ProgressLabeller: Visual Data Stream Annotation for Training Object-Centric 3D Perception
This addresses the time-intensive data annotation bottleneck for researchers and practitioners in robotics and computer vision, though it is incremental as it builds on existing pose estimation methods.
The paper tackles the problem of efficiently generating large-scale labeled 3D pose data for training object perception models, particularly for custom scenes and transparent objects, and demonstrates that using ProgressLabeller to create over 1M samples improves robotic grasp success rates.
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.