IFOT-SSeg: Induced Factor Optimal Transport for Unsupervised Semantic Segmentation of 3D Point Clouds

Unsupervised 3D point cloud segmentation often relies on clustering learned region features, but mixed latent factors and unreliable fragment-to-primitive pseudo-label assignment jointly limit the quality of pseudo supervision. To address this problem, we propose a latent factor-driven global pseudo-label inference framework that first recovers semantic-relevant latent factors from neural region features and then performs globally competitive fragment-to-primitive assignment to generate reliable pseudo-labels. Specifically, Bayesian Nonparametric Latent Factor Induction (BN-LFI) models neural region features as observations from an open-ended latent factor space and combines ICA-style factor construction with variational Indian Buffet Process inference to adaptively recover active factors underlying the current scene semantics. These factors are further calibrated using unsupervised quality signals, yielding a more reliable latent representation for subsequent pseudo-label inference. Based on the induced representation, Global Margin-Aware Optimal Transport (GM-OT) formulates fragment-to-primitive-slot matching as a globally competitive transport problem, suppressing dominant primitive absorption and slot redundancy while producing more stable hard pseudo-labels. The framework operates only during pseudo-label construction, introduces no additional inference overhead, and outperforms existing unsupervised 3D point cloud segmentation methods on ScanNet, S3DIS, and nuScenes.