Deformable Objects In Real Time Research Articles

Graph visual tracking using conditional uncertainty minimization and minibatch Monte Carlo inference

In this paper, we propose a novel visual tracking method based on conditional uncertainty minimization (CUM), minibatch Monte Carlo (MMC), and non-nested sampling (NNS). We represent a target as a Markov network with nodes and edges, where each node corresponds to the corresponding pixel of the target and each edge describes the relations among the pixels. The nodes are then grouped into optimal cliques using the proposed CUM, which minimizes the conditional uncertainty (i.e. the variance of the conditional expectation) between two cliques. The aforementioned minimization process is facilitated using the proposed NNS. During visual tracking, Markov networks evolve across frames and describe the geometrically varying appearances of the target. In many cases, these networks cannot represent the targets perfectly; however, the configurations of the target can be inferred accurately using the CUM and the best configuration can be found at an early stage of the Monte Carlo sampling using the proposed MMC. The numerical results demonstrate that our method qualitatively and quantitatively outperforms other state-of-the-art trackers on standard benchmark datasets. In particular, our method accurately tracks deformable objects in realtime.

Real‐time interaction of virtual and physical objects in mixed reality applications

SummaryWe present a real‐time method for computing the mechanical interaction between real and virtual objects in an augmented reality environment. Using model order reduction methods we are able to estimate the physical behavior of deformable objects in real time, with the precision of a high‐fidelity solver but working at the speed of a video sequence. We merge tools of machine learning, computer vision, and computer graphics in a single application to describe the behavior of deformable virtual objects allowing the user to interact with them in a natural way. Three examples are provided to test the performance of the method.

CPU-GPU mixed implementation of virtual node method for real-time interactive cutting of deformable objects using OpenCL.

Surgical simulators need to simulate interactive cutting of deformable objects in real time. The goal of this work was to design an interactive cutting algorithm that eliminates traditional cutting state classification and can work simultaneously with real-time GPU-accelerated deformation without affecting its numerical stability. A modified virtual node method for cutting is proposed. Deformable object is modeled as a real tetrahedral mesh embedded in a virtual tetrahedral mesh, and the former is used for graphics rendering and collision, while the latter is used for deformation. Cutting algorithm first subdivides real tetrahedrons to eliminate all face and edge intersections, then splits faces, edges and vertices along cutting tool trajectory to form cut surfaces. Next virtual tetrahedrons containing more than one connected real tetrahedral fragments are duplicated, and connectivity between virtual tetrahedrons is updated. Finally, embedding relationship between real and virtual tetrahedral meshes is updated. Co-rotational linear finite element method is used for deformation. Cutting and collision are processed by CPU, while deformation is carried out by GPU using OpenCL. Efficiency of GPU-accelerated deformation algorithm was tested using block models with varying numbers of tetrahedrons. Effectiveness of our cutting algorithm under multiple cuts and self-intersecting cuts was tested using a block model and a cylinder model. Cutting of a more complex liver model was performed, and detailed performance characteristics of cutting, deformation and collision were measured and analyzed. Our cutting algorithm can produce continuous cut surfaces when traditional minimal element creation algorithm fails. Our GPU-accelerated deformation algorithm remains stable with constant time step under multiple arbitrary cuts and works on both NVIDIA and AMD GPUs. GPU-CPU speed ratio can be as high as 10 for models with 80,000 tetrahedrons. Forty to sixty percent real-time performance and 100-200 Hz simulation rate are achieved for the liver model with 3,101 tetrahedrons. Major bottlenecks for simulation efficiency are cutting, collision processing and CPU-GPU data transfer. Future work needs to improve on these areas.

A Physics-driven Neural Networks-based Simulation System (PhyNNeSS) for multimodal interactive virtual environments involving nonlinear deformable objects.

BACKGROUND: While an update rate of 30 Hz is considered adequate for real time graphics, a much higher update rate of about 1 kHz is necessary for haptics. Physics-based modeling of deformable objects, especially when large nonlinear deformations and complex nonlinear material properties are involved, at these very high rates is one of the most challenging tasks in the development of real time simulation systems. While some specialized solutions exist, there is no general solution for arbitrary nonlinearities. METHODS: In this work we present PhyNNeSS - a Physics-driven Neural Networks-based Simulation System - to address this long-standing technical challenge. The first step is an off-line pre-computation step in which a database is generated by applying carefully prescribed displacements to each node of the finite element models of the deformable objects. In the next step, the data is condensed into a set of coefficients describing neurons of a Radial Basis Function network (RBFN). During real-time computation, these neural networks are used to reconstruct the deformation fields as well as the interaction forces. RESULTS: We present realistic simulation examples from interactive surgical simulation with real time force feedback. As an example, we have developed a deformable human stomach model and a Penrose-drain model used in the Fundamentals of Laparoscopic Surgery (FLS) training tool box. CONCLUSIONS: A unique computational modeling system has been developed that is capable of simulating the response of nonlinear deformable objects in real time. The method distinguishes itself from previous efforts in that a systematic physics-based pre-computational step allows training of neural networks which may be used in real time simulations. We show, through careful error analysis, that the scheme is scalable, with the accuracy being controlled by the number of neurons used in the simulation. PhyNNeSS has been integrated into SoFMIS (Software Framework for Multimodal Interactive Simulation) for general use.

A real-time multigrid finite hexahedra method for elasticity simulation using CUDA

We present a multigrid approach for simulating elastic deformable objects in real time on recent NVIDIA GPU architectures. To accurately simulate large deformations we consider the co-rotated strain formulation. Our method is based on a finite element discretization of the deformable object using hexahedra. It draws upon recent work on multigrid schemes for the efficient numerical solution of partial differential equations on such discretizations. Due to the regular shape of the numerical stencil induced by the hexahedral regime, and since we use matrix-free formulations of all multigrid steps, computations and data layout can be restructured to avoid execution divergence of parallel running threads and to enable coalescing of memory accesses into single memory transactions. This enables to effectively exploit the GPU’s parallel processing units and high memory bandwidth via the CUDA parallel programming API. We demonstrate performance gains of up to a factor of 27 and 4 compared to a highly optimized CPU implementation on a single CPU core and 8 CPU cores, respectively. For hexahedral models consisting of as many as 269,000 elements our approach achieves physics-based simulation at 11 time steps per second.

Perceptually validated global/local deformations

AbstractModal analysis techniques are often used to animate deformable objects in real time. In order to achieve performance improvements, the degrees of freedom of the problem may be reduced by using the main global deformations alone. However, this can lead to a reduction in quality and realism due to the lack of local behaviors. To solve this problem, we present a new method to add local deformations to modal analysis simulations. We perceptually evaluate our method with a set of experiments, thereby deriving guidelines for when and how local deformations can best be used. Copyright © 2010 John Wiley & Sons, Ltd.

Animation through space and time based on a space deformation model

AbstractTo animate deformable objects through time and space, a new technique that will be referred to as DOGMA is proposed here. It is well suited to animation for both interactive design and real‐time visualization.The concept of animation primitives based on an extension to animation of the space deformation model is introduced. Animation primitives can be added to any geometrical modeller because they are independent of the underlying geometrical model of the animated objects. It encapsulates animation through space and time under a unique formalism. A local control of deformations both through space and time is easily specified. A direct manipulation of objects is provided. At any time, animated objects are continuously computed from their corresponding animation primitives. This dispenses with the necessity for major storage of the interpolation process. A fully interactive system offers an interactive environment which enables the user to animate deformable objects in real time.