The input/output complexity of sorting and related problems
Communications of the ACM
I/O-complexity of graph algorithms
Proceedings of the tenth annual ACM-SIAM symposium on Discrete algorithms
External memory data structures
Handbook of massive data sets
I/O-efficient batched union-find and its applications to terrain analysis
Proceedings of the twenty-second annual symposium on Computational geometry
Streaming computation of Delaunay triangulations
ACM SIGGRAPH 2006 Papers
I/o efficient algorithms and applications in geographic information systems
I/o efficient algorithms and applications in geographic information systems
TerraStream: from elevation data to watershed hierarchies
Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems
Computational Geometry: Algorithms and Applications
Computational Geometry: Algorithms and Applications
Algorithms and data structures for external memory
Foundations and Trends® in Theoretical Computer Science
External-memory computational geometry
SFCS '93 Proceedings of the 1993 IEEE 34th Annual Foundations of Computer Science
Provable surface reconstruction from noisy samples
Computational Geometry: Theory and Applications
I/O-efficient construction of constrained delaunay triangulations
ESA'05 Proceedings of the 13th annual European conference on Algorithms
Simplifying massive contour maps
ESA'12 Proceedings of the 20th Annual European conference on Algorithms
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We consider the problem of automatically cleaning massive sonar data point clouds, that is, the problem of automatically removing noisy points that for example appear as a result of scans of (shoals of) fish, multiple reflections, scanner self-reflections, refraction in gas bubbles, and so on. We describe a new algorithm that avoids the problems of previous local-neighbourhood based algorithms. Our algorithm is theoretically I/O-efficient, that is, it is capable of efficiently processing massive sonar point clouds that do not fit in internal memory but must reside on disk. The algorithm is also relatively simple and thus practically efficient, partly due to the development of a new simple algorithm for computing the connected components of a graph embedded in the plane. A version of our cleaning algorithm has already been incorporated in a commercial product.