Philip Dutré

Meshless animation of fracturing solids

We present a new meshless animation framework for elastic and plastic materials that fracture. Central to our method is a highly dynamic surface and volume sampling method that supports arbitrary crack initiation, propagation, and termination, while avoiding many of the stability problems of traditional mesh-based techniques. We explicitly model advancing crack fronts and associated fracture surfaces embedded in the simulation volume. When cutting through the material, crack fronts directly affect the coupling between simulation nodes, requiring a dynamic adaptation of the nodal shape functions. We show how local visibility tests and dynamic caching lead to an efficient implementation of these effects based on point collocation. Complex fracture patterns of interacting and branching cracks are handled using a small set of topological operations for splitting, merging, and terminating crack fronts. This allows continuous propagation of cracks with highly detailed fracture surfaces, independent of the spatial resolution of the simulation nodes, and provides effective mechanisms for controlling fracture paths. We demonstrate our method for a wide range of materials, from stiff elastic to highly plastic objects that exhibit brittle and/or ductile fracture.

A Comparison of Methods for Generating Poisson Disk Distributions

Poisson disk distributions have many applications in the field of computer graphics. Besides sampling, Poisson disk distributions are used in object distribution, non‐photorealistic rendering and procedural texturing. Over the years, a large number of methods for generating Poisson disk distributions have been proposed, making it difficult to choose the right method for a given application. In this paper, we present a detailed comparison of most techniques for generating Poisson disk distributions. The methods we study include dart throwing, relaxation dart throwing, Lloyds relaxation, Shades Poisson disk tiles, tiled blue noise samples, fast hierarchical importance sampling with blue noise properties, edge‐based Poisson disk tiles, template Poisson disk tiles, corner‐based Poisson disk tiles and recursive Wang tiles for real‐time blue noise. Analysing all of these methods within a single framework is one of the major contributions of this work.

A unified Lagrangian approach to solid-fluid animation

We present a framework for physics-based animation of deforming solids and fluids. By merging the equations of solid mechanics with the Navier-Stokes equations using a particle-based Lagrangian approach, we are able to employ a unified method to animate both solids and fluids as well as phase transitions. Central to our framework is a hybrid implicit-explicit surface generation approach, which is capable of representing fine surface detail as well as handling topological changes in interactive time for moderately complex objects. The generated surface is represented by oriented point samples, which adapt to the new position of the particles by minimizing the potential energy of the surface subject to geometric constraints. We illustrate our algorithm on a variety of examples ranging from stiff elastic and plasto-elastic materials to fluids with variable viscosity.

Porous flow in particle-based fluid simulations

This paper presents the simulation of a fluid flowing through a porous deformable material. We introduce the physical principles governing porous flow, expressed by the Law of Darcy, into the Smoothed Particle Hydrodynamics (SPH) framework for simulating fluids and deformable objects. Contrary to previous SPH approaches, we simulate porous flow at a macroscopic scale, making abstraction of individual pores or cavities inside the material. Thus, the number of computational elements is kept low, while at the same time realistic simulations can be achieved. Our algorithm models the changing behavior of the wet material as well as the full two-way coupling between the fluid and the porous material. This enables various new effects, such as the simulation of sponge-like elastic bodies and water-absorbing sticky cloth.

Procedural noise using sparse Gabor convolution

Noise is an essential tool for texturing and modeling. Designing interesting textures with noise calls for accurate spectral control, since noise is best described in terms of spectral content. Texturing requires that noise can be easily mapped to a surface, while high-quality rendering requires anisotropic filtering. A noise function that is procedural and fast to evaluate offers several additional advantages. Unfortunately, no existing noise combines all of these properties. In this paper we introduce a noise based on sparse convolution and the Gabor kernel that enables all of these properties. Our noise offers accurate spectral control with intuitive parameters such as orientation, principal frequency and bandwidth. Our noise supports two-dimensional and solid noise, but we also introduce setup-free surface noise. This is a method for mapping noise onto a surface, complementary to solid noise, that maintains the appearance of the noise pattern along the object and does not require a texture parameterization. Our approach requires only a few bytes of storage, does not use discretely sampled data, and is nonperiodic. It supports anisotropy and anisotropic filtering. We demonstrate our noise using an interactive tool for noise design.

