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Part-driven Visual Perception of 3D Objects

During the last years, approaches based on convolutional ne ural networks (CNN) had substantial success in visual object perception. CNNs turned out to be capable of ex tracting high-level features of objects, which allow for fine-grained classification. However, some object classes exhibit tremendous variance with respect to their instances appearance. We believe that considering object parts as an intermediate representation could be helpful in these cases. In this work, a part-driven percep tion of everyday objects with a rotation estimation is implemented using deep convolution neural networks. The used network is trained and tested on artificially generated RGB-D data. The approach has a potential to be used for part recognition of realistic sensor recordings in present robot systems.