An Approximate Differentiable Renderer

Date

October 28, 2014

Speaker

Matthew Loper

Affiliation

Max Planck Institute for Intelligent Systems

Overview

Although computer vision can be posed as an inverse rendering problem, most renderers are not tailored to this task. Our framework makes it simple to express a forward graphics model, automatically obtain derivatives with respect to the model parameters, and optimize over them. Built on a new autodifferentiation package and OpenGL, OpenDR provides color, depth and boundary renderers, and promotes the ability to construct functions of the renderers. We demonstrate the power and simplicity of programming with OpenDR by using it to prototype a system for estimating human body shape from Kinect depth and RGB data.

Speakers

Matthew Loper

Matthew Loper is a PhD candidate at the Max Planck Institute for Intelligent Systems in the Perceiving Systems department. He received his ScM from Brown in 2008, and has worked since then on various aspects of parametric body modelling. His current research interests include differentiable rendering, efficient shape representations, and human body inference.

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