  Rich geometric understanding of the world is an important component of many
robotic applications such as planning and manipulation. In this paper, we
present a modular pipeline for pose and shape estimation of objects from RGB-D
images given their category. The core of our method is a generative shape
model, which we integrate with a novel initialization network and a
differentiable renderer to enable 6D pose and shape estimation from a single or
multiple views. We investigate the use of discretized signed distance fields as
an efficient shape representation for fast analysis-by-synthesis optimization.
Our modular framework enables multi-view optimization and extensibility. We
demonstrate the benefits of our approach over state-of-the-art methods in
several experiments on both synthetic and real data. We open-source our
approach at https://github.com/roym899/sdfest.
