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    Article

    Augmented Autoencoders: Implicit 3D Orientation Learning for 6D Object Detection

    We propose a real-time RGB-based pipeline for object detection and 6D pose estimation. Our novel 3D orientation estimation is based on a variant of the Denoising Autoencoder that is trained on simulated views ...

    Martin Sundermeyer, Zoltan-Csaba Marton in International Journal of Computer Vision (2020)

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    Chapter and Conference Paper

    Applicability of Deep Learned vs Traditional Features for Depth Based Classification

    In robotic applications, highly specific objects such as industrial parts, for example, often need to be recognized. In these cases methods can’t rely on the online availability of large labeled training data ...

    Fabio Bracci, Mo Li, Ingo Kossyk in Computational Modeling of Objects Presente… (2019)

  3. Chapter and Conference Paper

    Implicit 3D Orientation Learning for 6D Object Detection from RGB Images

    We propose a real-time RGB-based pipeline for object detection and 6D pose estimation. Our novel 3D orientation estimation is based on a variant of the Denoising Autoencoder that is trained on simulated views ...

    Martin Sundermeyer, Zoltan-Csaba Marton, Maximilian Durner in Computer Vision – ECCV 2018 (2018)

  4. No Access

    Chapter and Conference Paper

    On the Use of the Tree Structure of Depth Levels for Comparing 3D Object Views

    Today the simple availability of 3D sensory data, the evolution of 3D representations, and their application to object recognition and scene analysis tasks promise to improve autonomy and flexibility of robots...

    Fabio Bracci, Ulrich Hillenbrand in Computer Analysis of Images and Patterns (2017)