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arXiv · 1809.06893

SilhoNet: An RGB Method for 6D Object Pose Estimation

Gideon Billings·Matthew Johnson-Roberson
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Abstract

Autonomous robot manipulation involves estimating the translation and orientation of the object to be manipulated as a 6-degree-of-freedom (6D) pose. Methods using RGB-D data have shown great success in solving this problem. However, there are situations where cost constraints or the working environment may limit the use of RGB-D sensors. When limited to monocular camera data only, the problem of object pose estimation is very challenging. In this work, we introduce a novel method called SilhoNet that predicts 6D object pose from monocular images. We use a Convolutional Neural Network (CNN) pipeline that takes in Region of Interest (ROI) proposals to simultaneously predict an intermediate silhouette representation for objects with an associated occlusion mask and a 3D translation vector. The 3D orientation is then regressed from the predicted silhouettes. We show that our method achieves better overall performance on the YCB-Video dataset than two state-of-the art networks for 6D pose estimation from monocular image input.

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Gideon Billings, Matthew Johnson-Roberson. 2018-09-18. SilhoNet: An RGB Method for 6D Object Pose Estimation. https://doi.org/10.1109/lra.2019.2928776

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