Shape from photographs: a multi-view stereo pipeline

Carlos Hernández*, George Vogiatzis

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Acquiring 3D shape from images is a classic problem in Computer Vision occupying researchers for at least 20 years. Only recently however have these ideas matured enough to provide highly accurate results. We present a complete algorithm to reconstruct 3D objects from images using the stereo correspondence cue. The technique can be described as a pipeline of four basic building blocks: camera calibration, image segmentation, photo-consistency estimation from images, and surface extraction from photo-consistency. In this Chapter we will put more emphasis on the latter two: namely how to extract geometric information from a set of photographs without explicit camera visibility, and how to combine different geometry estimates in an optimal way.

Original languageEnglish
Title of host publicationComputer vision
Subtitle of host publicationdetection, recognition and reconstruction
EditorsRoberto Cipolla, Sebastiano Battiato, Giovanni Maria Farinella
Place of PublicationBerlin (US)
PublisherSpringer
Pages281-311
Number of pages31
ISBN (Electronic)978-3-642-12848-6
ISBN (Print)978-3-642-12847-9
DOIs
Publication statusPublished - 2010

Publication series

NameStudies in Computational Intelligence
PublisherSpringer
Volume285
ISSN (Print)1860-949X

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Hernández, C., & Vogiatzis, G. (2010). Shape from photographs: a multi-view stereo pipeline. In R. Cipolla, S. Battiato, & G. M. Farinella (Eds.), Computer vision: detection, recognition and reconstruction (pp. 281-311). (Studies in Computational Intelligence; Vol. 285). Berlin (US): Springer. https://doi.org/10.1007/978-3-642-12848-6_11