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A novel compression framework of the dense point-cloud model for cultural heritage artifacts

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Abstract

With the help of laser scanner, the accurate digital information of cultural relics can be obtained. However, how to transfer the enormous and dense data by an efficient way, is still the key problem for computer-aided cultural relic protection. In this paper, we proposed a novel framework for compression and reconstruction of the dense point cloud model for cultural heritage artifacts. Firstly, the collected point cloud model were regarded as 3D geometric signals, and an octree method based on a hash function is utilized to divide the neighborhood relationship. Then, the discrete Laplacian sparse basis of 3D geometric signals is constructed, and the sensing matrix is further obtained by the stochastic Gauss matrix. However, the sensing matrix is always enormous, which means that in practice, it will cause a huge amount of computation and slow recovery. To solve this problem, we proposed a Truncated Singular Value Decomposition (TSVD)-based low rank approximation approach for the inverse reconstruction. Further, a preconditioning method is investigated to reduce the coherence of the converted sensing matrix. In order to test the performance of our framework, the 3D point cloud model of terracotta warriors and tri-coloured glazed pottery of Hu people are adopted. Experimental results demonstrate that our method can well recover the weak texture cultural heritage artifacts of the 3D point cloud model. The results achieved here are significant for virtual display and data processing of the dense point cloud model.

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Acknowledgments

The authors would like to thank all the reviewers for their valuable comments. This research was funded by the National Key Research and Development Program of China (No. 2019YFC1521102; No.2019YFC1521103), the Key Research and Development Program of Shaanxi Province of China (No. 2019GY-215), the National Natural Science Foundation of China (No.61902317; No.61772421; No.2017YFB1402103; No.61731015); the Science and Technology Plan Program in Shaanxi Province of China (No.2019JQ-166); the Major Industrial Chain Projects in ShaanXi Province of China (No. 2019ZDLSF07-02).

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  1. Haibo Zhang and Kang Li contributed equally to this work.

Authors and Affiliations

  1. School of Information Sciences and Technology, Northwest University, Xi’an, China

    Haibo Zhang, Kang Li, Jiaojiao Kou, Linqi Hai, Junbo Zhang, Mingquan Zhou, Guohua Geng & Shunli Zhang

  2. Tencent Technology, Chendu, Sichuan, China

    Xiaoxue Chen

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  1. Haibo Zhang
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Correspondence to Shunli Zhang.

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Zhang, H., Li, K., Kou, J. et al. A novel compression framework of the dense point-cloud model for cultural heritage artifacts. Multimed Tools Appl 81, 32817–32839 (2022). https://doi.org/10.1007/s11042-022-13084-8

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