# Refined Prior Guided Category-Level 6D Pose Estimation and Its Application on Robotic Grasping
###### Abstract  
Estimating the 6D pose and size of objects is crucial in the task of visual grasping for robotic arms. Most current algorithms still require the 3D CAD model of the target object to match with the detected points, and they are unable to predict the object's size, which significantly limits the generalizability of these methods. In this paper, we introduce category priors and extract high-dimensional abstract features from both the observed point cloud and the prior to predict the deformation matrix of the reconstructed point cloud and the dense correspondence between the reconstructed and observed point clouds. Furthermore, we propose a staged geometric correction and dense correspondence refinement mechanism to enhance the accuracy of regression. In addition, a novel lightweight attention module is introduced to further integrate the extracted features and identify potential correlations between the observed point cloud and the category prior. Ultimately, the object's translation, rotation, and size are obtained by mapping the reconstructed point cloud to a normalized canonical coordinate system. Through extensive experiments, we demonstrate that our algorithm outperforms existing methods in terms of performance and accuracy on commonly used benchmarks for this type of problem. Additionally, we implement the algorithm in robotic arm-grasping simulations, further validating its effectiveness. pose estimation; robotic grasping; grasp detection; channel attention; scene understanding +
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rotation through a network, overlooks the object's geometric features. [PERSON] [7] proposed the Normalized Object Coordinate Space (NOCS), a shared canonical representation for all objects within the same category. In NOCS, each vertex of an object is scaled into a unit-length cube, with all objects within the same category aligned in orientation and centered. The authors also provided the dense correspondence between the NOCS coordinates and the real point cloud. Therefore, if the NOCS coordinates of an object can be accurately predicted, the real point cloud image can be obtained. Since NOCS coordinates are within a unit-length cube, they are more conducive to neural network regression. Building on NOCS, further research [8] introduced category priors as input. The network learns feature information from the observed point cloud and the prior, deforms the prior point cloud, and maps it to NOCS through dense correspondence to estimate the 6D object pose and size. Despite the significant progress made by previous researchers in this area, we observe that these studies primarily focus on the extraction and fusion of feature information from the observed object and the prior point clouds [9; 10], while lacking explicit deformation of the point clouds. Furthermore, deforming the prior point cloud and establishing correspondence with the NOCS model is a dense regression problem. The direct regression of high-dimensional features through a single network can easily lead to information loss and local optima. Although [11] attempts have been made to compensate for the missing feature information, the lack of RGB image data reduces the network's generalization capability. Moreover, the absence of RGB guidance makes the results susceptible to interference when the target object is occluded, which is particularly dangerous in robotic arm-grasping tasks. To address these issues, we propose a grasping detection method based on refined prior-guided category-level 6D object pose estimation. This method extracts the 6D pose of the target object from the category-level pose estimation network, which includes the object's translation and rotation in 3D space, as well as the object's 3D bounding box. We introduce category priors to predict the offsets between the prior point cloud and the observed object point cloud. The deformed point cloud is then mapped to the NOCS coordinate system through dense correspondence.