Abstract
Robust scene understanding is essential for intelligent vehicles operating in natural, unstructured environments. While semantic segmentation datasets for structured urban driving are abundant, the datasets for extremely unstructured wild environments remain scarce due to the difficulty and cost of generating pixel-accurate annotations. These limitations hinder the development of perception systems needed for intelligent ground vehicles tasked with forestry automation, agricultural robotics, disaster response, and all-terrain mobility. To address this gap, we present ForestSim, a high-fidelity synthetic dataset designed for training and evaluating semantic segmentation models for intelligent vehicles in forested off-road and no-road environments. ForestSim contains 2094 photorealistic images across 25 diverse environments, covering multiple seasons, terrain types, and foliage densities. Using Unreal Engine environments integrated with Microsoft AirSim, we generate consistent, pixel-accurate labels across 20 classes relevant to autonomous navigation. We benchmark ForestSim using state-of-the-art architectures and report strong performance despite the inherent challenges of unstructured scenes. ForestSim provides a scalable and accessible foundation for perception research supporting the next generation of intelligent off-road vehicles. The dataset and code are publicly available: Dataset: this https URL Code: this https URL
Abstract (translated)
鲁棒的环境理解能力对于在自然、非结构化环境中运行的智能车辆至关重要。尽管针对结构化城市驾驶的语义分割数据集已相当丰富，但针对极端非结构化野外环境的数据集仍然稀缺，这源于生成像素级精确标注的困难与高昂成本。这些限制阻碍了智能地面车辆在林业自动化、农业机器人、灾害响应及全地形机动等任务中所需的感知系统发展。为填补这一空白，我们推出了ForestSim——一个专为森林越野及无道路环境中智能车辆训练与评估语义分割模型而设计的高保真合成数据集。ForestSim包含涵盖25种多样环境的2094张照片级真实图像，覆盖多种季节、地形类型和植被密度。通过集成Microsoft AirSim的Unreal Engine环境，我们生成了与20个自主导航相关类别一致的像素级精确标注。我们使用最先进的架构在ForestSim上进行基准测试，报告了在非结构化场景固有挑战下仍表现优异的性能。ForestSim为支持下一代智能越野车辆的感知研究提供了可扩展且易用的基础。数据集及代码已公开：数据集：此链接 代码：此链接
URL
https://arxiv.org/abs/2603.27923
