# RSCS6D: Keypoint Extraction-Based 6D Pose Estimation
## 3 Proposed Method
### 6D Pose Estimation Network  
After extracting keypoint cloud using the RSCS algorithm, we construct a network for pose estimation. Inspired by ResNet and G2L-Net, the RSCS6D algorithm incorporates a translational residual to accelerate network convergence. Specifically, we design a network that predicts the translation by estimating the deviation between the predicted translation and the object's center. This residual helps refine the translation prediction.  
To address the limitations of global point features and inspired by G2L-Net, RSCS6D defines a point cloud key vector cluster to capture viewpoint information. We also design a rotation prediction network and a rotation residual prediction network based on keypoint cloud to extract features and predict rotation and residual information.  
Finally, the RSCS6D algorithm uses the Kabsch algorithm, combined with the eight vertices of the ground-truth 3D tight bounding box, to compute the predicted rotation matrix. This comprehensive approach ensures accurate and efficient pose estimation.  
The training process of the RSCS6D algorithm is divided into two main components: global localization and pose estimation.  
1. Global Localization: This component involves obtaining pixel-wise labels of the target object using a semantic segmentation algorithm. In this work, we employ the PIDNet algorithm [28], though other advanced methods can also be used.
2. Pose Estimation: The input to this component is the keypoint cloud extracted using the RSCS algorithm. The process begins with translation prediction, which estimates the deviation between the predicted translation and the object's center. This residual is then used to refine the translation prediction by combining it with the predicted object center during the detection phase.  
For rotation prediction, the network consists of three modules:  
* Key Vector Cluster Module: Predicts unit vectors pointing to keypoints. The loss function is the mean squared error (MSE) between the predicted and ground-truth direction vectors.
* Rotation Prediction Module: Integrates point-wise embedding features to predict the object's rotation. The predicted tight bounding box data from this module is compared with the ground-truth data using the Kabsch algorithm to estimate the point cloud rotation. The loss function is the MSE between the predicted and ground-truth tight bounding box data.
* Rotation Residual Prediction Module: Inspired by the residual structure in ResNet, this module estimates the residual between the predicted and ground-truth rotations. It leverages point cloud key vector clusters to improve efficiency and reduce computational overhead.  
This structured approach ensures accurate and efficient 6D pose estimation by combining semantic segmentation, keypoint extraction, and residual learning.  
The RSCS6D pose estimation network draws inspiration from G2L-Net but features two fundamental differences. First, to address the issue of poor point cloud segmentation by traditional object detection algorithms, especially when objects are occluded, RSCS6D employs the semantic segmentation algorithm PIDNet to mitigate the impact of occlusion. Additionally, it leverages pixel-level two-dimensional segmentation results to perform three-dimensional pixel-level segmentation on the local scene point clouds (i.e., coarse point clouds), thereby obtaining the target key point clouds. Second, to tackle the problem of redundant information in traditional pose estimation algorithms that use coarse point clouds, RSCS6D precisely extracts key points from the coarse point clouds. This approach effectively reduces information redundancy, suppresses noise interference, accelerates network convergence, and enhances the efficiency and accuracy of pose estimation.