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 leveragi...
| Main Authors: | , , , |
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| Format: | Conference item |
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2011
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| _version_ | 1797055682991620096 |
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| author | Flint, A Murray, D Reid, I IEEE |
| author_facet | Flint, A Murray, D Reid, I IEEE |
| author_sort | Flint, A |
| collection | OXFORD |
| description | 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. © 2011 IEEE. |
| first_indexed | 2024-03-06T19:13:17Z |
| format | Conference item |
| id | oxford-uuid:17826ba5-023f-4ec1-9a8a-fc00193a0988 |
| institution | University of Oxford |
| last_indexed | 2024-03-06T19:13:17Z |
| publishDate | 2011 |
| record_format | dspace |
| spelling | oxford-uuid:17826ba5-023f-4ec1-9a8a-fc00193a09882022-03-26T10:37:36ZManhattan Scene Understanding Using Monocular, Stereo, and 3D FeaturesConference itemhttp://purl.org/coar/resource_type/c_5794uuid:17826ba5-023f-4ec1-9a8a-fc00193a0988Symplectic Elements at Oxford2011Flint, AMurray, DReid, IIEEEThis 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. © 2011 IEEE. |
| spellingShingle | Flint, A Murray, D Reid, I IEEE Manhattan Scene Understanding Using Monocular, Stereo, and 3D Features |
| title | Manhattan Scene Understanding Using Monocular, Stereo, and 3D Features |
| title_full | Manhattan Scene Understanding Using Monocular, Stereo, and 3D Features |
| title_fullStr | Manhattan Scene Understanding Using Monocular, Stereo, and 3D Features |
| title_full_unstemmed | Manhattan Scene Understanding Using Monocular, Stereo, and 3D Features |
| title_short | Manhattan Scene Understanding Using Monocular, Stereo, and 3D Features |
| title_sort | manhattan scene understanding using monocular stereo and 3d features |
| work_keys_str_mv | AT flinta manhattansceneunderstandingusingmonocularstereoand3dfeatures AT murrayd manhattansceneunderstandingusingmonocularstereoand3dfeatures AT reidi manhattansceneunderstandingusingmonocularstereoand3dfeatures AT ieee manhattansceneunderstandingusingmonocularstereoand3dfeatures |