Resolving Long-Tail Ambiguity in Unsupervised 3D Point Cloud Segmentation with Language Priors

ArXi:2605.20737v1 Announce Type: new Existing approaches for unsupervised 3D point cloud segmentation predominantly rely on a purely visual similarity-based learning-by-clustering paradigm, which suffers from a fundamental limitation: long-tail ambiguity. In such a paradigm, features of minor classes are consistently absorbed by dominant clusters, leading to severely imbalanced predictions.