Triangle meshes are used in many important shape-related applications including geometric modeling, animation production, system simulation, and visualization. However, these meshes are typically generated in raw form with several defects and poor-quality elements, obstructing them from practical application. Over the past decades, different surface remeshing techniques have been presented to improve these poor-quality meshes prior to the downstream utilization. A typical surface remeshing algorithm converts an input mesh into a higher quality mesh with consideration of given quality requirements as well as an acceptable approximation to the input mesh. In recent years, surface remeshing has gained significant attention from researchers and engineers, and several remeshing algorithms have been proposed. However, there has been no survey article on remeshing methods in general with a defined search strategy and article selection mechanism covering the recent approaches in surface remeshing domain with a good connection to classical approaches. In this article, we present a survey on surface remeshing techniques, classifying all collected articles in different categories and analyzing specific methods with their advantages, disadvantages, and possible future improvements. Following the systematic literature review methodology, we define step-by-step guidelines throughout the review process, including search strategy, literature inclusion/exclusion criteria, article quality assessment, and data extraction. With the aim of literature collection and classification based on data extraction, we summarized collected articles, considering the key remeshing objectives, the way the mesh quality is defined and improved, and the way their techniques are compared with other previous methods. Remeshing objectives are described by angle range control, feature preservation, error control, valence optimization, and remeshing compatibility. The metrics used in the literature for the evaluation of surface remeshing algorithms are discussed. Meshing techniques are compared with other related methods via a comprehensive table with indices of the method name, the remeshing challenge met and solved, the category the method belongs to, and the year of publication. We expect this survey to be a practical reference for surface remeshing in terms of literature classification, method analysis, and future prospects.

Video recording is one of the most commonly used techniques for reflection, because video allows people to know what they look like to others and how they could improve their performance, but it is problematic because some people easily fall into negative emotions and worry about their performance, resulting in a low benefit. In this study, the possibility of applying a simple VR-based reflection method was explored. This method uses virtual reality (VR) and a head-mounted display (HMD) to allow presenters to watch their own presentations from the audience’s perspective and uses an avatar, which hides personal appearance, which has low relevance to the quality of presentation, to help reduce self-awareness during reflection. An experimental study was carried out, considering four personal characteristics—gender, personal anxiety, personal confidence and self-bias. The goal of this study is to discuss which populations can benefit more from this system and to assess the impact of the avatar and HMD-based VR. According to the results, the individuals with low self-confidence in their public speaking skills could benefit more on self-evaluation from VR reflection with HMD, while individuals with negative self-bias could reduce more anxiety by using an avatar.

Recent augmented reality (AR) advancements have enabled the development of effective training systems, especially in the medical, rehabilitation, and industrial fields. However, it is unclear from the literature what the intrinsic value of AR to training is and how it differs across multiple application fields. In this work, we gathered and reviewed the prototypes and applications geared towards training the intended user's knowledge, skills, and abilities. Specifically, from IEEE Xplore plus other digital libraries, we collected 64 research papers present in high-impact publications about augmented reality training systems (ARTS). All 64 papers were then categorized according to the training method used, and each paper's evaluations were identified by validity. The summary of the results shows trends in the training methods and evaluations that incorporate ARTS in each field. The narrative synthesis illustrates the different implementations of AR for each of the training methods. In addition, examples of the different evaluation types of the current ARTS are described for each of the aforementioned training methods. We also investigated the different training strategies used by the prevailing ARTS. The insights gleaned from this review can suggest standards for designing ARTS regarding training strategy, and recommendations are provided for the implementation and evaluation of future ARTS.

The perceived deliciousness of a food item is highly related to its appearance. Image processing has been widely used to make food images more appealing to the public, such as when capturing and posting images on social networking sites. This paper proposes a methodology and a system to enhance the degree of subjective deliciousness perceived by a person based on the appearance of a real food item by changing its appearance in a real environment. First, an online questionnaire survey was conducted to analyze the appearance factors that make food look delicious by using various food images. Based on this knowledge, a prototype system, which projects a computer-generated image onto the food item, was constructed for enhancing its subjective degree of deliciousness based on its appearance at a pixel level. Finally, a user study was conducted in which the subjective degree of deliciousness based on food appearance was compared under various appearance modification conditions. The results show that appropriate chroma and partial-color modifications highly increase this degree of deliciousness, thus implying that the proposed system can successfully be used to improve the appearance of food to make it look more delicious.

Lenslet array (LA) near-eye displays (NEDs) are a recent technical development that creates a virtual image in the field of view of one or both eyes. A problem occurs when the user’s pupil moves out of the LA-NED eye box (i.e., cross-talk) making the image look doubled or ghosted. It negatively impacts the user experience. Although eye-gaze tracking can mitigate this problem, the effect of the solution has not been studied to understand the impact of pupil size and human perception. In this paper, we redefine the cross-talk region as the practical pupil movable region (PPMR₅₀), which differs from eye box size because it considers pupil size and human visual perception. To evaluate the effect of eye-gaze tracking on subjective image quality, three user studies were conducted. From the results, PPMR₅₀ was found to be consistent with human perception, and cross-talk elimination via eye-gaze tracking was better understood in a static gaze scenario. Although the system latency prevented the complete elimination of cross-talk for fast movements or large pupil changes, the problem was greatly alleviated. We also analyzed system delays based on PPMR₅₀, which we newly defined in this paper and provided an optimization scheme to meet the maximum eyeball rotation speed.