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A unified framework for content-aware view selection and planning through view importance

Massimo Mauro, Hayko Riemenschneider, Luc Van Gool, Alberto Signoroni, Riccardo Leonardi
BMVC
2014

Abstract

In this paper we present new algorithms for Next-Best-View (NBV) planning and Image Selection (IS) aimed at image-based 3D reconstruction. In this context, NBV algorithms are needed to propose new unseen viewpoints to improve a partially reconstructed model, while IS algorithms are useful for selecting a subset of cameras from an unordered image collection before running an expensive dense reconstruction. Our methods are based on the idea of view importance: how important is a given viewpoint for a 3D reconstruction? We answer this by proposing a set of expressive quality features and formulate both problems as a search for views ranked by importance. Our methods are automatic and work directly on sparse Structure-from-Motion output. We can remove up to 90\% of the images and demonstrate improved speed at comparable reconstruction quality when compared with state of the art on multiple datasets.


Link to publisher's page
@InProceedings{eth_biwi_01131,
  author = {Massimo Mauro and Hayko Riemenschneider and Luc Van Gool and Alberto Signoroni and Riccardo Leonardi},
  title = {A unified framework for content-aware view selection and planning through view importance},
  booktitle = {BMVC},
  year = {2014},
  keywords = {}
}