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OpenDR: An Approximate Differentiable Renderer

MPG-Autoren
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Loper,  Matthew
Dept. Perceiving Systems, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Black,  Michael J.
Dept. Perceiving Systems, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Zitation

Loper, M., & Black, M. J. (2014). OpenDR: An Approximate Differentiable Renderer. In D. Fleet, T. Pajdla, B. Schiele, & T. Tuytelaars (Eds.), Computer Vision - ECCV 2014. Proceedings, Part 7 (pp. 154-169). Cham et al.: Springer International Publishing.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0024-C703-F
Zusammenfassung
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 differentiable 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 autodifferentiation 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.