Surfels: surface elements as rendering primitives
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
QSplat: a multiresolution point rendering system for large meshes
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
SETI@HOME—massively distributed computing for SETI
Computing in Science and Engineering
MPI on the I-WAY: A Wide-Area, Multimethod Implementation of the Message Passing Interface
MPIDC '96 Proceedings of the Second MPI Developers Conference
The Grid 2: Blueprint for a New Computing Infrastructure
The Grid 2: Blueprint for a New Computing Infrastructure
Distributed computing in practice: the Condor experience: Research Articles
Concurrency and Computation: Practice & Experience - Grid Performance
The Anatomy of the Grid: Enabling Scalable Virtual Organizations
International Journal of High Performance Computing Applications
Immersive Visualization and Analysis of LiDAR Data
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
A break in the clouds: towards a cloud definition
ACM SIGCOMM Computer Communication Review
Introduction to Grid Computing
Introduction to Grid Computing
Cloud Computing Strategies
International Journal of Remote Sensing
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Light detection and ranging (lidar) technologies have proven to be the most powerful tools to collect, within a short time, three-dimensional (3-D) point clouds with high-density, high-accuracy and significantly detailed surface information pertaining to terrain and objects. However, in terms of feature extraction and 3-D reconstruction in a computer-aided drawing (CAD) format, most of the existing stand-alone lidar data processing software packages are unable to process a large volume of lidar data in an effective and efficient fashion. To break this technical bottleneck, through the design of a Condor-based process virtualization platform, we presented in this paper a novel strategy that uses network-related computational resources to process, manage, and distribute vast quantities of lidar data in a cloud computing environment. Three extensive experiments with and without a cloud computing environment were compared. The experiment results demonstrated that the proposed process virtualization approach is promisingly applicable and effective in the management of large-scale lidar point clouds.