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[![Documenter](https://github.com/rwth-irt/PoseErrors.jl/actions/workflows/documenter.yml/badge.svg)](https://github.com/rwth-irt/PoseErrors.jl/actions/workflows/documenter.yml)
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[![](https://img.shields.io/badge/docs-stable-blue.svg)](https://rwth-irt.github.io/PoseErrors.jl)
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# About
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This code has been produced during while writing my Ph.D. (Dr.-Ing.) thesis at the institut of automatic control, RWTH Aachen University.
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If you find it helpful for your research please cite this:
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> T. Redick, „Bayesian inference for CAD-based pose estimation on depth images for robotic manipulation“, RWTH Aachen University, 2024. doi: 10.18154/RWTH-2024-04533.
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# PoseErrors.jl
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A good overview and rationale behind 6D pose error metrics can be found in the [BOP-challenge](https://bop.felk.cvut.cz/challenges/bop-challenge-2019/#evaluationmethodology).
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They prefer Maximum Symmetry-Aware Surface Distance (MSSD) over Averade Average Distance of Model Points with indistinguishable views (ADD-S = ADI), because they can yield low errors with bad visual alignment.

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