RGBD Camera based Material Recognition via Surface Roughness Estimation

Authors
Jungjun KimHwasup LimAhn, Sang ChulSeungkyu Lee
Issue Date
2018-03-14
Publisher
IEEE
Citation
18th IEEE Winter Conference on Applications of Computer Vision (WACV), pp.1963 - 1971
Abstract
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.
ISSN
2472-6737
URI
https://pubs.kist.re.kr/handle/201004/79438
DOI
10.1109/WACV.2018.00217
Appears in Collections:
KIST Conference Paper > 2018
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