Decoupled Detection and Category-Level 6D Pose Estimation for Robot Grasping
學年 114
學期 2
出版（發表）日期 2026-04-17
作品名稱 Decoupled Detection and Category-Level 6D Pose Estimation for Robot Grasping
作品名稱（其他語言）
著者 Chia-Tse Lai; Chen-Chien Hsu; Shao-Kang Huang; Yin-Tien Wang
單位
出版者
著錄名稱、卷期、頁數 Electronics 2026 15(8) , p. 1706
摘要 6D object pose estimation is an essential component for robotic grasping. Most existing deep learning-based approaches focus on instance-level pose estimation, which requires prior object models and consequently limits their applicability on unseen objects in real-world scenarios. In contrast, category-level 6D pose estimation adopts Normalized Object Coordinate Space (NOCS) maps to represent intra-class object geometry, enabling pose prediction without relying on predefined object models and thus improving generalization to unseen instances. However, the original NOCS-based category-level framework typically trains NOCS prediction and object classification in a joint manner, which introduces NOCS regression error among inter-class instances with similar appearances, thereby degrading pose estimation accuracy. To address this issue, we integrate the YOLOv8 object detection with SegFormer and propose a novel Category-Level SegFormer for 6D Object Pose Estimation (CLSF-6DPE). By decoupling object classification from NOCS regression through independent learning branches, the proposed framework significantly improves pose estimation performance. Furthermore, we validate the practical feasibility of CLSF-6DPE by integrating it with a robotic gripper via the Robot Operating System (ROS) in a Real-World grasping setup. Experimental results on the CAMERA and Real-World datasets demonstrate that the proposed method achieves mAP scores of 93.8% and 81.1%, respectively. Overall, the proposed method provides a modular and effective solution for category-level pose estimation in real-world robotic grasping applications.
關鍵字 6D object pose estimation;category-level pose estimation;robot grasping;object detection
語言 en_US
ISSN 2079-9292
期刊性質 國外
收錄於 SCI
產學合作
通訊作者 Yin-Tien Wang
審稿制度 否
國別 TWN
公開徵稿
出版型式 ,電子版
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/129242 )