We present an exact and interactive collision detection system, I-COLLIDE, for large-scale environments. Such environments are characterized by the number of objects undergoing rigid motion and the complexity of the mod-els. The algorithm does not assume the objects ’ motions can be expressed as a closed form function of time. The collision detection system is general and can be easily in-terfaced with a variety of applications. The algorithm uses a two-level approach based on pruning multiple-object pairs using bounding boxes and performing exact collision detection between selected pairs of polyhedral models. We demonstrate the performance of the system in walkthrough and simulation environments consisting of a large number of moving objects. In particular, the system takes less than l/20 of a second to determine all the collisions and contacts in an environment consisting of more than a 1000 moving polytopes, each consisting of more than 50 faces on an HP-9000/750. 1

Interactive environments for dynamically deforming objects play an important role in surgery simulation and entertainment technology. These environments require fast deformable models and very efficient collision handling techniques. While collision detection for rigid bodies is well-investigated, collision detection for deformable objects introduces additional challenging problems. This paper focusses on these aspects and summarizes recent research in the area of deformable collision detection. Various approaches based on bounding volume hierarchies, distance fields, and spatial partitioning are discussed. Further, image-space techniques and stochastic methods are considered. Applications in cloth modeling and surgical simulation are presented.

We describe the HUMANOID environment dedicated to human modeling and animation for general multimedia, VR, and CAD applications integrating virtual humans. We present the design of the system and the integration of the various features: generic modeling of a large class of entities with the BODY data structure, realistic skin deformation for body and hands, facial animation, collision detection, integrated motion control and parallelization of computation intensive tasks.

Object interactions are ubiquitous in interactive computer graphics, 3D object motion simulations, virtual reality and robotics applications. Most collision detection algorithms are based on geometrical object–space interference tests. Some algorithms have employed an image– space approach to the collision detection problem. In this article, we demonstrate an image–space collision detection process that allows substatial computational savings during the image–space interference test. This approach makes efficient use of the graphics rendering hardware for real–time complex object interactions.

We present several techniques for producing two visual and modeling e#ects in augmentedreality. The #rst e#ect involves interactively calculating the occlusions between real and virtual objects. The second e#ect utilizes acollision detection algorithm to automatically move dynamic virtual objects until they come in contact with static real objects in augmentedreality. All of the techniques utilize calibrated data derivedfrom images of a real-world environment. 1. Introduction Augmented reality #AR# is a combination of technologies distinct from virtual reality #VR#, that promises to support a wider range of applications. Interest in AR has substantially increased in the past few years, with research groups exploring diagnostic, manufacturing, medical and repair applications 1 . In augmented reality, the computer provides additional visual information that enhances or augments a user's view of the real world. Instead of replacing the world with a completely virtual environment, as i...

Please note: This document is ©2001 by David Baraff. This chapter may be freely duplicated and distributed so long as no consideration is received in return, and this copyright notice remains intact.

A general framework for collision detection is presented. Then, we look at each stage and compare different approaches by extensive benchmarks. The results suggest a way to optimize the performance of the overall framework.

This paper introduces a new model for rapid physical simulation called sparse dynamics. The method employs a quick first pass to identify likely object interactions. These are then flagged for more detailed analysis. As actual collisions are rare in a sparsely populated environment, efficiency is greatly increased. The first pass uses deterministic Newtonian mechanics to predict future collisions analytically, obviating the need to simulate small uniform time steps. Timings indicate this provides a large speed improvement over more traditional methods. In particular we were able to simulate full collision detections for 1000 polyhedra at real time speeds. We also discuss how to extend the sparse dynamics model to handle complex effects such as gravity, friction, and user interaction. 1 Motivation The graphics community has a tradition of viewing realtime dynamic simulation as intractable. While large strides have been made in real-time rendering and user interfaces, simulations invol...

This paper is about sensing and manipulation strategies using simple, modular robot hardware. RISC robotics is an attempt to fuse automation and robotic technologies. It uses traditional automation hardware such as parallel-jaw grippers and optical beam sensors, together with geometric planning and sensing algorithms. RISC systems should be cost-effective and reliable, and easy to setup and reconfigure. They should also be flexible enough to support small batch sizes and rapid changes in part design needed in forthcoming flexible-agile manufacturing systems. The RISC acronym, borrowed from computer architecture, suggests the parallels between the two technologies. RISC robots perform complex operations by composing simple elements. The elements may be individual light beam sensors, grouped together to form an array for recognition. Or a complex manipulation task may be performed via a sequence of grasp steps by different grippers specialized for acquisition and placement. This paper emphasizes three areas: (i) RISC sensing, primarily optical beam sensing (ii) RISC manipulation using simple parallel-jaw grippers or minimal configurations of fingers (iii) Computer-aided design of RISC workcells.

Abstract—This research work is aimed toward the development of a VR-based trainer for colon cancer removal. It enables the surgeons to interactively view and manipulate the concerned virtual organs as during a real surgery. First, we present a method for animating the small intestine and the mesentery (the tissue that connects it to the main vessels) in real-time, thus enabling user interaction through virtual surgical tools during the simulation. We present a stochastic approach for fast collision detection in highly deformable, self-colliding objects. A simple and efficient response to collisions is also introduced in order to reduce the overall animation complexity. Second, we describe a new method based on generalized cylinders for fast rendering of the intestine. An efficient curvature detection method, along with an adaptive sampling algorithm, is presented. This approach, while providing improved tessellation without the classical self-intersection problem, also allows for high-performance rendering thanks to the new 3D skinning feature available in recent GPUs. The rendering algorithm is also designed to ensure a guaranteed frame rate. Finally, we present the quantitative results of the simulations and describe the qualitative feedback obtained from the surgeons. Index Terms—Virtual reality, physically-based modeling, animation, curve and surface representation. 1