Extended reality (XR) is at the center of attraction in the research
community due to the emergence of augmented, mixed, and virtual reality
applications. The performance of such applications needs to be uptight to
maintain the requirements of latency, energy consumption, and freshness of
data. Therefore, a comprehensive performance analysis model is required to
assess the effectiveness of an XR application but is challenging to design due
to the dependence of the performance metrics on several difficult-to-model
parameters, such as computing resources and hardware utilization of XR and edge
devices, which are controlled by both their operating systems and the
application itself. Moreover, the heterogeneity in devices and wireless access
networks brings additional challenges in modeling. In this paper, we propose a
novel modeling framework for performance analysis of XR applications
considering edge-assisted wireless networks and validate the model with
experimental data collected from testbeds designed specifically for XR
applications. In addition, we present the challenges associated with
performance analysis modeling and present methods to overcome them in detail.
Finally, the performance evaluation shows that the proposed analytical model
can analyze XR applications' performance with high accuracy compared to the
state-of-the-art analytical models.

本文提出了一种新颖的建模框架，用于考虑边缘辅助无线网络的扩展现实应用的性能分析，并通过专门为扩展现实应用设计的测试平台收集的实验数据对模型进行验证。此外，我们详细介绍了与性能分析建模相关的挑战，并提出了克服这些挑战的方法。最后，性能评估表明，与现有的分析模型相比，所提出的分析模型可以以高精度分析扩展现实应用的性能。

边缘辅助无线网络中扩展现实应用的性能分析建模框架

A Performance Analysis Modeling Framework for Extended Reality  Applications in Edge-Assisted Wireless Networks

Real-time Stereo Matching is a cornerstone algorithm for many Extended
Reality (XR) applications, such as indoor 3D understanding, video pass-through,
and mixed-reality games. Despite significant advancements in deep stereo
methods, achieving real-time depth inference with high accuracy on a low-power
device remains a major challenge. One of the major difficulties is the lack of
high-quality indoor video stereo training datasets captured by head-mounted
VR/AR glasses. To address this issue, we introduce a novel video stereo
synthetic dataset that comprises photorealistic renderings of various indoor
scenes and realistic camera motion captured by a 6-DoF moving VR/AR
head-mounted display (HMD). This facilitates the evaluation of existing
approaches and promotes further research on indoor augmented reality scenarios.
Our newly proposed dataset enables us to develop a novel framework for
continuous video-rate stereo matching.
As another contribution, our dataset enables us to proposed a new video-based
stereo matching approach tailored for XR applications, which achieves real-time
inference at an impressive 134fps on a standard desktop computer, or 30fps on a
battery-powered HMD. Our key insight is that disparity and contextual
information are highly correlated and redundant between consecutive stereo
frames. By unrolling an iterative cost aggregation in time (i.e. in the
temporal dimension), we are able to distribute and reuse the aggregated
features over time. This approach leads to a substantial reduction in
computation without sacrificing accuracy. We conducted extensive evaluations
and comparisons and demonstrated that our method achieves superior performance
compared to the current state-of-the-art, making it a strong contender for
real-time stereo matching in VR/AR applications.

实时立体匹配是许多扩展现实（XR）应用的基础算法，本研究介绍了一个包含室内场景的真实视频立体合成数据集，并提出了一种在低功耗设备上实现高准确度实时深度推测的新方法。

时域立体匹配：超过 100 帧每秒的扩展现实视频立体匹配

Stereo Matching in Time: 100+ FPS Video Stereo Matching for Extended  Reality

Radiance Fields (RFs) have shown great potential to represent scenes from
casually captured discrete views. Compositing parts or whole of multiple
captured scenes could greatly interest several XR applications. Prior works can
generate new views of such scenes by tracing each scene in parallel. This
increases the render times and memory requirements with the number of
components. In this work, we provide a method to create a single, compact,
fused RF representation for a scene composited using multiple RFs. The fused RF
has the same render times and memory utilizations as a single RF. Our method
distills information from multiple teacher RFs into a single student RF while
also facilitating further manipulations like addition and deletion into the
fused representation.

本文介绍基于 Radiance Fields 的方法，用于将多个场景的 RF 合成为单个压缩的 RF 表示形式，提高 XR 应用的渲染效率。