HFBRI-MAE substitutes handcrafted rotation-invariant local and global features into a masked autoencoder, letting it classify and segment arbitrarily rotated point clouds without alignment failures.
Exploring geometry-aware contrast and clustering harmonization for self-supervised 3D object detection,
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HFBRI-MAE: Handcrafted Feature Based Rotation-Invariant Masked Autoencoder for 3D Point Cloud Analysis
HFBRI-MAE substitutes handcrafted rotation-invariant local and global features into a masked autoencoder, letting it classify and segment arbitrarily rotated point clouds without alignment failures.