Non-manifold Level Sets: A multivalued implicit surface representation with applications to self-collision processing

Nathan Mitchell, Mridul Aanjaneya, Rajsekhar Setaluri, Eftychios Sifakis

Level sets have been established as highly versatile implicit surface representations, with widespread use in graphics applications including modeling and dynamic simulation. Nevertheless, level sets are often presumed to be limited, compared to explicit meshes, in their ability to represent domains with thin topological features (e.g. narrow slits and gaps) or, even worse, material overlap. Geometries with such features may arise from modeling tools that tolerate occasional self-intersections, fracture modeling algorithms that create narrow or zero-width cuts by design, or as transient states in collision processing pipelines for deformable objects. Converting such models to level sets can alter their topology if thin features are not resolved by the grid size. We argue that this ostensible limitation is not an inherent defect of the implicit surface concept, but a collateral consequence of the standard Cartesian lattice used to store the level set values. We propose storing signed distance values on a regular hexahedral mesh which can have multiple collocated cubic elements and non-manifold bifurcation to accommodate non-trivial topology. We show how such non-manifold level sets can be systematically generated from convenient alternative geometric representations. Finally we demonstrate how this representation can facilitate fast and robust treatment of self-collision in simulations of volumetric elastic deformable bodies.

Non-manifold Level Sets: A multivalued implicit surface representation with applications to self-collision processing

TightCCD: Efficient and Robust Continuous Collision Detection using Tight Error Bounds

Zhendong Wang, Min Tang , Ruofeng Tong, and Dinesh Manocha

We present a realtime and reliable continuous collision detection (CCD) algorithm between triangulated models that exploits the floating point hardware capability of current CPUs and GPUs. Our formulation is based on Bernstein Sign Classification that takes advantage of the geometry properties of Bernstein basis and Bézier curves to perform Boolean collision queries. We derive tight numerical error bounds on the computations and employ those bounds to design an accurate algorithm using finite-precision arithmetic. Compared with prior floatingpoint CCD algorithms, our approach eliminates all the false negatives and 90-95% of the false positives. We integrated our algorithm (TightCCD) with physically-based simulation system and observe speedups in collision queries of 5-15X compared with prior reliable CCD algorithms. Furthermore, we demonstrate its benefits in terms of improving the performance or robustness of cloth simulation systems.

TightCCD: Efficient and Robust Continuous Collision Detection using Tight Error Bounds

Deformable Objects Collision Handling with Fast Convergence

Siwang Li, Zherong Pan, Jin Huang,  Hujun Bao, Xiaogang Jin

We present a stable and efficient simulator for deformable objects with collisions and contacts. For stability, an optimization derived from the implicit time integrator is solved in each timestep under the inequality constraints coming from collisions. To achieve fast convergence, we extend the MPRGP based solver from handling boxconstraints only to handling general linear constraints and prove its convergence. This generalization introduces a cost of solving dense linear systems in each step, but these systems can be reduced into diagonal ones for efficiency without affecting the general stability via pruning redundant collisions. Our solver is an order of magnitude faster, especially for elastic objects under large deformation compared with iterative constraint anticipation method (ICA), a typical method for stability. The efficiency, robustness and stability are further verified by our results.

Deformable Objects Collision Handling with Fast Convergence

An Efficient Boundary Handling with a Modified Density Calculation for SPH

Makoto Fujisawa, Kenjiro T. Miura

We propose a new boundary handling method for smoothed particle hydrodynamics (SPH). Previous approaches required the use of boundary particles to prevent particles from sticking to the boundary. We address this issue by correcting the fundamental equations of SPH with the integration of a kernel function. Our approach is able to directly handle triangle mesh boundaries without the need for boundary particles. We also show how our approach can be integrated into a position-based fluid framework.

An Efficient Boundary Handling with a Modified Density Calculation for SPH

 

Quadratic Contact Energy Model for Multi-Impact Simulation

Tianxiang Zhang, Sheng Li, Guoping Wang, Dinesh Manocha, Hanqiu Sun

Simultaneous multi-impact simulation is a challenging problem in modeling collision for rigid bodies. There are several physical criteria for an ideal model of rigid body collision, but existing models generally fail to meet one or more of them. In order to reveal the inner process of potential energy variation, which is the physical fundamental of collision in a multi-impact system, we propose a novel quadratic contact energy model for rigid body simulation. Through constructing quadratic energy functions with respect to impulse, post-impact reactions of rigid bodies can be computed efficiently. Our model can fulfil all the physical criteria and can simulate various natural phenomena including wave effect in particular. Besides, our model has high compatibility to be embedded into the Linear Complementary Problem (LCP) easily and can provide feasible results with any restitution coefficient. With a solid physical base, our model can solve the simultaneous multi-impact problem efficiently with high fidelity and robustness, as demonstrated in the experiment results.

Quadratic Contact Energy Model for Multi-Impact Simulation

Model Reduced Variational Fluid Simulation

Beibei Liu, Gemma Mason, Julian Hodgson, Yiying Tong, Mathieu Desbrun

We present a model-reduced variational Eulerian integrator for incompressible fluids, which combines the efficiency gains of dimension reduction, the qualitative robustness of coarse spatial and temporal resolutions of geometric integrators, and the simplicity of sub-grid accurate boundary conditions on regular grids to deal with arbitrarily-shaped domains. At the core of our contributions is a functional map approach to fluid simulation for which scalar- and vector-valued eigenfunctions of the Laplacian operator can be easily used as reduced bases. Using a variational integrator in time to preserve liveliness and a simple, yet accurate embedding of the fluid domain onto a Cartesian grid, our model-reduced fluid simulator can achieve realistic animations in significantly less computational time than full-scale non-dissipative methods but without the numerical viscosity from which current reduced methods suffer. We also demonstrate the versatility of our approach by showing how it easily extends to magnetohydrodynamics and turbulence modeling in 2D, 3D and curved domains.

Model Reduced Variational Fluid Simulation