Weakly supervised 6D pose estimation for robotic grasping

VRCAI '18: International Conference on Virtual Reality Continuum and its Applications in Industry Tokyo Japan December, 2018, pp. 1-8, 2018.

Cited by: 0|Bibtex|Views21|DOI:https://doi.org/10.1145/3284398.3284408
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Other Links: dblp.uni-trier.de|academic.microsoft.com|dl.acm.org

Abstract:

Learning based robotic grasping methods achieve substantial progress with the development of the deep neural networks. However, the requirement of large-scale training data in the real world limits the application scopes of these methods. Given the 3D models of the target objects, we propose a new learning-based grasping approach built on...More

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