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Found 3 papers, 2 papers with code
MatchU: Matching Unseen Objects for 6D Pose Estimation from RGB-D Images
MatchU is a generic approach that fuses 2D texture and 3D geometric cues for 6D pose prediction of unseen objects.
Robotic Pick-and-Place of Novel Objects in Clutter with Multi-Affordance Grasping and Cross-Domain Image Matching
Since product images are readily available for a wide range of objects (e. g., from the web), the system works out-of-the-box for novel objects without requiring any additional training data.
Multi-view Self-supervised Deep Learning for 6D Pose Estimation in the Amazon Picking Challenge
The approach was part of the MIT-Princeton Team system that took 3rd- and 4th- place in the stowing and picking tasks, respectively at APC 2016.
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