Reproducibility Companion Paper: Kalman Filter-based Head Motion Prediction for Cloud-based Mixed Reality

被引：2

作者：

Guel, Serhan ^{[1
]}

Bosse, Sebastian ^{[1
]}

Podborski, Dimitri ^{[1
]}

Schierl, Thomas ^{[1
]}

Hellge, Cornelius ^{[1
]}

Kastner, Marc A. ^{[2
]}

Zahalka, Jan ^{[3
]}

机构：

[1] Fraunhofer HHI, Berlin, Germany

[2] Natl Inst Informat, Tokyo, Japan

[3] Czech Tech Univ, Prague, Czech Republic

来源：

PROCEEDINGS OF THE 29TH ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA, MM 2021 | 2021年

关键词：

volumetric video; mixed reality; augmented reality; remote rendering; head motion prediction; Kalman filter; reproducible research;

D O I：

10.1145/3474085.3477936

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In our MM'20 paper [4], we presented a Kalman filter-based approach for prediction of head motion in 6DoF. The proposed approach was employed in our cloud-based volumetric video streaming system to reduce the interaction latency experienced by the user. In this companion paper, we present the dataset collected for our experiments and our simulation framework that reproduces the obtained experimental results. Our implementation is freely available on Github to facilitate further research.

引用

页码：3619 / 3621

页数：3

共 12 条

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