Alvaro G. Perez

Star-contours for efficient hierarchical self-collision detection

Collision detection is a problem that has often been addressed efficiently with the use of hierarchical culling data structures. In the subproblem of self-collision detection for triangle meshes, however, such hierarchical data structures lose much of their power, because triangles adjacent to each other cannot be distinguished from actually colliding ones unless individually tested. Shape regularity of surface patches, described in terms of orientation and contour conditions, was proposed long ago as a culling criterion for hierarchical self-collision detection. However, to date, algorithms based on shape regularity had to trade conservativeness for efficiency, because there was no known algorithm for efficiently performing 2D contour self-intersection tests. In this paper, we introduce a star-contour criterion that is amenable to hierarchical computations. Together with a thorough analysis of the tree traversal process in hierarchical self-collision detection, it has led us to novel hierarchical data structures and algorithms for efficient yet conservative self-collision detection. We demonstrate the application of our algorithm to several example animations, and we show that it consistently outperforms other approaches.

Soft finger tactile rendering for wearable haptics

This paper introduces a tactile rendering algorithm for wearable cutaneous devices that stimulate the skin through local contact surface modulation. The first step in the algorithm simulates contact between a skin model and virtual objects, and computes the contact surface to be rendered. The accuracy of this surface is maximized by simulating soft skin with its characteristic nonlinear behavior. The second step takes the desired contact surface as input, and computes the device configuration by solving an optimization problem, i.e., minimizing the deviation between the contact surface in the virtual environment and the contact surface rendered by the device. The method is implemented on a thimble-like wearable device.

Strain limiting for soft finger contact simulation

The command of haptic devices for rendering direct interaction with the hand requires thorough knowledge of the forces and deformations caused by contact interactions on the fingers. In this paper, we propose an algorithm to simulate nonlinear elasticity under frictional contact, with the goal of establishing a model-based strategy to command haptic devices and to render direct hand interaction. The key novelty in our algorithm is an approach to model the extremely nonlinear elasticity of finger skin and flesh using strain-limiting constraints, which are seamlessly combined with frictional contact constraints in a standard constrained dynamics solver. We show that our approach enables haptic rendering of rich and compelling deformations of the fingertip.

Fast deformation of volume data using tetrahedral mesh rasterization

Many inherently deformable structures, such as human anatomy, are often represented using a regular volumetric discretization, e.g., in medical imaging. While deformation algorithms employ discretizations that deform themselves along with the material, visualization algorithms are optimized for regular undeformed discretizations. In this paper, we propose a method to transform highresolution volume data embedded in a deformable tetrahedral mesh. We cast volume deformation as a problem of tetrahedral rasterization with 3D texture mapping. Then, the core of our solution to volume data deformation is a very fast algorithm for tetrahedral rasterization. We perform rasterization as a massively parallel operation on target voxels, and we minimize the number of voxels to be handled using a multi-resolution culling approach. Our method allows the deformation of volume data with over 20 million voxels at interactive rates.

Optimization-Based Wearable Tactile Rendering

Novel wearable tactile interfaces offer the possibility to simulate tactile interactions with virtual environments directly on our skin. But, unlike kinesthetic interfaces, for which haptic rendering is a well explored problem, they pose new questions about the formulation of the rendering problem. In this work, we propose a formulation of tactile rendering as an optimization problem, which is general for a large family of tactile interfaces. Based on an accurate simulation of contact between a finger model and the virtual environment, we pose tactile rendering as the optimization of the device configuration, such that the contact surface between the device and the actual finger matches as close as possible the contact surface in the virtual environment. We describe the optimization formulation in general terms, and we also demonstrate its implementation on a thimble-like wearable device. We validate the tactile rendering formulation by analyzing its force error, and we show that it outperforms other approaches.

Efficient nonlinear skin simulation for multi-finger tactile rendering

Recent advances in tactile rendering span, among others, wearable cutaneous interfaces, tactile rendering algorithms, or nonlinear soft skin models. However, the adoption of these advances for multi-finger tactile rendering of dexterous grasping and manipulation is hampered by the computational cost incurred with nonlinear skin models when applied to the full hand. We have observed that classic constrained dynamics solvers, typically designed for contact mechanics, fail to perform efficiently on deformation constraints of nonlinear skin models. In this paper, we propose a novel constrained dynamics solver designed to perform well with highly nonlinear deformation constraints. In practice, we achieve more than 10× speed-up over previous approaches, and as a result we enable multi-finger tactile rendering of manipulation actions that capture the nonlinearity of skin.