Work

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

Year: 2022

Type: article

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 athttps://github.com/roym899/sdfest.

Source: IEEE Robotics and Automation Letters

DOI: 10.1109/lra.2022.3189792

Authors: Leonard Bruns, Patric Jensfelt

Institution: KTH Royal Institute of Technology

Language: English

Topic: 3D Shape Modeling and Analysis

Subfield: Computational Mechanics

Field: Engineering

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $1,256

Funder: Knut och Alice Wallenbergs Stiftelse