Efficient Propagation Method for Angularly Consistent 4D Light Field Disparity Maps
Light Field (LF) imaging, since it conveys both spatial and angular scene information, can facilitate computer vision tasks such as depth/disparity estimation. Although disparity maps can be estimated for all LF views, most existing methods merely estimate depth/disparity for the central view and do...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10156827/ |
| _version_ | 1797792190392434688 |
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| author | Maryam Hamad Caroline Conti Paulo Nunes Luis Ducla Soares |
| author_facet | Maryam Hamad Caroline Conti Paulo Nunes Luis Ducla Soares |
| author_sort | Maryam Hamad |
| collection | DOAJ |
| description | Light Field (LF) imaging, since it conveys both spatial and angular scene information, can facilitate computer vision tasks such as depth/disparity estimation. Although disparity maps can be estimated for all LF views, most existing methods merely estimate depth/disparity for the central view and do not adequately deal with other LF views. However, having depth/disparity maps for all LF views can be useful for enhancing immersive multimedia applications, such as 3D reconstruction and LF editing. To overcome this limitation, in this paper, an efficient and occlusion-aware disparity propagation method is proposed. The proposed method generates disparity maps for all LF views given a single disparity map for one reference view (e.g., the central view). The disparity map for the reference view is propagated first into the four corner views to ensure angular consistency. Afterwards, an off-the-shelf existing disparity estimation model is used to fill any remaining holes in the corner views. Finally, disparity maps for the remaining views are recursively generated through a fast propagation step, which is followed by a final refinement step to regularize the generated disparity maps. The proposed method not only generates disparity maps for all LF views but also handles occlusions and ensures angular consistency. Experimental results on synthetic and real LF datasets with different disparity ranges, using several accuracy and angular consistency metrics, show outperforming or competitive results compared to the benchmark methods with a significant complexity reduction. |
| first_indexed | 2024-03-13T02:29:38Z |
| format | Article |
| id | doaj.art-e6acb19451a74fd4959464a3db13c432 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-13T02:29:38Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-e6acb19451a74fd4959464a3db13c4322023-06-29T23:00:38ZengIEEEIEEE Access2169-35362023-01-0111634636347410.1109/ACCESS.2023.328792010156827Efficient Propagation Method for Angularly Consistent 4D Light Field Disparity MapsMaryam Hamad0https://orcid.org/0000-0003-2952-9680Caroline Conti1https://orcid.org/0000-0002-9197-2627Paulo Nunes2https://orcid.org/0000-0003-3982-5723Luis Ducla Soares3https://orcid.org/0000-0001-9738-639XInstituto de Telecomunicações, Instituto Universitário de Lisboa (ISCTE-IUL), Lisbon, PortugalInstituto de Telecomunicações, Instituto Universitário de Lisboa (ISCTE-IUL), Lisbon, PortugalInstituto de Telecomunicações, Instituto Universitário de Lisboa (ISCTE-IUL), Lisbon, PortugalInstituto de Telecomunicações, Instituto Universitário de Lisboa (ISCTE-IUL), Lisbon, PortugalLight Field (LF) imaging, since it conveys both spatial and angular scene information, can facilitate computer vision tasks such as depth/disparity estimation. Although disparity maps can be estimated for all LF views, most existing methods merely estimate depth/disparity for the central view and do not adequately deal with other LF views. However, having depth/disparity maps for all LF views can be useful for enhancing immersive multimedia applications, such as 3D reconstruction and LF editing. To overcome this limitation, in this paper, an efficient and occlusion-aware disparity propagation method is proposed. The proposed method generates disparity maps for all LF views given a single disparity map for one reference view (e.g., the central view). The disparity map for the reference view is propagated first into the four corner views to ensure angular consistency. Afterwards, an off-the-shelf existing disparity estimation model is used to fill any remaining holes in the corner views. Finally, disparity maps for the remaining views are recursively generated through a fast propagation step, which is followed by a final refinement step to regularize the generated disparity maps. The proposed method not only generates disparity maps for all LF views but also handles occlusions and ensures angular consistency. Experimental results on synthetic and real LF datasets with different disparity ranges, using several accuracy and angular consistency metrics, show outperforming or competitive results compared to the benchmark methods with a significant complexity reduction.https://ieeexplore.ieee.org/document/10156827/Light field disparity estimationangular consistencyfast disparity propagationdeep learning |
| spellingShingle | Maryam Hamad Caroline Conti Paulo Nunes Luis Ducla Soares Efficient Propagation Method for Angularly Consistent 4D Light Field Disparity Maps IEEE Access Light field disparity estimation angular consistency fast disparity propagation deep learning |
| title | Efficient Propagation Method for Angularly Consistent 4D Light Field Disparity Maps |
| title_full | Efficient Propagation Method for Angularly Consistent 4D Light Field Disparity Maps |
| title_fullStr | Efficient Propagation Method for Angularly Consistent 4D Light Field Disparity Maps |
| title_full_unstemmed | Efficient Propagation Method for Angularly Consistent 4D Light Field Disparity Maps |
| title_short | Efficient Propagation Method for Angularly Consistent 4D Light Field Disparity Maps |
| title_sort | efficient propagation method for angularly consistent 4d light field disparity maps |
| topic | Light field disparity estimation angular consistency fast disparity propagation deep learning |
| url | https://ieeexplore.ieee.org/document/10156827/ |
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