用普通相机拍的植物图，生成逼真温室模拟环境，助力农业机器人训练
GreenhouseSplat: A Dataset of Photorealistic Greenhouse Simulations for Mobile Robotics
- 基于RGB图像和高斯点云技术，重建逼真温室场景
- 包含82株黄瓜、多种种植布局，支持相机与激光雷达渲染
- 适合农业机器人定位、导航研究者使用
为发展农业机器人，仿真温室环境至关重要，但现有方法多依赖简化的合成资产，难以实现仿真到现实的迁移。近期辐射场技术（如高斯点云）可实现照片级重建，但此前仅限于单株植物或实验室环境。本文提出GreenhouseSplat框架与数据集，直接从低成本RGB图像生成逼真温室资产，并集成至基于ROS的仿真系统，支持摄像头与激光雷达渲染，可用于带标识符的定位任务。提供82株黄瓜在多个行配置下的数据，验证其在机器人评估中的有效性。GreenhouseSplat是首个面向温室尺度的辐射场仿真方案，为未来农业机器人研究奠定基础。
原文摘要 · Abstract (English)
Simulating greenhouse environments is critical for developing and evaluating robotic systems for agriculture, yet existing approaches rely on simplistic or synthetic assets that limit simulation-to-real transfer. Recent advances in radiance field methods, such as Gaussian splatting, enable photorealistic reconstruction but have so far been restricted to individual plants or controlled laboratory conditions. In this work, we introduce GreenhouseSplat, a framework and dataset for generating photorealistic greenhouse assets directly from inexpensive RGB images. The resulting assets are integrated into a ROS-based simulation with support for camera and LiDAR rendering, enabling tasks such as localization with fiducial markers. We provide a dataset of 82 cucumber plants across multiple row configurations and demonstrate its utility for robotics evaluation. GreenhouseSplat represents the first step toward greenhouse-scale radiance-field simulation and offers a foundation for future research in agricultural robotics.
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