UAVScenes: A Multi-Modal Dataset for UAVs
Abstract
Multi-modal perception is essential for unmanned aerial vehicle (UAV) operations, as it enables a comprehensive understanding of the UAVs' surrounding environment. However, most existing multi-modal UAV datasets are primarily biased toward localization and 3D reconstruction tasks, or only support map-level semantic segmentation due to the lack of frame-wise annotations for both camera images and LiDAR point clouds. This limitation prevents them from being used for high-level scene understanding tasks. To address this gap and advance multi-modal UAV perception, we introduce UAVScenes, a large-scale dataset designed to benchmark various tasks across both 2D and 3D modalities. Our benchmark dataset is built upon the well-calibrated multi-modal UAV dataset MARS-LVIG, originally developed only for simultaneous localization and mapping (SLAM). We enhance this dataset by providing manually labeled semantic annotations for both frame-wise images and LiDAR point clouds, along with accurate 6-degree-of-freedom (6-DoF) poses. These additions enable a wide range of UAV perception tasks, including segmentation, depth estimation, 6-DoF localization, place recognition, and novel view synthesis (NVS). Our dataset is available at https://github.com/sijieaaa/UAVScenes
Cite
@article{arxiv.2507.22412,
title = {UAVScenes: A Multi-Modal Dataset for UAVs},
author = {Sijie Wang and Siqi Li and Yawei Zhang and Shangshu Yu and Shenghai Yuan and Rui She and Quanjiang Guo and JinXuan Zheng and Ong Kang Howe and Leonrich Chandra and Shrivarshann Srijeyan and Aditya Sivadas and Toshan Aggarwal and Heyuan Liu and Hongming Zhang and Chujie Chen and Junyu Jiang and Lihua Xie and Wee Peng Tay},
journal= {arXiv preprint arXiv:2507.22412},
year = {2025}
}
Comments
Accepted by ICCV 2025