Jungdae Lee — accepted AI-conference papers

2025

CityNav: A Large-Scale Dataset for Real-World Aerial Navigation

Jungdae Lee, Taiki Miyanishi, Shuhei Kurita, Koya Sakamoto, Daichi Azuma, Yutaka Matsuo +1

ICCV 2025poster

Vision-and-language navigation (VLN) aims to develop agents capable of navigating in realistic environments. While recent cross-modal training approaches have significantly improved navigation performance in both indoor and outdoor scenarios, aerial navigation over real-world cities remains underexp…

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2023

CityRefer: Geography-aware 3D Visual Grounding Dataset on City-scale Point Cloud Data

Taiki Miyanishi, Fumiya Kitamori, Shuhei Kurita, Jungdae Lee, Motoaki Kawanabe, Nakamasa Inoue

NeurIPS 2023poster

City-scale 3D point cloud is a promising way to express detailed and complicated outdoor structures. It encompasses both the appearance and geometry features of segmented city components, including cars, streets, and buildings that can be utilized for attractive applications such as user-interactive…

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