The research on 3D scene viewpoints has been a frontier problem in computer graphics and virtual reality technology. In a pioneering study, it had been extensively used in virtual scene understanding, image-based modeling, and visualization computing. With the development of computer graphics and the human-computer interaction, the viewpoint evaluation becomes more significant for the comprehensive understanding of complex scenes. The high-quality viewpoints could navigate observers to the region of interest, help subjects to seek the hidden relations of hierarchical structure, and improve the efficiency of virtual exploration. These studies later contributed to research such as robot vision, dynamic scene planning, virtual driving and artificial intelligence navigation.The introduction of visual perception had The introduction of visual perception had contributed to the inspiration of viewpoints research, and the combination with machine learning made significant progress in the viewpoints selection. The viewpoints research also has been significant in the optimization of global lighting, visualization calculation, 3D supervising rendering, and reconstruction of a virtual scene. Additionally, it has a huge potential in novel fields such as 3D model retrieval, virtual tactile analysis, human visual perception research, salient point calculation, ray tracing optimization, molecular visualization, and intelligent scene computing.

Although VSLAM/VISLAM has achieved great success, it is still difficult to quantitatively evaluate the localization results of different kinds of SLAM systems from the aspect of augmented reality due to the lack of an appropriate benchmark. For AR applications in practice, a variety of challenging situations (e.g., fast motion, strong rotation, serious motion blur, dynamic interference) may be easily encountered since a home user may not carefully move the AR device, and the real environment may be quite complex. In addition, the frequency of camera lost should be minimized and the recovery from the failure status should be fast and accurate for good AR experience. Existing SLAM datasets/benchmarks generally only provide the evaluation of pose accuracy and their camera motions are somehow simple and do not fit well the common cases in the mobile AR applications. With the above motivation, we build a new visual-inertial dataset as well as a series of evaluation criteria for AR. We also review the existing monocular VSLAM/VISLAM approaches with detailed analyses and comparisons. Especially, we select 8 representative monocular VSLAM/VISLAM approaches/systems and quantitatively evaluate them on our benchmark. Our dataset, sample code and corresponding evaluation tools are available at the benchmark website http://www.zjucvg.net/eval-vislam/.

Visual reality (VR) health-monitoring by flexible electronics provides a new avenue to remote and wearable medicine. The combination of flexible electronics and VR could facilitate smart remote disease diagnosis by real-time monitoring of the physiological signals and remote interaction between patient and physician. The flexible healthcare sensor is the most crucial unit in the flexible and wearable health-monitoring system, which has attracted much attention in recent years. This paper briefly reviews the progress in flexible healthcare sensors and VR healthcare devices. The flexible healthcare sensor is introduced with basic flexible materials, manufacturing techniques, and their applications in health-monitoring (such as blood/sweat detection and heart-rate tracking). VR healthcare devices for telemedicine diagnosis are discussed, and the smart remote diagnosis system using flexible and wearable healthcare sensors, and a VR device, is addressed.