G2L-Net: Global to Local Network for Real-time 6D Pose Estimation with Embedding Vector Features

• URL: http://arxiv.org/abs/2003.11089v2
• Date: Thu, 26 Mar 2020 08:36:23 GMT
• Title: G2L-Net: Global to Local Network for Real-time 6D Pose Estimation with Embedding Vector Features
• Authors: Wei Chen, Xi Jia, Hyung Jin Chang, Jinming Duan, Ales Leonardis
• Abstract summary: We propose a novel real-time 6D object pose estimation framework, named G2L-Net. Our network operates on point clouds from RGB-D detection in a divide-and-conquer fashion. G2L-Net achieves state-of-the-art performance in terms of both accuracy and speed.
• Score: 39.77987181390717
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we propose a novel real-time 6D object pose estimation framework, named G2L-Net. Our network operates on point clouds from RGB-D detection in a divide-and-conquer fashion. Specifically, our network consists of three steps. First, we extract the coarse object point cloud from the RGB-D image by 2D detection. Second, we feed the coarse object point cloud to a translation localization network to perform 3D segmentation and object translation prediction. Third, via the predicted segmentation and translation, we transfer the fine object point cloud into a local canonical coordinate, in which we train a rotation localization network to estimate initial object rotation. In the third step, we define point-wise embedding vector features to capture viewpoint-aware information. To calculate more accurate rotation, we adopt a rotation residual estimator to estimate the residual between initial rotation and ground truth, which can boost initial pose estimation performance. Our proposed G2L-Net is real-time despite the fact multiple steps are stacked via the proposed coarse-to-fine framework. Extensive experiments on two benchmark datasets show that G2L-Net achieves state-of-the-art performance in terms of both accuracy and speed.

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