This paper proposes an approach for RGB-D object recognition by integrating a CNN model with recursive neural networks. It first employs a pre-trained CNN model as the underlying feature extractor to get visual features at different layers for RGB and depth modalities. Then, a deep recursive model is applied to map these features into highlevel representations. Finally, multi-level information is fused to produce a strong global representation of the entire object image. In order to utilize the

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... CNN model trained on large-scale RGB datasets for depth domain, depth images are converted to a representation similar to RGB images. Experimental results on the Washington RGB-D Object dataset show that the proposed approach outperforms previous approaches.