构建高保真森林场景数据集，助力无人车在复杂野外环境的感知能力提升。
ForestSim: A Synthetic Benchmark for Intelligent Vehicle Perception in Unstructured Forest Environments
- 基于UE引擎与AirSim生成25种多样化森林场景的逼真图像与像素级标签。
- 包含2094张图像、20类语义标签，覆盖多季节、地形与植被密度变化。
- 适用于林地自动化、农业机器人等野外智能车辆研究，公开可下载。
智能车辆在自然非结构化环境中的鲁棒场景理解至关重要。尽管城市道路语义分割数据集丰富，但极端非结构化野外环境的数据集仍稀缺，主要因像素级标注成本高、难度大。这制约了林业自动化、农业机器人及灾后救援等任务中自主地面车辆的感知系统发展。为此，我们提出ForestSim，一个高保真合成数据集，用于训练和评估森林等无路非结构化环境中智能车辆的语义分割模型。ForestSim包含2094张逼真图像，覆盖25种多样环境，涵盖多个季节、地形类型与叶密度。通过集成Unreal Engine与Microsoft AirSim，生成20类与自主导航相关的像素级准确标签。我们使用先进模型对ForestSim进行基准测试，结果表明即使在非结构化场景下仍表现良好。该数据集为下一代野外智能车辆感知研究提供了可扩展、易获取的基础。数据集与代码已公开：数据集: https://vailforestsim.github.io 代码: https://github.com/pragatwagle/ForestSim
原文摘要 · Abstract (English)
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: https://vailforestsim.github.io Code: https://github.com/pragatwagle/ForestSim
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