Sparse Volume Data Structures
Sparse Volume Data Structures is a computational representation method that organizes volumetric data using hierarchical or compressed spatial indexing to efficiently store and access non-uniformly sampled or sparsely populated regions of a volume, distinguishing itself from dense volume representations by optimizing memory usage and traversal operations for datasets with significant empty or homogeneous regions.
21
Publications
1.4K
Citations
55
Authors
33
Institutions
Publications per year
2017–2026
8
Parents
21
Ken Museth · ACM Transactions on Graphics · 2013 · 342 citations
Yuanming Hu, Tzu‐Mao Li, Luke Anderson et al. · ACM Transactions on Graphics · 2019 · 249 citations · Full text
Ben Houston, Michael B. Nielsen, Christopher Batty et al. · ACM Transactions on Graphics · 2006 · 112 citations
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55
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King Abdullah University of Science and Technology, Saudi Arabia
1 publications · 15 citations
1
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1
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1–5 of 55
33

University of Cologne
Cologne, Germany
2 h-index
4 publications
62 citations
University of California, Berkeley
Berkeley, United States
2 h-index
4 publications
830 citations
Nvidia (United States)
Santa Clara, United States
2 h-index
4 publications
81 citations
Robotics Research (United States)
Cincinnati, United States
1 h-index
2 publications
76 citations
Institut national de recherche en sciences et technologies du numérique
Le Chesnay-Rocquencourt, France
1 h-index
2 publications
332 citations

Corvinus University of Budapest
Budapest, Hungary
1 h-index
1 publications
21 citations
University College London
London, United Kingdom
1 h-index
1 publications
15 citations

University of British Columbia
Vancouver, Canada
1 h-index
1 publications
112 citations
1–8 of 33
TOG · 5 publications · 817 citations
5
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TVCG · 2 publications · 29 citations
2
h-index
1 publications · 15 citations
1
h-index
1 publications · 15 citations
1
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Conference · 1 publications · 27 citations
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