6 Result(s)
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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 ...
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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 ...
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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 ...
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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...
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Article
Improving object classification robustness in RGB-D using adaptive SVMs
Nowadays object recognition is a fundamental capability for an autonomous robot in interaction with the physical world. Taking advantage of new sensing technologies providing RGB-D data, the object recognition...
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Chapter and Conference Paper
Object Categorization in Clutter Using Additive Features and Hashing of Part-Graph Descriptors
Detecting objects in clutter is an important capability for a household robot executing pick and place tasks in realistic settings. While approaches from 2D vision work reasonably well under certain lighting c...
