  Contemporary point cloud segmentation approaches largely rely on richly
annotated 3D training data. However, it is both time-consuming and challenging
to obtain consistently accurate annotations for such 3D scene data. Moreover,
there is still a lack of investigation into fully unsupervised scene
segmentation for point clouds, especially for holistic 3D scenes. This paper
presents U3DS$^3$, as a step towards completely unsupervised point cloud
segmentation for any holistic 3D scenes. To achieve this, U3DS$^3$ leverages a
generalized unsupervised segmentation method for both object and background
across both indoor and outdoor static 3D point clouds with no requirement for
model pre-training, by leveraging only the inherent information of the point
cloud to achieve full 3D scene segmentation. The initial step of our proposed
approach involves generating superpoints based on the geometric characteristics
of each scene. Subsequently, it undergoes a learning process through a spatial
clustering-based methodology, followed by iterative training using
pseudo-labels generated in accordance with the cluster centroids. Moreover, by
leveraging the invariance and equivariance of the volumetric representations,
we apply the geometric transformation on voxelized features to provide two sets
of descriptors for robust representation learning. Finally, our evaluation
provides state-of-the-art results on the ScanNet and SemanticKITTI, and
competitive results on the S3DIS, benchmark datasets.
