BOP: Benchmark for 6D Object Pose Estimation

@inproceedings{Hodan2018BOPBF,
  title={BOP: Benchmark for 6D Object Pose Estimation},
  author={Tom{\'a}s Hodan and Frank Michel and Eric Brachmann and Wadim Kehl and A. Buch and D. Kraft and B. Drost and Joel Vidal and Stephan Ihrke and X. Zabulis and Caner Sahin and Fabian Manhardt and Federico Tombari and Tae-Kyun Kim and Jiri Matas and C. Rother},
  booktitle={ECCV},
  year={2018}
}
We propose a benchmark for 6D pose estimation of a rigid object from a single RGB-D input image. The training data consists of a texture-mapped 3D object model or images of the object in known 6D poses. The benchmark comprises of: (i) eight datasets in a unified format that cover different practical scenarios, including two new datasets focusing on varying lighting conditions, (ii) an evaluation methodology with a pose-error function that deals with pose ambiguities, (iii) a comprehensive… Expand
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