论文标题
HRPOSE：使用知识蒸馏的实时高分辨率6D姿势估计网络
HRPose: Real-Time High-Resolution 6D Pose Estimation Network Using Knowledge Distillation
论文作者
论文摘要
实时6D对象姿势估计对于许多现实世界应用至关重要，例如机器人抓握和增强现实。为了实时从RGB图像中实现准确的对象姿势估计，我们提出了一个有效且轻巧的模型，即高分辨率6D姿势估计网络（HRPOSE）。我们采用高效且小的HRNETV2-W18作为特征提取器，以减轻计算负担，同时产生准确的6D姿势。与最先进的模型相比，我们的HRPOSE只有33％的模型大小和较低的计算成本，可以实现可比的性能。此外，通过将知识从大型模型转移到我们提出的HRPOSE通过输出和相似性蒸馏，我们的HRPOSE的性能在有效性和效率上提高了。在广泛使用的基准线条上进行的数值实验证明了我们提出的HRPOSE与最新方法的优越性。
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.
