Large-scale 6D Object Pose Estimation Dataset for Industrial Bin-Picking

Autor:	Kleeberger, Kilian, Landgraf, Christian, Huber, Marco F.
Rok vydání:	2019
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Artificial Intelligence Computer Science - Robotics
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted at 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2019)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1912.12125 Zobrazit plný text záznamu View this record from Arxiv