Abstract：To tackle the issues of depth quality and non-linear classification in the large-scale RGB-D dataset, a 3D object recognition method is designed on the basis of convolutional-recursive neural network(CNN-RNN) and kernel extreme learning machine(KELM). Firstly, a depth coding algorithm is introduced to correct the numerical losses and noises in the original depth cue and unify the point cloud into the standard angle. And the original depth and the encoded depth are fused as the new depth cue. Secondly,multi-cue hierarchical features are learned using CNN-RNN. Meanwhile, the two-way spatial pyramid pooling method is exploited for each cue. Finally, KELM is constructed as the classifier to recognize 3D objects. The experimental results demonstrate the proposed method effectively improves the 3D object recognition accuracy and the classification efficiency.
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2017, Vol. 30 
