@inproceedings{23e5b3e68630459cbf4ac640e773beb0,
title = "TA-MDet: Terrain-Aware Multimodal 3D Object Detection for UAV Platforms",
abstract = "Multimodal 3D object detection method combines the spatial geometric information from LiDAR with the visual semantic information from cameras. However, existing multimodal methods are mainly designed for vehicle platforms. In UAV top-down scenarios, they often suffer from inaccurate feature alignment and high computational cost. To address these challenges, we propose TA-MDet, a terrain-aware multimodal 3D object detection method for UAV platforms. It uses sparse BEV features as queries and generates near-ground 3D query points from the predicted terrain, enabling accurate cross-modal feature alignment without depth estimation. Furthermore, we construct a UAV-based multimodal dataset UAV-LC3D to evaluate the proposed method. Experimental results demonstrate that TA-MDet outperforms all compared single-modal and multimodal methods on UAV-LC3D, validating the effectiveness of the proposed approach.",
keywords = "3D object detection, Multimodal method, Terrain-Aware, UAV platform",
author = "Yanze Jiang and Yanfeng Gu and Xian Li",
note = "Publisher Copyright: {\textcopyright} 2026 IEEE.; 2026 China Aerospace Information Technology Conference, CAIT 2026 ; Conference date: 08-05-2026 Through 10-05-2026",
year = "2026",
doi = "10.1109/CAIT70489.2026.11553615",
language = "英语",
series = "2026 China Aerospace Information Technology Conference, CAIT 2026",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2026 China Aerospace Information Technology Conference, CAIT 2026",
address = "美国",
}