OmniOcc: Cylindrical Voxel-Based Semantic Occupancy Prediction for Omnidirectional Vision Systems
Accurate 3D perception is essential for autonomous driving. Traditional methods often struggle with geometric ambiguity due to a lack of geometric prior. To address these challenges, we use omnidirectional depth estimation to introduce geometric prior. Based on the depth information, we propose a Sk...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11113275/ |
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| author | Chaofan Wu Jiaheng Li Jinghao Cao Ming Li Sidan Du Yang Li |
| author_facet | Chaofan Wu Jiaheng Li Jinghao Cao Ming Li Sidan Du Yang Li |
| author_sort | Chaofan Wu |
| collection | DOAJ |
| description | Accurate 3D perception is essential for autonomous driving. Traditional methods often struggle with geometric ambiguity due to a lack of geometric prior. To address these challenges, we use omnidirectional depth estimation to introduce geometric prior. Based on the depth information, we propose a Sketch-Coloring framework OmniOcc. Additionally, our approach introduces a cylindrical voxel representation based on polar coordinate to better align with the radial nature of panoramic camera views. To address the lack of fisheye camera dataset in autonomous driving tasks, we also build a virtual scene dataset with six fisheye cameras. Experimental results demonstrate that our Sketch-Coloring network significantly enhances 3D perception performance. |
| format | Article |
| id | doaj-art-79e80d2dff6344bfb13b3628ae9d9329 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-79e80d2dff6344bfb13b3628ae9d93292025-08-20T03:41:40ZengIEEEIEEE Access2169-35362025-01-011313994413995210.1109/ACCESS.2025.359589811113275OmniOcc: Cylindrical Voxel-Based Semantic Occupancy Prediction for Omnidirectional Vision SystemsChaofan Wu0https://orcid.org/0009-0004-7215-329XJiaheng Li1Jinghao Cao2https://orcid.org/0000-0002-9408-4553Ming Li3https://orcid.org/0000-0002-1341-5585Sidan Du4https://orcid.org/0000-0002-9966-3765Yang Li5https://orcid.org/0000-0001-6769-4076School of Electronic Science and Engineering, Nanjing University, Nanjing, ChinaSchool of Electronic Science and Engineering, Nanjing University, Nanjing, ChinaSchool of Electronic Science and Engineering, Nanjing University, Nanjing, ChinaSchool of Electronic Science and Engineering, Nanjing University, Nanjing, ChinaSchool of Electronic Science and Engineering, Nanjing University, Nanjing, ChinaSchool of Electronic Science and Engineering, Nanjing University, Nanjing, ChinaAccurate 3D perception is essential for autonomous driving. Traditional methods often struggle with geometric ambiguity due to a lack of geometric prior. To address these challenges, we use omnidirectional depth estimation to introduce geometric prior. Based on the depth information, we propose a Sketch-Coloring framework OmniOcc. Additionally, our approach introduces a cylindrical voxel representation based on polar coordinate to better align with the radial nature of panoramic camera views. To address the lack of fisheye camera dataset in autonomous driving tasks, we also build a virtual scene dataset with six fisheye cameras. Experimental results demonstrate that our Sketch-Coloring network significantly enhances 3D perception performance.https://ieeexplore.ieee.org/document/11113275/Omnidirectional depthcylindrical voxel representationfisheye cameraoccupancy |
| spellingShingle | Chaofan Wu Jiaheng Li Jinghao Cao Ming Li Sidan Du Yang Li OmniOcc: Cylindrical Voxel-Based Semantic Occupancy Prediction for Omnidirectional Vision Systems IEEE Access Omnidirectional depth cylindrical voxel representation fisheye camera occupancy |
| title | OmniOcc: Cylindrical Voxel-Based Semantic Occupancy Prediction for Omnidirectional Vision Systems |
| title_full | OmniOcc: Cylindrical Voxel-Based Semantic Occupancy Prediction for Omnidirectional Vision Systems |
| title_fullStr | OmniOcc: Cylindrical Voxel-Based Semantic Occupancy Prediction for Omnidirectional Vision Systems |
| title_full_unstemmed | OmniOcc: Cylindrical Voxel-Based Semantic Occupancy Prediction for Omnidirectional Vision Systems |
| title_short | OmniOcc: Cylindrical Voxel-Based Semantic Occupancy Prediction for Omnidirectional Vision Systems |
| title_sort | omniocc cylindrical voxel based semantic occupancy prediction for omnidirectional vision systems |
| topic | Omnidirectional depth cylindrical voxel representation fisheye camera occupancy |
| url | https://ieeexplore.ieee.org/document/11113275/ |
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