From 2D to 3D: AISG-SLA Visual Localization Challenge

Jialin Gao, Bill Ong, Darld Lwi, Zhen Hao Ng, Xun Wei Yee +11 more
7/26/2024
cs.CV

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Abstract

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 [email protected].

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Cite this paper

@article{gao2024from,
  title  = {From 2D to 3D: AISG-SLA Visual Localization Challenge},
  author = {Jialin Gao and Bill Ong and Darld Lwi and Zhen Hao Ng and Xun Wei Yee and Mun-Thye Mak and Wee Siong Ng and See-Kiong Ng and Hui Ying Teo and Victor Khoo and Georg Bökman and Johan Edstedt and Kirill Brodt and Clémentin Boittiaux and Maxime Ferrera and Stepan Konev},
  year   = {2024},
  eprint = {2407.18590},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2407.18590v1}
}

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