RNNPose: Recurrent 6-DoF Object Pose Refinement with Robust Correspondence Field Estimation and Pose Optimization

• URL: http://arxiv.org/abs/2203.12870v2
• Date: Fri, 25 Mar 2022 11:11:07 GMT
• Title: RNNPose: Recurrent 6-DoF Object Pose Refinement with Robust Correspondence Field Estimation and Pose Optimization
• Authors: Yan Xu, Kwan-Yee Lin, Guofeng Zhang, Xiaogang Wang, Hongsheng Li
• Abstract summary: We propose a framework based on a recurrent neural network (RNN) for object pose refinement. The problem is formulated as a non-linear least squares problem based on the estimated correspondence field. The correspondence field estimation and pose refinement are conducted alternatively in each iteration to recover accurate object poses.
• Score: 46.144194562841435
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Direct estimating the 6-DoF object pose from a single color image is challenging, and post-refinement is generally needed to achieve high-precision estimation. In this paper, we propose a framework based on a recurrent neural network (RNN) for object pose refinement, which is robust to erroneous initial poses and occlusions. During the recurrent iterations, object pose refinement is formulated as a non-linear least squares problem based on the estimated correspondence field (between a rendered image and the observed image). The problem is then solved by a differentiable Levenberg-Marquardt (LM) algorithm for end-toend training. The correspondence field estimation and pose refinement are conducted alternatively in each iteration to recover accurate object poses. Furthermore, to improve the robustness to occlusions, we introduce a consistencycheck mechanism based on the learned descriptors of the 3D model and observed 2D image, which downweights the unreliable correspondences during pose optimization. Extensive experiments on LINEMOD, Occlusion-LINEMOD, and YCB-Video datasets validate the effectiveness of our method and demonstrate state-of-the-art performance.

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