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Efficient Geometry-based Similarity Search of 3D Spatial Databases

Summary: Geometry-based similarity search for 3D-volume databases uses hierarchical approximations to speed up 3D-object matching. Cuboid and octree instantiations, with formal correctness proofs and empirical evaluation, show gains over existing indexes in medical and CAD workloads. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
3180
Venue
SIGMOD
Year
1999
Pagerank
5.4246876e-05
Overall Rank
8,442 | 42.09%
DOI
10.1145/304182.304219

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{keim_sigmod99,
        title = {{Efficient Geometry-based Similarity Search of 3D Spatial Databases}},
        author = {Keim, Daniel A.},
        series = {{SIGMOD} '99},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/304182.304219},
        url = {https://dl.acm.org/doi/10.1145/304182.304219},
        year = {1999}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
9,262 Using Sets of Feature Vectors for Similarity Search on Voxelized CAD Objects 2003 SIGMOD 5.2968259e-05
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Outgoing Citations (Sorted by Pagerank)

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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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