Improving Structure from Motion with Reliable Resectioning

Rajbir Kataria, Joseph Degol, Derek Hoiem

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

Abstract

A common cause of failure in structure-from-motion (SfM) is misregistration of images due to visual patterns that occur in more than one scene location. Most work to solve this problem ignores image matches that are inconsistent according to the statistics of the tracks graph, but these methods often need to be tuned for each dataset and can lead to reduced completeness of normally good reconstructions when valid matches are removed. Our key idea is to address ambiguity directly in the reconstruction process by using only a subset of reliable matches to determine resectioning order and the initial pose. We also introduce a new measure of similarity that adjusts the influence of feature matches based on their track length. We show this improves reconstruction robustness for two state-of-the-art SfM algorithms on many diverse datasets.

Original languageEnglish (US)
Title of host publicationProceedings - 2020 International Conference on 3D Vision, 3DV 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages41-50
Number of pages10
ISBN (Electronic)9781728181288
DOIs
StatePublished - Nov 2020
Event8th International Conference on 3D Vision, 3DV 2020 - Virtual, Fukuoka, Japan
Duration: Nov 25 2020Nov 28 2020

Publication series

NameProceedings - 2020 International Conference on 3D Vision, 3DV 2020

Conference

Conference8th International Conference on 3D Vision, 3DV 2020
CountryJapan
CityVirtual, Fukuoka
Period11/25/2011/28/20

Keywords

  • Geometry
  • SfM
  • Structure from motion

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition

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