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Volume: 27 | Article ID: art00027
Removing gloss using Deep Neural Network for 3D Reconstruction
  DOI :  10.2352/issn.2169-2629.2019.27.27  Published OnlineOctober 2019

3D reconstruction is used for inspection of industrial products. The demand for measuring 3D shapes is increased. There are many methods for 3D reconstruction using RGB images. However, it is difficult to reconstruct 3D shape using RGB images with gloss. In this paper, we use the deep neural network to remove the gloss from the image group captured by the RGB camera, and reconstruct the 3D shape with high accuracy than conventional method. In order to do the evaluation experiment, we use CG of simple shape and create images which changed geometry such as illumination direction. We removed gloss on these images and corrected defect parts after gloss removal for accurately estimating 3D shape. Finally, we compared 3D estimation using proposed method and conventional method by photo metric stereo. As a result, we show that the proposed method can estimate 3D shape more accurately than the conventional method.

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Futa Matsushita, Ryo Takahasshi, Mari Tsunomura, Norimichi Tsumura, "Removing gloss using Deep Neural Network for 3D Reconstructionin Proc. IS&T 27th Color and Imaging Conf.,  2019,  pp 143 - 148,

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Copyright © Society for Imaging Science and Technology 2019
Color and Imaging Conference
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