Bart Adams

Adaptively sampled particle fluids

We present novel adaptive sampling algorithms for particle-based fluid simulation. We introduce a sampling condition based on geometric local feature size that allows focusing computational resources in geometrically complex regions, while reducing the number of particles deep inside the fluid or near thick flat surfaces. Further performance gains are achieved by varying the sampling density according to visual importance. In addition, we propose a novel fluid surface definition based on approximate particle-to-surface distances that are carried along with the particles and updated appropriately. The resulting surface reconstruction method has several advantages over existing methods, including stability under particle resampling and suitability for representing smooth flat surfaces. We demonstrate how our adaptive sampling and distance-based surface reconstruction algorithms lead to significant improvements in time and memory as compared to single resolution particle simulations, without significantly affecting the fluid flow behavior.

Robust single-view geometry and motion reconstruction

We present a framework and algorithms for robust geometry and motion reconstruction of complex deforming shapes. Our method makes use of a smooth template that provides a crude approximation of the scanned object and serves as a geometric and topological prior for reconstruction. Large-scale motion of the acquired object is recovered using a novel space-time adaptive, non-rigid registration method. Fine-scale details such as wrinkles and folds are synthesized with an efficient linear mesh deformation algorithm. Subsequent spatial and temporal filtering of detail coefficients allows transfer of persistent geometric detail to regions not observed by the scanner. We show how this two-scale process allows faithful recovery of small-scale shape and motion features leading to a high-quality reconstruction. We illustrate the robustness and generality of our algorithm on a variety of examples composed of different materials and exhibiting a large range of dynamic deformations.

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.

Non-rigid registration under isometric deformations

We present a robust and efficient algorithm for the pairwise non‐rigid registration of partially overlapping 3D surfaces. Our approach treats non‐rigid registration as an optimization problem and solves it by alternating between correspondence and deformation optimization. Assuming approximately isometric deformations, robust correspondences are generated using a pruning mechanism based on geodesic consistency. We iteratively learn an appropriate deformation discretization from the current set of correspondences and use it to update the correspondences in the next iteration. Our algorithm is able to register partially similar point clouds that undergo large deformations, in just a few seconds. We demonstrate the potential of our algorithm in various applications such as example based articulated segmentation, and shape interpolation.

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.

Efficient reconstruction of nonrigid shape and motion from real-time 3D scanner data

We present a new technique for reconstructing a single shape and its nonrigid motion from 3D scanning data. Our algorithm takes a set of time-varying unstructured sample points that capture partial views of a deforming object as input and reconstructs a single shape and a deformation field that fit the data. This representation yields dense correspondences for the whole sequence, as well as a completed 3D shape in every frame. In addition, the algorithm automatically removes spatial and temporal noise artifacts and outliers from the raw input data. Unlike previous methods, the algorithm does not require any shape template but computes a fitting shape automatically from the input data. Our reconstruction framework is based upon a novel topology-aware adaptive subspace deformation technique that allows handling long sequences with complex geometry efficiently. The algorithm accesses data in multiple sequential passes, so that long sequences can be streamed from hard disk, not being limited by main memory. We apply the technique to several benchmark datasets, significantly increasing the complexity of the data that can be handled efficiently in comparison to previous work.

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.

Shape Decomposition Using Modal Analysis

We introduce a novel algorithm that decomposes a deformable shape into meaningful parts requiring only a single input pose. Using modal analysis, we are able to identify parts of the shape that tend to move rigidly. We define a deformation energy on the shape, enabling modal analysis to find the typical deformations of the shape. We then find a decomposition of the shape such that the typical deformations can be well approximated with deformation fields that are rigid in each part of the decomposition. We optimize for the best decomposition, which captures how the shape deforms. A hierarchical refinement scheme makes it possible to compute more detailed decompositions for some parts of the shape.

Meshless modeling of deformable shapes and their motion

We present a new framework for interactive shape deformation modeling and key frame interpolation based on a meshless finite element formulation. Starting from a coarse nodal sampling of an objects volume, we formulate rigidity and volume preservation constraints that are enforced to yield realistic shape deformations at interactive frame rates. Additionally, by specifying key frame poses of the deforming shape and optimizing the nodal displacements while targeting smooth interpolated motion, our algorithm extends to a motion planning framework for deformable objects. This allows reconstructing smooth and plausible deformable shape trajectories in the presence of possibly moving obstacles. The presented results illustrate that our framework can handle complex shapes at interactive rates and hence is a valuable tool for animators to realistically and efficiently model and interpolate deforming 3D shapes.

Interactive 3D painting on point-sampled objects

We present a novel painting system for 3D objects. In order to overcome parameterization problems of existing applications, we propose a unified sample-based approach to represent geometry and appearance of the 3D object as well as the brush surface. The generalization of 2D pixel-based paint models to point samples allows us to elegantly simulate paint transfer for 3D objects. In contrast to mesh-based painting systems, an efficient dynamic resampling scheme permits arbitrary levels of painted detail. Our system provides intuitive user interaction with a six degree-of-freedom (DOF) input device. As opposed to other 3D painting systems, real brushes are simulated including their dynamics and collision handling.