AI RESEARCH
Hierarchical Feature Learning for Medical Point Clouds via State Space Model
arXiv CS.CV
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ArXi:2504.13015v3 Announce Type: replace Deep learning-based point cloud modeling has been widely investigated as an indispensable component of general shape analysis. Recently, transformer and state space model (SSM) have shown promising capacities in point cloud learning. However, limited research has been conducted on medical point clouds, which have great potential in disease diagnosis and treatment. This paper presents an SSM-based hierarchical feature learning framework for medical point cloud understanding.