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http://dx.doi.org/10.9717/kmms.2020.23.2.166

Sequential Point Cloud Generation Method for Efficient Representation of Multi-view plus Depth Data  

Kang, Sehui (School of Computer Science and Electrical Engineering, Handong Global University)
Han, Hyunmin (School of Computer Science and Electrical Engineering, Handong Global University)
Kim, Binna (School of Computer Science and Electrical Engineering, Handong Global University)
Lee, Minhoe (School of Computer Science and Electrical Engineering, Handong Global University)
Hwang, Sung Soo (School of Computer Science and Electrical Engineering, Handong Global University)
Bang, Gun (Electronics and Telecommunications Research Institute)
Publication Information
Journal of Korea Multimedia Society / v.23, no.2, 2020 , pp. 166-173 More about this Journal
Abstract
Multi-view images, which are widely used for providing free-viewpoint services, can enhance the quality of synthetic views when the number of views increases. However, there needs an efficient representation method because of the tremendous amount of data. In this paper, we propose a method for generating point cloud data for the efficient representation of multi-view color and depth images. The proposed method conducts sequential reconstruction of point clouds at each viewpoint as a method of deleting duplicate data. A 3D point of a point cloud is projected to a frame to be reconstructed, and the color and depth of the 3D point is compared with the pixel where it is projected. When the 3D point and the pixel are similar enough, then the pixel is not used for generating a 3D point. In this way, we can reduce the number of reconstructed 3D points. Experimental results show that the propose method generates a point cloud which can generate multi-view images while minimizing the number of 3D points.
Keywords
Multi-view plus Depth Data; Point Cloud; Sequential Reconstruction; Color Thresholding; Depth Thresholding;
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