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Compensation for latency in XR offloaded tasks using pose prediction

Yazdanian, Yas LU and Péter, Bálint LU (2024) MAMM15 20241
Department of Design Sciences
Abstract
As Augmented Reality (AR) and Mixed Reality (MR) glasses continue to advance, becoming more compact and user-friendly, certain computationally demanding tasks are being offloaded to edge networks or the cloud. This shift, while enhancing the capabilities of AR/MR glasses, will introduce a new challenge—latency.
Latency occurs when there is a need to transmit data to a remote processing unit, perform tasks, and then send the processed information back to the device for rendering. Poor network conditions prevent the timely rendering of content on AR/MR glasses which negatively impacts the user experience and in worst case could lead to
Virtual Reality (VR) sickness.
In this thesis, we will focus on minimizing the perceived latency to... (More)
As Augmented Reality (AR) and Mixed Reality (MR) glasses continue to advance, becoming more compact and user-friendly, certain computationally demanding tasks are being offloaded to edge networks or the cloud. This shift, while enhancing the capabilities of AR/MR glasses, will introduce a new challenge—latency.
Latency occurs when there is a need to transmit data to a remote processing unit, perform tasks, and then send the processed information back to the device for rendering. Poor network conditions prevent the timely rendering of content on AR/MR glasses which negatively impacts the user experience and in worst case could lead to
Virtual Reality (VR) sickness.
In this thesis, we will focus on minimizing the perceived latency to enhance the user experience in AR/MR applications. Our primary approach involves using head movement tracking with available sensors to minimize latency and synchronize content with user movements, leading to a seamless and enjoyable immersive experience. (Less)
Popular Abstract
Imagine wearing smart glasses and spotting a cute cat. Your glasses recognize the cat and draw a box around it, but there’s a catch:
your glasses don’t handle the heavy computation themselves, instead, they send images to another computer far away, called a remote server, that performs these tasks.
However, there’s a problem: by the time your glasses get the data back from the server, you
might have moved your head, and now the box is no longer around the cat.
This delay, known as latency, often disrupts the seamless integration of virtual content with the
real world. This thesis focuses on minimizing perceived latency.
Please use this url to cite or link to this publication:
author
Yazdanian, Yas LU and Péter, Bálint LU
supervisor
organization
course
MAMM15 20241
year
type
H2 - Master's Degree (Two Years)
subject
keywords
compensation, latency, XR, offloading, pose prediction
language
English
id
9157476
date added to LUP
2024-06-05 10:29:44
date last changed
2024-06-05 10:29:44
@misc{9157476,
  abstract     = {{As Augmented Reality (AR) and Mixed Reality (MR) glasses continue to advance, becoming more compact and user-friendly, certain computationally demanding tasks are being offloaded to edge networks or the cloud. This shift, while enhancing the capabilities of AR/MR glasses, will introduce a new challenge—latency.
Latency occurs when there is a need to transmit data to a remote processing unit, perform tasks, and then send the processed information back to the device for rendering. Poor network conditions prevent the timely rendering of content on AR/MR glasses which negatively impacts the user experience and in worst case could lead to
Virtual Reality (VR) sickness.
In this thesis, we will focus on minimizing the perceived latency to enhance the user experience in AR/MR applications. Our primary approach involves using head movement tracking with available sensors to minimize latency and synchronize content with user movements, leading to a seamless and enjoyable immersive experience.}},
  author       = {{Yazdanian, Yas and Péter, Bálint}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Compensation for latency in XR offloaded tasks using pose prediction}},
  year         = {{2024}},
}