An Iterative Multiresolution Scheme for SFM with Missing Data

  • Authors:
  • Carme Julià;Angel D. Sappa;Felipe Lumbreras;Joan Serrat;Antonio López

  • Affiliations:
  • Intelligent Robotics and Computer Vision Group, Dept. of Computer Science and Mathematics, ETSE-URV, Tarragona, Spain 43007;Computer Vision Center, UAB Edifici O Campus UAB, Barcelona, Spain 08193;Computer Vision Center, Computer Science Department, UAB Edifici O Campus UAB, Barcelona, Spain 08193;Computer Vision Center, Computer Science Department, UAB Edifici O Campus UAB, Barcelona, Spain 08193;Computer Vision Center, Computer Science Department, UAB Edifici O Campus UAB, Barcelona, Spain 08193

  • Venue:
  • Journal of Mathematical Imaging and Vision
  • Year:
  • 2009

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Abstract

Several techniques have been proposed for tackling the Structure from Motion problem through factorization in the case of missing data. However, when the percentage of unknown data is high, most of them may not perform as well as expected. Focussing on this problem, an iterative multiresolution scheme, which aims at recovering missing entries in the originally given input matrix, is proposed. Information recovered following a coarse-to-fine strategy is used for filling in the missing entries. The objective is to recover, as much as possible, missing data in the given matrix. Thus, when a factorization technique is applied to the partially or totally filled in matrix, instead of to the originally given input one, better results will be obtained. An evaluation study about the robustness to missing and noisy data is reported. Experimental results obtained with synthetic and real video sequences are presented to show the viability of the proposed approach.