Spatial computing is a technological advancement that facilitates the
seamless integration of devices into the physical environment, resulting in a
more natural and intuitive digital world user experience. Spatial computing has
the potential to become a significant advancement in the field of computing.
From GPS and location-based services to healthcare, spatial computing
technologies have influenced and improved our interactions with the digital
world. The use of spatial computing in creating interactive digital
environments has become increasingly popular and effective. This is explained
by its increasing significance among researchers and industrial organisations,
which motivated us to conduct this review. This review provides a detailed
overview of spatial computing, including its enabling technologies and its
impact on various applications. Projects related to spatial computing are also
discussed. In this review, we also explored the potential challenges and
limitations of spatial computing. Furthermore, we discuss potential solutions
and future directions. Overall, this paper aims to provide a comprehensive
understanding of spatial computing, its enabling technologies, their impact on
various applications, emerging challenges, and potential solutions.

该论文旨在全面了解空间计算、其支持技术、对各种应用的影响、新兴挑战以及潜在解决方案。

空间计算：概念、应用、挑战与未来方向

Spatial Computing: Concept, Applications, Challenges and Future  Directions

Originally designed for applications in computer graphics, visual computing
(VC) methods synthesize information about physical and virtual worlds, using
prescribed algorithms optimized for spatial computing. VC is used to analyze
geometry, physically simulate solids, fluids, and other media, and render the
world via optical techniques. These fine-tuned computations that operate
explicitly on a given input solve so-called forward problems, VC excels at. By
contrast, deep learning (DL) allows for the construction of general algorithmic
models, side stepping the need for a purely first principles-based approach to
problem solving. DL is powered by highly parameterized neural network
architectures -- universal function approximators -- and gradient-based search
algorithms which can efficiently search that large parameter space for optimal
models. This approach is predicated by neural network differentiability, the
requirement that analytic derivatives of a given problem's task metric can be
computed with respect to neural network's parameters. Neural networks excel
when an explicit model is not known, and neural network training solves an
inverse problem in which a model is computed from data.

通过使用计算机图形学 （Computer Graphics, CG）技术，视觉计算（Visual Computing, VC）方法通过经过优化的算法综合了有关物理世界和虚拟世界的信息，用于分析几何结构、模拟物体、流体和其他媒体，并通过光学技术渲染世界。与之相对，深度学习（Deep Learning, DL）通过使用高度参数化的神经网络架构和基于梯度的搜索算法来构建通用算法模型，避开了纯粹基于第一原理的问题解决方法的需要。当缺乏明确模型时，神经网络表现出色，并且通过神经网络训练解决了一个由数据计算模型的逆问题。

可微可视计算用于逆问题和机器学习

Differentiable Visual Computing for Inverse Problems and Machine  Learning

Since Facebook was renamed Meta, a lot of attention, debate, and exploration
have intensified about what the Metaverse is, how it works, and the possible
ways to exploit it. It is anticipated that Metaverse will be a continuum of
rapidly emerging technologies, usecases, capabilities, and experiences that
will make it up for the next evolution of the Internet. Several researchers
have already surveyed the literature on artificial intelligence (AI) and
wireless communications in realizing the Metaverse. However, due to the rapid
emergence and continuous evolution of technologies, there is a need for a
comprehensive and in-depth survey of the role of AI, 6G, and the nexus of both
in realizing the immersive experiences of Metaverse. Therefore, in this survey,
we first introduce the background and ongoing progress in augmented reality
(AR), virtual reality (VR), mixed reality (MR) and spatial computing, followed
by the technical aspects of AI and 6G. Then, we survey the role of AI in the
Metaverse by reviewing the state-of-the-art in deep learning, computer vision,
and Edge AI to extract the requirements of 6G in Metaverse. Next, we
investigate the promising services of B5G/6G towards Metaverse, followed by
identifying the role of AI in 6G networks and 6G networks for AI in support of
Metaverse applications, and the need for sustainability in Metaverse. Finally,
we enlist the existing and potential applications, usecases, and projects to
highlight the importance of progress in the Metaverse. Moreover, in order to
provide potential research directions to researchers, we underline the
challenges, research gaps, and lessons learned identified from the literature
review of the aforementioned technologies.

本次调查研究了 AI、6G 技术和与其相关的 Metaverse 在增强现实（AR）、虚拟现实（VR）、混合现实（MR）和空间计算方面的作用，并对 Metaverse 的现有和潜在应用进行了列举和总结。

AI 和 6G 进入元宇宙：基础、挑战和未来研究趋势

AI and 6G into the Metaverse: Fundamentals, Challenges and Future Research Trends

Remote telepresence via next-generation mixed reality platforms can provide
higher levels of immersion for computer-mediated communications, allowing
participants to engage in a wide spectrum of activities, previously not
possible in 2D screen-based communication methods. However, as mixed reality
experiences are limited to the local physical surrounding of each user, finding
a common virtual ground where users can freely move and interact with each
other is challenging. In this paper, we propose a novel mutual scene synthesis
method that takes the participants' spaces as input, and generates a virtual
synthetic scene that corresponds to the functional features of all
participants' local spaces. Our method combines a mutual function optimization
module with a deep-learning conditional scene augmentation process to generate
a scene mutually and physically accessible to all participants of a mixed
reality telepresence scenario. The synthesized scene can hold mutual walkable,
sittable and workable functions, all corresponding to physical objects in the
users' real environments. We perform experiments using the MatterPort3D dataset
and conduct comparative user studies to evaluate the effectiveness of our
system. Our results show that our proposed approach can be a promising research
direction for facilitating contextualized telepresence systems for
next-generation spatial computing platforms.

本文提出了一种新方法，通过深度学习条件场景增强过程和相互功能优化模块相结合，生成适用于所有混合现实远程实时视频会议场景的场景合成方法，以促进下一代空间计算平台上的情境化遥控系统的发展。