From 2D to 3D: AISG-SLA Visual Localization Challenge
Paper in proceeding, 2024

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 vlcdataset@aisingapore.org.

Author

Jialin Gao

AI Singapore

Bill Ong

AI Singapore

Darld Lwi

Singapore Land Authority

Zhen Hao Ng

Singapore Land Authority

Xun Wei Yee

AI Singapore

Mun Thye Mak

AI Singapore

Wee Siong Ng

Agency for Science, Technology and Research (A*STAR)

See Kiong Ng

National University of Singapore (NUS)

Hui Ying Teo

Singapore Land Authority

Victor Khoo

Singapore Land Authority

Georg Bökman

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering

Other publications Research

Johan Edstedt

Linköping University

Kirill Brodt

Université de Montréal

Clémentin Boittiaux

IFREMER Institut Francais de Recherche pour l'Exploitation de la Mer

Maxime Ferrera

IFREMER Institut Francais de Recherche pour l'Exploitation de la Mer

Stepan Konev

Booking.com

IJCAI International Joint Conference on Artificial Intelligence

10450823 (ISSN)

8661-8664
9781956792041 (ISBN)

33rd International Joint Conference on Artificial Intelligence, IJCAI 2024
Jeju, South Korea,

Subject Categories (SSIF 2025)

Robotics and automation

Computer graphics and computer vision

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Latest update

5/20/2025

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