In order to reduce the file size of three-dimensional (3D) shape data, we propose two compression algorithms: two-channel phase coding algorithm and three-channel phase coding algorithm. In these two algorithms, 3D sh...
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In order to reduce the file size of three-dimensional (3D) shape data, we propose two compression algorithms: two-channel phase coding algorithm and three-channel phase coding algorithm. In these two algorithms, 3D shape information is encoded into the color channels of a single 24-bit color image based on virtualstructured-light projection system. Tested with a hemisphere of 1 mm diameter used in another virtual structured-light coding algorithm, which was proposed by Nikolaus Karpinsky and Song Zhang in 'composite phase-shifting algorithm for three-dimensional shape compression' in 2010, the two-channel phase coding algorithm achieves compression ratio 1:56.6 with reconstruction error of +/- 4.91 x 10(-4) mm, and the three-channel algorithm gains compression ratio 1:33.8 with reconstruction error of +/- 0.36 x 10(-4) mm. The theoretical analyses demonstrate that the relative reconstruction errors of our coding algorithms can be reduced to 7.66 x 10(-6) and 2.99 x 10(-8) of the height of the 3D object, respectively. With these theoretical analyses, the virtual structured-light coding algorithms can be used to achieve desired reconstruction qualities with high compression ratios in storing, transmitting, encrypting 3D shapes and constructing 3D face databases. (C) 2012 Elsevier Ltd. All rights reserved.
Converting three-dimensional (3D) point cloud data to two-dimensional (2D) image based on virtualstructured-light system is a popular method in 3D data compression because the image format is easier to process by the...
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Converting three-dimensional (3D) point cloud data to two-dimensional (2D) image based on virtualstructured-light system is a popular method in 3D data compression because the image format is easier to process by the exiting storing and transmitting methods. When this method is used in the 3D point cloud compression, the quantization error is introduced during the coordinate mapping. To solve this problem, a virtualstructured-light 3D point cloud compression algorithm based on geometric reshaping is presented. And a least-square system parameter optimization method is proposed to further improve the data decoding accuracy. In the proposed method, the 3D spatial coordinates are reshaped to a 2D matrix first, and then the 2D matrix is stored as a "Holoimage" by using the parameter optimized virtualstructured-light system. The quantization error introduced by coordinate mapping between X and Y coordinates of the 3D point cloud and the 2D image pixel coordinates is suppressed, so the decoding accuracy is improved. In addition, the geometric information of the 3D point cloud is hidden synchronously when the 3D point cloud is compressed, which is of great significance in the copyright protection of the point cloud data. Experiments verify the effectiveness of the proposed algorithm. The decoding root mean square error (RMSE) of the proposed method is decreased by 79.86% on average compared with the traditional one under the same compression ratio, and the compression ratio of the proposed method is 1.96 times bigger than the traditional one under the similar decoding accuracy. (C) 2022 SPIE and IS&T
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