I-HS4PCS: Object 6D Pose Estimation Method Based on Harris3D-ikdTree Optimization

Object 6D pose estimation is a critical task in image processing and various other fields. It involves determining both the three-degree-of-freedom translation and the three-degree-of-freedom rotation information of the target object within the camera coordinate system. I-HS4PCS algorithm is introduced to address the issue of extended execution times in the current pose estimation algorithm. Initially, adapted Harris3D is employed to identify and filter feature points that effectively encapsulate point cloud feature information from the original point cloud dataset. These feature points are subsequently utilized as input data for Super4PCS coarse pose estimation step. Additionally, ikd-Tree data structure and point-wise deletion strategy are leveraged to enhance the ICP fine pose estimation procedure, significantly accelerating the search for the closest points. By maintaining the accuracy of pose estimation while simultaneously boosting algorithmic efficiency, I-HS4PCS have achieved notable improvements. The experimental validation, conducted using the Stanford University dataset and ShapeNet dataset, demonstrates significant advancements in comparison to other algorithms that employ Super4PCS for coarse pose estimation. Specifically, when compared to ICP, AA-ICP, and Sparse ICP, I-HS4PCS showcased a remarkable improvement in execution time by 80%, 66.8% and 88.3%.