Visual Completion Of 3D Object Shapes From A Single View For Robotic Tasks
Résumé
The goal of this paper is to predict 3D object shape to improve the visual perception of robots in grasping and manipulation tasks. The planning of image-based robotic manipulation tasks depends on the recognition of the object's shape. Mostly, the manipulator robots usually use a camera with configuration eye-in-hand. This fact limits the calculation of the grip on the visible part of the object. In this paper, we present a 3D Deep Convolutional Neural Network to predict the hidden parts of objects from a single-view and to accomplish recovering the complete shape of them. We have tested our proposal with both previously seen objects and novel objects from a well-known dataset.
Mots clés
convolutional neural nets
learning (artificial intelligence)
manipulators
object recognition
robot vision
shape recognition
stereo image processing
manipulator robots
configuration eye-in-hand
3D Deep Convolutional Neural Network
single-view
visual completion
image-based robotic manipulation tasks
3D object shape recognition
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