Depth sensing is an important issue in many applications, such as Augmented Reality (AR), eXtended Reality (XR), and Metaverse. For 3D reconstruction, a depth map can be acquired by a stereo camera and a Time-of-Flight (ToF) sensor. We used both sensors complementarily to improve the accuracy of 3D information of the data. First, we applied a generalized multi-camera calibration method that uses both color and depth information. Next, depth maps of two sensors were fused by 3D registration and reprojection approach. Then, hole-filling was applied to refine the new depth map from the ToF-stereo fused data. Finally, the surface reconstruction technique was used to generate mesh data from the ToF-stereo fused pointcloud data. The proposed procedure was implemented and tested with real-world data and compared with various algorithms to validate its efficiency.

Monitoring and predicting the environment in an indoor space plays an important role in securing big data and detecting abnormal conditions in the industrial environment and living space. This study proposes an indoor multi-environment sensor system based on intelligent edge computing that collects and predicts environmental data. The system collects data using 14 types of environmental sensors and object detection technology models and implements a model that predicts indoor air quality based on the bi-directional LSTM network. The trained model shows high performance in predicting indoor air quality (IAQ) factors, such as CO${}_2$, PM2.5, and total volatile organic compounds (TVOC). The indoor multi-environment sensor system based on intelligent edge computing is available for data collection and environmental prediction in various spaces without restrictions on specific locations. This study proposes an integrated approach with various functions by applying edge computing to indoor environment monitoring. We verify the proposed system through various experiments.

As augmented reality technologies develop, real-time interactions between objects present in the real world and virtual space are required. Generally, recognition and location estimation in augmented reality are carried out using tracking techniques, typically markers. However, using markers creates spatial constraints in simultaneous tracking of space and objects. Therefore, we propose a system that enables camera tracking in the real world and visualizes virtual visual information through the recognition and positioning of objects. We scanned the space using an RGB-D camera. A three-dimensional (3D) dense point cloud map is created using point clouds generated through video images. Among the generated point cloud information, objects are detected and retrieved based on the pre-learned data. Finally, using the predicted pose of the detected objects, other information may be augmented. Our system estimates object recognition and 3D pose based on simple camera information, enabling the viewing of virtual visual information based on object location.

Wearable devices are being explored and investigated as a promising computing platform as well as a source of personal big data for the post smartphone era. To deal with a series of rapidly developed wearable prototypes, a well-structured strategy is required to assess the prototypes at various development stages. In this paper, we first design and develop variants of advanced wearable user interface prototypes, including joystick-embedded, potentiometer-embedded, motion-gesture and contactless infrared user interfaces for rapidly assessing hands-on user experience of potential futuristic user interfaces. To achieve this goal systematically, we propose a conceptual test framework and present a case study of using the proposed framework in an iterative cyclic process to prototype, test, analyze, and refine the wearable user interface prototypes. We attempt to improve the usability of the user interface prototypes by integrating initial user feedback into the leading phase of the test framework. In the following phase of the test framework, we track signs of improvements through the overall results of usability assessments, task workload assessments and user experience evaluation of the prototypes. The presented comprehensive and in-depth case study demonstrates that the iterative approach employed by the test framework was effective in assessing and enhancing the prototypes, as well as gaining insights on potential applications and establishing practical guidelines for effective and usable wearable user interface development.

Traffic sign recognition is a pivotal technology in the advancement of autonomous vehicles as it is critical for adhering to country- or region-specific traffic regulations. Defined as an image classification problem in computer vision, traffic sign recognition is a technique that determines the class of a given traffic sign from input data processed by a neural network. Although image classification has been considered a relatively manageable task with the advent of neural networks, traffic sign classification presents its own unique set of challenges due to the similar visual features inherent in traffic signs. This can make designing a softmax-based classifier problematic. To address this challenge, this paper presents a novel traffic sign recognition model that employs angular margin loss. This model optimizes the necessary hyperparameters for the angular margin loss via Bayesian optimization, thereby maximizing the effectiveness of the loss and achieving a high level of classification performance. This paper showcases the impressive performance of the proposed method through experimental results on benchmark datasets for traffic sign classification.