
Incorporating radius-normalized distance and directional vectors as additional local neighborhood features can significantly improve the classification accuracy of 3D point cloud models, particularly on real-world datasets.


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leveraging-local-neighborhood-features-to-enhance-3d-point-cloud-classification


Leveraging Local Neighborhood Features to Enhance 3D Point Cloud Classification


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The PMT-MAE framework, featuring a dual-branch architecture that integrates Transformer and MLP components, along with a two-stage distillation strategy, achieves high accuracy and efficiency in point cloud classification tasks.


efficient-point-cloud-classification-with-dual-branch-self-supervised-learning-and-knowledge-distillation


Efficient Point Cloud Classification with Dual-Branch Self-Supervised Learning and Knowledge Distillation
