SegICP: Integrated deep semantic segmentation and pose estimation

@article{Wong2017SegICPID,
  title={SegICP: Integrated deep semantic segmentation and pose estimation},
  author={Jay Ming Wong and Vincent Kee and Tiffany Le and Syler Wagner and Gian Luca Mariottini and Abraham Schneider and Lei Hamilton and Rahul Chipalkatty and Mitchell Hebert and David M. S. Johnson and Jimmy Wu and Bolei Zhou and Antonio Torralba},
  journal={2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  year={2017},
  pages={5784-5789}
}
Recent robotic manipulation competitions have highlighted that sophisticated robots still struggle to achieve fast and reliable perception of task-relevant objects in complex, realistic scenarios. To improve these systems' perceptive speed and robustness, we present SegICP, a novel integrated solution to object recognition and pose estimation. SegICP couples convolutional neural networks and multi-hypothesis point cloud registration to achieve both robust pixel-wise semantic segmentation as… CONTINUE READING
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