Improving the performance of non-rigid 3D shape recovery by points classification

Junjie Hu, Terumasa Aoki

研究成果: Conference contribution

抄録

The goal of Non-Rigid Structure from Motion (NRSfM) is to recover 3D shapes of a deformable object from a monocular video sequence. Procrustean Normal Distribution (PND) is one of the best algorithms for NRSfM. It uses Generalized Procrustes Analysis (GPA) model to accomplish this task. But the biggest problem of this method is that just a few non-rigid points in 2D observations can largely affect the reconstruction performance. We believe that PND can achieve better reconstruction performance by eliminating the affection of these points. In this paper, we present a novel reconstruction method to solve this problem. We present two solutions to simply classify the points into non-rigid and nearly rigid points. After that, we use EM algorithm of PND to recover 3D structure again for nearly rigid points. Experimental results show that the proposed method outperforms the existing state-of-the-art algorithms.

本文言語English
ホスト出版物のタイトルProceedings of the 15th IAPR International Conference on Machine Vision Applications, MVA 2017
出版社Institute of Electrical and Electronics Engineers Inc.
ページ266-269
ページ数4
ISBN(電子版)9784901122160
DOI
出版ステータスPublished - 2017 7月 19
イベント15th IAPR International Conference on Machine Vision Applications, MVA 2017 - Nagoya, Japan
継続期間: 2017 5月 82017 5月 12

出版物シリーズ

名前Proceedings of the 15th IAPR International Conference on Machine Vision Applications, MVA 2017

Other

Other15th IAPR International Conference on Machine Vision Applications, MVA 2017
国/地域Japan
CityNagoya
Period17/5/817/5/12

ASJC Scopus subject areas

  • コンピュータ サイエンスの応用
  • コンピュータ ビジョンおよびパターン認識

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