Year: 2025
Type: preprint
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
Source: arXiv (Cornell University)
Authors: Wang, Sijie, Li, Siqi, Zhang, Yawei, Yu, Shangshu, Yuan, Shenghai, She, Rui, Guo, Quanjiang, Zheng, JinXuan, Howe, Ong Kang, Chandra, Leonrich, Srijeyan, Shrivarshann, Sivadas, Aditya, Aggarwal, Toshan, Liu, Heyuan, Zhang, Hongming, Chen, Chujie, Jiang, Junyu, Xie, Lihua, Tay, Wee Peng
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6979304684
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