GrowSP++: Growing Superpoints and Primitives for Unsupervised 3D Semantic Segmentation

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 proposes GrowSP++, an unsupervised method to successfully identify complex semantic classes for every point in 3D scenes, without needing any type of human labels. Our method is composed of three major components: 1) a feature extractor incorporating 2D-3D feature distillation, 2) a superpoint constructor featuring progressively growing superpoints, and 3) a semantic primitive constructor with an additional growing strategy. The key to our method is the superpoint constructor together with the progressive growing strategy on both superpoints and semantic primitives, driving the feature extractor to progressively learn similar features for 3D points belonging to the same semantic class. We extensively evaluate our method on five challenging indoor and outdoor datasets, demonstrating state-of-the-art performance over all unsupervised baselines. We hope our work could inspire more advanced methods for unsupervised 3D semantic learning.