3D convolutional object recognition using volumetric representations of depth data

Abstract

Hand-crafted features are widely used in object recognition field. Recent advances in convolutional neural networks allow to extract features automatically and produce better results in object recognition without considering about feature design. Although RGB and depth data are used in some convolutional network based approaches, volumetric information hidden in depth data is not fully utilized. We present a 3D convolutional neural network based approach to utilize volumetric information extracted from depth data. Using a single depth image, a view-based incomplete 3D model is constructed. Although this method does not provide enough information to build a complete 3D model, it is still useful to recognize objects. To the best of our knowledge, the proposed approach is the first volumetric study on the Washington RGB-D Object Dataset and achieves results as competitive as the state-of-the-art works.