RealTime Hand Model Estimation from Depth Images for Wearable Augmented Reality Glasses This work presents a hand model estimation method designed specifically with augmented reality (AR) glasses and 3D AR interface in mind. The proposed work is capable of estimating the 3D positions of all ten finger from a single depth image. By leveraging a lowdimensional hand model and exploiting hand geometries from an egocentric view, we build a lightweight algorithm that is accurate, environment agnostic, and runs in real time on mobile hardware. One major consideration in our design for AR is that the useru0027s hand is likely to interact with planar surfaces since they serve as ideal touchscreens. As a result, our method will not fail to detect the hand even when the hand is in physical contact with a surface such as a table, wall, or even another palm. Our experiment shows using the CVAR database that the accuracy with clear background at 98% and with cluttered background at around 85%

Tangible and Visible 3D Object Reconstruction in Augmented Reality Many crucial applications in the fields of filmmaking, game design, education, and cultural preservation xe2x80x94 among others xe2x80x94 involve the modeling, authoring, or editing of 3D objects and scenes. The two major methods of creating 3D models are 1) modeling, using computer software, and 2) reconstruction, generally using highquality 3D scanners. Scanners of sufficient quality to support the latter method remain unaffordable to the general public. Since the emergence of consumergrade RGBD cameras, there has been a growing interest in 3D reconstruction systems using depth cameras. However, most such systems are not userfriendly, and require intense efforts and practice if good reconstruction results are to be obtained. In this paper, we propose to increase the accessibility of depthcamerabased 3D reconstruction by assisting its users with augmented reality (AR) technology. Specifically, the proposed approach will allow users to rotatemove a target object freely with their hands and see the object being overlapped with its reconstructing model during the reconstruction process. As well as being more instinctual than conventional reconstruction systems, our proposed system will provide useful hints on complete 3D reconstruction of an object, including the best capturing range; reminder of moving and rotating the object at a steady speed; and which model regions are complex enough to require zoomingin. We evaluated our system via a user study that compared its performance against those of three other stateof theart approaches, and found our system outperforms the other approaches. Specifically, the participants rated it highest in usability, understandability, and model satisfaction.