Bin Picking based on Harmonic Shape Contexts and Graph Based Matching
In this work we address the general bin picking problem where 3D data is available. We apply harmonic shape contexts (HSC) features since these are invariant to translation, scale, and 3D rotation. Each object is divided into a number of sub models each represented by a number of HSC features. These are compared with HSC features extracted in the current data using a graph based scheme. Results show that the approach is somewhat sensitive to noise, but works in presence of occlusion
18th International Conference on Pattern Recognition (ICPR'06)