We propose an unsupervised method for parsing large 3D scans of real-world
scenes into interpretable parts. Our goal is to provide a practical tool for
analyzing 3D scenes with unique characteristics in the context of aerial
surveying and mapping, without relying on application-specific 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
shapes. Our model provides an interpretable reconstruction of complex scenes
and leads to relevant instance and semantic segmentations. To demonstrate the
usefulness of our results, we introduce a novel dataset of seven diverse aerial
LiDAR scans. We show that our method outperforms state-of-the-art unsupervised
methods in terms of decomposition accuracy while remaining visually
interpretable. Our method offers significant advantage over existing
approaches, as it does not require any manual annotations, making it a
practical and efficient tool for 3D scene analysis. Our code and dataset are
available at https://imagine.enpc.fr/~loiseaur/learnable-earth-parse