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

2019/12/06 by Kleeberger, Kilian, Landgraf, Christian, Huber, Marco F. · 4 citations
#Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Robotics (cs.RO)

paper · doi:10.48550/arxiv.1912.12125

Abstract

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.

Cited by

• Geo6DPose: Fast Zero-Shot 6D Object Pose Estimation via Geometry-Filtered Feature Matching
• Object Pose Distribution Estimation for Determining Revolution and Reflection Uncertainty in Point Clouds
• Gen2Real: Towards Demo-Free Dexterous Manipulation by Harnessing Generated Video
• Quaternion Approximation Networks for Enhanced Image Classification and Oriented Object Detection

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