Robust Reflectance Estimation for Projection-Based Appearance Control in a Dynamic Light Environment.

We present a novel method that robustly estimates the reflectance, even in an environment with dynamically changing light. To control the appearance of an object by using a projector-camera system, an appropriate estimate of the object's reflectance is vital to the creation of an appropriate projection image. Most conventional estimation methods assume static light conditions; however, in practice, the appearance is affected by both the reflectance and environmental light. In an environment with dynamically changing light, conventional reflectance estimation methods require calibration every time the conditions change. In contrast, our method requires no additional calibration because it simultaneously estimates both the reflectance and environmental light. Our method is based on the concept of creating two different light conditions by switching the projection at a rate higher than that perceived by the human eye and captures the images of a target object separately under each condition. The reflectance and environmental light are then simultaneously estimated by using the pair of images acquired under these two conditions. We implemented a projector-camera system that switches the projection on and off at 120 Hz. Experiments confirm the robustness of our method when changing the environmental light. Further, our method can robustly estimate the reflectance under practical indoor lighting conditions.

Handheld Guides in Inspection Tasks: Augmented Reality versus Picture.

Inspection tasks focus on observation of the environment and are required in many industrial domains. Inspectors usually execute these tasks by using a guide such as a paper manual, and directly observing the environment. The effort required to match the information in a guide with the information in an environment and the constant gaze shifts required between the two can severely lower the work efficiency of inspector in performing his/her tasks. Augmented reality (AR) allows the information in a guide to be overlaid directly on an environment. This can decrease the amount of effort required for information matching, thus increasing work efficiency. AR guides on head-mounted displays (HMDs) have been shown to increase efficiency. Handheld AR (HAR) is not as efficient as HMD-AR in terms of manipulability, but is more practical and features better information input and sharing capabilities. In this study, we compared two handheld guides: an AR interface that shows 3D registered annotations, that is, annotations having a fixed 3D position in the AR environment, and a non-AR picture interface that displays non-registered annotations on static images. We focused on inspection tasks that involve high information density and require the user to move, as well as to perform several viewpoint alignments. The results of our comparative evaluation showed that use of the AR interface resulted in lower task completion times, fewer errors, fewer gaze shifts, and a lower subjective workload. We are the first to present findings of a comparative study of an HAR and a picture interface when used in tasks that require the user to move and execute viewpoint alignments, focusing only on direct observation. Our findings can be useful for AR practitioners and psychology researchers.

Augmented reality as multimedia: the case for situated vocabulary learning.

Augmented reality (AR) has the potential to create compelling learning experiences. However, there are few research works exploring the design and evaluation of AR for educational settings. In our research, we treat AR as a type of multimedia that is situated in authentic environments and apply multimedia learning theory as a framework for developing our educational applications. We share our experiences in developing a handheld AR system and one specific use case, namely, situated vocabulary learning. Results of our evaluations show that we are able to create AR applications with good system usability. More importantly, our preliminary evaluations show that AR may lead to better retention of words and improve student attention and satisfaction.

Toward Standard Usability Questionnaires for Handheld Augmented Reality.

Usability evaluations are important to improving handheld augmented reality (HAR) systems. However, no standard questionnaire considers perceptual and ergonomic issues found in HAR. The authors performed a systematic literature review to enumerate these issues. Based on these issues, they created a HAR usability scale that consists of comprehensibility and manipulability scales. These scales measure general system usability, ease of understanding the information presented, and ease of handling the device. The questionnaires' validity and reliability were evaluated in four experiments, and the results show that the questionnaires consistently correlate with other subjective and objective measures of usability. The questionnaires also have good reliability based on the Cronbach's alpha. Researchers and professionals can directly use these questionnaires to evaluate their own HAR applications or modify them with the insights presented in this article.

Geometrically-correct projection-based texture mapping onto a deformable object.

Projection-based Augmented Reality commonly employs a rigid substrate as the projection surface and does not support scenarios where the substrate can be reshaped. This investigation presents a projection-based AR system that supports deformable substrates that can be bent, twisted or folded. We demonstrate a new invisible marker embedded into a deformable substrate and an algorithm that identifies deformations to project geometrically correct textures onto the deformable object. The geometrically correct projection-based texture mapping onto a deformable marker is conducted using the measurement of the 3D shape through the detection of the retro-reflective marker on the surface. In order to achieve accurate texture mapping, we propose a marker pattern that can be partially recognized and can be registered to an object’s surface. The outcome of this work addresses a fundamental vision recognition challenge that allows the underlying material to change shape and be recognized by the system. Our evaluation demonstrated the system achieved geometrically correct projection under extreme deformation conditions. We envisage the techniques presented are useful for domains including prototype development, design, entertainment and information based AR systems.