  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
https://georgegu1997.github.io/OSSID/
