From 2D to 3D: AISG-SLA Visual Localization Challenge
<|reference_start|>From 2D to 3D: AISG-SLA Visual Localization Challenge: 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.<|reference_end|>
@article{gao2024from,
    title={From 2D to 3D: AISG-SLA Visual Localization Challenge},
    author={Jialin Gao, Bill Ong, Darld Lwi, Zhen Hao Ng, Xun Wei Yee, Mun-Thye
  Mak, Wee Siong Ng, See-Kiong Ng, Hui Ying Teo, Victor Khoo, Georg B"okman,
  Johan Edstedt, Kirill Brodt, Cl'ementin Boittiaux, Maxime Ferrera, Stepan
  Konev},
    journal={Proceedings of the Thirty-Third International Joint Conference on
  Artificial Intelligence Demo Track (2024) 8661-8664},
    year={2024},
    doi={10.24963/ijcai.2024/1003},
    archivePrefix={arXiv},
    eprint={2407.18590},
    primaryClass={cs.CV}
}