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Convolutional-Recursive Deep Learning for 3D Object Classification
2012
Neural Information Processing Systems
Recent advances in 3D sensing technologies make it possible to easily record color and depth images which together can improve object recognition. Most current methods rely on very well-designed features for this new 3D modality. We introduce a model based on a combination of convolutional and recursive neural networks (CNN and RNN) for learning features and classifying RGB-D images. The CNN layer learns low-level translationally invariant features which are then given as inputs to multiple,
dblp:conf/nips/SocherHBMN12
fatcat:52b6veja5zbfffbujjkutfuuxu