52 research outputs found

    GPU-based volume deformation.

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    Animating Coupling between Inviscid Free-Surface Liquids and Elastic Deformable Bodies

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    Driven by demand for high-fidelity computer-generated imagery, physics-based animation has become an exciting frontier of research in computer science. Simulations of fluids and their interactions with other objects in the environment have particularly enjoyed much attention and investigation. Consequently, effective techniques have been developed to efficiently simulate two-way coupling between fluids and rigid bodies, allowing for convincing animation of, for instance, ships on the ocean. On the other hand, accurately capturing interactions between fluids and deformable solids has proven to be much more elusive. In particular, satisfaction of boundary conditions poses a significant difficulty, as the straightforward voxelized treatment suffers from visible grid artefacts, whereas use of a conforming mesh greatly increases the computational overhead of a simulation. This thesis investigates the problem of animating two-way coupling effects between free-surface liquids and linearly elastic solids. Aside from presenting simulation techniques for such liquids and solids separately, we introduce a new approach to simulating their interactions that exhibits several notable advantages over previous techniques. By fully incorporating the dynamics of the solid into pressure projection, we simultaneously handle fluid incompressibility and solid elasticity and damping. Thanks to this strong coupling, our method does not suffer from instability, even in very taxing scenarios. Furthermore, use of a cut-cell discretization methodology allows us to accurately apply proper free-slip boundary conditions at the exact solid-fluid interface. Consequently, our method is capable of correctly simulating inviscid tangential flow, devoid of grid artefacts or artificial sticking. Lastly, we present an efficient algebraic transformation to convert the indefinite coupled pressure projection system into positive-definite form. The thesis also contains an evaluation of our proposed method, including several animation scenarios, as well as comparisons to previous techniques

    Real-time hybrid cutting with dynamic fluid visualization for virtual surgery

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    It is widely accepted that a reform in medical teaching must be made to meet today's high volume training requirements. Virtual simulation offers a potential method of providing such trainings and some current medical training simulations integrate haptic and visual feedback to enhance procedure learning. The purpose of this project is to explore the capability of Virtual Reality (VR) technology to develop a training simulator for surgical cutting and bleeding in a general surgery

    Semantic models for texturing volume objects

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    EThOS - Electronic Theses Online ServiceGBUnited Kingdo

    Novel Paradigms in Physics-Based Animation: Pointwise Divergence-Free Fluid Advection and Mixed-Dimensional Elastic Object Simulation

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    This thesis explores important but so far less studied aspects of physics-based animation: a simulation method for mixed-dimensional and/or non-manifold elastic objects, and a pointwise divergence-free velocity interpolation method applied to fluid simulation. Considering the popularity of single-type models e.g., hair, cloths, soft bodies, etc., in deformable body simulations, more complicated coupled models have gained less attention in graphics research, despite their relative ubiquity in daily life. This thesis presents a unified method to simulate such models: elastic bodies consisting of mixed-dimensional components represented with potentially non-manifold simplicial meshes. Building on well-known simplicial rod, shell, and solid models, this thesis categorizes and defines a comprehensive palette expressing all possible constraints and elastic energies for stiff and flexible connections between the 1D, 2D, and 3D components of a single conforming simplicial mesh. For fluid animation, this thesis proposes a novel methodology to enhance grid-based fluid animation with pointwise divergence-free velocity interpolation. Unlike previous methods which interpolate discrete velocity values directly for advection, this thesis proposes using intermediate steps involving vector potentials: first build a discrete vector potential field, interpolate these values to form a pointwise potential, and apply the continuous curl to recover a pointwise divergence-free flow field. Particles under these pointwise divergence-free flows exhibit significantly better particle distributions than divergent flows over time. To accelerate the use of vector potentials, this thesis proposes an efficient method that provides boundary-satisfying and smooth discrete potential fields on uniform and cut-cell grids. This thesis also introduces an improved ramping strategy for the “Curl-Noise” method of Bridson et al. (2007), which enforces exact no-normal-flow on the exterior domain boundaries and solid surfaces. The ramping method in the thesis effectively reduces the incidence of particles colliding with obstacles or creating erroneous gaps around the obstacles, while significantly alleviating the artifacts the original ramping strategy produces

    Shape Deformation Statistics and Regional Texture-Based Appearance Models for Segmentation

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    Transferring identified regions of interest (ROIs) from planning-time MRI images to the trans-rectal ultrasound (TRUS) images used to guide prostate biopsy is difficult because of the large difference in appearance between the two modalities as well as the deformation of the prostate's shape caused by the TRUS transducer. This dissertation describes methods for addressing these difficulties by both estimating a patient's prostate shape after the transducer is applied and then locating it in the TRUS image using skeletal models (s-reps) of prostate shapes. First, I introduce a geometrically-based method for interpolating discretely sampled s-reps into continuous objects. This interpolation is important for many tasks involving s-reps, including fitting them to new objects as well as the later applications described in this dissertation. This method is shown to be accurate for ellipsoids where an analytical solution is known. Next, I create a method for estimating a probability distribution on the difference between two shapes. Because s-reps live in a high-dimensional curved space, I use Principal Nested Spheres (PNS) to transform these representations to instead live in a flat space where standard techniques can be applied. This method is shown effective both on synthetic data as well as for modeling the deformation caused by the TRUS transducer to the prostate. In cases where appearance is described via a large number of parameters, such as intensity combined with multiple texture features, it is computationally beneficial to be able to turn these large tuples of descriptors into a scalar value. Using the inherent localization properties of s-reps, I develop a method for using regionally-trained classifiers to turn appearance tuples into the probability that the appearance tuple in question came from inside the prostate boundary. This method is shown to be able to accurately discern inside appearances from outside appearances over a large majority of the prostate boundary. Finally, I combine these techniques into a deformable model-based segmentation framework to segment the prostate in TRUS. By applying the learned mean deformation to a patient's prostate and then deforming it so that voxels with high probability of coming from the prostate's interior are also in the model's interior, I am able to generate prostate segmentations which are comparable to state of the art methods.Doctor of Philosoph

