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ICCV
2011
IEEE

Manhattan Scene Understanding Using Monocular, Stereo, and 3D Features

8 years 5 months ago
Manhattan Scene Understanding Using Monocular, Stereo, and 3D Features
This paper addresses scene understanding in the context of a moving camera, integrating semantic reasoning ideas from monocular vision with 3D information available through structure–from–motion. We combine geometric and photometric cues in a Bayesian framework, building on recent successes leveraging the indoor Manhattan assumption in monocular vision. We focus on indoor environments and show how to extract key boundaries while ignoring clutter and decorations. To achieve this we present a graphical model that relates photometric cues learned from labeled data, stereo photo–consistency across multiple views, and depth cues derived from structure–from–motion point clouds. We show how to solve MAP inference using dynamic programming, allowing exact, global inference in ∼100 ms (in addition to feature computation of under one second) without using specialized hardware. Experiments show our system out–performing the state–of–the–art.
Alex Flint, David Murray, Ian Reid
Added 12 Dec 2011
Updated 12 Dec 2011
Type Journal
Year 2011
Where ICCV
Authors Alex Flint, David Murray, Ian Reid
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