• Computer Science
  • Published in BMVC 2018

Multi-Task Deep Networks for Depth-Based 6D Object Pose and Joint Registration in Crowd Scenarios

@article{Sock2018MultiTaskDN,
  title={Multi-Task Deep Networks for Depth-Based 6D Object Pose and Joint Registration in Crowd Scenarios},
  author={Juil Sock and Kwang In Kim and Caner Sahin and Tae-Kyun Kim},
  journal={ArXiv},
  year={2018},
  volume={abs/1806.03891}
}
In bin-picking scenarios, multiple instances of an object of interest are stacked in a pile randomly, and hence, the instances are inherently subjected to the challenges: severe occlusion, clutter, and similar-looking distractors. Most existing methods are, however, for single isolated object instances, while some recent methods tackle crowd scenarios as post-refinement which accounts multiple object relations. In this paper, we address recovering 6D poses of multiple instances in bin-picking… CONTINUE READING
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