The input/output complexity of sorting and related problems
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LEDA: a platform for combinatorial and geometric computing
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Triangle: Engineering a 2D Quality Mesh Generator and Delaunay Triangulator
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External-memory computational geometry
SFCS '93 Proceedings of the 1993 IEEE 34th Annual Foundations of Computer Science
I/O-efficient batched union-find and its applications to terrain analysis
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Illustrating the streaming construction of 2D delaunay triangulations
Proceedings of the twenty-second annual symposium on Computational geometry
Streaming computation of Delaunay triangulations
ACM SIGGRAPH 2006 Papers
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Algorithms and data structures for external memory
Foundations and Trends® in Theoretical Computer Science
Optimal in-place algorithms for 3-D convex hulls and 2-D segment intersection
Proceedings of the twenty-fifth annual symposium on Computational geometry
Optimal in-place and cache-oblivious algorithms for 3-d convex hulls and 2-d segment intersection
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Proceedings of the 1st International Conference and Exhibition on Computing for Geospatial Research & Application
I/O-efficient batched union-find and its applications to terrain analysis
ACM Transactions on Algorithms (TALG)
Cleaning massive sonar point clouds
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
Natural neighbor interpolation based grid DEM construction using a GPU
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
Generating raster DEM from mass points via TIN streaming
GIScience'06 Proceedings of the 4th international conference on Geographic Information Science
Fast segment insertion and incremental construction of constrained delaunay triangulations
Proceedings of the twenty-ninth annual symposium on Computational geometry
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In this paper, we designed and implemented an I/O-efficient algorithm for constructing constrained Delaunay triangulations. If the number of constraining segments is smaller than the memory size, our algorithm runs in expected $O(\frac{N}{B}{\rm log}_{M/B}\frac{N}{B})$ I/Os for triangulating N points in the plane, where M is the memory size and B is the disk block size. If there are more constraining segments, the theoretical bound does not hold, but in practice the performance of our algorithm degrades gracefully. Through an extensive set of experiments with both synthetic and real data, we show that our algorithm is significantly faster than existing implementations.