Learning-Based 3D Reconstruction of Power Networks from Aerial Point Clouds

arXiv:2609.23915 · eess.IV, cs.CV, cs.LG · Submitted 2026-09-20 · Read on arXiv

eess.IV, cs.CV, cs.LG

Submitted: 2026-09-20

Updated: 2026-09-20

Comments: 5 pages, 6 figures. Accepted for oral presentation at IEEE IGARSS 2026

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Point Clouds — 11× in this paper · explained in 2 episodes
• Accuracy — 4× in this paper · explained in 1 episode
• Robustness — 3× in this paper · explained in 5 episodes
• Clustering — 2× in this paper · explained in 2 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Failure Modes — 2× in this paper · explained in 3 episodes
• Loss Function — 2× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes

Sources

• Deep Projective 3D Semantic Segmentation
• Feature Pyramid Networks for Object Detection
• DALES: A Large-scale Aerial LiDAR Data Set for Semantic Segmentation

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