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A Survey of Label-Efficient Deep Learning for 3D Point Clouds.

Aoran Xiao, Xiaoqin Zhang, Ling Shao

IEEE Transactions on Pattern Analysis and Machine Intelligence

|June 18, 2024

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Label-efficient learning is crucial for advancing deep neural networks in point cloud processing, overcoming the high cost of data annotation. This survey explores methods to train models effectively with less labeled point cloud data.

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Area of Science:

Computer Vision
Machine Learning
3D Data Processing

Background:

Deep neural networks have advanced point cloud learning.
Collecting large-scale, precisely-annotated point clouds is costly and time-consuming.
This annotation bottleneck limits the scalability and application of point cloud datasets.

Purpose of the Study:

To provide the first comprehensive survey of label-efficient learning for point clouds.
To address the importance, scope, and progress in this emerging field.
To organize methods based on data prerequisites and label types.

Main Methods:

Categorization of four key label-efficient learning approaches: data augmentation, domain transfer learning, weakly-supervised learning, and pretrained foundation models.
Proposal of a taxonomy for organizing methods based on label requirements.
Extensive literature review of existing approaches, challenges, and progress.

Main Results:

Identification of data augmentation, domain transfer learning, weakly-supervised learning, and pretrained foundation models as key strategies.
A structured overview of the current landscape of label-efficient point cloud learning.
Highlighting of research challenges and future directions in the field.

Conclusions:

Label-efficient learning is essential for overcoming annotation costs in point cloud processing.
The proposed taxonomy offers a structured understanding of diverse methodologies.
Further research is needed to address current challenges and unlock future potential.