SemiGMMPoint: Semi-supervised point cloud segmentation based on Gaussian mixture models

doi:10.1016/j.patcog.2024.111045

2025-02-01

PRE

DOI：10.1016/j.patcog.2024.111045

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摘要

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Existing semi-supervised point cloud segmentation methods emphasize on discriminative learning, which overlooks the underlying class-conditional distributions and distribution similarities. In this paper, we propose SemiGMMPoint, the first generative framework for semi-supervised 3D point cloud segmentation in real-world and large-scale settings. Specifically, we propose a point dense generative classifier based on Gaussian mixture models (GMMs) to explicitly estimate class-conditional distributions. On top of it, we incorporate a novel similarity-minimization algorithm into the Expectation-Maximization (EM) based GMM parameter estimation, which minimizes the inter-class distribution similarity in the representation space. Moreover, we utilize the well-calibrated posterior to develop a modified point contrastive loss to mitigate sampling bias in semi- supervised settings. Extensive experiments show that SemiGMMPoint significantly boosts performance for semi-supervised point cloud segmentation on many state-of-the-art backbones without requiring architectural changes. Codes are available at https://github.com/jojodidli/SemiGMMPoint.

Keyword:

3D point cloud segmentation

Semi-supervised learning

Generative classifier

Gaussian mixture models

Distribution similarity minimization

Contrastive learning