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Communication Dans Un Congrès Année : 2019

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.
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hal-03083304 , version 1 (19-12-2020)

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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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