Abstract
A number of recent papers have investigated reconstruction under Manhattan world assumption, in which surfaces in the world are assumed to be aligned with one of three dominant directions [1,2,3,4]. In this paper we present a dynamic programming solution to the reconstruction problem for "indoor" Manhattan worlds (a sub-class of Manhattan worlds). Our algorithm deterministically finds the global optimum and exhibits computational complexity linear in both model complexity and image size. This is an important improvement over previous methods that were either approximate [3] or exponential in model complexity [4]. We present results for a new dataset containing several hundred manually annotated images, which are released in conjunction with this paper. © 2010 Springer-Verlag.
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CITATION STYLE
Flint, A., Mei, C., Murray, D., & Reid, I. (2010). A dynamic programming approach to reconstructing building interiors. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6315 LNCS, pp. 394–407). Springer Verlag. https://doi.org/10.1007/978-3-642-15555-0_29
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