论文标题
通过对齐本地预测的分布来对6D姿势估计的知识蒸馏
Knowledge Distillation for 6D Pose Estimation by Aligning Distributions of Local Predictions
论文作者
论文摘要
知识蒸馏通过使用深层老师的培训来促进对紧凑的学生网络的培训。尽管这在许多任务中取得了巨大的成功，但对于基于图像的6D对象姿势估计，它仍然完全没有研究。在这项工作中，我们介绍了由6D姿势估计任务驱动的第一种知识蒸馏方法。为此，我们观察到，大多数现代6D构成估计框架都会输出本地预测，例如稀疏的2D关键点或密集的表示，紧凑的学生网络通常会精确地预测此类局部数量。因此，我们建议将教师的本地预测的\ emph {Distribution}提炼到学生网络中，而不是对学生对学生进行预测的预测监督，以促进其培训。我们对几个基准测试的实验表明，我们的蒸馏方法通过不同的紧凑型学生模型以及基于关键的基于密集和密集的预测架构产生最先进的结果。
Knowledge distillation facilitates the training of a compact student network by using a deep teacher one. While this has achieved great success in many tasks, it remains completely unstudied for image-based 6D object pose estimation. In this work, we introduce the first knowledge distillation method driven by the 6D pose estimation task. To this end, we observe that most modern 6D pose estimation frameworks output local predictions, such as sparse 2D keypoints or dense representations, and that the compact student network typically struggles to predict such local quantities precisely. Therefore, instead of imposing prediction-to-prediction supervision from the teacher to the student, we propose to distill the teacher's \emph{distribution} of local predictions into the student network, facilitating its training. Our experiments on several benchmarks show that our distillation method yields state-of-the-art results with different compact student models and for both keypoint-based and dense prediction-based architectures.
