dc.contributor.author | Mendes, O | |
dc.contributor.author | Hadfield, S | |
dc.contributor.author | Pugeault, N | |
dc.contributor.author | Bowden, R | |
dc.date.accessioned | 2016-08-31T09:58:12Z | |
dc.date.issued | 2016-09 | |
dc.description.abstract | Most 3D reconstruction approaches passively optimise over all data, exhaustively
matching pairs, rather than actively selecting data to process. This is costly both in terms
of time and computer resources, and quickly becomes intractable for large datasets.
This work proposes an approach to intelligently filter large amounts of data for 3D
reconstructions of unknown scenes using monocular cameras. Our contributions are twofold:
First, we present a novel approach to efficiently optimise the Next-Best View (NBV)
in terms of accuracy and coverage using partial scene geometry. Second, we extend this to
intelligently selecting stereo pairs by jointly optimising the baseline and vergence to find
the NBV’s best stereo pair to perform reconstruction. Both contributions are extremely
efficient, taking 0.8ms and 0.3ms per pose, respectively.
Experimental evaluation shows that the proposed method allows efficient selection
of stereo pairs for reconstruction, such that a dense model can be obtained with only
a small number of images. Once a complete model has been obtained, the remaining
computational budget is used to intelligently refine areas of uncertainty, achieving results
comparable to state-of-the-art batch approaches on the Middlebury dataset, using as little
as 3.8% of the views. | en_GB |
dc.description.sponsorship | The presentation of this paper was made possible
by the BMVC 2016 student bursary | en_GB |
dc.identifier.citation | British Machine Vision Conference, 19 - 22 September 2016, York, UK | en_GB |
dc.identifier.doi | 10.5244/C.30.65 | |
dc.identifier.uri | http://hdl.handle.net/10871/23230 | |
dc.language.iso | en | en_GB |
dc.publisher | British Machine Vision Association | en_GB |
dc.rights | © 2016. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms. | en_GB |
dc.subject | Next best view | en_GB |
dc.subject | 3D reconstruction | en_GB |
dc.subject | UAV | en_GB |
dc.subject | Autonomous systems | en_GB |
dc.title | Next-Best Stereo: Extending Next-Best View Optimisation For Collaborative Sensors | en_GB |
dc.type | Article | en_GB |
dc.description | This is the final version of the article. Available from the publisher via the DOI in this record. | |
dc.identifier.journal | Proceedings of the British Machine Vision Conference | en_GB |