Uncertainty-guided Contrastive Learning for Weakly Supervised Point Cloud Segmentation

3D point cloud data are widely used in many fields as they can be easily obtained and contain rich semantic information. Recently, weakly supervised segmentation has attracted lots of attention because it only requires very few labels, thus reducing time-consuming and expensive data annotation effor...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2024-01, Vol.62, p.1-1
Main Authors: Yao, Baochen, Dong, Li, Qiu, Xiaojie, Song, Kangkang, Yan, Diqun, Peng, Chengbin
Format: Article
Language:English
Subjects:
Annotations
contrastive learning
Image segmentation
Labels
Learning
Measurement
Modules
negative pseudo-label
Perturbation method
Perturbation methods
Point cloud compression
Point cloud segmentation
Predictions
Predictive models
Prototypes
Reliability
Segmentation
Training
Uncertainty
weakly supervised
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Summary:3D point cloud data are widely used in many fields as they can be easily obtained and contain rich semantic information. Recently, weakly supervised segmentation has attracted lots of attention because it only requires very few labels, thus reducing time-consuming and expensive data annotation efforts for huge amounts of point cloud data. Existing approaches typically adopt softmax scores from the last layer as the confidence for selecting high-confident point predictions. However, such approaches can ignore the potential value of a large number of low-confidence point predictions under traditional metrics. In this work, we propose an Uncertainty-guided Contrastive Learning (UCL) framework for weakly supervised point cloud segmentation. A novel uncertainty metric based on prototype entropy is presented to estimate the reliability of model predictions. With this metric, we propose a negative contrastive learning module exploiting negative pseudo-labels of predictions with low reliability and an active contrastive learning module enhancing feature learning of segmentation models by predictions with high reliability. We also propose a generic multi-scale feature perturbation method to expand a wider perturbation space. Extensive experimental results on indoor and outdoor point cloud datasets demonstrate that the proposed method achieves competitive performance.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2024.3416219