pi-Lisco: parallel and incremental stream-based point-cloud clustering
Paper in proceeding, 2022
The simplicity of Lisco, along with the potential of improvements through utilization of computational overlaps, form the motivation of a more challenging objective studied here. We propose Parallel and Incremental Lisco (pi-Lisco), which, with a simple yet efficient approach, clusters LiDAR data in streaming sliding windows, reusing the results from overlapping portions of the data, thus, enabling single-window (i.e., in-place) processing. Moreover, pi-Lisco employs efficient work-sharing among threads, facilitated by the ScaleGate data structure, and embeds a customised version of the STINGER concurrent data structure. Through an orchestration of these key ideas, pi-Lisco is able to lead to significant performance improvements. We complement with an evaluation of pi-Lisco, using the Ford Campus real-world extensive data-set, showing (i) the computational benefits from incrementally processing the consecutive point-clouds; and (ii) the fact that pi-Lisco' parallelization leads to continuously increasing sustainable rates with increasing number of threads, shifting the saturation point of the baseline.
Clustering
Point-cloud analysis
Data-stream processing
Author
Hannaneh Najdataei
Network and Systems
Vincenzo Massimiliano Gulisano
Network and Systems
Philippas Tsigas
Network and Systems
Marina Papatriantafilou
Network and Systems
Proceedings of the ACM Symposium on Applied Computing
460-469
9781450387132 (ISBN)
Virtual, ,
Future factories in the Cloud (FiC)
Swedish Foundation for Strategic Research (SSF) (GMT14-0032), 2016-01-01 -- 2020-12-31.
Subject Categories
Other Computer and Information Science
Media Engineering
Computer Vision and Robotics (Autonomous Systems)
Areas of Advance
Information and Communication Technology
DOI
10.1145/3477314.3507093