Metaverse study
Computer vision (CV) and machine learning plays a big role in the development of Virtual Reality (VR) experiences for users. In the Metaverse, users often use VR devices such as headsets and motion capture devices to enhance the immersion of such digital spaces. However, computer vision and machi...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | |
| التنسيق: | Final Year Project |
| اللغة: | English |
| منشور في: |
Nanyang Technological University
2023
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://hdl.handle.net/10356/166805 |
| الوسوم: |
إضافة وسم
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| الملخص: | Computer vision (CV) and machine learning plays a big role in the development of
Virtual Reality (VR) experiences for users. In the Metaverse, users often use VR devices
such as headsets and motion capture devices to enhance the immersion of such digital
spaces. However, computer vision and machine learning are computationally intensive
tasks given the amount of data required to be processed in real time. Given the limited
computing power and battery life of wireless VR devices, these devices are inefficient in
handling some of these tasks in real-time. However, with the development of mobileedge computing (MEC), it may serve as a solution to distribute computational resources
across networks, to improve the data processing capability of the wireless VR headsets.
In this paper, we explore a deep reinforcement learning algorithm to offload the
computationally and resource-intensive tasks to MEC servers to reduce the latency and
improve the performance of these VR devices, hoping to make connections and
interactions to the Metaverse a better experience for users. |
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