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

Junjie Hu, Terumasa Aoki

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

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the 15th IAPR International Conference on Machine Vision Applications, MVA 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages266-269
Number of pages4
ISBN (Electronic)9784901122160
DOIs
Publication statusPublished - 2017 Jul 19
Event15th IAPR International Conference on Machine Vision Applications, MVA 2017 - Nagoya, Japan
Duration: 2017 May 82017 May 12

Publication series

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

Other

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

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Vision and Pattern Recognition

Fingerprint Dive into the research topics of 'Improving the performance of non-rigid 3D shape recovery by points classification'. Together they form a unique fingerprint.

Cite this