3D Object Pose Estimation Using Viewpoint Generative Learning

  • Dissaphong Thachasongtham
  • Takumi Yoshida
  • François de Sorbier
  • Hideo Saito
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7944)


Conventional local features such as SIFT or SURF are robust to scale and rotation changes but sensitive to large perspective change. Because perspective change always occurs when 3D object moves, using these features to estimate the pose of a 3D object is a challenging task. In this paper, we extend one of our previous works on viewpoint generative learning to 3D objects. Given a model of a textured object, we virtually generate several patterns of the model from different viewpoints and select stable keypoints from those patterns. Then our system learns a collection of feature descriptors from the stable keypoints. Finally, we are able to estimate the pose of a 3D object by using these robust features. In our experimental results, we demonstrate that our system is robust against large viewpoint change and even under partial occlusion.


pose estimation generative learning stable keypoint 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Dissaphong Thachasongtham
    • 1
  • Takumi Yoshida
    • 1
  • François de Sorbier
    • 1
  • Hideo Saito
    • 1
  1. 1.Graduate School of Science and TechnologyKeio University YokohamaJapan

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