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Visual Completion Of 3D Object Shapes From A Single View For Robotic Tasks

Abstract : 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.
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https://hal.uca.fr/hal-03083304
Contributor : Juan Antonio Corrales Ramon <>
Submitted on : Saturday, December 19, 2020 - 12:06:49 AM
Last modification on : Wednesday, February 24, 2021 - 4:16:03 PM

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Mohamed Tahoun, Carlos Mateo, Juan Antonio Corrales Ramon, Omar Tahri, Youcef Mezouar, et al.. Visual Completion Of 3D Object Shapes From A Single View For Robotic Tasks. 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO), Dec 2019, Dali, China. pp.1777-1782, ⟨10.1109/ROBIO49542.2019.8961378⟩. ⟨hal-03083304⟩

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