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The paper identifies a theoretical incompatibility between 3D point cloud masked autoencoding and SE(3)-equivariant networks, proving that standard masking triggers a topological collapse, and introduces SIEVE, an equivariant self-supervised framework using a spherical projection mask to preserve global pose while obfuscating local semantics, improving downstream generalization to unseen shape categories.