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The paper identifies a theoretical incompatibility between 3D point cloud masked autoencoding and SE(3)-equivariant networks, showing that standard masking causes a topological collapse that forces models to hallucinate orientation. To address this, the authors introduce SIEVE, an SE(3)-equivariant self-supervised framework that compresses point cloud geometry into invariant scalar embeddings using a spherical projection mask that preserves global pose while obfuscating local semantics. Experiments demonstrate that SIEVE avoids the predicted collapse where constant and noise masks fail, and its embeddings outperform coordinate-based baselines and hand-crafted geometric descriptors on downstream point cloud tasks, especially in generalization to unseen shape categories.
arXiv:2606. 31570v1 Announce Type: cross Abstract: Masked autoencoding has emerged as a prominent paradigm for self-supervised learning on 3D point clouds, achieving competitive performance across downstream tasks.
arXiv:2603.25165v3 Announce Type: replace Abstract: Recent advances in self-supervised learning (SSL) for point clouds have substantially improved 3D scene understanding without human annotations. Ex...
arXiv:2608.24093v1 Announce Type: cross Abstract: Self-supervised representation learning for 4D point cloud videos is challenging because annotations are costly and reconstruction-based pretraining...
arXiv:2605. 17131v2 Announce Type: replace-cross Abstract: Point cloud stands as the most widely adopted format for representing 3D shapes and scenes due to its simplicity and geometric fidelity.
P-CORE introduces a self‑supervised surface consistency technique for point‑based neural representations, enabling robust adaptation to large deformations without needing ground‑truth deformed images. By generating random deformations and enforcing that the predicted surface after deformation matches the deformation applied to the original surface prediction, the method leverages attention‑based point representations with a learned interpolation kernel. Experiments on synthetic benchmarks and real‑world datasets show improved zero‑shot editing performance and reduced artifacts compared to existing point‑based approaches.