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
DINOcular is a self‑supervised framework that learns joint visuospatial representations from RGB‑D data. It fuses depth‑derived geometric priors with a visual backbone using inter‑patch and intra‑patch techniques, 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.
Geometry-Aligned Semantic Matching for Cross-Modal Planar Image Registration proposes CDPM, a method that first aligns semantic representations across modalities and then refines correspondences with fine-grained CNN features. CDPM adapts DINOv3 using geometrically consistent cross-modal patch pairs, builds a DINO-Centric Feature Pyramid for stable cross-modal matching, and adds a lightweight CNN branch for precise local refinement. Experiments on three cross-modal datasets show that CDPM outperforms existing dense matchers, improving AUC metrics and reducing mACE while using fewer FLOPs.
By Zhiwei Wang, Defeng He, Yuxing Li, Meilu Zhu, Edmund Y. Lam
TokenMatch is a transformer-based model that estimates 3D shape correspondences by adaptively tokenising meshes into curvature-guided patches. Trained only on the BeCoS partial-to-partial dataset, it generalises to full-shape matching without retraining, using self‑ and cross‑attention to learn patch‑ and point‑level relations. Evaluated on CP2P, PSMAL, BeCoS, FAUST, SCAPE, and SHREC'19, TokenMatch consistently outperforms existing methods in mean geodesic error and intersection‑over‑union while achieving sub‑second inference speeds.
By Adeela Islam, Zorah L\"ahner, Vittorio Murino, Vladislav Golyanik
arXiv:2605.26949v2 Announce Type: replace
Abstract: 3D shape completion from partial scans remains challenging for unseen categories and noisy real-world observations, where geometry alone is often i...
By Furkan Mert Algan, Eckehard Steinbach
arXiv:2608.23850v1 Announce Type: new
Abstract: Foundational visual features such as DINO have played a critical role across modern computer vision, and have recently become key components in multi-v...
By Jeong-gi Kwak, Sho Kagami, Yuki Ono, Kwang Moo Yi
The paper introduces a generalizable deformation learning framework that reconstructs 3D objects by deforming a category-level shape template to match a monocular observation. It employs a geometry-guided feature modeling mechanism to enrich foundation features with template topology, creating a geometry-aware representation that is explicitly correlated with the target observation for precise deformation. A view-adaptive feature aggregation module further bridges the gap between the fixed template and arbitrary target views by leveraging multi-view template features and camera poses, ensuring robust feature alignment across diverse viewpoints.
By Yiyao Ma, Kai Chen, Zhongxiang Zhou, Zhuheng Song, Dongsheng Xie, Zelong Tan, Rong Xiong, Qi Dou
arXiv:2312. 08230v2 Announce Type: replace-cross Abstract: Detecting partial extrinsic symmetry in 3D geometry is a fundamental yet persistent challenge in computer vision and graphics, critical for tasks ranging from shape completion to procedural generation.
By Gregor Kobsik, Isaak Lim, Leif Kobbelt
arXiv:2607. 13449v1 Announce Type: cross Abstract: 6-DoF pose estimation is a critical task in autonomous rendezvous and proximity operations.
By Josiane Uwumukiza, Jocelyn Zhao, Giovanni Lavezzi, Giacomo Battaglia, Paolo Panicucci, Minduli C. Wijayatunga, Victor Rodriguez-Fernandez, Richard Linares
arXiv:2609.28187v1 Announce Type: new
Abstract: Self-supervised vision transformers trained with DINO-style objectives exhibit striking emergent semantic representation quality across visual tasks, y...
By Basavaraj Sunagad, Artur Jesslen, Adam Kortylewski
ShapeY is a benchmarking framework that evaluates shape-based recognition in object recognition systems using 68,200 grayscale images of 200 3D objects rendered from multiple viewpoints, with optional appearance changes. It employs a nearest‑neighbor matching task to probe how well an embedding space clusters views by 3D shape similarity across varying viewpoints and non‑shape alterations, providing quantitative and qualitative performance readouts such as error rates, viewpoint tuning curves, and matching score histograms. Testing 321 pre‑trained networks shows that even state‑of‑the‑art models struggle to generalize robustly across 3D viewpoint and appearance changes, highlighting gaps in current shape recognition capabilities.
By Jong Woo Nam, Amanda S. Rios, Bartlett W. Mel
arXiv:2609.01530v1 Announce Type: new
Abstract: Self-supervised pre-training via cross-view completion learns strong features for 3D vision from co-visible regions of image pairs. However, the refere...
By Thibaut Loiseau, Guillaume Bourmaud, Vincent Lepetit