  Visual perception tasks often require vast amounts of labelled data,
including 3D poses and image space segmentation masks. The process of creating
such training data sets can prove difficult or time-intensive to scale up to
efficacy for general use. Consider the task of pose estimation for rigid
objects. Deep neural network based approaches have shown good performance when
trained on large, public datasets. However, adapting these networks for other
novel objects, or fine-tuning existing models for different environments,
requires significant time investment to generate newly labelled instances.
Towards this end, we propose ProgressLabeller as a method for more efficiently
generating large amounts of 6D pose training data from color images sequences
for custom scenes in a scalable manner. ProgressLabeller is intended to also
support transparent or translucent objects, for which the previous methods
based on depth dense reconstruction will fail. We demonstrate the effectiveness
of ProgressLabeller by rapidly create a dataset of over 1M samples with which
we fine-tune a state-of-the-art pose estimation network in order to markedly
improve the downstream robotic grasp success rates. ProgressLabeller is
open-source at https://github.com/huijieZH/ProgressLabeller.
