Corpus ID: 139106812

Differentiable Visual Computing

  title={Differentiable Visual Computing},
  author={Tzu-Mao Li},
  • Tzu-Mao Li
  • Published 2019
  • Computer Science
  • ArXiv
  • Derivatives of computer graphics, image processing, and deep learning algorithms have tremendous use in guiding parameter space searches, or solving inverse problems. As the algorithms become more sophisticated, we no longer only need to differentiate simple mathematical functions, but have to deal with general programs which encode complex transformations of data. This dissertation introduces three tools for addressing the challenges that arise when obtaining and applying the derivatives for… CONTINUE READING
    6 Citations
    A differential theory of radiative transfer
    • 18
    • PDF
    Generalized Physics-Informed Learning through Language-Wide Differentiable Programming
    • 2
    • PDF
    Physics-based differentiable rendering: from theory to implementation


    Opt: A Domain Specific Language for Non-linear Least Squares Optimization in Graphics and Imaging
    • 40
    • PDF
    Differentiable Monte Carlo ray tracing through edge sampling
    • 114
    • PDF
    OpenDR: An Approximate Differentiable Renderer
    • 258
    • Highly Influential
    • PDF
    Deep bilateral learning for real-time image enhancement
    • 214
    • Highly Influential
    • PDF
    A Versatile Scene Model with Differentiable Visibility Applied to Generative Pose Estimation
    • 35
    • PDF
    Evaluating derivatives - principles and techniques of algorithmic differentiation, Second Edition
    • 2,561
    • PDF
    Manifold exploration
    • 124
    • Highly Influential
    • PDF
    Shape, Illumination, and Reflectance from Shading
    • J. Barron, Jitendra Malik
    • Computer Science, Medicine
    • IEEE Transactions on Pattern Analysis and Machine Intelligence
    • 2015
    • 447
    • PDF