3D object pose estimation using viewpoint generative learning

Dissaphong Thachasongtham, Takumi Yoshida, François De Sorbier, Hideo Saito

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationImage Analysis - 18th Scandinavian Conference, SCIA 2013, Proceedings
Number of pages10
Publication statusPublished - 2013 Sept 26
Event18th Scandinavian Conference on Image Analysis, SCIA 2013 - Espoo, Finland
Duration: 2013 Jun 172013 Jun 20

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7944 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other18th Scandinavian Conference on Image Analysis, SCIA 2013


  • generative learning
  • pose estimation
  • stable keypoint

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


Dive into the research topics of '3D object pose estimation using viewpoint generative learning'. Together they form a unique fingerprint.

Cite this