  We propose an unsupervised method for parsing large 3D scans of real-world
scenes with easily-interpretable shapes. This work aims to provide a practical
tool for analyzing 3D scenes in the context of aerial surveying and mapping,
without the need for user annotations. Our approach is based on a probabilistic
reconstruction model that decomposes an input 3D point cloud into a small set
of learned prototypical 3D shapes. The resulting reconstruction is visually
interpretable and can be used to perform unsupervised instance and low-shot
semantic segmentation of complex scenes. We demonstrate the usefulness of our
model on a novel dataset of seven large aerial LiDAR scans from diverse
real-world scenarios. Our approach outperforms state-of-the-art unsupervised
methods in terms of decomposition accuracy while remaining visually
interpretable. Our code and dataset are available at
https://romainloiseau.fr/learnable-earth-parser/
