SCA 16: Eurographics/SIGGRAPH Symposium on Computer Animation

Permanent URI for this collection

https://diglib.eg.org/handle/10.2312/2630803

Browse

Browsing SCA 16: Eurographics/SIGGRAPH Symposium on Computer Animation by Subject "Computational Geometry and Object Modeling"

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    Accurate Simulation of Wound Healing and Skin Deformation
    (The Eurographics Association, 2016) Feess, Stefan; Kurfiss, Kathrin; Fedkiw, Ronald P.; Michels, Dominik L.; Ladislav Kavan and Chris Wojtan
    We devise a method for the accurate simulation of wound healing and skin deformation. This is based on adequate formulations modeling the underlying biological processes. Cell movements and proliferation are described by a biochemical model whereas a biomechanical model covers effects like wound contraction and the influence of the healing process on the surrounding skin. The resulting simulation framework is very efficient and can be used with realistic input parameters like those measured in biochemistry and biophysics. The accurate behavior of our approach is shown by reproducing regenerative healing processes as well as specific effects such as anisotropic wound contraction, scarring and scab formation. Its efficiency and robustness is illustrated on a broad spectrum of complex examples.
  • Thumbnail Image
    Item
    A Macroblock Optimization for Grid-based Nonlinear Elasticity
    (The Eurographics Association, 2016) Mitchell, Nathan; Doescher, Michael; Sifakis, Eftychios; Ladislav Kavan and Chris Wojtan
    We introduce a new numerical approach for the solution of grid-based discretizations of nonlinear elastic models. Our method targets the linearized system of equations within each iteration of the Newton method, and combines elements of a direct factorization scheme with an iterative Conjugate Gradient method. The goal of our hybrid scheme is to inherit as many of the advantages of its constituent approaches, while curtailing several of their respective drawbacks. In particular, our algorithm converges in far fewer iterations than Conjugate Gradients, especially for systems with less-than-ideal conditioning. On the other hand, our approach largely avoids the storage footprint and memory-bound nature of direct methods, such as sparse Cholesky factorization, while offering very direct opportunities for both SIMD and thread-based parallelism. Conceptually, our method aggregates a rectangular neighborhood of grid cells (typically a 16x8x8 subgrid) into a composite element that we refer to as a ''macroblock''. Similar to conventional tetrahedral or hexahedral elements, macroblocks receive nodal inputs (e.g., displacements) and compute nodal outputs (e.g., forces). However, this input/output interface now only includes nodes on the boundary of the 16x8x8 macroblock; interior nodes are always solved exactly, by means of a direct, highly optimized solver. Models built from macroblocks are solved using Conjugate Gradients, which is accelerated due to the reduced number of degrees of freedom and improved robustness against poor conditioning thanks to the direct solver within each macroblock. We explain how we attain these benefits with just a small increase of the per-iteration cost over the simplest traditional solvers.
  • Thumbnail Image
    Item
    Topology-Aware Neighborhoods for Point-Based Simulation and Reconstruction
    (The Eurographics Association, 2016) Canezin, Florian; Guennebaud, Gaël; Barthe, Loïc; Ladislav Kavan and Chris Wojtan
    Particle based simulations are widely used in computer graphics. In this field, several recent results have improved the simulation itself or improved the tension of the final fluid surface. In current particle based implementations, the particle neighborhood is computed by considering the Euclidean distance between fluid particles only. Thus particles from different fluid components interact, which generates both local incorrect behavior in the simulation and blending artifacts in the reconstructed fluid surface. Our method introduces a better neighborhood computation for both the physical simulation and surface reconstruction steps. We track and store the local fluid topology around each particle using a graph structure. In this graph, only particles within the same local fluid component are neighbors and other disconnected fluid particles are inserted only if they come into contact. The graph connectivity also takes into account the asymmetric behavior of particles when they merge and split, and the fluid surface is reconstructed accordingly, thus avoiding their blending at distance before a merge. In the simulation, this neighborhood information is exploited for better controlling the fluid density and the force interactions at the vicinity of its boundaries. For instance, it prevents the introduction of collision events when two distinct fluid components are crossing without contact, and it avoids fluid interactions through thin waterproof walls. This leads to an overall more consistent fluid simulation and reconstruction.