Towards 3D Scene Understanding Using Differentiable Rendering

Abstract

Deep learning methods have achieved significant results in many 2D computer vision tasks. To realize similar results in 3D tasks, equipping deep learning pipelines with components that incorporate knowledge about 2D image generation from the 3D scene description is a promising research direction. Rasterization, the standard formulation of the image generation process is not differentiable, and thus not compatible with the deep learning models trained using gradient-based optimization schemes. In recent years, many new approximate differentiable renderers have been proposed to enable compatibility between deep learning methods and image rendering techniques. Differentiable renderers fit naturally into the render-and-compare framework where the 3D scene parameters are estimated iteratively by minimizing the error between the observed image and the image rendered according to the current scene parameter estimate. In this article, we present StilllebenDR, a light-weight, scalable differentiable renderer built as an extension to the openly available Stillleben library. We demonstrate the usability of the proposed differentiable renderer for the task of iterative 3D deformable registration using a latent shape-space model and occluded object pose refinement using order-independent transparency based on analytical gradients and learned scene aggregation.