The extended reality (XR) is one of the most widely used approaches for accessing the metaverse world. The metaverse and XR aim to blend the virtual and real parts, offering an immersive and interactive experience. Diminished reality (DR) is a subset of XR that specifically addresses the real-time occlusion, removal, and transparency of objects in the environment. As an immersive technology, DR has been utilized in academia and industry to tackle a wide range of engineering problems. However, there is a little investigative work about DR technique evaluations. In this survey, we categorize the state-of-the-art research into two major categories and six subcategories, providing a novel perspective. We further analyze and evaluate the application effects and performance of these approaches from both quantitative and qualitative perspectives, considering the technical performance and user experience of DR techniques. Finally, we provide an overview of potential future directions for DR applications.

Numerous physical objects in our daily lives are grouped or ranked according to a stereotyped presentation style. For example, in a library, books are typically grouped and ranked based on classification numbers. However, for better comparison, we often need to re-group or re-rank the books using additional attributes such as ratings, publishers, comments, publication years, keywords, prices, etc., or a combination of these factors. In this article, we propose a novel mobile DR/MR-based application framework named DRCmpVis to achieve in-context multi-attribute comparisons of physical objects with text labels or textual information. The physical objects are scanned in the real world using mobile cameras. All scanned objects are then segmented and labeled by a convolutional neural network and replaced (diminished) by their virtual avatars in a DR environment. We formulate three visual comparison strategies, including filtering, re-grouping, and re-ranking, which can be intuitively, flexibly, and seamlessly performed on their avatars. This approach avoids breaking the original layouts of the physical objects. The computation resources in virtual space can be fully utilized to support efficient object searching and multi-attribute visual comparisons. We demonstrate the usability, expressiveness, and efficiency of DRCmpVis through a user study, NASA TLX assessment, quantitative evaluation, and case studies involving different scenarios.

Augmented reality (AR) is a burgeoning interaction technology with the ability to provide users with immersive everyday experiences. Shopping is among the most common experiences. When consumers purchase products, they often encounter difficulties in obtaining detailed information relevant to their interests, such as ingredients, origin, and product comparisons, leading to frustration. This situation can be undeniably frustrating. We can use visualization technology [18] to organize it more orderly and friendly. This paper proposes a mobile augmented reality-based application framework, Product Magic Mirror (PMM), which helps to integrate basic visualization design into the application. The augmented information can be rich, e.g. they can be some visualizations and vivid data videos. In the evaluation, we simulated a goods purchase scene, and applied information to the real world by using AR. The real environment and virtual objects were superimposed on the same screen in real time, so as to achieve an experience beyond reality [24]. In our user survey, users rated our tools as a way to make better shopping choices, giving them a positive score for an immersive shopping experience. We predict that displaying information such as instructions, ingredients, and/or user review information next to products could allow consumers to make better consumption choices while reducing decision time and making shopping more immersive.

We propose eBoF, a novel time-varying ensemble data visualization approach based on the Bag-of-Features (BoF) model. In the eBoF model, we extract a simple and monotone interval from all target variables of ensemble scalar data as a local feature patch. Each local feature of a semantically simple single interval can be defined as a feature patch within the BoF model, with the duration of each interval (i.e., feature patch) serving as its frequency. Feature clusters in ensemble runs are then identified based on the similarity of temporal correlations. eBoF generates clusters along with their probability distributions across all feature patches while preserving the geo-spatial information, which is often lost in traditional topic modeling or clustering algorithms. The probability distribution across different clusters can help to generate reasonable clustering results, evaluated by domain knowledge. We conduct case studies and performance tests to evaluate the eBoF model and gather feedback from domain experts to further refine it. Evaluation results suggest the proposed eBoF can provide insightful and comprehensive evidence on ensemble simulation data analysis.