TY - GEN
T1 - QoS-Aware Point Cloud Streaming of Wild Animals/Humans for Interactions in Virtual Space
AU - Ishimaru, Hiroki
AU - Nakamura, Yugo
AU - Fujimoto, Manato
AU - Suwa, Hirohiko
AU - Yasumoto, Keiichi
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 3D applications such as VR and AR are attracting increasing commercial attention, and point cloud video is expected to be one of the most suitable representations for real-time applications due to its simplicity and versatility. However, point cloud data is large in size and difficult to stream in a mobile network environment with limited bandwidth. Therefore, a method for streaming point clouds with low bandwidth consumption while maintaining the quality of the user experience is needed. In this paper, we present a point cloud streaming method of real-space objects such as humans and animals for real-time 3D reconstruction in VR space. The system uses a depth camera to scan a human or animal, divides the point cloud into parts of the body, and then controls the quality of the point cloud (i.e., resolution and frame rate) for each part in real-time according to the object's motion and context. This enables point cloud streaming with limited resources (computational and network resources) and maximizes the user's quality of experience. We exhibit a series of systems that enhance the user experience in remote communication in realistic environments and scenarios while maintaining interactivity between real-space objects and remote users.
AB - 3D applications such as VR and AR are attracting increasing commercial attention, and point cloud video is expected to be one of the most suitable representations for real-time applications due to its simplicity and versatility. However, point cloud data is large in size and difficult to stream in a mobile network environment with limited bandwidth. Therefore, a method for streaming point clouds with low bandwidth consumption while maintaining the quality of the user experience is needed. In this paper, we present a point cloud streaming method of real-space objects such as humans and animals for real-time 3D reconstruction in VR space. The system uses a depth camera to scan a human or animal, divides the point cloud into parts of the body, and then controls the quality of the point cloud (i.e., resolution and frame rate) for each part in real-time according to the object's motion and context. This enables point cloud streaming with limited resources (computational and network resources) and maximizes the user's quality of experience. We exhibit a series of systems that enhance the user experience in remote communication in realistic environments and scenarios while maintaining interactivity between real-space objects and remote users.
KW - 3D point cloud
KW - QoE
KW - Real-time communication
KW - Streaming
KW - VR
UR - http://www.scopus.com/inward/record.url?scp=85164150760&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85164150760&partnerID=8YFLogxK
U2 - 10.1109/PerComWorkshops56833.2023.10150405
DO - 10.1109/PerComWorkshops56833.2023.10150405
M3 - Conference contribution
AN - SCOPUS:85164150760
T3 - 2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2023
SP - 325
EP - 327
BT - 2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2023
Y2 - 13 March 2023 through 17 March 2023
ER -