This thesis investigates a user-centered, low-complexity point cloud adaptive streaming method to improve the quality of experience in VR remote communication. By spatially segmenting point clouds and estimating surface orientation in real-time, combined with an auxiliary utility function for bandwidth allocation, the delivery of remote user reconstructions is optimized. The contributions include a novel subjective evaluation methodology for dynamic point clouds in immersive environments and a low-complexity adaptive streaming approach. Results demonstrate significant bitrate savings and improved user experience, highlighting the potential of the method for real-time VR communication.
Improving immersion and co-presence in remote communication
Remote communication applications have become a necessity in a globalised and connected world exemplified by the popularity of video conferencing applications. In recent years, virtual reality (VR) remote communication applications have emerged that aim to deliver a greater sense of co-presence and immersion in a shared virtual space where users are able to navigate freely while employing both verbal and non-verbal communication. Such applications require a volumetric user representation and point clouds have emerged as a popular format to represent real-time user reconstructions. However, volumetric point clouds are challenging to deliver over bandwidth-limited networks owing to the large volume of data required for dynamic streams. This challenge can be addressed by combining real-time compression and user-adaptive streaming. Adaptive streaming is the process of segmenting an object spatially and temporally in order to optimize the delivery of content by prioritizing the quality of spatial segments that are visible from a given viewport.
