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September 20, 2025

Exploring Semantic Masked Autoencoder for Self-supervised Point Cloud Understanding

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The semantic masked autoencoder improves point cloud understanding, enhancing the semantics captured.
Experiments on datasets like ScanObjectNN show significant improvement in performance with the proposed model.
The approach combines semantic modeling with enhanced masking strategies to preserve component structures.
The introduction of prototypes in semantic guidance addresses challenges in restoring corrupted point cloud inputs.

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Proposed model improves point cloud understanding by addressing random masking limitations, enhancing semantics.

Zha et al. (2025) studied this question.

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