Iterative pose refinement for object pose estimation based on RGBD data

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

*Corresponding author for this work

Research output: Contribution to journalLetterpeer-review

3 Citations (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.

Original languageEnglish
Article number4114
Pages (from-to)1-12
Number of pages12
JournalSensors (Switzerland)
Issue number15
Publication statusPublished - 2020 Aug


  • Convolution neural network
  • Deep learning
  • Object pose estimation

ASJC Scopus subject areas

  • Analytical Chemistry
  • Information Systems
  • Atomic and Molecular Physics, and Optics
  • Biochemistry
  • Instrumentation
  • Electrical and Electronic Engineering


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