ObjectNet3D: A Large Scale Database for 3D Object Recognition

  • Yu Xiang
  • Wonhui Kim
  • Wei Chen
  • Jingwei Ji
  • Christopher Choy
  • Hao Su
  • Roozbeh Mottaghi
  • Leonidas Guibas
  • Silvio Savarese
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9912)

Abstract

We contribute a large scale database for 3D object recognition, named ObjectNet3D, that consists of 100 categories, 90,127 images, 201,888 objects in these images and 44,147 3D shapes. Objects in the 2D images in our database are aligned with the 3D shapes, and the alignment provides both accurate 3D pose annotation and the closest 3D shape annotation for each 2D object. Consequently, our database is useful for recognizing the 3D pose and 3D shape of objects from 2D images. We also provide baseline experiments on four tasks: region proposal generation, 2D object detection, joint 2D detection and 3D object pose estimation, and image-based 3D shape retrieval, which can serve as baselines for future research using our database. Our database is available online at http://cvgl.stanford.edu/projects/objectnet3d.

Keywords

Database construction 3D object recognition 

Supplementary material

419983_1_En_10_MOESM1_ESM.mp4 (5.2 mb)
Supplementary material 1 (mp4 5337 KB)
419983_1_En_10_MOESM2_ESM.pdf (8.3 mb)
Supplementary material 2 (pdf 8489 KB)

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Yu Xiang
    • 1
  • Wonhui Kim
    • 1
  • Wei Chen
    • 1
  • Jingwei Ji
    • 1
  • Christopher Choy
    • 1
  • Hao Su
    • 1
  • Roozbeh Mottaghi
    • 1
  • Leonidas Guibas
    • 1
  • Silvio Savarese
    • 1
  1. 1.Stanford UniversityStanfordUSA

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