Introduction The field of Augmented Reality (AR) has existed for just over one decade, but the growth and progress in the past few years has been remarkable. In 1997, the first author published a survey [3] (based on a 1995 SIGGRAPH course lecture) that defined the field, described many problems, and summarized the developments up to that point. Since then, the field has grown rapidly. In the late 1990s, several conferences specializing in this area were started, including the International Workshop and Symposium on Augmented Reality [29], the International Symposium on Mixed Reality [30], and the Designing Augmented Reality Environments workshop. Some wellfunded interdisciplinary consortia were formed that focused on AR, notably the Mixed Reality Systems Laboratory [50] in Japan and Project ARVIKA [61] in Germany. A freely-available software toolkit (the ARToolkit) for rapidly building AR applications is now available [2]. Because of this wealth of new developments, an updated survey

this article, we provide a comprehensive solution to the problem of transferring the observations of the motion capture sensors to an animated character whose size and proportion may be different from the performer's. Our goal is to map as many of the important aspects of the motion to the target character as possible, while meeting the online, real-time demands of computer puppetry. We adopt a Kalman filter scheme that addresses motion capture noise issues in this setting. We provide the notion of dynamic importance of an end-effector that allows us to determine what aspects of the performance must be kept in the resulting motion. We introduce a novel inverse kinematics solver that realizes these important aspects within tight real-time constraints. Our approach is demonstrated by its application to broadcast television performances

Our HiBall Tracking System generates over 2000 head-pose estimates per second with less than one millisecond of latency, and less than 0.5 millimeters and 0.02 degrees of position and orientation noise, everywhere in a 4.5 by 8.5 meter room. The system is remarkably responsive and robust, enabling VR applications and experiments that previously would have been difficult or even impossible. Previously we published descriptions of only the Kalman filter-based software approach that we call Single-Constraint-at-a-Time tracking. In this paper we describe the complete tracking system, including the novel optical, mechanical, electrical, and algorithmic aspects that enable the unparalleled performance. 1.1 Keywords virtual environments, tracking, calibration, autocalibration, delay, latency, sensor fusion, Kalman filter, optical sensor 2. INTRODUCTION In 1991 the University of North Carolina demonstrated a working scalable optoelectronic head-tracking system in the Tomorrow'...

BRENT EDWARD INSKO: Passive Haptics Significantly Enhances Virtual Environments (Under the direction of Frederick P. Brooks, Jr.) One of the most disconcertingly unnatural properties of most virtual environments (VEs) is the ability of the user to pass through objects. I hypothesize that passive haptics, augmenting a high-fidelity visual virtual environment with low-fidelity physical objects, will markedly improve both sense of presence and spatial knowledge training transfer. The low-fidelity physical models can be constructed from cheap, easy- to-assemble materials such as styrofoam, plywood, and particle board.

We study the problem of tracking a moving device under two indoor location architectures: an active mobile architecture and a passive mobile architecture. In the former, the infrastructure has receivers at known locations, which estimate distances to a mobile device based on an active transmission from the device. In the latter, the infrastructure has active beacons that periodically transmit signals to a passively listening mobile device, which in turn estimates distances to the beacons. Because the active mobile architecture receives simultaneous distance estimates at multiple receivers from the mobile device, it is likely to perform better tracking than the passive mobile system in which the device obtains only one distance estimate at a time and may have moved between successive estimates. However, an passive mobile system scales better with the number of mobile devices and puts users in control of whether their whereabouts are tracked.

This paper presents a new approach for continuous tracking of moving objects observed by multiple, heterogeneous cameras. Our approach simultaneously processes video streams from stationary and Pan-Tilt-Zoom cameras. The detection of moving objects from moving camera streams is performed by defining an adaptive background model that takes into account the camera motion approximated by an affine transformation. We address the tracking problem by separately modeling motion and appearance of the moving objects using two probabilistic models. For the appearance model, multiple color distribution components are proposed for ensuring a more detailed description of the object being tracked. The motion model is obtained using a Kalman Filter (KF) process, which predicts the position of the moving object. The tracking is performed by the maximization of a joint probability model. The novelty of our approach consists in modeling the multiple trajectories observed by the moving and stationary cameras in the same KF framework. It allows deriving a more accurate motion measurement for objects simultaneously viewed by the two cameras and an automatic handling of occlusions, errors in the detection and camera handoff. We demonstrate the performances of the system on several video surveillance sequences. 1.

Almost all previous Augmented Reality (AR) systems work indoors. Outdoor AR systems offer the potential for new application areas. However, building an outdoor AR system is difficult due to portability constraints, the inability to modify the environment, and the greater range of operating conditions. We demonstrate a hybrid tracker that stabilizes an outdoor AR display with respect to user motion, achieving more accurate registration than previously shown in an outdoor AR system. The hybrid tracker combines rate gyros with a compass and tilt orientation sensor in a near real-time system. Sensor distortions and delays required compensation to achieve good results. The measurements from the two sensors are fused together to compensate for each other's limitations. From static locations with moderate head rotation rates, peak registration errors are ~2 degrees, with typical errors under 1 degree, although errors can become larger over long time periods due to compass drift. Without our s...

We compare two technological approaches to augmented reality for 3-D medical visualization: optical and video see-through devices. We provide a context to discuss the technology by reviewing several medical applications of augmented-reality research efforts driven by real needs in the medical �eld, both in the United States and in Europe. We then discuss the issues for each approach, optical versus video, from both a technology and human-factor point of view. Finally, we point to potentially promising future developments of such devices including eye tracking and multifocus planes capabilities, as well as hybrid optical/video technology. 1

We introduce a method to imperceptibly embed arbitrary binary patterns into ordinary color images displayed by unmodified off-the-shelf Digital Light Processing (DLP) projectors. The encoded images are visible only to cameras synchronized with the projectors and exposed for a short interval, while the original images appear only minimally degraded to the human eye. To achieve this goal, we analyze and exploit the micro-mirror modulation pattern used by the projection technology to generate intensity levels for each pixel and color channel. Our real-time embedding process maps the user’s original color image values to the nearest values whose camera-perceived intensities are the ones desired by the binary image to be embedded. The color differences caused by this mapping process are compensated by error-diffusion dithering. The non-intrusive nature of our novel approach allows simultaneous (immersive) display and acquisition under controlled lighting conditions, as defined on a pixel level by the binary patterns. We therefore introduce structured light techniques into human-inhabited mixed and augmented reality environments, where they previously often were too intrusive. 1.

The field of view (FOV) in most head-mounted displays (HMDs) is no more than 60 degrees wide -- far narrower than our normal FOV of about 200 wide. This mismatch arises mostly from the difficulty and expense of building wide-FOV HMDs. Restricting a person's FOV, however, has been shown in real environments to affect people's behavior and degrade task performance. Previous work in virtual reality too has shown that restricting FOV to 50 or less in an HMD can degrade performance. I conducted experiments with a custom, wide-FOV HMD and found that performance is degraded even at the relatively high FOV of 112, and further at 48. The experiments used a prototype tiled wide-FOV HMD to measure performance in VR at up to 176 total horizontal FOV, and a custom large-area tracking system to establish new findings on performance while walking about a large virtua...