arXiv Computer Vision
ShelfChange3D: Object-Level 3D Change Detection for Retail Shelf Monitoring
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arXiv AI
Jun 2
ShelfAware: Real-Time Semantic Localization in Quasi-Static Environments with Low-Cost Sensors
arXiv:2512. 09065v2 Announce Type: replace-cross Abstract: Many indoor workspaces are quasi-static: their global geometric layout is stable, but local semantics change continually, producing repetitive geometry, dynamic clutter, and perceptual noise that defeat standard vision-based localization.
arXiv AI
Sep 4
AnyBox: Efficient Zero-Shot 9DoF Pose Estimation of Boxes for Robotic Manipulation
AnyBox is a zero‑shot framework that estimates the full 9DoF pose (6D pose plus 3D dimensions) of boxes from a single RGB‑D image, leveraging the geometric regularity of boxes. It alternates between pose and scale estimation, using a binary search guided by the discrepancy between a reprojected template and the observed mask, and employs a depth‑consistency filter and an early‑stopping rule to prune implausible hypotheses. On public benchmarks and a warehouse dataset, AnyBox improves detection AP by up to 36 points and boosts robotic box‑shelving success by 28%.
arXiv Computer Vision
1d ago
OpenBox: Annotate Any Bounding Boxes in 3D
arXiv:2512.01352v2 Announce Type: replace Abstract: Unsupervised and open-vocabulary 3D object detection have recently gained attention, particularly in autonomous driving, where reducing annotation...
arXiv Computer Vision
4d ago
GenCOPE: Syn2Real Generalized Category-Level Object Pose Estimation for Robotic Picking
GenCOPE introduces a synthetic-to-real (Syn2Real) approach for category-level object pose estimation (COPE) that eliminates the need for labor-intensive real-world data collection. By learning domain-invariant representations through 2D and 3D semantic consistency constraints and employing an end-to-end pose regression framework with 2D-3D cross consistency, the model achieves robust generalization across synthetic and real domains. The architecture relies solely on global features, resulting in a lightweight and efficient design validated on REAL275, Wild6D, and real-world robotic manipulation scenes.
arXiv Machine Learning
Sep 23
CODA: Depth-Aligned Scene Completion and Object Decomposition from a Single RGB-D Image
arXiv:2609.25654v1 Announce Type: cross Abstract: Robots operating safely in cluttered everyday environments often need to infer scene geometry from partial observations. Methods that detect objects...
arXiv AI
Jul 7
R3D: Quantitative 3D Spatial Reasoning for Egocentric Wearables
arXiv:2607. 02921v1 Announce Type: cross Abstract: Quantitative 3D spatial reasoning from egocentric RGB-D video is a critical capability for next-generation wearable assistants.