive images or using active depth sensors such as structured light systems that use external infrared projectors. Although active methods provide very robust and reliable depth information, they have limited use cases and heavy power requirements, which makes passive techniques more suitable for day-to-day user applications. Image-based depth acquisition systems usually face challenges representing thin, textureless, or specular surfaces and regions in shadows or low-light environments. While scene depth information can be extracted from the set of passive images, fusion of depth information from several views into a consistent 3D representation remains a challenging task. The most common challenges in 3D environment capture include the use of efficient scene representation that preserves the details, thin structures, and ensures overall completeness of the reconstruction. In this thesis, we illustrate the use of deep learning techniques to resolve some of the challenges of image-based depth acquisition and 3D scene representation. We use a deep learning framework to learn priors over scene geometry and scene global context for solving several ambiguous and ill-posed problems such as estimating depth on textureless surfaces and producing complete 3D reconstruction for partially observed scenes. More specifically, we propose that using deep learning priors, a simple stereo camera system can be used to reconstruct a typical apartment size indoor scene environments with the fidelity that approaches the quality of a much more expensive state-of-the-art active depth-sensing system. Furthermore, we describe how deep learning priors on local shapes can represent 3D environments more efficiently than with traditional systems while at the same time preserving details and completing surfaces.Doctor of Philosoph