# Refined Prior Guided Category-Level 6D Pose Estimation and Its Application on Robotic Grasping
## 5 Discussion  
This paper presents a refined prior-guided category-level 6D object pose estimation method and applies it to robotic arm grasping. The model takes RGB images, depth images, and category priors as inputs, and outputs a deformation matrix and a dense correspondence matrix for the point cloud. Subsequently, the instance's NOCS coordinates are obtained, and the instance's 6D pose, including translation, rotation, and 3D size, is computed using the Umeyama algorithm and RANSAC. We address the intra-class variation problem by introducing an explicit deformation point cloud. Furthermore, this paper proposes a novel attention module and refine modules for point cloud deformation and dense correspondence to improve prediction accuracy. Extensive experiments conducted on the widely used CAMERA25 and REAL275 datasets demonstrate that our method outperforms existing approaches. Finally, we implement the model on a robotic arm, and grasping simulation experiments further validate the model's effectiveness and practicality. A key advantage of this model is its ability to generalize to unseen objects, enabling pose estimation for all objects within a category rather than being limited to objects with known, precise 3D CAD models. This makes it broadly applicable to augmented reality, virtual reality, 3D reconstruction, and robotic arm grasping. The model's versatility is particularly important for robotic arm grasping, as the robot must handle a wide variety of objects in different environments. Our model assists the robotic arm in accurately identifying and understanding the precise location and orientation of objects, thereby enhancing its operational flexibility in complex environments. The simulation results indicate that our method can improve recognition accuracy, which in turn increases the success rate of robotic arm grasping. Analyzing the cases of failed simulated grasps by the robotic arm, we found that many of them were caused by the target objects being heavily occluded. Looking ahead, while our method can already perform pose estimation among different instances within a category, its robustness under extreme conditions (such as partial occlusion, significant lighting changes, or reflective object surfaces) still needs improvement. Additionally, increasing the model's capacity can enhance its performance, but it may also reduce computational efficiency and real-time capability. Moreover, when the robotic arm needs to grasp fragile objects, the algorithm can only predict the 6D bounding box of the target object, without the ability to avoid specific parts of the object. To address the issue of partial occlusion, we may explore three directions: segmentation, feature extraction, and model capacity. Since our model currently uses Mask R-CNN for segmenting target objects and PSP-Net for feature extraction, future work could involve employing MAE (Masked Autoencoder) or SAM (Segment Anything Model) for more precise segmentation and feature extraction. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & **Bowl** & **Bottle** & **Can** & **Camera** & **Laptop** & **Mug** \\ \hline \multirow{3}{*}{Detection Success Rate} & SPD [8] & 23/25 & 24/25 & 24/25 & 15/25 & 20/25 & 21/25 \\ \cline{2-7}  & ours & 23/25 & 25/25 & 24/25 & 20/25 & 23/25 & 22/25 \\ \hline \multirow{3}{*}{Grasp Success Rate} & SPD [8] & 22/25 & 24/25 & 23/25 & 13/25 & 20/25 & 20/25 \\ \cline{2-7}  & ours & 20/25 & 24/25 & 23/25 & 19/25 & 22/25 & 22/25 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of grasping and detection success rates with SPD. For the model itself, the incorporation of depth images and point cloud information can alleviate, to some extent, the severe impact on prediction results caused by occlusion and lighting condition variations when only RGB images are used. In future work, we will further refine the model structure to ensure more accurate deformation of the prior when adapting to partially missing instances.