Header logo is ps

{OpenDR}: An Approximate Differentiable Renderer


Conference Paper


Inverse graphics attempts to take sensor data and infer 3D geometry, illumination, materials, and motions such that a graphics renderer could realistically reproduce the observed scene. Renderers, however, are designed to solve the forward process of image synthesis. To go in the other direction, we propose an approximate di fferentiable renderer (DR) that explicitly models the relationship between changes in model parameters and image observations. We describe a publicly available OpenDR framework that makes it easy to express a forward graphics model and then automatically obtain derivatives with respect to the model parameters and to optimize over them. Built on a new autodiff erentiation package and OpenGL, OpenDR provides a local optimization method that can be incorporated into probabilistic programming frameworks. We demonstrate the power and simplicity of programming with OpenDR by using it to solve the problem of estimating human body shape from Kinect depth and RGB data.

Author(s): Matthew M. Loper and Michael J. Black
Book Title: Computer Vision – ECCV 2014
Volume: 8695
Pages: 154--169
Year: 2014
Month: September

Series: Lecture Notes in Computer Science
Editors: D. Fleet and T. Pajdla and B. Schiele and T. Tuytelaars
Publisher: Springer International Publishing

Department(s): Perceiving Systems
Research Project(s): Differentiable Rendering
Bibtex Type: Conference Paper (inproceedings)
Paper Type: Conference

DOI: 10.1007/978-3-319-10584-0_11
Event Name: 13th European Conference on Computer Vision
Event Place: Zürich, Switzerland

Links: pdf
video of talk


  title = {{OpenDR}: An Approximate Differentiable Renderer},
  author = {Loper, Matthew M. and Black, Michael J.},
  booktitle = {Computer Vision -- ECCV 2014},
  volume = {8695},
  pages = {154--169},
  series = {Lecture Notes in Computer Science},
  editors = {D. Fleet  and T. Pajdla and B. Schiele  and T. Tuytelaars },
  publisher = {Springer International Publishing},
  month = sep,
  year = {2014},
  month_numeric = {9}