The way a user is represented in virtual reality is a key element for applications ranging from entertainment to immersive communication and remote collaboration. Having realistic and expressive avatars leads to a stronger sense of embodiment and presence. However, they also lead to increased system latency, which can cause several negative effects, including cybersickness and a reduced sense of embodiment. We conducted a user study to investigate the (interaction) effects of avatar representation/quality and latency on embodiment, task efficiency, and cybersickness in VR. Specifically, we compared a high-quality, personalized point cloud avatar with a lower-quality pre-modeled mesh avatar and latency settings between 150 and 300 ms. We found that the avatar quality had a greater effect on all components of embodiment than latency, and that the perception of the latter was influenced by the avatar representation. High-quality avatars were consistently and significantly rated superior and led to a less severe perception of latency. In contrast, avatar type and latency level had little effect on task efficiency and no notable one on cybersickness. Our work has practical implications for researchers and developers as it shows that having a high-quality avatar in VR is crucial, even at the cost of higher latency, as its benefits outweigh and reduce the negative effects of latency.

Abstract Registration is an essential prerequisite for many applications when a multiple-camera setup is used. Due to the noise in depth images, registration procedures for depth sensors frequently rely on the detection of a target object in color or infrared images. However, this prohibits use cases where color and infrared images are not available or where there is no mapping between the pixels of different image types, e.g., due to separate sensors or different projections. We present our novel registration method that requires only the point cloud resulting from the depth image of each camera. For feature detection, we propose a combination of a custom-designed 3D registration target and an algorithm that is able to reliably detect that target and its features in noisy point clouds. Our evaluation indicates that our lattice detection is very robust (with a precision of more than 0.99) and very fast (on average about 20 ms with a single core). We have also compared our registration method with known methods: Our registration method achieves an accuracy of 1.6 mm at a distance of 2 m using only the noisy depth image, while the most accurate registration method achieves an accuracy of 0.7 mm requiring both the infrared and depth image.

The precise registration between multiple depth sensors is a crucial prerequisite for many applications. Previous techniques frequently rely on RGB or IR images and checkerboard targets for feature detection. However, this prohibits the usage for use-cases where neither is available or where IR and depth images have different projections. Therefore, we present a novel registration approach that uses depth data exclusively for feature detection, making it more universally applicable while still achieving robust and precise results. We propose a combination of a custom 3D registration target — a lattice with regularly-spaced holes — and a feature detection algorithm that is able to reliably extract the lattice and its features from noisy depth images. In addition, we have integrated the registration procedure to a publicly available Unreal Engine 4 plugin that allows multiple point clouds captured by several depth cameras to be registered in a virtual environment. Despite the rather noisy depth images, we are able to quickly obtain a robust registration that yields an average deviation of 3.8 mm to 4.4 mm in our test scenarios.

Abstract Advances in computer technology and increasing usage of computer graphics in a broad field of applications lead to rapidly rising demands regarding size and detail of virtual landscapes. Manually creating huge, realistic looking terrains and populating them densely with assets is an expensive and laborious task. In consequence, (semi-)automatic procedural terrain generation is a popular method to reduce the amount of manual work. However, such methods are usually highly specialized for certain terrain types and especially the procedural generation of landscapes composed of different biomes is a scarcely explored topic. We present a novel system, called AutoBiomes, which is capable of efficiently creating vast terrains with plausible biome distributions and therefore different spatial characteristics. The main idea is to combine several synthetic procedural terrain generation techniques with digital elevation models (DEMs) and a simplified climate simulation. Moreover, we include an easy-to-use asset placement component which creates complex multi-object distributions. Our system relies on a pipeline approach with a major focus on usability. Our results show that our system allows the fast creation of realistic looking terrains.