Scalable Model Representation

Scalable Model Representation is a modeling framework that enables the adaptive refinement and simplification of geometric or structural detail in accordance with varying levels of resolution or resource constraints, distinguishing itself by supporting continuous and efficient transitions across levels of detail without loss of semantic integrity.

74

Publications

5.3K

Citations

315

Authors

96

Institutions

Publications per year

2017–2026

38

Authors

315

  1. JP
    Jodilyn Peralta

    Google (United States), United States

    2 publications · 559 citations

    2

    h-index

  2. YL
    Yao Lu

    Nantong University, China

    2 publications · 559 citations

    2

    h-index

  3. KL
    Kuang-Huei Lee

    Google (United States), United States

    2 publications · 559 citations

    2

    h-index

  4. CT
    Clayton Tan

    Google (United States), United States

    2 publications · 559 citations

    2

    h-index

  5. RE
    Richard E. Turner

    University of Cambridge, United Kingdom

    2 publications · 30 citations

    2

    h-index

1–5 of 315

Venues

  1. arXiv (Cornell University)

    Repository · 7 publications · 588 citations

    7

    h-index

  2. bioRxiv (Cold Spring Harbor Laboratory)

    Repository · 3 publications · 80 citations

    3

    h-index

  3. IEEE Access

    2 publications · 31 citations

    2

    h-index

  4. Marketing Science

    1 publications · 11 citations

    1

    h-index

  5. Journal of Medical Devices

    JMED · 1 publications · 30 citations

    1

    h-index

1–5