Implementation of Real-time Computer-generated Integral Imaging System for Head Mounted Display using OpenGL Shading Language

Shin, SeungSeob; Yeom, Jiwoon; Son, Yeseul; Choi, Kwang-Soon

doi:10.1109/ictc46691.2019.8939803

Cited by 2 publications

(4 citation statements)

References 8 publications

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“…The main application, installed on the HMD and the server is developed through the engine Unreal Engine (UE) at version 4.27 6 . It is a demo version, where a user in VR can interact with another user connected through the PC version of the game passing a big ball.…”

Section: A Equipmentmentioning

confidence: 99%

“…Instead, the device will only send input action, like head movement or controller button pressed, to the server to be processed. The usage of Virtual and Augmented Reality technologies is reaching more application from the gaming field to engineering and medical sector thanks to its immersiveness, that captures the attention of the customers in the case of retail industry [1]- [3], or used as support to improve and facilitate work of medical teams in the case of trauma surgeon inspection [4]- [6], but also for therapy and mental stimulation [7].…”

Section: Introductionmentioning

confidence: 99%

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Local and Remote Fog based Trade-offs for QOE in VR Applications by using CloudXR and Oculu Air Link

Maiorano

Mattia

Beraldi

2022

2022 International Conference on Edge Computing and Applications (ICECAA)

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Virtual Reality (VR) is now a well-established technology which offers realistic and immersive virtual worlds to the user usually by means of Head-Mounted Displays (HMDs). Actually, these devices can also be backed by a cloud server which can host the game server or even directly render the virtual world, as in the well-known cloud gaming paradigm. However, due to the drastically low latencies that technology requires, it is more convenient, when possible, to use servers that are as close to the users as possible. As a consequence, implementing the game or the render server in the Fog Computing layer is a concrete possibility. In this paper, we investigate, by using the Meta Quest 2 device, which are the QoE trade-offs, in terms of graphic quality and network performance, both in the case in which the HMD performs the 3d rendering locally by using a sample game written with Unreal Engine and in the case in which the 3d rendering is done in the Fog by means of the nVidia CloudXR framework and Oculus Air Link. From the results of our experiments, we found that remote rendering offers a stable frame rate against a higher quality image. Instead, local rendering sets the best possible graphics quality against the optimal frame rate. Additionally, we saw how remote rendering uses video compression in the case of decreasing bandwidth available to adjust graphics quality and FPS. The same does not hold for Motion-to-Photon latency, which increases with distance, reducing the general QoE.

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Section: A Equipmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Local and Remote Fog based Trade-offs for QOE in VR Applications by using CloudXR and Oculu Air Link

Maiorano

Mattia

Beraldi

2022

2022 International Conference on Edge Computing and Applications (ICECAA)

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“…(i) Viewpoints can be parallelly processed, suggesting exploiting GPU-based parallel computing [9][10][11][12]. However, the system complexity and power consumption are inevitably increased.…”

Section: Introductionmentioning

confidence: 99%

“…Concerning the tradeoff, this study revisits the very original EIA generation method-point retracing rendering (PRR) [22], which retraces from every point on a 3D object, also known as a voxel, to the lens array and then to the EIA. This method was thought inefficient and soon upgraded to viewpoint-based methods [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Real-time computer-generated integral imaging light field displays: revisiting the point retracing rendering method from a signal processing point of view

Qin¹,

Cheng²,

Dong³

et al. 2023

Preprint

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Integral imaging light field displays (InIm-LFDs) can provide realistic 3D images by showing an elemental image array (EIA) under a lens array. However, it is always challenging to computationally generate an EIA in real-time with entry-level computing hardware because the current practice that projects many viewpoints to the EIA induces heavy computations. This study discards the viewpoint-based strategy and revisits the early point retracing rendering method, and then proposes that InIm-LFDs and regular 2D displays share two similar signal processing phases: sampling and reconstructing. An InIm-LFD is demonstrated to create a finite number of static voxels for signal sampling. And each voxel is invariantly formed by homogeneous pixels for signal reconstructing. We obtain the static voxel-pixel mapping through arbitrarily accurate raytracing in advance and store it as a lookup table (LUT). Our EIA rendering method first resamples input 3D data with the pre-defined voxels and then assigns every voxel’s value to its homogeneous pixels through the LUT. As s result, the proposed method reduces the computational complexity by several orders of magnitude. The experimental rendering speed is as fast as 7 ms for a full-HD EIA frame on an entry-level laptop. Finally, considering a voxel may not be perfectly integrated by its homogeneous pixels, called the sampling error, the proposed and conventional viewpoint-based methods are analyzed in the Fourier domain. We prove that even with severe sampling errors, the two methods negligibly differ in the output signal’s frequency spectrum. The proposed method breaks the long-standing tradeoff among rendering speed, accuracy, and system complexity for computer-generated integral imaging (CGII), expected to remove the hinder for real-time CGII.

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Implementation of Real-time Computer-generated Integral Imaging System for Head Mounted Display using OpenGL Shading Language

Cited by 2 publications

References 8 publications

Local and Remote Fog based Trade-offs for QOE in VR Applications by using CloudXR and Oculu Air Link

Local and Remote Fog based Trade-offs for QOE in VR Applications by using CloudXR and Oculu Air Link

Real-time computer-generated integral imaging light field displays: revisiting the point retracing rendering method from a signal processing point of view

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