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Neural Computing and Applications

, Volume 28, Issue 5, pp 941–952 | Cite as

Multi-sensor 3D object dataset for object recognition with full pose estimation

  • Alberto Garcia-Garcia
  • Sergio Orts-Escolano
  • Sergiu Oprea
  • Jose Garcia-Rodriguez
  • Jorge Azorin-Lopez
  • Marcelo Saval-Calvo
  • Miguel Cazorla
Computational Intelligence for Vision and Robotics

Abstract

In this work, we propose a new dataset for 3D object recognition using the new high-resolution Kinect V2 sensor and some other popular low-cost devices like PrimeSense Carmine. Since most already existing datasets for 3D object recognition lack some features such as 3D pose information about objects in the scene, per pixel segmentation or level of occlusion, we propose a new one combining all this information in a single dataset that can be used to validate existing and new 3D object recognition algorithms. Moreover, with the advent of the new Kinect V2 sensor we are able to provide high-resolution data for RGB and depth information using a single sensor, whereas other datasets had to combine multiple sensors. In addition, we will also provide semiautomatic segmentation and semantic labels about the different parts of the objects so that the dataset could be used for testing robot grasping and scene labeling systems as well as for object recognition.

Keywords

3D computer vision Object recognition 3D object dataset Kinect V2 PrimeSense Carmine 

Notes

Acknowledgments

This work was partially funded by the Spanish Government DPI2013-40534-R Grant. This work has also been funded by the grant “Ayudas para Estudios de Mster e Iniciacin a la Investigacin” from the University of Alicante.

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Copyright information

© The Natural Computing Applications Forum 2016

Authors and Affiliations

  1. 1.Computer Technology DepartmentUniversity of AlicanteAlicanteSpain
  2. 2.Computer Science and Artificial Intelligence DepartmentUniversity of AlicanteAlicanteSpain

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