CPPF++: Uncertainty-Aware Sim2Real Object Pose Estimation by Vote Aggregation

Published in TPAMI, 2024

Yang You, Wenhao He, Jin Liu, Hongkai Xiong, Weiming Wang, Cewu Lu

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Abstract

Object pose estimation is crucial in 3D vision, but real-world data collection is costly. This paper presents CPPF++, a new sim-to-real pose estimation method using only 3D CAD models. CPPF++ enhances the point-pair voting scheme with probabilistic modeling to address voting collision and iterative noise filtering. We introduce N-point tuples for richer voting context and a new dataset, DiversePose 300, to test current methods in diverse scenarios. Our results show CPPF++ significantly reduces the gap between simulation and real-world performance.

BibTeX

@article{you2024cppf,
  title     = {CPPF++: Uncertainty-Aware Sim2Real Object Pose Estimation by Vote Aggregation},
  author    = {Yang You and Wenhao He and Jin Liu and Hongkai Xiong and Weiming Wang and Cewu Lu},
  journal   = {IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},
  year      = {2024},
  eprint    = {2211.13398},
  archivePrefix = {arXiv}
}

Recommended citation: You, Y., He, W., Liu, J., Xiong, H., Wang, W., & Lu, C. (2022). CPPF++: Uncertainty-Aware Sim2Real Object Pose Estimation by Vote Aggregation. TPAMI 2024.