# Refined Prior Guided Category-Level 6D Pose Estimation and Its Application on Robotic Grasping
## 4 Results
### Simulation of Robotic Grasping  
To further validate the effectiveness of the model, we applied the model proposed in this paper to robotic arm-grasping experiments, conducting simulations through PyBullet. The experiments utilized a UR3 robotic arm equipped with a Robotiq 85 gripper. The models used in the simulation were bottle, bowl, camera, can, laptop, and mug, with three instances of each model. Some of these models are shown in Figure 9. Two cameras were used, a Realsense 415 positioned above the robotic arm, and a Realsense 435i positioned directly in front of the robotic arm, both fixed and not moving with the arm. The simulation involved 50 scenes, each loaded with three objects, with random object positions and rotations. Each object category was subjected to 25 grasping attempts. Figure 9 shows some models we used during the simulation. Figure 10 shows some of the scenes used in the simulation.  
The 6D pose estimation of symmetrical objects is a significant challenge in 6D pose estimation. This is because a symmetrical object may have multiple correct rotation predictions, while there is only one ground truth. If the algorithm does not effectively address the mismatch between the correct predictions and the ground truth, it can lead to difficulties in training convergence and result in inaccurate final rotation predictions. Figure 11 illustrates the grasping process of a symmetrical object (bottle) in two different scenarios with varying positions and rotations, demonstrating that our algorithm effectively handles the challenges associated with predicting the rotation of symmetrical objects.  
The Low-rank Transformer used in SGPA increases inference time. Hence, we chose another relatively lightweight model, SPD, for comparison with our method. Table 4 presents the detection success rates and grasping success rates for each object category, comparing these results with those obtained when implementing the SPD algorithm.  
To ensure a fair comparison, we implemented the SPD algorithm using the same parameters and structures for the shared components. Specifically, we utilized Mask R-CNN for segmenting the target objects. For semantic feature extraction from RGB images, we employed a PSPNet with a ResNet-18 backbone. The input priors were also generated using the information obtained from our trained encoder, with the prior point cloud consisting of 1024 points.  
Figure 11: Illustration of the process of robotic grasping. Three subfigures represent the grasping process of the bottle in two different scenarios. From left to right, they show detection, grasping, and removal.  
Figure 10: Illustration of part of scenes we used for simulation.  
The experimental results indicate that our proposed model can effectively identify target objects, achieving a detection success rate of 91.3%, which is an improvement of 6.6% over SPD. As seen in Table 3, both models have relatively low detection success rates for the camera category, primarily because the camera models selected for the simulation differ significantly in shape from the category prior point cloud. This highlights that intra-class variation remains a bottleneck problem for category-level 6D pose estimation. However, our algorithm demonstrates a better capability to address the intra-class variation issue compared to SPD, thereby proving the rationale and effectiveness of our approach.