Rigidity controllable as-rigid-as-possible shape deformations

Shape deformation is one of the fundamental techniques in geometric processing. One principle of deformation is to preserve the geometric details while distributing the necessary distortions uniformly. To achieve this, state-of-the-art techniques deform shapes in a locally as-rigid-as-possible (ARAP) manner. Existing ARAP deformation methods optimize rigid transformations in the 1-ring neighborhoods and maintain the consistency between adjacent pairs of rigid transformations by single overlapping edges. In this paper, we make one step further and propose to use larger local neighborhoods to enhance the consistency of adjacent rigid transformations. This is helpful to keep the geometric details better and distribute the distortions more uniformly. Moreover, the size of the expanded local neighborhoods provides an intuitive parameter to adjust physical stiffness. The larger the neighborhood is, the more rigid the material is. Based on these, we propose a novel rigidity controllable mesh deformation method where shape rigidity can be flexibly adjusted. The size of the local neighborhoods can be learned from datasets of deforming objects automatically or specified by the user, and may vary over the surface to simulate shapes composed of mixed materials. Various examples are provided to demonstrate the effectiveness of our method

A Revisit of Shape Editing Techniques: from the Geometric to the Neural Viewpoint

3D shape editing is widely used in a range of applications such as movie production, computer games and computer aided design. It is also a popular research topic in computer graphics and computer vision. In past decades, researchers have developed a series of editing methods to make the editing process faster, more robust, and more reliable. Traditionally, the deformed shape is determined by the optimal transformation and weights for an energy term. With increasing availability of 3D shapes on the Internet, data-driven methods were proposed to improve the editing results. More recently as the deep neural networks became popular, many deep learning based editing methods have been developed in this field, which is naturally data-driven. We mainly survey recent research works from the geometric viewpoint to those emerging neural deformation techniques and categorize them into organic shape editing methods and man-made model editing methods. Both traditional methods and recent neural network based methods are reviewed

Soft volume simulation using a deformable surface model

The aim of the research is to contribute to the modelling of deformable objects, such as soft tissues in medical simulation. Interactive simulation for medical training is a concept undergoing rapid growth as the underlying technologies support the increasingly more realstic and functional training environments. The prominent issues in the deployment of such environments centre on a fine balance between the accuracy of the deformable model and real-time interactivity. Acknowledging the importance of interacting with non-rigid materials such as the palpation of a breast for breast assessment, this thesis has explored the physics-based modelling techniques for both volume and surface approach. This thesis identified that the surface approach based on the mass spring system (MSS) has the benefits of rapid prototyping, reduced mesh complexity, computational efficiency and the support for large material deformation compared to the continuum approach. However, accuracy relative to real material properties is often over looked in the configuration of the resulting model. This thesis has investigated the potential and the feasibility of surface modelling for simulating soft objects regardless of the design of the mesh topology and the non-existence of internal volume discretisation. The assumptions of the material parameters such as elasticity, homogeneity and incompressibility allow a reduced set of material values to be implemented in order to establish the association with the surface configuration. A framework for a deformable surface model was generated in accordance with the issues of the estimation of properties and volume behaviour corresponding to the material parameters. The novel extension to the surface MSS enables the tensile properties of the material to be integrated into an enhanced configuration despite its lack of volume information. The benefits of the reduced complexity of a surface model are now correlated with the improved accuracy in the estimation of properties and volume behaviour. Despite the irregularity of the underlying mesh topology and the absence of volume, the model reflected the original material values and preserved volume with minimal deviations. Global deformation effect which is essential to emulate the run time behaviour of a real soft material upon interaction, such as the palpation of a generic breast, was also demonstrated, thus indicating the potential of this novel technique in the application of soft tissue simulation

Simulation-Based Joint Estimation of Body Deformation and Elasticity Parameters for Medical Image Analysis

Elasticity parameter estimation is essential for generating accurate and controlled simulation results for computer animation and medical image analysis. However, finding the optimal parameters for a particular simulation often requires iterations of simulation, assessment, and adjustment and can become a tedious process. Elasticity values are especially important in medical image analysis, since cancerous tissues tend to be stiffer. Elastography is a popular type of method for finding stiffness values by reconstructing a dense displacement field from medical images taken during the application of forces or vibrations. These methods, however, are limited by the imaging modality and the force exertion or vibration actuation mechanisms, which can be complicated for deep-seated organs. In this thesis, I present a novel method for reconstructing elasticity parameters without requiring a dense displacement field or a force exertion device. The method makes use of natural deformations within the patient and relies on surface information from segmented images taken on different days. The elasticity value of the target organ and boundary forces acting on surrounding organs are optimized with an iterative optimizer, within which the deformation is always generated by a physically-based simulator. Experimental results on real patient data are presented to show the positive correlation between recovered elasticity values and clinical prostate cancer stages. Furthermore, to resolve the performance issue arising from the high dimensionality of boundary forces, I propose to use a reduced finite element model to improve the convergence of the optimizer. To find the set of bases to represent the dimensions for forces, a statistical training based on real patient data is performed. I demonstrate the trade-off between accuracy and performance by using different numbers of bases in the optimization using synthetic data. A speedup of more than an order of magnitude is observed without sacrificing too much accuracy in recovered elasticity.Doctor of Philosoph

Soft volume simulation using a deformable surface model

The aim of the research is to contribute to the modelling of deformable objects, such as soft tissues in medical simulation. Interactive simulation for medical training is a concept undergoing rapid growth as the underlying technologies support the increasingly more realstic and functional training environments. The prominent issues in the deployment of such environments centre on a fine balance between the accuracy of the deformable model and real-time interactivity. Acknowledging the importance of interacting with non-rigid materials such as the palpation of a breast for breast assessment, this thesis has explored the physics-based modelling techniques for both volume and surface approach. This thesis identified that the surface approach based on the mass spring system (MSS) has the benefits of rapid prototyping, reduced mesh complexity, computational efficiency and the support for large material deformation compared to the continuum approach. However, accuracy relative to real material properties is often over looked in the configuration of the resulting model. This thesis has investigated the potential and the feasibility of surface modelling for simulating soft objects regardless of the design of the mesh topology and the non-existence of internal volume discretisation. The assumptions of the material parameters such as elasticity, homogeneity and incompressibility allow a reduced set of material values to be implemented in order to establish the association with the surface configuration. A framework for a deformable surface model was generated in accordance with the issues of the estimation of properties and volume behaviour corresponding to the material parameters. The novel extension to the surface MSS enables the tensile properties of the material to be integrated into an enhanced configuration despite its lack of volume information. The benefits of the reduced complexity of a surface model are now correlated with the improved accuracy in the estimation of properties and volume behaviour. Despite the irregularity of the underlying mesh topology and the absence of volume, the model reflected the original material values and preserved volume with minimal deviations. Global deformation effect which is essential to emulate the run time behaviour of a real soft material upon interaction, such as the palpation of a generic breast, was also demonstrated, thus indicating the potential of this novel technique in the application of soft tissue simulation.EThOS - Electronic Theses Online ServiceUniversiti Malaysia Sarawak (UMS)Malaysia. Jabatan Perkhidmatan Awam (JPA)Malaysia. Kementerian Pengajian Tinggi (KPT)GBUnited Kingdo