Efficient 3D Reconstruction, Streaming and Visualization of Static and Dynamic Scene Parts for Multi-client Live-telepresence in Large-scale Environments

Despite the impressive progress of telepresence systems for room-scale scenes with static and dynamic scene entities, expanding their capabilities to scenarios with larger dynamic environments beyond a fixed size of a few square-meters remains challenging. In this paper, we aim at sharing 3D live-telepresence experiences in large-scale environments beyond room scale with both static and dynamic scene entities at practical bandwidth requirements only based on light-weight scene capture with a single moving consumer-grade RGB-D camera. To this end, we present a system which is built upon a novel hybrid volumetric scene representation in terms of the combination of a voxel-based scene representation for the static contents, that not only stores the reconstructed surface geometry but also contains information about the object semantics as well as their accumulated dynamic movement over time, and a point-cloud-based representation for dynamic scene parts, where the respective separation from static parts is achieved based on semantic and instance information extracted for the input frames. With an independent yet simultaneous streaming of both static and dynamic content, where we seamlessly integrate potentially moving but currently static scene entities in the static model until they are becoming dynamic again, as well as the fusion of static and dynamic data at the remote client, our system is able to achieve VR-based live-telepresence at close to real-time rates. Our evaluation demonstrates the potential of our novel approach in terms of visual quality, performance, and ablation studies regarding involved design choices.