At the core of every object recognition system lies the development and integration of distinct feature descriptors to create object representations robust against varying perspectives or lightning conditions. Recent work has primarily focused on the development of distinct point features. While these features achieve impressive recognition results, point features fail to capture the shape and appearance of an object with less or even without texture. This paper proposes a novel method for the rapid and dense computation of 2D and 3D image cues from RGB-D data to target the recognition of objects without rich texture and a global histogram-based descriptor for the distinct description of object models.
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