HRPose: Real-Time High-Resolution 6D Pose Estimation Network Using Knowledge Distillation

• URL: http://arxiv.org/abs/2204.09429v1
• Date: Wed, 20 Apr 2022 12:43:39 GMT
• Title: HRPose: Real-Time High-Resolution 6D Pose Estimation Network Using Knowledge Distillation
• Authors: Qi Guan, Zihao Sheng, and Shibei Xue
• Abstract summary: We propose an effective and lightweight model, namely High-Resolution 6D Pose Estimation Network (HRPose) With only 33% of the model size and lower computational costs, our HRPose achieves comparable performance compared with state-of-the-art models. Numerical experiments on the widely-used benchmark LINEMOD demonstrate the superiority of our proposed HRPose against state-of-the-art methods.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Real-time 6D object pose estimation is essential for many real-world applications, such as robotic grasping and augmented reality. To achieve an accurate object pose estimation from RGB images in real-time, we propose an effective and lightweight model, namely High-Resolution 6D Pose Estimation Network (HRPose). We adopt the efficient and small HRNetV2-W18 as a feature extractor to reduce computational burdens while generating accurate 6D poses. With only 33\% of the model size and lower computational costs, our HRPose achieves comparable performance compared with state-of-the-art models. Moreover, by transferring knowledge from a large model to our proposed HRPose through output and feature-similarity distillations, the performance of our HRPose is improved in effectiveness and efficiency. Numerical experiments on the widely-used benchmark LINEMOD demonstrate the superiority of our proposed HRPose against state-of-the-art methods.

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