While the availability of large and diverse datasets has contributed to significant breakthroughs in autonomous driving and indoor applications, forestry applications are still lagging behind and new forest datasets would most certainly contribute to achieving significant progress in the development of data-driven methods for forest-like scenarios. This paper introduces a forest dataset called \textit{FinnWoodlands}, which consists of RGB stereo images, point clouds, and sparse depth maps, as well as ground truth manual annotations for semantic, instance, and panoptic segmentation. \textit{FinnWoodlands} comprises a total of 4226 objects manually annotated, out of which 2562 objects (60.6\%) correspond to tree trunks classified into three different instance categories, namely "Spruce Tree", "Birch Tree", and "Pine Tree". Besides tree trunks, we also annotated "Obstacles" objects as instances as well as the semantic stuff classes "Lake", "Ground", and "Track". Our dataset can be used in forestry applications where a holistic representation of the environment is relevant. We provide an initial benchmark using three models for instance segmentation, panoptic segmentation, and depth completion, and illustrate the challenges that such unstructured scenarios introduce.

翻译：尽管大规模多样化数据集的出现推动了自动驾驶和室内应用领域的重大突破，但林业应用仍相对滞后。新构建的森林数据集将极有可能促进面向林地场景的数据驱动方法取得显著进展。本文提出名为《FinnWoodlands》的森林数据集，包含RGB立体图像、点云与稀疏深度图，以及用于语义分割、实例分割和全景分割的人工标注真值。该数据集共包含4226个手动标注对象，其中2562个对象（占比60.6%）为树干实例，分为"云杉"、"白桦"和"松树"三类。除树干外，我们还标注了"障碍物"实例，以及"湖泊"、"地面"和"路径"等语义类别。该数据集可应用于需要环境整体表征的林业场景。我们基于三个模型（分别用于实例分割、全景分割与深度补全）提供了初始基准测试，揭示了此类非结构化场景所带来的挑战。