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A SAM-based Solution for Hierarchical Panoptic Segmentation of Crops and Weeds Competition
Our best-performing model achieved a PQ+ score of 81. 33 based on the evaluation metrics of the competition.
Reinforcement Learning for Picking Cluttered General Objects with Dense Object Descriptors
We conduct experiments to demonstrate that our CODs is able to consistently represent seen and unseen cluttered objects, which allowed for the picking policy to robustly pick cluttered general objects.
Learning Sim-to-Real Dense Object Descriptors for Robotic Manipulation
In this paper, we present Sim-to-Real Dense Object Nets (SRDONs), a dense object descriptor that not only understands the object via appropriate representation but also maps simulated and real data to a unified feature space with pixel consistency.
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