3D Shape Estimation with an Enhanced Sparse Representation Approach

Jia Xiang Wang, Zhan Li Sun, Zhi Gang Zeng, Kin Man Lam

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

In this paper, an enhanced sparse representation approach is proposed to estimate the 3D shapes of objects in 2D image sequences. In the proposed method, the unknown 3D shape is estimated via a two-stage scheme, namely the main 3D shape estimation stage and the compensatory 3D shape estimation stage. Moreover, a reweighted sparse representation model is constructed to extract the shape bases for each estimation stage. In the sparse model, a reweighted constraint is enforced to enhance the coefficient sparsity of the shape bases. Experimental results on the well-known CMU image sequences demonstrate the effectiveness and feasibility of the proposed approach.

Original languageEnglish
Article number9511830
Pages (from-to)1685-1688
Number of pages4
JournalIEEE Signal Processing Letters
Volume28
DOIs
Publication statusPublished - Aug 2021

Keywords

  • 3D reconstruction
  • Non-rigid structure from motion
  • sparse representation model

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Applied Mathematics

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