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Distributed 3D Rendering for the Metaverse

Majlesi Koupaei, Ali | 2024

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 57450 (05)
  4. University: Sharif University of Technology
  5. Department: Electrical Engineering
  6. Advisor(s): Hossein Khalaj, Babak
  7. Abstract:
  8. In recent years, virtual reality (VR) and the Metaverse have gained increased attention in industry and exhibitions. However, rendering high-quality scenes demands significant computing power that cannot be directly handled by VR headsets. In this paper, we propose a novel approach to distribute rendering between Mobile Edge Computing (MEC) and cloud servers. Our method involves the cloud server predicting head positions in advance to reduce round-trip time, while the MEC server re-renders frames where the predicted head location is substantial. We evaluate this resource allocation scheme using two prediction methods: Kalman filter and LSTM Encoder-Decoder. By considering users’ preferred accuracy, we ensure fairness in experienced location errors. Our evaluation, based on a dataset containing over 4500 seconds of recorded head poses from users exploring VR, demonstrates that this strategy significantly increases the maximum number of users each MEC
    server can support, from 5 to 30
  9. Keywords:
  10. Virtual Reality (VR)Environment ; Long Short Term Memory (LSTM) ; Distributed Processing ; Edge Computing ; Augmented Reality ; Head Position Prediction ; Metaverse

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