    Sketch-based path design

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    We first present a novel approach to sketching 2D curves with minimally varying curvature as piecewise clothoids. A stable and efficient algorithm fits a sketched piecewise linear curve using a number of clothoid segments with G2 continuity based on a specified error tolerance. We then present a system for conceptually sketching 3D layouts for road and other path networks. Our system makes four key contributions. First, we generate paths with piecewise linear curvature by fitting 2D clothoid curves to strokes sketched on a terrain. Second, the height of paths above the terrain is automatically determined using a new constraint optimization formulation of the occlusion relationships between sketched strokes. Third, we present the break-out lens, a novel widget inspired by break-out views used in engineering visualization, to facilitate the in-context and interactive manipulation of paths from alternate view points. Finally, our path construction is terrain sensitive. ii Acknowledgements I would like to acknowledge the efforts of my supervisor, Karan Singh, and thank him for his guidance over the duration of the Masters program. I learned much from him a

    Non-isometric 3D shape registration.

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    3D shape registration is an important task in computer graphics and computer vision. It has been widely used in the area of film industry, 3D animation, video games and AR/VR assets creation. Manually creating the 3D model of a character from scratch is tedious and time consuming, and it can only be completed by professional trained artists. With the development of 3D geometry acquisition technology, it becomes easier and cheaper to capture high-resolution and highly detailed 3D geometries. However, the scanned data are often incomplete or noisy and therefore cannot be employed directly. To deal with the above two problems, one typical and efficient solution is to deform an existing high-quality model (template) to fit the scanned data (target). Shape registration as an essential technique to do so has been arousing intensive attention. In last decades, various shape registration approaches have been proposed for accurate template fitting. However, there are still some remaining challenges. It is well known that the template can be largely different with the target in respect of size and pose. With the large (usually non-isometric) deformation between them, the shear distortion can easily occur, which may lead to poor results, such as degenerated triangles, fold-overs. Before deforming the template towards the target, reliable correspondences between them should be found first. Incorrect correspondences give the wrong deformation guidance, which can also easily produce fold-overs. As mentioned before, the target always comes with noise. This is the part we want to filter out and try not to fit the template on it. Hence, non-isometric shape registration robust to noise is highly desirable in the scene of geometry modelling from the scanned data. In this PhD research, we address existing challenges in shape registration, including how to prevent the deformation distortion, how to reduce the foldover occurrence and how to deal with the noise in the target. Novel methods including consistent as-similar as-possible surface deformation and robust Huber-L1 surface registration are proposed, which are validated through experimental comparison with state-of-the-arts. The deformation technique plays an important role in shape registration. In this research, a consistent as similar-as-possible (CASAP) surface deformation approach is proposed. Starting from investigating the continuous deformation energy, we analyse the existing term to make the discrete energy converge to the continuous one, whose property we called as energy consistency. Based on the deformation method, a novel CASAP non-isometric surface registration method is proposed. The proposed registration method well preserves the angles of triangles in the template surface so that least distortion is introduced during the surface deformation and thus reduce the risk of fold-over and self-intersection. To reduce the noise influence, a Huber-L1 based non-isometric surface registration is proposed, where a Huber-L1 regularized model constrained on the transformation variation and position difference. The proposed method is robust to noise and produces piecewise smooth results while still preserving fine details on the target. We evaluate and validate our methods through extensive experiments, whose results have demonstrated that the proposed methods in this thesis are more accurate and robust to noise in comparison of the state-of-the arts and enable us to produce high quality models with little efforts

    State of the Art in Dense Monocular Non-Rigid 3D Reconstruction

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    3D reconstruction of deformable (or non-rigid) scenes from a set of monocular2D image observations is a long-standing and actively researched area ofcomputer vision and graphics. It is an ill-posed inverse problem,since--without additional prior assumptions--it permits infinitely manysolutions leading to accurate projection to the input 2D images. Non-rigidreconstruction is a foundational building block for downstream applicationslike robotics, AR/VR, or visual content creation. The key advantage of usingmonocular cameras is their omnipresence and availability to the end users aswell as their ease of use compared to more sophisticated camera set-ups such asstereo or multi-view systems. This survey focuses on state-of-the-art methodsfor dense non-rigid 3D reconstruction of various deformable objects andcomposite scenes from monocular videos or sets of monocular views. It reviewsthe fundamentals of 3D reconstruction and deformation modeling from 2D imageobservations. We then start from general methods--that handle arbitrary scenesand make only a few prior assumptions--and proceed towards techniques makingstronger assumptions about the observed objects and types of deformations (e.g.human faces, bodies, hands, and animals). A significant part of this STAR isalso devoted to classification and a high-level comparison of the methods, aswell as an overview of the datasets for training and evaluation of thediscussed techniques. We conclude by discussing open challenges in the fieldand the social aspects associated with the usage of the reviewed methods.<br
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