From 2D to 3D: AISG-SLA Visual Localization Challenge
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Abstract (arXiv)
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].
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 11 of its 21 references that are in the map. First posted on arXiv 26 Jul 2024.
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References · 11
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- 2020TartanAir: A Dataset to Push the Limits of Visual SLAMcited by 506
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- 2023DeDoDe: Detect, Don't Describe -- Describe, Don't Detect for Local Feature Matchingcited by 63
- 2019Direct Sparse Mappingcited by 25
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