From 2D to 3D: AISG-SLA Visual Localization Challenge
Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence Demo Track (2024) 8661-8664 Research in 3D mapping is crucial for smart city applications, yet the cost of acquiring 3D data often hinders progress. Visual localization, particularly monocular camera position e...
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| creator | Gao, Jialin Ong, Bill Lwi, Darld Ng, Zhen Hao Yee, Xun Wei Mak, Mun-Thye Ng, Wee Siong Ng, See-Kiong Teo, Hui Ying Khoo, Victor Bökman, Georg Edstedt, Johan Brodt, Kirill Boittiaux, Clémentin Ferrera, Maxime Konev, Stepan |
| description | Proceedings of the Thirty-Third International Joint Conference on
Artificial Intelligence Demo Track (2024) 8661-8664 Research in 3D mapping is crucial for smart city applications, yet the cost
of acquiring 3D data often hinders progress. Visual localization, particularly
monocular camera position estimation, offers a solution by determining the
camera's pose solely through visual cues. However, this task is challenging due
to limited data from a single camera. To tackle these challenges, we organized
the AISG-SLA Visual Localization Challenge (VLC) at IJCAI 2023 to explore how
AI can accurately extract camera pose data from 2D images in 3D space. The
challenge attracted over 300 participants worldwide, forming 50+ teams. Winning
teams achieved high accuracy in pose estimation using images from a car-mounted
camera with low frame rates. The VLC dataset is available for research purposes
upon request via vlc-dataset@aisingapore.org. |
| doi_str_mv | 10.48550/arxiv.2407.18590 |
| format | Article |
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Artificial Intelligence Demo Track (2024) 8661-8664 Research in 3D mapping is crucial for smart city applications, yet the cost
of acquiring 3D data often hinders progress. Visual localization, particularly
monocular camera position estimation, offers a solution by determining the
camera's pose solely through visual cues. However, this task is challenging due
to limited data from a single camera. To tackle these challenges, we organized
the AISG-SLA Visual Localization Challenge (VLC) at IJCAI 2023 to explore how
AI can accurately extract camera pose data from 2D images in 3D space. The
challenge attracted over 300 participants worldwide, forming 50+ teams. Winning
teams achieved high accuracy in pose estimation using images from a car-mounted
camera with low frame rates. The VLC dataset is available for research purposes
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Artificial Intelligence Demo Track (2024) 8661-8664 Research in 3D mapping is crucial for smart city applications, yet the cost
of acquiring 3D data often hinders progress. Visual localization, particularly
monocular camera position estimation, offers a solution by determining the
camera's pose solely through visual cues. However, this task is challenging due
to limited data from a single camera. To tackle these challenges, we organized
the AISG-SLA Visual Localization Challenge (VLC) at IJCAI 2023 to explore how
AI can accurately extract camera pose data from 2D images in 3D space. The
challenge attracted over 300 participants worldwide, forming 50+ teams. Winning
teams achieved high accuracy in pose estimation using images from a car-mounted
camera with low frame rates. The VLC dataset is available for research purposes
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Artificial Intelligence Demo Track (2024) 8661-8664 Research in 3D mapping is crucial for smart city applications, yet the cost
of acquiring 3D data often hinders progress. Visual localization, particularly
monocular camera position estimation, offers a solution by determining the
camera's pose solely through visual cues. However, this task is challenging due
to limited data from a single camera. To tackle these challenges, we organized
the AISG-SLA Visual Localization Challenge (VLC) at IJCAI 2023 to explore how
AI can accurately extract camera pose data from 2D images in 3D space. The
challenge attracted over 300 participants worldwide, forming 50+ teams. Winning
teams achieved high accuracy in pose estimation using images from a car-mounted
camera with low frame rates. The VLC dataset is available for research purposes
upon request via vlc-dataset@aisingapore.org.</abstract><doi>10.48550/arxiv.2407.18590</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Computer Vision and Pattern Recognition |
| title | From 2D to 3D: AISG-SLA Visual Localization Challenge |
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