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Accurate and fast estimations or predictions of the (near) future location of the users of head-mounted devices within the virtual omnidirectional environment open a plethora of opportunities in application domains such as interactive immersive gaming and tele-surgery. Therefore, the past years have seen growing attention to models for viewport prediction in 360֯ environments. Among the approaches, content-agnostic, trajectory-based methods have the potential to provide very fast solutions, as they do not require complex analysis of the videos to provide a prediction. However, accurate trajectory-based viewport prediction is rather difficult due to the intrinsic variability in user behaviour. Furthermore, even when making use of machine learning, current approaches tend to be brute-force and heavily tailored to specific datasets with little comparison to existing benchmarks or publicly available studies. This article presents a generic, content-agnostic viewport prediction method consisting of a window-based approach combined with a preprocessing system to classify behavioural patterns in terms of user clustering and trajectory correlation. Moreover, as the state of the art does not provide a comparative analysis of different approaches, this work contributes to this. Based on the obtained results, a combined prediction model is proposed and evaluated. Our method shows a 36.8% to 53.9% improvement when compared to the static prediction baseline for a prediction horizon of 8 seconds. In addition, a 11.5% to 24.0% improvement to a brute-force machine learning prediction approach is obtained. As such, this work contributes towards the creation of more generic and structured solutions for content-agnostic viewport prediction in terms of data representation, preprocessing and modelling.

This research investigates whether the interaction methods of eXtended Reality (XR) headsets can be improved by using haptic feedback. As a first and most common technique, indirect interactions are considered. Indirect interactions correspond to manipulations of virtual objects from a virtual distance using pre-defined hand gestures. As a second interaction technique, direct interaction (namely Direct Interaction Method (DIM)) has been implemented where the user manipulates objects by virtually touching these with their hands. A third interaction method extends the previous one with haptic feedback (namely Haptic Enhanced Direct Interaction Method (HEDIM)). These 3 methods are compared with each other based on objective and subjective user tests, also taking into account financial considerations. This research concludes that the DIM improves upon the standard indirect method. Additionally, it has been observed that haptic feedback could enhance the DIM in specific situations. Nevertheless, when considering the current financial cost, our subjects were not convinced of the small improvements haptic feedback brings.

Point Cloud (PC) streaming has recently attracted research attention as it has the potential to provide six degrees of freedom (6DoF), which is essential for truly immersive media. PCs require high-bandwidth connections, and adaptive streaming is a promising solution to cope with fluctuating bandwidth conditions. Thus, understanding the impact of different factors in adaptive streaming on the Quality of Experience (QoE) becomes fundamental. Mixed Reality (MR) is a novel technology and has recently become popular. However, quality evaluations of PCs in MR environments are still limited to static images. In this paper, we perform a subjective study on four impact factors on the QoE of PC video sequences in MR conditions, including quality switches, viewing distance, and content characteristics. The experimental results show that these factors significantly impact QoE. The QoE decreases if the sequence switches to lower quality and/or is viewed at a shorter distance, and vice versa. Additionally, the end user might not distinguish the quality differences between two quality levels at a specific viewing distance. Regarding content characteristics, objects with lower contrast seem to provide better quality scores.

3D objects are important components in Mixed Reality (MR) environments as they allow users to inspect and interact with them in a six degrees of freedom (6DoF) system. Point clouds (PCs) and meshes are two common 3D object representations that can be compressed to reduce the delivered data at the cost of quality degradation. In addition, as the end users can move around in 6DoF applications, the viewing distance can vary. Quality assessment is necessary to evaluate the impact of the compressed representation and viewing distance on the Quality of Experience (QoE) of end users. This paper presents a demonstrator for subjective quality assessment of dynamic PC and mesh objects under different conditions in MR environments. Our platform allows conducting subjective tests to evaluate various QoE influence factors, including encoding parameters, quality switching, viewing distance, and content characteristics, with configurable settings for these factors.

Dynamic point cloud delivery can provide the required interactivity and realism to six degrees of freedom (6DoF) interactive applications. However, dynamic point cloud rendering imposes stringent requirements (e.g., frames per second (FPS) and quality) that current hardware cannot handle. A possible solution is to convert point cloud into meshes before rendering on the head-mounted display (HMD). However, this conversion can induce degradation in quality perception such as a change in depth, level of detail, or presence of artifacts. This paper, as one of the first, presents an extensive subjective study of the effects of converting point cloud to meshes with different quality representations. In addition, we provide a novel in-session content rating methodology, providing a more accurate assessment as well as avoiding post-study bias. Our study shows that both compression level and observation distance have their influence on subjective perception. However, the degree of influence is heavily entangled with the content and geometry at hand. Furthermore, we also noticed that while end users are clearly aware of quality switches, the influence on their quality perception is limited. As a result, this has the potential to open up possibilities in bringing the adaptive video streaming paradigm to the 6DoF environment.

In recent years, Virtual Reality (VR) has gained attention as a tool for a plethora of applications such as first-aid, firefighting and in the automotive industry. End-user immersion is a key factor in these applications to make the experience representative for its real-life counterpart. By enhancing the traditional audiovisual cues with additional sensory inputs in terms of haptic vibro-tactile and kinesthetic feedback, this immersion can be improved. But are current haptic implementations sufficient to provide the required added value? And how do they compare to other types of VR interaction? In this paper, we present a multi-modal VR training framework able to provide subjective and objective comparisons among three different interaction options: (i) haptic gloves, (ii) traditional VR controllers, and (iii) non-haptic handtracking. We performed a user test where the different interactivity flavours were compared in terms of their influence on both subjective perception and objective performance of the end-user by means of three VR training scenarios. The subjective results show an aversion towards non-haptic handtracking for constrained, cognitively light tasks while a preference towards controllers exist for more cognitively heavy multi-tasking. This is however not reflected in objective results, where differences between interaction methods are far less pronounced.