SDFEst: Categorical Pose and Shape Estimation of Objects from RGB-D using Signed Distance Fields

• URL: http://arxiv.org/abs/2207.04880v1
• Date: Mon, 11 Jul 2022 13:53:50 GMT
• Title: SDFEst: Categorical Pose and Shape Estimation of Objects from RGB-D using Signed Distance Fields
• Authors: Leonard Bruns and Patric Jensfelt
• Abstract summary: We present a modular pipeline for pose and shape estimation of objects from RGB-D images. We integrate a generative shape model with a novel network to enable 6D pose and shape estimation from a single or multiple views. We demonstrate the benefits of our approach over state-of-the-art methods in several experiments on both synthetic and real data.
• Score: 5.71097144710995
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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