GridNet-HD: A High-Resolution Multi-Modal Dataset for LiDAR-Image Fusion on Power Line Infrastructure

• URL: http://arxiv.org/abs/2601.13052v1
• Date: Mon, 19 Jan 2026 13:41:52 GMT
• Title: GridNet-HD: A High-Resolution Multi-Modal Dataset for LiDAR-Image Fusion on Power Line Infrastructure
• Abstract summary: GridNet-HD is a dataset for 3D semantic segmentation of overhead electrical infrastructures.<n>The dataset comprises 7,694 images and 2.5 billion points annotated into 11 classes.<n>On GridNet-HD, fusion models outperform the best unimodal baseline by +5.55 mIoU.
• Score: 0.4793962338847224
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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