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Survey of self-supervised learning for point cloud data

Survey of self-supervised learning for point cloud data


Self-Supervised Learning for Point Clouds Data: A Survey

arXiv paper abstract https://arxiv.org/abs/2305.11881



3D point clouds are a crucial ... data collected by LiDAR sensors and widely used in transportation applications due to its concise descriptions and accurate localization.


... Self-Supervised Learning (SSL), an unsupervised training paradigm that mines effective information from the data itself, is ... an essential solution to solve the time-consuming and labor-intensive data labelling problems via smart pre-training task design.


This paper provides a comprehensive survey of recent advances on SSL for point clouds.


... first present an innovative taxonomy, categorizing the existing SSL methods into four broad categories based on the pretexts' characteristics.


... then further categorize the methods into more fine-grained groups and summarize the strength and limitations of the representative methods.


... also compare the performance of the notable SSL methods in literature on multiple downstream tasks on benchmark datasets both quantitatively and qualitatively ...



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