GreenhouseSplat: A Dataset of Photorealistic Greenhouse Simulations for Mobile Robotics

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.

Fiducial Marker Splatting for High-Fidelity Robotics Simulations

High-fidelity 3D simulation is critical for training mobile robots, but its traditional reliance on mesh-based representations often struggle in complex environments, such as densely packed greenhouses featuring occlusions and repetitive structures. Recent neural rendering methods, like Gaussian Splatting (GS), achieve remarkable visual realism but lack flexibility to incorporate fiducial markers, which are essential for robotic localization and control. We propose a hybrid framework that combines the photorealism of GS with structured marker representations. Our core contribution is a novel algorithm for efficiently generating GS-based fiducial markers (e.g., AprilTags) within cluttered scenes. Experiments show that our approach outperforms traditional image-fitting techniques in both efficiency and pose-estimation accuracy. We further demonstrate the framework's potential in a greenhouse simulation. This agricultural setting serves as a challenging testbed, as its combination of dense foliage, similar-looking elements, and occlusions pushes the limits of perception, thereby highlighting the framework's value for real-world applications.