Urban 3D semantic modelling using stereo vision
In this paper we propose a robust algorithm that generates an efficient and accurate dense 3D reconstruction with associated semantic labellings. Intelligent autonomous systems require accurate 3D reconstructions for applications such as navigation and localisation. Such systems also need to recogni...
| Main Authors: | , , , |
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| Format: | Conference item |
| Language: | English |
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IEEE
2013
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| _version_ | 1826314875407695872 |
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| author | Sengupta, S Greveson, E Shahrokni, A Torr, PHS |
| author_facet | Sengupta, S Greveson, E Shahrokni, A Torr, PHS |
| author_sort | Sengupta, S |
| collection | OXFORD |
| description | In this paper we propose a robust algorithm that generates an efficient and accurate dense 3D reconstruction with associated semantic labellings. Intelligent autonomous systems require accurate 3D reconstructions for applications such as navigation and localisation. Such systems also need to recognise their surroundings in order to identify and interact with objects of interest. Considerable emphasis has been given to generating a good reconstruction but less effort has gone into generating a 3D semantic model. The inputs to our algorithm are street level stereo image pairs acquired from a camera mounted on a moving vehicle. The depth-maps, generated from the stereo pairs across time, are fused into a global 3D volume online in order to accommodate arbitrary long image sequences. The street level images are automatically labelled using a Conditional Random Field (CRF) framework exploiting stereo images, and label estimates are aggregated to annotate the 3D volume. We evaluate our approach on the KITTI odometry dataset and have manually generated ground truth for object class segmentation. Our qualitative evaluation is performed on various sequences of the dataset and we also quantify our results on a representative subset. |
| first_indexed | 2024-12-09T03:12:48Z |
| format | Conference item |
| id | oxford-uuid:f203dff9-8db3-4766-a872-46ee51c87ff8 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-12-09T03:12:48Z |
| publishDate | 2013 |
| publisher | IEEE |
| record_format | dspace |
| spelling | oxford-uuid:f203dff9-8db3-4766-a872-46ee51c87ff82024-10-14T14:57:42ZUrban 3D semantic modelling using stereo visionConference itemhttp://purl.org/coar/resource_type/c_5794uuid:f203dff9-8db3-4766-a872-46ee51c87ff8EnglishSymplectic ElementsIEEE2013Sengupta, SGreveson, EShahrokni, ATorr, PHSIn this paper we propose a robust algorithm that generates an efficient and accurate dense 3D reconstruction with associated semantic labellings. Intelligent autonomous systems require accurate 3D reconstructions for applications such as navigation and localisation. Such systems also need to recognise their surroundings in order to identify and interact with objects of interest. Considerable emphasis has been given to generating a good reconstruction but less effort has gone into generating a 3D semantic model. The inputs to our algorithm are street level stereo image pairs acquired from a camera mounted on a moving vehicle. The depth-maps, generated from the stereo pairs across time, are fused into a global 3D volume online in order to accommodate arbitrary long image sequences. The street level images are automatically labelled using a Conditional Random Field (CRF) framework exploiting stereo images, and label estimates are aggregated to annotate the 3D volume. We evaluate our approach on the KITTI odometry dataset and have manually generated ground truth for object class segmentation. Our qualitative evaluation is performed on various sequences of the dataset and we also quantify our results on a representative subset. |
| spellingShingle | Sengupta, S Greveson, E Shahrokni, A Torr, PHS Urban 3D semantic modelling using stereo vision |
| title | Urban 3D semantic modelling using stereo vision |
| title_full | Urban 3D semantic modelling using stereo vision |
| title_fullStr | Urban 3D semantic modelling using stereo vision |
| title_full_unstemmed | Urban 3D semantic modelling using stereo vision |
| title_short | Urban 3D semantic modelling using stereo vision |
| title_sort | urban 3d semantic modelling using stereo vision |
| work_keys_str_mv | AT senguptas urban3dsemanticmodellingusingstereovision AT grevesone urban3dsemanticmodellingusingstereovision AT shahroknia urban3dsemanticmodellingusingstereovision AT torrphs urban3dsemanticmodellingusingstereovision |