Robust 6D Object Pose Estimation by Learning RGB-D Features

• URL: http://arxiv.org/abs/2003.00188v2
• Date: Mon, 9 Mar 2020 14:25:38 GMT
• Title: Robust 6D Object Pose Estimation by Learning RGB-D Features
• Authors: Meng Tian, Liang Pan, Marcelo H Ang Jr and Gim Hee Lee
• Abstract summary: We propose a novel discrete-continuous formulation for rotation regression to resolve this local-optimum problem. We uniformly sample rotation anchors in SO(3), and predict a constrained deviation from each anchor to the target, as well as uncertainty scores for selecting the best prediction. Experiments on two benchmarks: LINEMOD and YCB-Video, show that the proposed method outperforms state-of-the-art approaches.
• Score: 59.580366107770764
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate 6D object pose estimation is fundamental to robotic manipulation and grasping. Previous methods follow a local optimization approach which minimizes the distance between closest point pairs to handle the rotation ambiguity of symmetric objects. In this work, we propose a novel discrete-continuous formulation for rotation regression to resolve this local-optimum problem. We uniformly sample rotation anchors in SO(3), and predict a constrained deviation from each anchor to the target, as well as uncertainty scores for selecting the best prediction. Additionally, the object location is detected by aggregating point-wise vectors pointing to the 3D center. Experiments on two benchmarks: LINEMOD and YCB-Video, show that the proposed method outperforms state-of-the-art approaches. Our code is available at https://github.com/mentian/object-posenet.

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