Multi-User Cross-Device Remote Rendering with Local Positioning Assistance for Mixed Reality Experiences

NCUIR > College of Electrical Engineering & Computer Science > Graduate Institute of Communication Engineering > Electronic Thesis & Dissertation > Item 987654321/92972

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/92972

Title:	Multi-User Cross-Device Remote Rendering with Local Positioning Assistance for Mixed Reality Experiences
Authors:	黃正婷;Huang, Zheng-Ting
Contributors:	通訊工程學系
Keywords:	擴增實境;虛擬實境;混合實境;多人遠端渲染;延遲補償;傳輸資料最佳化;Augmented Reality;Virtual Reality;Mixed Reality;Multi-user Remote Rendering;latency compensation;Data Optimization for Transmission
Date:	2023-08-15
Issue Date:	2024-09-19 16:36:13 (UTC+8)
Publisher:	國立中央大學
Abstract:	隨著科技快速的發展下，增強現實（AR）、虛擬現實（VR）和混合現實（MR）等技術成為一個備受關注的領域。然而這些技術在遠端算繪應用中仍然需要面臨一些困難和問題，例如延遲和預測不準確等。為了防止出現這些問題，本文提出了一種多人遠端算繪架構，目標於提供良好的MR應用體驗。該架構利用伺服器端物件串流相機設計和本地端定位輔助，幾乎消除了延遲帶來的不順暢物件位移現象。同時，通過最佳化網路流量，考慮「人與物件」之間的關係，透過動態調整解析度以及視野剔除或合併物件等方法，提高了網路使用效率和提升用戶體驗。實驗結果顯示，該架構的數據最佳化方法平均降低了80%的資料量，與傳統的MR串流架構相比減少了70%。此外，該架構還支持多個用戶使用不同廠牌的頭戴顯示設備或行動設備加入虛擬空間並進行物件互動，並減少了伺服器上的硬體消耗。;In the wave of rapid development of modern technology, augmented reality (AR), virtual reality (VR), and mixed reality (MR) have become prominent fields. However, these technologies still face some challenges in remote rendering applications, such as latency and inaccurate prediction. To address these issues, this paper proposes a multi-user remote rendering architecture aimed at providing users with a high-quality MR application experience. The architecture utilizes a server-side object streaming camera design and local positioning assistance to virtually eliminate the noticeable object displacement caused by latency. Simultaneously, by optimizing network traffic, considering the relationship between users and objects, dynamically adjusting resolutions, and performing view frustum culling or object merging, the network efficiency is improved, and the user experience is enhanced. Experimental results demonstrate that the data optimization method of this architecture reduces the average data volume by 80%, compared to a traditional MR streaming architecture, which reduces the data volume by 70%. Additionally, this architecture supports multiple users using different brands of head-mounted displays or mobile devices to enter the virtual space and interact with objects, while reducing hardware consumption on the server.
Appears in Collections:	[Graduate Institute of Communication Engineering] Electronic Thesis & Dissertation

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