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

Authors

55

  1. KM
    Ken Museth

    Linköping University, Sweden

    3 publications · 472 citations

    3

    h-index

  2. UL
    Ulrich Lang

    University of Cologne, Germany

    3 publications · 42 citations

    3

    h-index

  3. SZ
    Stefan Zellmann

    University of Cologne, Germany

    3 publications · 42 citations

    3

    h-index

  4. MH
    Markus Hadwiger

    King Abdullah University of Science and Technology, Saudi Arabia

    1 publications · 15 citations

    1

    h-index

  5. CB
    Christopher Batty

    University of Waterloo, Canada

    1 publications · 112 citations

    1

    h-index

1–5 of 55

Venues

  1. ACM Transactions on Graphics

    TOG · 5 publications · 817 citations

    5

    h-index

  2. IEEE Transactions on Visualization and Computer Graphics

    TVCG · 2 publications · 29 citations

    2

    h-index

  3. Image and Vision Computing

    1 publications · 15 citations

    1

    h-index

  4. Computer Graphics Forum

    1 publications · 15 citations

    1

    h-index

  5. High Performance Graphics

    Conference · 1 publications · 27 citations

    1

    h-index

1–5