SDFEst: Categorical Pose and Shape Estimation of Objects From RGB-D Using Signed Distance Fields

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.

Automated summary

This paper presents a modular pipeline for pose and shape estimation of objects from RGB-D images. The method incorporates a generative shape model, an initialization network, and a differentiable renderer to enable accurate estimation of 6D pose and shape from single or multiple views. The use of discretized signed distance fields as a shape representation is investigated and proved to be efficient.