Large-scale 6D Object Pose Estimation Dataset for Industrial Bin-Picking
This dataset addresses the lack of annotated data for learning-based approaches in industrial bin-picking, enabling better robotic manipulation.
The authors introduced a new public dataset for 6D object pose estimation and instance segmentation in industrial bin-picking, comprising synthetic and real-world scenes with annotations like poses and masks, and it is one of the largest such datasets available.
In this paper, we introduce a new public dataset for 6D object pose estimation and instance segmentation for industrial bin-picking. The dataset comprises both synthetic and real-world scenes. For both, point clouds, depth images, and annotations comprising the 6D pose (position and orientation), a visibility score, and a segmentation mask for each object are provided. Along with the raw data, a method for precisely annotating real-world scenes is proposed. To the best of our knowledge, this is the first public dataset for 6D object pose estimation and instance segmentation for bin-picking containing sufficiently annotated data for learning-based approaches. Furthermore, it is one of the largest public datasets for object pose estimation in general. The dataset is publicly available at http://www.bin-picking.ai/en/dataset.html.