UAVScenes: A Multi-Modal Dataset for UAVs

• URL: http://arxiv.org/abs/2507.22412v1
• Date: Wed, 30 Jul 2025 06:29:52 GMT
• Title: UAVScenes: A Multi-Modal Dataset for UAVs
• Abstract summary: UAVScenes is a large-scale dataset designed to benchmark various tasks across both 2D and 3D modalities.<n>We enhance this dataset by providing manually labeled semantic annotations for both frame-wise images and LiDAR point clouds.<n>These additions enable a wide range of UAV perception tasks, including segmentation, depth estimation, 6-DoF localization, place recognition, and novel view synthesis.
• Score: 45.752766099526525
• License: http://creativecommons.org/licenses/by/4.0/
• 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

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