Abstract
In order to prevent illegal dissemination and misappropriation of 3D models, this paper presents a novel 3D model zero-watermarking method using geometrical and statistical features. It firstly obtains some feature vertices according to an adaptive sampling scheme based on the Gaussian curvature of an input 3D model. Such vertices can describe the basic shape of the model. And then, it uses FPFH (fast point feature histograms) to generate a multidimensional histogram descriptor representing the statistical characteristics of the neighborhood of the feature vertices. After that, a binary watermark information can be generated to achieve the aim of copyright protection for the input 3D model. Experimental results show that the proposed method has good robustness against various attacks including similarity transformation, element reordering, noise, simplification, smoothing, cropping attacks, etc. Furthermore, it is very competitive with the state-of-the-art watermarking methods for 3D models.
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Acknowledgements
We would like to thank the anonymous reviewers for their helpful comments. This work is supported by Natural Science Foundation of Jilin Province in China (No. 20210101472JC).
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Li, H., Hu, J., Wang, X., Xie, Q. (2023). Novel 3D Model Zero-Watermarking Using Geometrical and Statistical Features. In: Lu, H., et al. Image and Graphics . ICIG 2023. Lecture Notes in Computer Science, vol 14357. Springer, Cham. https://doi.org/10.1007/978-3-031-46311-2_23
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DOI: https://doi.org/10.1007/978-3-031-46311-2_23
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