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.

We study the problem of unsupervised 3D semantic segmentation on raw point clouds without needing human labels in training. Existing methods usually formulate this problem into learning per-point local features followed by a simple grouping strategy, lacking the ability to discover additional and possibly richer semantic priors beyond local features. In this paper, we introduce LogoSP to learn 3D semantics from both local and global point features. The key to our approach is to discover 3D semantic information by grouping superpoints according to their global patterns in the frequency domain, thus generating highly accurate semantic pseudo-labels for training a segmentation network. Extensive experiments on two indoor and an outdoor datasets show that our LogoSP surpasses all existing unsupervised methods by large margins, achieving the state-of-the-art performance for unsupervised 3D semantic segmentation. Notably, our investigation into the learned global patterns reveals that they truly represent meaningful 3D semantics in the absence of human labels during training. Our code and data are available at https://github.com/vLAR-group/LogoSP