CPPF++: Uncertainty-Aware Sim-to-Real 6D Object Pose Estimation by Probabilistic Vote Aggregation

The core message of this paper is to introduce a novel method called CPPF++ that leverages a probabilistic approach to model the uncertainty in the voting process for sim-to-real 6D object pose estimation. CPPF++ builds upon the foundational point-pair voting scheme of CPPF, reformulating it through a probabilistic view to address the challenge of vote collision. It also incorporates several innovative modules, including noisy pair filtering, online alignment optimization, and a tuple feature ensemble, to enhance the robustness and accuracy of the model.