A DR algorithm based on artificial potential field method

Shi, Xiaohu; Wang, Xue; Bi, Jing; Liu, Fang; Yang, Dan; Liu, Xian-Yan

doi:10.1007/s11042-009-0296-6

Cited by 3 publications

(4 citation statements)

References 3 publications

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“…This provided similar features to rollback, often used in online fighting games [44]. Li et al [27] and Shi et al [48] proposed using potential fields to predict player motion. Ohlenburg [36] extended dead-reckoning to include collision responses.…”

Section: Smart Clientsmentioning

confidence: 99%

Quality of Service Impact on Edge Physics Simulations for VR

Friston

Griffith

Swapp

et al. 2021

IEEE Trans. Visual. Comput. Graphics

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Mobile HMDs must sacrifice compute performance to achieve ergonomic and power requirements for extended use. Consequently, applications must either reduce rendering and simulation complexity -along with the richness of the experience -or offload complexity to a server. Within the context of edge-computing, a popular way to do this is through render streaming. Render streaming has been demonstrated for desktops and consoles. It has also been explored for HMDs. However, the latency requirements of head tracking make this application much more challenging. While mobile GPUs are not yet as capable as their desktop counterparts, we note that they are becoming more powerful and efficient. With the hard requirements of VR, it is worth continuing to investigate what schemes could optimally balance load, latency and quality. We propose an alternative we call edge-physics: streaming at the scene-graph level from a simulation running on edge-resources, analogous to cluster rendering. Scene streaming is not only straightforward, but compute and bandwidth efficient. The most demanding loops run locally. Jobs that hit the power-wall of mobile CPUs are off-loaded, while improving GPUs are leveraged, maximising compute utilisation. In this paper we create a prototypical implementation and evaluate its potential in terms of fidelity, bandwidth and performance. We show that an effective system which maintains high consistencies on typical edge-links can be easily built, but that some traditional concepts are not applicable, and a better understanding of the perception of motion is required to evaluate such a system comprehensively.

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Section: Smart Clientsmentioning

confidence: 99%

Quality of Service Impact on Edge Physics Simulations for VR

Friston

Griffith

Swapp

et al. 2021

IEEE Trans. Visual. Comput. Graphics

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show abstract

“…We chose this many players for our studies because it is an authentic representative set of the actual players that would be seen in an online game playing community. Furthermore, this size of sample follows suit with the sample sizes of similar works [18,21,26,29].…”

Section: The Testing Methodsology Play Testingmentioning

confidence: 99%

“…And so we turn to compensation methods that can reduce and mask the effects of delay and loss. The use of a dead reckoning (or path prediction) algo rithm is one such compensation method, and can hide the effects of delay and loss considerably [5,8,18,21,26].…”

Section: Motivationmentioning

confidence: 99%

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Play patterns for path prediction in multiplayer online games

Agar¹

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Play patterns for path prediction in multiplayer online games

Agar

Corriveau

Shi

2012

7th International Conference on Communications and Networking in China

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Traditional dead reckoning schemes predict a player's position by assuming that players move with constant force or velocity. However, because player movement is rarely linear in nature, using linear prediction fails to produce an accurate result. Among existing dead reckoning methods, only few focus on improving prediction accuracy via genuinely nontraditional methods for predicting the path of a player. In this paper, we propose a new prediction method based on play patterns. We implemented a 2D top-down multiplayer online game to act as a test harness that we used to collect play data from 44 experienced players. From the data for half of these players, we extracted play patterns, which we used to create our dead reckoning algorithm. A comparative evaluation proceeding from an extensive set of simulations (using the other half of our play data) suggests that our EKB algorithm yields more accurate predictions than the IEEE standard dead reckoning algorithm and the recent "Interest Scheme" algorithm.

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A DR algorithm based on artificial potential field method

Cited by 3 publications

References 3 publications

Quality of Service Impact on Edge Physics Simulations for VR

Quality of Service Impact on Edge Physics Simulations for VR

Play patterns for path prediction in multiplayer online games

Play patterns for path prediction in multiplayer online games

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