ProgressLabeller: Visual Data Stream Annotation for Training Object-Centric 3D Perception

TL;DR

ProgressLabeller is a scalable, efficient tool for generating large 6D pose datasets from color images, supporting transparent objects, and improving robotic grasping performance through fine-tuning.

Contribution

It introduces ProgressLabeller, a novel method for rapid, scalable 6D pose data annotation from color images, including transparent objects, enhancing training efficiency.

Findings

Created over 1 million training samples rapidly.

Fine-tuning with ProgressLabeller improves robotic grasp success.

Supports transparent and translucent objects.

Abstract

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.