arXiv:2605.13018v2 Announce Type: replace Abstract: Object-centric scene understanding is a fundamental challenge in computer vision. Existing approaches often rely on multi-stage pipelines that firs...
arXiv:2607. 04930v1 Announce Type: cross Abstract: In the pursuit of robust and generalizable category-level object pose estimation, most existing methods adopt parametric formulations that learn effective representations from data, yet they primarily encode category-level patterns into fixed shape priors or static parameter weights, which limits their scalability to highly diverse instances.
In the pursuit of robust and generalizable category-level object pose estimation, most existing methods adopt parametric formulations that learn effective representations from data, yet they primarily encode category-level patterns into fixed shape priors or static parameter weights, which limits their scalability to highly diverse instances. In this paper, we rethink category-level pose estimation from a memory-centric perspective and present MemPose, a memory-augmented framework that explicitly incorporates category-level geometric memory into the pose estimation pipeline.
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.
Category-level object pose estimation seeks to recover a similarity transform $(R,t,s)$ for unseen instances without instance-specific CAD models. Most competitive methods are correspondence-based: pr...
MessyKitchens introduces a new dataset of cluttered real-world kitchen scenes with detailed 3D object shapes, poses, and accurate contact information. The authors extend the SAM 3D single-object reconstruction method with a Multi-Object Decoder (MOD) to jointly reconstruct entire scenes, achieving better registration accuracy and reduced inter-object penetration compared to prior work. The dataset, benchmark, code, and pretrained models will be publicly released on the project website.
PriorPose introduces a reference-guided correspondence framework for category-level object pose estimation that jointly performs canonicalization and alignment in a shared feature space. By embedding partial observations and a category prior as token sets in a seeded transformer, the network predicts per-point NOCS fields and a canonical deformation, while a deep pose head regresses the similarity transform. A two-part shape consistency objective couples correspondence, deformation, and pose, reducing reliance on memorized canonical orientations and avoiding error cascades, leading to state-of-the-art results on standard and larger-category benchmarks, especially under strict pose thresholds.
arXiv:2604. 04690v2 Announce Type: replace-cross Abstract: Bin picking in real industrial environments remains challenging due to severe clutter, occlusions, and the high cost of traditional 3D sensing setups.
The paper presents a generative framework that estimates category-level 6D pose and 3D size of objects from a single RGB image, using score-based diffusion models to produce a multi-hypothesis pose distribution. It replaces costly likelihood pruning with a Mean Shift approach to isolate the mode as the final pose estimate, achieving state-of-the-art results on the REAL275 benchmark. The method also decouples detection from pose estimation, enabling robust zero-shot generalisation on the Wild6D dataset and extending naturally to video sequences by propagating the pose distribution over time.
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.
G6D is a learning‑free, geometry‑driven RGB‑D 6D pose solver designed for robotic manipulation. It generates pose hypotheses via template‑based geometric matching and refines them using silhouette and depth consistency, requiring only an RGB‑D observation, an object mask, camera intrinsics, and a CAD model. The method offers adjustable accuracy‑computation trade‑offs, can run on CPU without GPUs, and has shown strong performance on LineMOD and BOP19 datasets, as well as in real‑world pick‑and‑place experiments.
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%.