Iterative pose refinement for object pose estimation based on RGBD data

Shao Kang Huang, Chen Chien Hsu*, Wei Yen Wang, Cheng Hung Lin


研究成果: 雜誌貢獻通訊期刊論文同行評審

3 引文 斯高帕斯(Scopus)


Accurate estimation of 3D object pose is highly desirable in a wide range of applications, such as robotics and augmented reality. Although significant advancement has been made for pose estimation, there is room for further improvement. Recent pose estimation systems utilize an iterative refinement process to revise the predicted pose to obtain a better final output. However, such refinement process only takes account of geometric features for pose revision during the iteration. Motivated by this approach, this paper designs a novel iterative refinement process that deals with both color and geometric features for object pose refinement. Experiments show that the proposed method is able to reach 94.74% and 93.2% in ADD(-S) metric with only 2 iterations, outperforming the state-of-the-art methods on the LINEMOD and YCB-Video datasets, respectively.

頁(從 - 到)1-12
期刊Sensors (Switzerland)
出版狀態已發佈 - 2020 8月

ASJC Scopus subject areas

  • 分析化學
  • 資訊系統
  • 原子與分子物理與光學
  • 生物化學
  • 儀器
  • 電氣與電子工程


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