# Special Issue on \"Augmented Reality, Virtual Reality & Semantic 3D Reconstruction\"
## 2 Augmented Reality, Virtual Reality and Semantic 3D Reconstruction
The Diagnosis of Attention Deficit/Hyperactivity Disorder (ADHD) requires an exhaustive and objective assessment in order to design an intervention that is adapted to the peculiarities of the patients. The authors of [6] aimed to determine if the most commonly used ADHD observation scale--the Evaluation of Attention Deficit and Hyperactivity (EDAH) scale--is able to predict performance in a Continuous Performance Test based on Virtual Reality (VR-CPT). The findings may partially explain why the impulsive-hyperactive and the combined presentations of ADHD might be considered as unique and qualitatively different subcategories of ADHD. These results also highlighted the importance of measuring not only the observable behaviors of ADHD individuals, but also the scores in performance tests that are attained by the patients themselves. Image matching techniques offer valuable opportunities for the construction industry. [PERSON] et al. [7] developed and evaluated an orientation and positioning approach that decreased the variation in camera viewpoints and image transformation on construction sites. The results show that images captured while using this approach had less image transformation in contrast to images not captured using this approach. Super-resolution reconstruction is an increasingly important area in computer vision. To alleviate the problems that super-resolution reconstruction models based on generative adversarial networks are difficult to train and contain artifacts in reconstruction results, [PERSON] and [PERSON] [8] presented a TSRGAN model which was based on generative adversarial networks. The author redefined the generator network and discriminator network. The experimental results show that the method made the average Peak Signal to Noise Ratio of reconstructed images reach 27.99 dB and the average Structural Similarity Index reach 0.778 without losing too much speed, which was superior to other comparison algorithms in objective evaluation index. What is more, TSRGAN significantly improved subjective visual evaluations. Experimental results prove the effectiveness and superiority of TSRGAN algorithm. As virtual reality (VR) and the corresponding 3D documentation and modelling technologies evolve into increasingly powerful and established tools for numerous applications in architecture, monument preservation, conservation/restoration and the presentation of cultural heritage, new methods for creating information-rich interactive 3D environments are increasingly in demand. In [9], the authors describe the development of an immersive virtual reality application for the Imperial Cathedral in Konigslutter. A specialized technical workflow was developed to build the virtual environment in Unreal Engine 4 (UE4) and integrate the panorama photographs. A simple mechanic was developed using the native UE4 node-based programming language to switch between these two modes of visualization. Semantic modeling is a challenging task that has received widespread attention in recent years. With the help of mini Unmanned Aerial Vehicles (UAVs), multiview high-resolution aerial images of large-scale scenes can be conveniently collected. In [10], [PERSON] et al. propose a semantic Multi-View Stereo (MVS) method to reconstruct 3D semantic models from 2D images. The graph-based semantic fusion procedure and refinement based on local and global information can suppress and reduce the reprojection error. In the work by [PERSON] et al. [11] a group of images captured from an eye-in-hand vision system carried on a robotic manipulator are segmented by deep learning and geometric features and create a semantic 3D reconstruction using a map stitching method. The results demonstrate that the quality of segmented images and the precision of semantic 3D reconstruction are effectively improved by their method. Consumer depth cameras bring about cheap and fast acquisition of 3D models. However, the precision and resolution of these consumer depth cameras cannot satisfy the requirements of some 3D face applications. [PERSON] et al.