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.
By Jongann Lee, Sun Woo Park, Yun Young Choi
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.
By Xu Yan, Huiqun Wang, Chen Wang, Lei Ren, Di Huang
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...
By Bin Yang, Mohamed Abdelsamad, Miao Zhang, Alexandru Paul Condurache
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...
By Jheng-Ling Lee, Shang-Tse Chen
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.
By Minhas Kamal, Hiranya Garbha Kumar, Balakrishnan Prabhakaran
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.
By Yanshu Zhang, Shichong Peng, Mehran Aghabozorgi, Alireza Moazeni, Ke Li
The paper introduces Poincar3, a self‑supervised method that learns multi‑view representations through self‑distillation rather than RGB reconstruction. By combining masked patch and image‑level distillation with a teacher that sees additional views, it trains from scratch without explicit 3D supervision. Poincar3 surpasses prior single‑ and multi‑view self‑supervised methods on tasks such as correspondence estimation, camera pose estimation, and 3D reconstruction, and its features encode camera motion more accurately thanks to a lightweight Poincaré adapter.
By David Nordstr\"om, Thibaut Loiseau, Vincent Lepetit, Michael Felsberg, Guillaume Bourmaud, Fredrik Kahl
arXiv:2608. 05774v1 Announce Type: cross Abstract: Joint-embedding predictive architectures learn by predicting latent representations of missing observations, yet many masked JEPAs are evaluated primarily through the encoders they produce.
By Zihan Zhou, Qifu Wen, Xi Zeng
arXiv:2610.03717v1 Announce Type: cross
Abstract: This paper examines the role of Novel View Synthesis (NVS) in geometric representation learning. In principle, NVS should reason about 3D scene struc...
By Keerthi Kaashyap, Dennis Anthony, Akshay Krishnan, Nhi Ngoc Nguyen, Jeremy Collins, James Hays, Shreyas Kousik, Animesh Garg
arXiv:2609.26039v1 Announce Type: cross
Abstract: Point cloud generation has emerged as a crucial task for accurately capturing and reproducing the complexity of the physical world. However, existing...
By Ilias Mitsouras, Nikolaos Chaidos, Giorgos Stamou, Athanasios Voulodimos
The paper introduces a self‑supervised, geometry‑aware method that refines DINOv3 features with LoRA to better handle partial 3D shapes. By training on partial observations, the resulting descriptors outperform traditional and other vision‑foundation features in partial shape matching tasks. When incorporated into existing pipelines, the approach achieves state‑of‑the‑art results on both partial shape matching and left‑right prediction benchmarks.
By Viktoria Ehm, Dongliang Cao, Riccardo Marin, Daniel Scholz, Weikang Wang, Florian Bernard, Daniel Cremers
DINOcular is a self‑supervised framework that learns joint visuospatial representations from RGB‑D observations. It fuses depth‑derived geometric priors with a visual backbone using inter‑patch and intra‑patch fusion, allowing the model to encode both appearance and spatial structure efficiently. The resulting representation improves 3D awareness on multiple geometry benchmarks while staying competitive on standard RGB‑D semantic segmentation tasks.
By Farkhat Almukhamedov, Sami Azirar, Hermann Blum