Two-stage Monocular 6D Pose Estimation for Small Cubic Objects

M-VTOP: Modular Visuo-Tactile Object Pose Estimation for High-Precision Robotic Manipulation

Experiments show that M-VTOP achieves sub-millimeter accuracy under complex geometries, occlusions, and tight tolerances, demonstrating its promise for high-precision robotic manipulation.

Model-agnostic pose estimation for enhanced collaborative robot grasping via binocular vision

A novel 7-DoF grasping pose generation framework that integrates sparse attention and null convolution is introduced, which enhances the model’s ability to capture fine-grained features from point clouds, significantly improving the accuracy of parallel gripping pose estimation.

Accurate 6D pose estimation using consumer-grade depth cameras in real-world scenarios

Enhanced YOLOv11 with global geometric perception for 6D object pose estimation

Precise 6D object pose estimation from RGB images remains a formidable challenge due to complex backgrounds and severe occlusions. To address these issues, our study presents an enhanced YOLOv11 framework specifically designed for global geometric perception and high-fidelity single-stage 6D pose regression. The core of our architecture is the C3k2SW module, which innovatively synergizes local convolutional features with global long-range dependencies through windowbased self-attention, significantly enhancing the network's geometric perception of spatial topologies. Furthermore, to optimize multi-scale feature interaction, an adaptive ConcatA module and a Bi-directional Feature Pyramid Attention Network (BFPAN) are proposed to suppress background noise while preserving fine-grained geometric details across different scales. Experimental results on the LineMod benchmark demonstrate that our method achieves an optimal tradeoff between inference efficiency and accuracy, reaching an average ADD(-S) accuracy of 76.50% and 84.92% on the 5cm 5° metric, respectively. These results validate that the integration of global geometric awareness consistently outperforms the vanilla YOLOv11 and other classical baselines in complex scenarios.

PIXIE: A Zero-Shot texture-invariant 6D pose estimation framework for unseen objects with assembly defects

6D pose estimation remains a key challenge in robotics and computer vision, particularly in industrial environments. The deployment of currently available data-driven methods is often limited by resource-intensive data pipelines, reliance on textured 3D models, and sensitivity to geometric deviations caused by damages or assembly defects. We present PIXIE, a zero-shot framework that estimates the 6D pose of an object from an RGB image using only an untextured 3D model. Synthetic depth and normal maps are rendered from sampled reference viewpoints and matched to the query image via a pretrained cross-modality feature matcher. Matched keypoints are back-projected to obtain 2D--3D correspondences for PnP-based pose estimation. Relying exclusively on geometry makes the method inherently robust to lighting and texture variation, while correspondence filtering handles geometric deviations between the model and physical object. We evaluate on widely-used public benchmarks, reporting state-of-the-art results on texture-less objects without object-specific training, and introduce a novel dataset with assembly defects, texture variations, and occlusion to demonstrate real-world applicability.

FreeTrack6d: a training-free framework for 6D pose tracking and robotic grasping of moving objects on conveyor belts

For robotic dynamic grasping of moving objects in conveyor-belt scenarios, accurate and robust 6D pose estimation and tracking are essential for reliable grasping. However, existing deep-learning-based methods usually rely on large amounts of supervised data for specific objects or categories, which limits their generalization, deployment efficiency, and flexibility for rapid object changeover in industrial applications. To address these challenges, this paper proposes FreeTrack6D, a training-free unified segmentation and 6D pose tracking framework. Relying only on the CAD model of the target object, FreeTrack6D can be applied to dynamic tracking and grasping of unseen objects without additional object-specific training. Specifically, an adaptive multi-cue mask generation module is first introduced to generate frame-wise target masks in real time, which provides target-region constraints for initial pose registration and subsequent pose refinement. This helps reduce the influence of background interference and target-region misalignment caused by rapid motion. Based on the generated mask, RGB-D observations, and the CAD model, FoundationPose is used for initial 6D pose registration and subsequent pose refinement. To improve tracking robustness under large inter-frame motion and rotational variations, a Kalman-guided multi-hypothesis refinement strategy is further designed, where multiple candidate poses predicted from historical motion states are refined and selected according to mask consistency. In addition, a Pose Consistency-aware Association and Gating mechanism is developed to reject abnormal detections, protect the filter state, and trigger re-initialization when consecutive mismatches occur. By integrating frame-wise mask generation, multi-hypothesis pose refinement, motion prediction, observation gating, and visual-servo-based robot control, FreeTrack6D forms a closed-loop training-free dynamic grasping framework.