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Dual-Branch Point Cloud Semantic Segmentation: An EMA-Based Teacher-Student Collaborative Learning Framework.

Xiaoying Zhang¹, Yu Hu¹, Yuzhuo Li¹

¹School of Mechanical Engineering, Jiangsu University of Technology, Changzhou 213001, China.

Sensors (Basel, Switzerland)

|January 28, 2026

Summary

This summary is machine-generated.

This study introduces a dual-branch consistency learning (DBCL) framework for point cloud semantic segmentation. DBCL significantly improves segmentation accuracy with minimal labels by unifying consistency regularization and preserving structural integrity.

Keywords:

data augmentation deep learning point-cloud semantic segmentation weak supervision

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Area of Science:

Computer Vision
Machine Learning
3D Data Analysis

Background:

Point cloud semantic segmentation is crucial for 3D data understanding.
Existing methods struggle with extremely low annotation budgets and data augmentation noise.
Efficient utilization of sparse labels is a key challenge.

Purpose of the Study:

To develop a novel framework for semi-supervised point cloud semantic segmentation.
To address the challenges of low annotation budgets and data noise.
To enhance the utilization of sparse labels in 3D segmentation tasks.

Main Methods:

Proposed a dual-branch consistency learning (DBCL) framework.
Incorporated an Exponential Moving Average (EMA) teacher model.
Implemented a unified consistency regularization scheme using JS divergence and contrastive learning.
Introduced a geometry-aware Laplacian smoothing term for structural consistency.

Main Results:

Achieved 68.56% mIoU on the S3DIS dataset with only 0.1% labels.
Outperformed existing semi-supervised point cloud segmentation methods.
Demonstrated performance comparable to some fully supervised approaches.

Conclusions:

The DBCL framework effectively handles extremely low annotation budgets in point cloud semantic segmentation.
The proposed methods significantly improve segmentation accuracy and robustness.
DBCL offers a promising direction for efficient 3D data annotation and analysis.