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

Authors :
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
Source :
Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence Demo Track (2024) 8661-8664
Publication Year :
2024

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 vlc-dataset@aisingapore.org.

Details

Database :
arXiv
Journal :
Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence Demo Track (2024) 8661-8664
Publication Type :
Report
Accession number :
edsarx.2407.18590
Document Type :
Working Paper
Full Text :
https://doi.org/10.24963/ijcai.2024/1003