MH6D: Multi-Hypothesis Consistency Learning for Category-Level 6-D Object Pose Estimation
Jian Liu, Wei Sun, Chongpei Liu, Hui Yang, Xing Zhang, Ajmal Mian

• We propose a multi-hypothesis consistency learning framework for category-level 6-D object pose estimation, which utilizes a parallel consistency learning structure, alleviating the uncertainty problem of single-shot feature extraction and promoting self-adaptation of domain to reduce the synthetic-to-real domain gap.

Robotic Continuous Grasping System by Shape Transformer-Guided Multi-Object Category-Level 6D Pose Estimation
Jian Liu, Wei Sun, Chongpei Liu, Xing Zhang, Qiang Fu

• A transformer-guided shape reconstruction network is proposed to reconstruct the NOCS shape of intra-class known objects, which can fully use the prior feature, current observation feature, and their feature difference by internal self-attention, as well as strengthen their correlation by mutual cross-attention. By doing so, the shape variation can be explicitly highlighted.

HFF6D: Hierarchical Feature Fusion Network for Robust 6D Object Pose Tracking
Jian Liu, Wei Sun, Chongpei Liu, Xing Zhang, Shimeng Fan, Wei Wu

• We propose a lightweight and robust hierarchical feature fusion network for 6D object pose tracking. It establishes sufficient spatial-temporal information interaction between adjacent frames and explicitly highlights the feature differences between adjacent frames, thus improving the robustness of relative pose estimation in challenging scenes.

Robotic Continuous Grasping System (Demo can be seen through link1 or link2)

• We build an end-to-end robotic continuous grasping system, which achieves high-accuracy 6D pose estimation for multiple intra-class unknown objects and high-efficiency robotic grasping in 3D space. For continuous grasping, we propose a low-computation and effective grasping strategy based on the pre-defined vector orientation, and develop a GUI for monitoring and control.