Real-time object pose estimation is necessary for many robot manipulation
algorithms. However, state-of-the-art methods for object pose estimation are
trained for a specific set of objects; these methods thus need to be retrained
to estimate the pose of each new object, often requiring tens of GPU-days of
training for optimal performance. In this paper, we propose the OSSID
framework, leveraging a slow zero-shot pose estimator to self-supervise the
training of a fast detection algorithm. This fast detector can then be used to
filter the input to the pose estimator, drastically improving its inference
speed. We show that this self-supervised training exceeds the performance of
existing zero-shot detection methods on two widely used object pose estimation
and detection datasets, without requiring any human annotations. Further, we
show that the resulting method for pose estimation has a significantly faster
inference speed, due to the ability to filter out large parts of the image.
Thus, our method for self-supervised online learning of a detector (trained
using pseudo-labels from a slow pose estimator) leads to accurate pose
estimation at real-time speeds, without requiring human annotations.
Supplementary materials and code can be found at
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本研究提出了一种名为 OSSID 的框架，通过使用基于零样本学习的缓慢姿态估计器来自我监督训练快速检测算法，这使得估计器的过滤效果得到大幅改善，从而大大提高了姿态估计的速度。