pi-Lisco: parallel and incremental stream-based point-cloud clustering
Paper i 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
Författare
Hannaneh Najdataei
Nätverk och System
Vincenzo Massimiliano Gulisano
Nätverk och System
Philippas Tsigas
Nätverk och System
Marina Papatriantafilou
Nätverk och System
Proceedings of the ACM Symposium on Applied Computing
460-469
9781450387132 (ISBN)
Virtual, ,
Molnbaserade produkter och produktion (FiC)
Stiftelsen för Strategisk forskning (SSF) (GMT14-0032), 2016-01-01 -- 2020-12-31.
Ämneskategorier
Annan data- och informationsvetenskap
Mediateknik
Datorseende och robotik (autonoma system)
Styrkeområden
Informations- och kommunikationsteknik
DOI
10.1145/3477314.3507093