SLAMCast: Large-Scale, Real-Time 3D Reconstruction and Streaming for Immersive Multi-Client Live Telepresence

In: arXiv preprint arXiv:1805.03709 (2018)
 

Abstract

Real-time 3D scene reconstruction from RGB-D sensor data, as well as the exploration of such data in VR/AR settings, has seen tremendous progress in recent years. The combination of both these components into telepresence systems, however, comes with significant technical challenges. All approaches proposed so far are extremely demanding on input and output devices, compute resources and transmission bandwidth, and they do not reach the level of immediacy required for applications such as remote collaboration. Here, we introduce what we believe is the first practical client-server system for real-time capture and many-user exploration of static 3D scenes. Our system is based on the observation that interactive frame rates are sufficient for capturing and reconstruction, and real-time performance is only required on the client site to achieve lag-free view updates when rendering the 3D model. Starting from this insight, we extend previous voxel block hashing frameworks by overcoming internal dependencies and introducing, to the best of our knowledge, the first thread-safe GPU hash map data structure that is robust under massively concurrent retrieval, insertion and removal of entries on a thread level. We further propose a novel transmission scheme for volume data that is specifically targeted to Marching Cubes geometry reconstruction and enables a 90% reduction in bandwidth between server and exploration clients. The resulting system poses very moderate requirements on network bandwidth, latency and client-side computation, which enables it to rely entirely on consumer-grade hardware, including mobile devices. We demonstrate that our technique achieves state-of-the-art representation accuracy while providing, for any number of clients, an immersive and fluid lag-free viewing experience even during network outages.

The full-text of the publication is available here.

Bibtex

@ARTICLE{stotko-2018-SLAMCast_arxiv,
    author = {Stotko, Patrick and Krumpen, Stefan and Hullin, Matthias B. and Weinmann, Michael and Klein,
              Reinhard},
     title = {SLAMCast: Large-Scale, Real-Time 3D Reconstruction and Streaming for Immersive Multi-Client Live
              Telepresence},
   journal = {arXiv preprint arXiv:1805.03709},
      year = {2018},
  abstract = {Real-time 3D scene reconstruction from RGB-D sensor data, as well as the exploration of such data in
              VR/AR settings, has seen tremendous progress in recent years. The combination of both these
              components into telepresence systems, however, comes with significant technical challenges. All
              approaches proposed so far are extremely demanding on input and output devices, compute resources
              and transmission bandwidth, and they do not reach the level of immediacy required for applications
              such as remote collaboration. Here, we introduce what we believe is the first practical
              client-server system for real-time capture and many-user exploration of static 3D scenes. Our system
              is based on the observation that interactive frame rates are sufficient for capturing and
              reconstruction, and real-time performance is only required on the client site to achieve lag-free
              view updates when rendering the 3D model. Starting from this insight, we extend previous voxel block
              hashing frameworks by overcoming internal dependencies and introducing, to the best of our
              knowledge, the first thread-safe GPU hash map data structure that is robust under massively
              concurrent retrieval, insertion and removal of entries on a thread level. We further propose a novel
              transmission scheme for volume data that is specifically targeted to Marching Cubes geometry
              reconstruction and enables a 90% reduction in bandwidth between server and exploration clients. The
              resulting system poses very moderate requirements on network bandwidth, latency and client-side
              computation, which enables it to rely entirely on consumer-grade hardware, including mobile devices.
              We demonstrate that our technique achieves state-of-the-art representation accuracy while providing,
              for any number of clients, an immersive and fluid lag-free viewing experience even during network
              outages.}
}