Data-driven Reflectance Models

Data-Driven Reflectance Models is a reflectance modeling approach that belongs to the class of computational methods for estimating surface appearance from measured data, distinguished by its reliance on empirical observations and statistical learning rather than analytical physical simulations to characterize light-surface interactions.

41

Publications

5.3K

Citations

155

Authors

98

Institutions

Publications per year

2017–2026

9

Authors

155

  1. HW
    Henrik Wann Jensen

    University of California San Diego, United States

    4 publications · 525 citations

    4

    h-index

  2. HP
    Hanspeter Pfister

    Harvard University Press, United States

    4 publications · 1.6K citations

    4

    h-index

  3. WM
    Wojciech Matusik

    Massachusetts Institute of Technology, United States

    4 publications · 1.6K citations

    4

    h-index

  4. XT
    Xin Tong

    Microsoft Research Asia (China), China

    3 publications · 207 citations

    3

    h-index

  5. BG
    Baining Guo

    Microsoft Research Asia (China), China

    2 publications · 120 citations

    2

    h-index

1–5 of 155

Venues

  1. ACM Transactions on Graphics

    TOG · 7 publications · 1.3K citations

    7

    h-index

  2. Remote Sensing of Environment

    6 publications · 1.1K citations

    6

    h-index

  3. Remote Sensing

    2 publications · 41 citations

    2

    h-index

  4. 2

    h-index

  5. International Journal of Earth Sciences

    GR · 1 publications · 53 citations

    1

    h-index

1–5