An empirical analysis of deep feature learning for RGB-D object recognition

Abstract

Conventional deep feature learning methods use the same model parameters for both RGB and depth domains in RGB-D object recognition. Since the characteristics of RGB and depth data are different, the suitability of such approaches is suspicious. In this paper, we empirically investigate the effects of different model parameters on RGB and depth domains using the Washington RGB-D Object Dataset. We have explored the effects of different filter learning approaches, rectifier functions, pooling methods, and classifiers for RGB and depth data separately. We have found that individual model parameters fit best for RGB and depth data.