RGBD Camera based Material Recognition via Surface Roughness Estimation - Jungjun Kim; Hwasup Lim; Ahn, Sang Chul; Seungkyu Lee (2018 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV 2018), 2018)
Real world objects can be characterized effectively by their shape, color and material types. Material recognition of an arbitrary object at a distance is an important task for the improvement of object recognition, scene understanding, realistic rendering and various virtual and augmented reality applications. Researchers have tried to recognize material types based on color features, however material type of an object is not completely correlated with its visual appearance. In this paper, we propose a simple but effective surface roughness estimation method using single time-offlight(ToF) camera. A set of features extracted from the estimated roughness together with conventional color features are used for material type recognition. Experimental results on our material data set with 122 subjects show promising material type recognition results.