3D object pose estimation using viewpoint generative learning

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

研究成果: Conference contribution

11 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトルImage Analysis - 18th Scandinavian Conference, SCIA 2013, Proceedings
ページ512-521
ページ数10
DOI
出版ステータスPublished - 2013 9 26
イベント18th Scandinavian Conference on Image Analysis, SCIA 2013 - Espoo, Finland
継続期間: 2013 6 172013 6 20

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
7944 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Other

Other18th Scandinavian Conference on Image Analysis, SCIA 2013
国/地域Finland
CityEspoo
Period13/6/1713/6/20

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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