A procedural object distribution function

In this article, we present a procedural object distribution function, a new texture basis function that distributes procedurally generated objects over a procedurally generated texture. The objects are distributed uniformly over the texture, and are guaranteed not to overlap. The scale, size, and orientation of the objects can be easily manipulated. The texture basis function is efficient to evaluate, and is suited for real-time applications. The new texturing primitive we present extends the range of textures that can be generated procedurally.The procedural object distribution function we propose is based on Poisson disk tiles and a direct stochastic tiling algorithm for Wang tiles. Poisson disk tiles are square tiles filled with a precomputed set of Poisson disk distributed points, inspired by Wang tiles. A single set of Poisson disk tiles enables the real-time generation of an infinite amount of Poisson disk distributions of arbitrary size. With the direct stochastic tiling algorithm, these Poisson disk distributions can be evaluated locally, at any position in the Euclidean plane.Poisson disk tiles and the direct stochastic tiling algorithm have many other applications in computer graphics. We briefly explore applications in object distribution, primitive distribution for illustration, and environment map sampling.

An alternative for Wang tiles: colored edges versus colored corners

In this article we revisit the concept of Wang tiles and introduce corner tiles, square tiles with colored corners. During past years, Wang tiles have become a valuable tool in computer graphics. Important applications of Wang tiles include texture synthesis, tile-based texture mapping, and generating Poisson disk distributions. Through their colored edges, Wang tiles enforce continuity with their direct neighbors. However, Wang tiles do not directly constrain their diagonal neighbors. This leads to continuity problems near tile corners, a problem commonly known as the corner problem. Corner tiles, on the other hand, do impose restrictions on their diagonal neighbors, and thus are not subject to the corner problem. In this article we show that previous applications of Wang tiles can also be done using corner tiles, but that corner tiles have distinct advantages for each of these applications. Compared to Wang tiles, corner tiles are easier to tile, textures synthesized with corner tiles contain more samples from the original texture, corner tiles reduce the required texture memory by a factor of two for tile-based texture mapping, and Poisson disk distributions generated with corner tiles have better spectral properties. Corner tiles result in cleaner, simpler, and more efficient applications.

Wavelet environment matting

In this paper we present a novel approach for capturing the environment matte of a scene. We impose no restrictions on material properties of the objects in the captured scene and exploit scene characteristics (e.g. material properties and self-shadowing) to minimize recording time and to bound the error. Using a CRT monitor, wavelet patterns are emitted onto the scene in order of importance to efficiently construct the environment matte. This order of importance is obtained by means of a feedback loop that takes advantage of the knowledge learned from previously recorded photographs. Once the recording process is finished, new backdrops can be efficiently placed behind the scene.

Interactive Rendering with Bidirectional Texture Functions

We propose a new technique for efficiently rendering bidirectional texture functions (BTFs). A 6D BTF describesthe appearance of a material as a texture that depends on the lighting and viewing directions. As such, a BTFaccommodates self‐shadowing, interreflection, and masking effects of a complex material without needing anexplicit representation of the small scale geometry. Our method represents the BTF as a set of spatially varyingapparent BRDFs, that each encode the reflectance field of a single pixel in the BTF. Each apparent BRDF isdecomposed into a product of three or more two‐dimensional positive factors using a novel factorization technique,which we call chained matrix factorization (CMF). The proposed factorization technique is fully automatic andsuitable for both BRDFs and apparent BRDFs (which typically exhibit off‐specular peaks and non‐reciprocity).The main benefit of CMF is that it delivers factors well suited for the limited dynamic range of conventionaltexture maps. After factorization, an efficient representation of the BTF is obtained by clustering the factors intoa compact set of 2D textures. With this compact representation, BTFs can be rendered on recent consumer levelhardware with arbitrary viewing and lighting directions at interactive rates.

A compact factored representation of heterogeneous subsurface scattering

Many translucent materials exhibit heterogeneous subsurface scattering, which arises from complex internal structures. The acquisition and representation of these scattering functions is a complex problem that has been only partially addressed in previous techniques. Unlike homogeneous materials, the spatial component of heterogeneous subsurface scattering can vary arbitrarily over surface locations. Storing the spatial component without compression leads to impractically large datasets. In this paper, we address the problem of acquiring and compactly representing the spatial component of heterogeneous subsurface scattering functions. We propose a material model based on matrix factorization that can be mapped onto arbitrary geometry, and, due to its compact form, can be incorporated into most visualization systems with little overhead. We present results of several real-world datasets that are acquired using a projector and a digital camera.