TY - GEN
T1 - Characterizing real-time dense point cloud capture and streaming on mobile devices
AU - Hu, Jinhan
AU - Shaikh, Aashiq
AU - Bahremand, Alireza
AU - Likamwa, Robert
N1 - Publisher Copyright:
© 2021 ACM.
PY - 2021/10/25
Y1 - 2021/10/25
N2 - Point clouds are a dense compilation of millions of points that can advance content creation and interaction in various emerging applications such as Augmented Reality (AR). However, point clouds consist of per-point real-world spatial and color information that are too computationally intensive to meet real-time specifications, especially on mobile devices. To stream dense point cloud (PtCl) to mobile devices, existing solutions encode pre-captured point clouds, yet with PtCl capturing treated as a separate offline operation. To discover more insights, we combine PtCl capturing and streaming as an entire pipeline and build a research prototype to study the bottlenecks of its real-time usage on mobile devices, consisting of a depth sensor with high precision and resolution, an edge-computing development board, and a smartphone. In a custom Unity app, we monitor the latency of each operation from the capturing to the rendering, as well as the energy efficiency of the board and the smartphone working at different point cloud resolutions. Results reveal that a toolset helping users efficiently capture, stream, and process color and depth data is the key enabler to real-time PtCl capturing and streaming on mobile devices.
AB - Point clouds are a dense compilation of millions of points that can advance content creation and interaction in various emerging applications such as Augmented Reality (AR). However, point clouds consist of per-point real-world spatial and color information that are too computationally intensive to meet real-time specifications, especially on mobile devices. To stream dense point cloud (PtCl) to mobile devices, existing solutions encode pre-captured point clouds, yet with PtCl capturing treated as a separate offline operation. To discover more insights, we combine PtCl capturing and streaming as an entire pipeline and build a research prototype to study the bottlenecks of its real-time usage on mobile devices, consisting of a depth sensor with high precision and resolution, an edge-computing development board, and a smartphone. In a custom Unity app, we monitor the latency of each operation from the capturing to the rendering, as well as the energy efficiency of the board and the smartphone working at different point cloud resolutions. Results reveal that a toolset helping users efficiently capture, stream, and process color and depth data is the key enabler to real-time PtCl capturing and streaming on mobile devices.
KW - dense point cloud streaming prototype
KW - performance and energy characterization
KW - point cloud rendering on mobile devices
UR - http://www.scopus.com/inward/record.url?scp=85134079585&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85134079585&partnerID=8YFLogxK
U2 - 10.1145/3477083.3480155
DO - 10.1145/3477083.3480155
M3 - Conference contribution
AN - SCOPUS:85134079585
T3 - HotEdgeVideo 2021 - Proceedings of the 2021 3rd ACM Workshop on Hot Topics in Video Analytics and Intelligent Edges
SP - 1
EP - 6
BT - HotEdgeVideo 2021 - Proceedings of the 2021 3rd ACM Workshop on Hot Topics in Video Analytics and Intelligent Edges
PB - Association for Computing Machinery, Inc
T2 - 3rd ACM Workshop on Hot Topics in Video Analytics and Intelligent Edges, HotEdgeVideo 2021
Y2 - 25 October 2021
ER -