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Efficient topology-aware simplification of large triangulated terrains

Song, Yunting and Fellegara, Riccardo and Iuricich, Federico and De Floriani, Leila (2021) Efficient topology-aware simplification of large triangulated terrains. In: Efficient topology-aware simplification of large triangulated terrains, pp. 576-587. ACM. SIGSPATIAL ’21: 29th International Conference on Advances in Geographic Information Systems, Virtual Event / Beijing, China, November 2-5, 2021, November 2-5, 2021, Beijing, China. doi: 10.1145/3474717.3484261.

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Official URL: https://dl.acm.org/doi/10.1145/3474717.3484261

Abstract

A common first step in the terrain processing pipeline of large Triangulated Irregular Networks (TINs) is simplifying the TIN to make it manageable for further processing. The major problem with TIN simplification algorithms is that they create or remove critical points in an uncontrolled way. Topology-aware operators have been defined to solve this issue by coarsening a TIN without affecting the topology of its underlying terrain, i.e., without modifying critical simplices describing pits, saddles, peaks, and their connectivity. While effective, existing algorithms are sequential in nature and are not scalable enough to perform well with large terrains on multicore systems. Here, we consider the problem of topology-aware simplification of very large meshes. We define a topology-aware simplification algorithm on a compact and distributed data structure for triangle meshes, namely the Terrain trees. Terrain trees reduce both the memory and time requirements of the simplification procedure by adopting a batched processing strategy of the mesh elements. Furthermore, we define a new parallel topology-aware simplification algorithm that takes advantage of the spatial domain decomposition at the basis of Terrain trees. Scalability and efficiency are experimentally demonstrated on real-world TINs originated from topographic and bathymetric LiDAR data. Our experiments show that topology-aware simplification on Terrain trees uses 40% less memory and half the time than the same approach implemented on the most compact and efficient connectivity-based data structure for TINs. Beyond that, our parallel algorithm on the Terrain trees reaches a 12x speedup when using 20 threads.

Item URL in elib:https://elib.dlr.de/146785/
Document Type:Conference or Workshop Item (Speech)
Title:Efficient topology-aware simplification of large triangulated terrains
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Song, Yuntingytsong (at) umd.eduhttps://orcid.org/0000-0002-3053-1748
Fellegara, RiccardoRiccardo.Fellegara (at) dlr.dehttps://orcid.org/0000-0002-8758-2802
Iuricich, Federicofiurici (at) clemson.eduhttps://orcid.org/0000-0002-6605-9131
De Floriani, Leiladeflo (at) umiacs.umd.eduhttps://orcid.org/0000-0002-1361-2888
Date:2021
Journal or Publication Title:Efficient topology-aware simplification of large triangulated terrains
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
DOI :10.1145/3474717.3484261
Page Range:pp. 576-587
Publisher:ACM
Series Name:SIGSPATIAL '21: Proceedings of the 29th International Conference on Advances in Geographic Information Systems
Status:Published
Keywords:terrain simplification, edge contraction, spatial indexes, topological methods, shared memory processing
Event Title:SIGSPATIAL ’21: 29th International Conference on Advances in Geographic Information Systems, Virtual Event / Beijing, China, November 2-5, 2021
Event Location:Beijing, China
Event Type:international Conference
Event Dates:November 2-5, 2021
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space System Technology
DLR - Research area:Raumfahrt
DLR - Program:R SY - Space System Technology
DLR - Research theme (Project):R - Tasks SISTEC
Location: Braunschweig
Institutes and Institutions:Institute for Software Technology > Software for Space Systems and Interactive Visualisation
Institute for Software Technology
Deposited By: Fellegara, Dr Riccardo
Deposited On:08 Dec 2021 10:09
Last Modified:08 Dec 2021 10:09

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