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This paper investigates the value of depth modality in object classification in RGB-D images. We use a simple model based on a multi-layered convolutional neural network which we train on a dataset of segmented RGB-D images of household and office objects. We evaluate and quantify the benefit of additional depth modality and its effect on classification accuracy on this dataset. Also, we compare the benefit of depth channel against the addition of color to grayscale image. Our experimental results support a conclusion, that for these categories of objects the depth modality provides a significant benefit to classification, which also outweighs the benefit of color information. Similar supporting evidence found in recent research is shown in comparison along with the resulting quantified benefit of depth modality.

EVALUATION OF DEPTH MODALITY IN CONVOLUTIONAL NEURAL NETWORK CLASSIFICATION OF RGB-D IMAGES