构建首个大尺度林区低空RGB-D数据集，助力火灾应急定位与森林测绘。
WildfireX-SLAM: A Large-scale Low-altitude RGB-D Dataset for Wildfire SLAM and Beyond
- 基于虚幻引擎5合成大规模林区低空影像，支持多种环境参数调控。
- 包含5.5k张16km²范围内的低空RGB-D图像，含真实相机位姿。
- 专为3D高斯溅射在林区的SLAM应用设计，适合火灾响应与森林管理研究者。
3D高斯溅射（3DGS）及其后续变体在同时定位与地图构建（SLAM）中取得显著进展。然而，当前多数3DGS-based SLAM工作集中于小规模室内场景，针对大尺度森林场景的研究潜力巨大，尤其在火灾应急响应与森林管理中具有重要应用前景。受限于缺乏高质量、大规模的真实数据集，实际采集成本高且技术难度大。为此，我们构建了一个大规模、综合性、高质量的合成数据集——WildfireX-SLAM，用于林区与火灾环境下的SLAM研究。基于虚幻引擎5的Electric Dreams环境样本项目，我们开发了一套可灵活控制光照、天气、火势类型与状态的采集管线，可生成无人机拍摄的空中与地面视图，包含真实相机位姿及多种模态数据。该数据集包含5.5k张低空RGB-D图像，覆盖16km²的大规模森林区域。在此基础上，我们开展了全面的基准测试，揭示了3DGS在林区环境中面临的独特挑战，并为未来研究提供改进方向。数据集与代码将公开。项目主页：https://zhicongsun.github.io/wildfirexslam。
原文摘要 · Abstract (English)
3D Gaussian splatting (3DGS) and its subsequent variants have led to remarkable progress in simultaneous localization and mapping (SLAM). While most recent 3DGS-based SLAM works focus on small-scale indoor scenes, developing 3DGS-based SLAM methods for large-scale forest scenes holds great potential for many real-world applications, especially for wildfire emergency response and forest management. However, this line of research is impeded by the absence of a comprehensive and high-quality dataset, and collecting such a dataset over real-world scenes is costly and technically infeasible. To this end, we have built a large-scale, comprehensive, and high-quality synthetic dataset for SLAM in wildfire and forest environments. Leveraging the Unreal Engine 5 Electric Dreams Environment Sample Project, we developed a pipeline to easily collect aerial and ground views, including ground-truth camera poses and a range of additional data modalities from unmanned aerial vehicle. Our pipeline also provides flexible controls on environmental factors such as light, weather, and types and conditions of wildfire, supporting the need for various tasks covering forest mapping, wildfire emergency response, and beyond. The resulting pilot dataset, WildfireX-SLAM, contains 5.5k low-altitude RGB-D aerial images from a large-scale forest map with a total size of 16 km2. On top of WildfireX-SLAM, a thorough benchmark is also conducted, which not only reveals the unique challenges of 3DGS-based SLAM in the forest but also highlights potential improvements for future works. The dataset and code will be publicly available. Project page: https://zhicongsun.github.io/wildfirexslam.
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