A unified framework for content-aware view selection and planning through view importance
In Proceedings British Machine Vision Conference 2014
http://dx.doi.org/10.5244/C.28.69
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 recon- structed 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.
Session
Poster Session
Files
Extended Abstract (PDF, 1 page, 2.2M)Paper (PDF, 11 pages, 4.5M)
Bibtex File
Citation
Massimo Mauro, Hayko Riemenschneider, Alberto Signoroni, Riccardo Leonardi and Luc Van Gool. A unified framework for content-aware view selection and planning through view importance. Proceedings of the British Machine Vision Conference. BMVA Press, September 2014.
BibTex
@inproceedings{BMVC.28.69
title = {A unified framework for content-aware view selection and planning through view importance},
author = {Mauro, Massimo and Riemenschneider, Hayko and Signoroni, Alberto and Leonardi, Riccardo and Van Gool, Luc},
year = {2014},
booktitle = {Proceedings of the British Machine Vision Conference},
publisher = {BMVA Press},
editors = {Valstar, Michel and French, Andrew and Pridmore, Tony}
doi = { http://dx.doi.org/10.5244/C.28.69 }
}