Deep Learning for Ultra-Large-Scale Semantic Segmentation of Geographic 3D Point Clouds With Missing Labels
TL;DR
This research advances 3D deep learning by tackling semantic segmentation of ultra-large-scale geographic point clouds with missing labels, using approximately 36,369 million points and 29.5 square kilometers, surpassing previous dataset scales.
Deep Learning for Ultra-Large-Scale Semantic Segmentation of Geographic 3D Point Clouds With Missing Labels
Alberto M. Esmorís; Miguel Yermo; Silvia R. Alcaraz; Samuel Soutullo; Francisco F. Rivera
https://doi.org/10.1109/ACCESS.2025.3647154
Volume 14
Semantic segmentation of 3D point clouds is a critical task essential for research and industry in a wide variety of domains. Most works until now use datasets where the concept of large-scale varies between 1.17 and $9,261$ million points and 0.31 and 250 square kilometers. In this research, we push the limits of 3D deep learning by considering approximately $36,369$ million points and $29,5...