GrowSP: Unsupervised Semantic Segmentation of 3D Point Clouds
Zihui Zhang Bo Yang Bing Wang Bo Li
We study the problem of 3D semantic segmentation from raw point clouds.
Unlike existing methods which primarily rely on a large amount of human
annotations for training neural networks, we propose the first purely
unsupervised method, called GrowSP, to successfully identify complex semantic
classes for every point in 3D scenes, without needing any type of human labels
or pretrained models. The key to our approach is to discover 3D semantic
elements via progressive growing of superpoints. Our method consists of three
major components, 1) the feature extractor to learn per-point features from
input point clouds, 2) the superpoint constructor to progressively grow the
sizes of superpoints, and 3) the semantic primitive clustering module to group
superpoints into semantic elements for the final semantic segmentation. We
extensively evaluate our method on multiple datasets, demonstrating superior
performance over all unsupervised baselines and approaching the classic
fully-supervised PointNet. We hope our work could inspire more advanced methods
for unsupervised 3D semantic learning.