This paper presents GridNet-HD, a multi-modal dataset for 3D semantic segmentation of overhead electrical infrastructures, pairing high-density LiDAR with high-resolution oblique imagery. The dataset comprises 7,694 images and 2.5 billion points annotated into 11 classes, with predefined splits and mIoU metrics. Unimodal (LiDAR-only, image-only) and multi-modal fusion baselines are provided. On GridNet-HD, fusion models outperform the best unimodal baseline by +5.55 mIoU, highlighting the complementarity of geometry and appearance. As reviewed in Sec. 2, no public dataset jointly provides high-density LiDAR and high-resolution oblique imagery with 3D semantic labels for power-line assets. Dataset, baselines, and codes are available: https://huggingface.co/collections/heig-vd-geo/gridnet-hd.

翻译：本文提出了GridNet-HD，一个用于架空电力基础设施三维语义分割的多模态数据集，它将高密度激光雷达与高分辨率倾斜影像进行配对。该数据集包含7,694张图像和25亿个点，被标注为11个类别，并提供了预定义的划分和平均交并比指标。我们提供了单模态（仅LiDAR、仅图像）和多模态融合的基线模型。在GridNet-HD上，融合模型比最佳单模态基线的平均交并比高出+5.55，凸显了几何信息与外观信息的互补性。如第2节所述，目前尚无公开数据集能同时为电力线资产提供高密度激光雷达、高分辨率倾斜影像以及三维语义标签。数据集、基线模型和代码已发布：https://huggingface.co/collections/heig-vd-geo/gridnet-hd。