arXiv:2610.34305v1 Announce Type: new
Abstract: Object re-identification requires embeddings that are robust to intra-class variation in viewpoint, illumination, and occlusion, yet preserve sharp sep...
By Athena Psalta, Vasileios Tsironis, Konstantinos Karantzalos
RYOPO is an end‑to‑end query‑based RGB‑D set predictor that jointly detects, segments, and estimates 9‑DoF poses of unseen instances within known categories without relying on external instance segmentation or CAD priors. It uses shared image and scene encoding, a query‑conditioned geometry pathway, and object‑centric refinement with pose‑conditioned cross‑attention to achieve accurate pose estimation. On benchmark datasets such as NOCS, REAL275, and HouseCat6D, RYOPO outperforms published methods and runs in real time at 31.8 FPS on an RTX A6000.
By Hakjin Lee, Junghoon Seo, Jaehoon Sim
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
SUFLECA is a weakly supervised framework that improves zero‑shot CAD‑to‑image alignment by scaling geometry‑grounded feature learning using Normalized Object Coordinates across up to 12 real and synthetic datasets. It introduces a geometrically consistent matching algorithm that reliably establishes CAD‑to‑image correspondences, enabling accurate, sub‑second alignment without iterative pose refinement. On the ScanNet25k benchmark, SUFLECA achieves 32.8%/42.6% category/instance accuracy, outperforming the strongest zero‑shot baseline by 9.7/12.5 percentage points and surpassing existing pose‑supervised methods for the first time.
By Saad Ejaz, Miguel Fernandez-Cortizas, Javier Civera, Holger Voos, Jose Luis Sanchez-Lopez
The paper introduces SADGE, a quantitative similarity metric that predicts how well synthetic image datasets will perform on real-world computer vision tasks without requiring downstream model training. SADGE combines appearance and geometric similarity measures through a constrained bilinear interaction, outperforming appearance-only and geometry-only baselines across 79,000 image pairs in five synthetic-to-real benchmark families. The study demonstrates that the non‑linear interplay between appearance and geometry, rather than either alone, best predicts downstream performance in tasks such as object detection, semantic segmentation, and pose estimation.
By Patryk Bartkowiak, Dominik Michels, Soren Pirk, Wojtek Palubicki
The paper introduces a CAD‑free 3D shape prior that enhances object recognition by reconstructing each object with 3D Gaussian Splatting (3DGS) from short RGB‑D scans and fusing the resulting shape prototype with frozen DINOv2 image features. Experiments on T‑LESS and HOPE datasets show that geometry alone can match or exceed CAD‑based recognition, and that the combined approach improves performance, especially on shape‑distinctive or partially occluded objects. The study demonstrates that the benefit comes from the geometric information rather than rendered pixels, and that the prior is complementary to frozen vision features.
By Chenxi Tao, Seung-Kyum Choi