In this paper, we review diminished reality (DR) studies that visually remove, hide, and see through real objects from the real world. We systematically analyze and classify publications and present a technology map as a reference for future research. We also discuss future directions, including multimodal diminished reality. We believe that this paper will be useful mainly for students who are interested in DR, beginning DR researchers, and teachers who introduce DR in their classes.

In recent years, the development of Augmented Reality (AR) frameworks made AR application development widely accessible to developers without AR expert background. With this development, new application fields for AR are on the rise. This comes with an increased need for visualization techniques that are suitable for a wide range of application areas. It becomes more important for a wider audience to gain a better understanding of existing AR visualization techniques. Within this work we provide a taxonomy of existing works on visualization techniques in AR. The taxonomy aims to give researchers and developers without an in-depth background in Augmented Reality the information to successively apply visualization techniques in Augmented Reality environments. We also describe required components and methods and analyze common patterns.

Remote assistance represents an important use case for mixed reality. With the rise of handheld and wearable devices, remote assistance has become practical in the wild. However, spontaneous provisioning of remote assistance requires an easy, fast and robust approach for capturing and sharing of unprepared environments. In this work, we make a case for utilizing interactive light fields for remote assistance. We demonstrate the advantages of object representation using light fields over conventional geometric reconstruction. Moreover, we introduce an interaction method for quickly annotating light fields in 3D space without requiring surface geometry to anchor annotations. We present results from a user study demonstrating the effectiveness of our interaction techniques, and we provide feedback on the usability of our overall system.

We present a system for generating and visualizing interactive 3D Augmented Reality tutorials based on 2D video input, which allows viewpoint control at runtime. Inspired by assembly planning, we analyze the input video using a 3D CAD model of the object to determine an assembly graph that encodes blocking relationships between parts. Using an assembly graph enables us to detect assembly steps that are otherwise difficult to extract from the video, and generally improves object detection and tracking by providing prior knowledge about movable parts. To avoid information loss, we combine the 3D animation with relevant parts of the 2D video so that we can show detailed manipulations and tool usage that cannot be easily extracted from the video. To further support user orientation, we visually align the 3D animation with the real-world object by using texture information from the input video. We developed a presentation system that uses commonly available hardware to make our results accessible for home use and demonstrate the effectiveness of our approach by comparing it to traditional video tutorials.

Presenting tutorials in augmented reality is a compelling application area, but previous attempts have been limited to objects with only a small numbers of parts. Scaling augmented reality tutorials to complex assemblies of a large number of parts is difficult, because it requires automatically discriminating many similar-looking object configurations, which poses a challenge for current object detection techniques. In this paper, we seek to lift this limitation. Our approach is inspired by the observation that, even though the number of assembly steps may be large, their order is typically highly restricted: Some actions can only be performed after others. To leverage this observation, we enhance a state-of-the-art object detector to predict the current assembly state by conditioning on the previous one, and to learn the constraints on consecutive states. This learned ‘consecutive state prior’ helps the detector disambiguate configurations that are otherwise too similar in terms of visual appearance to be reliably discriminated. Via the state prior, the detector is also able to improve the estimated probabilities that a state detection is correct. We experimentally demonstrate that our technique enhances the detection accuracy for assembly sequences with a large number of steps and on a variety of use cases, including furniture, Lego and origami. Additionally, we demonstrate the use of our algorithm in an interactive augmented reality application.

This work introduces the first approach to video see-through mixed reality with full support for focus cues. By combining the flexibility to adjust the focus distance found in varifocal designs with the robustness to eye-tracking error found in multifocal designs, our novel display architecture reliably delivers focus cues over a large workspace. In particular, we introduce gaze-contingent layered displays and mixed reality focal stacks, an efficient representation of mixed reality content that lends itself to fast processing for driving layered displays in real time. We thoroughly evaluate this approach by building a complete end-to-end pipeline for capture, render, and display of focus cues in video see-through displays that uses only off-the-shelf hardware and compute components.