Tiled Frustum Culling for Differential Rendering on Mobile Devices

Rohmer, Kai; Grosch, Thorsten

doi:10.1109/ismar.2015.13

Cited by 6 publications

(2 citation statements)

References 14 publications

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“…Accordingly, this approach groups users with similar preferences at each time point and adjusts the quality of objects within the virtual-space by reflecting the popularity of the respective group. This method, like frustum culling commonly used in resource-intensive games, selectively provides only the necessary objects within the virtual-space to the group, preventing unnecessary use of computing and communication resources and contributing to the overall stability of the system [24].…”

Section: Preliminaries a Metaverse Network Architecturementioning

confidence: 99%

Quantum Reinforcement Learning for Spatio-Temporal Prioritization in Metaverse

Park,

Baek,

Kim

2024

IEEE Access

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A metaverse is composed of a physical-space and virtual-space, with the aim of having users in both the virtual reality and the real world experience. Prioritization is essential, but it is not straight-forwarded due to the limitation of computing resources in real-world, making it impossible to synchronize all data. Therefore, it is crucial to allocate resources based on regional preferences in physical-space and content preferences of users in virtual-space. The referencing system consists of two-stage sub-computations, (i) spatial-prioritization for more data gathering under the consideration of avatar-popularity one top of physical-space and (ii) temporal-prioritization for virtual-space rendering under the avatar-popularity. Both prioritization tasks are combinatorics problems and are well-known NP-hard. The problem scale is also large, making it difficult to solve within given times. The classical deep learning cannot be the solution. Quantum-based learning algorithms can be the potential solutions due to high-performance computing capabilities, because a small number of qubits can represent an exponentially large amount of information. On top of these advantages, an improved quantum reinforcement learning (QRL) algorithm is proposed for reducing the control dimensions into a logarithmic scale. We corroborate that our proposed QRL-based algorithm for low-dimensional spatio-temporal prioritization improves convergence and performance.

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Section: Preliminaries a Metaverse Network Architecturementioning

confidence: 99%

Quantum Reinforcement Learning for Spatio-Temporal Prioritization in Metaverse

Park,

Baek,

Kim

2024

IEEE Access

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“…The development of differential rendering extended to mobile devices. Rohmer et al [90,91] reduced the computational cost for each light using tile-based rendering, in addition to frustum culling techniques tailored for AR systems and applications. Monroy et al [76] presented a similar system which work in dynamic environment with the ability to scan the real scene and then projected onto a two-dimensional environment map that contain RGB+Depth data.…”

Section: Interactive Differential Renderingmentioning

confidence: 99%

Real-time Illumination and Visual Coherence for Photorealistic Augmented/Mixed Reality

Alhakamy

Tüceryan

2020

ACM Comput. Surv.

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A realistically inserted virtual object in the real-time physical environment is a desirable feature in augmented reality (AR) applications and mixed reality (MR) in general. This problem is considered a vital research area in computer graphics, a field that is experiencing ongoing discovery. The algorithms and methods used to obtain dynamic and real-time illumination measurement, estimating, and rendering of augmented reality scenes are utilized in many applications to achieve a realistic perception by humans. We cannot deny the powerful impact of the continuous development of computer vision and machine learning techniques accompanied by the original computer graphics and image processing methods to provide a significant range of novel AR/MR techniques. These techniques include methods for light source acquisition through image-based lighting or sampling, registering and estimating the lighting conditions, and composition of global illumination. In this review, we discussed the pipeline stages with the details elaborated about the methods and techniques that contributed to the development of providing a photo-realistic rendering, visual coherence, and interactive real-time illumination results in AR/MR.

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Differential G-Buffer Rendering for Mediated Reality Applications

Schwandt

Broll

2017

Lecture Notes in Computer Science

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Tiled Frustum Culling for Differential Rendering on Mobile Devices

Cited by 6 publications

References 14 publications

Quantum Reinforcement Learning for Spatio-Temporal Prioritization in Metaverse

Quantum Reinforcement Learning for Spatio-Temporal Prioritization in Metaverse

Real-time Illumination and Visual Coherence for Photorealistic Augmented/Mixed Reality

Differential G-Buffer Rendering for Mediated Reality Applications

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