What question did this study set out to answer?

The goal is to improve the protection of 3D point cloud models through robust watermarking and information hiding techniques.

May 9, 2026Open Access

A Dual‐Protection Framework for 3D Point Clouds: Robust Watermarking via RDWT‐SVD and High‐Capacity Steganography With Incremental Point Clouds

Key Points

The goal is to improve the protection of 3D point cloud models through robust watermarking and information hiding techniques.
Utilized affine invariant processing for robustness against rotation, translation, and scaling attacks.
Embedded digital watermark into a processed 2D image using techniques like principal component analysis and redundant discrete wavelet transform.
Developed an incremental information hiding algorithm to enhance embedding capacity in 3D models.
Peak signal-to-noise ratios of watermarked models exceeded 47 dB, with the Happy_recon model reaching 56.83 dB.
Achieved an embedding capacity of 1643 bits in the Happy_recon model, 57.22% higher than the label clustering method.
Bit accuracy rate against a 40% sheet-cutting attack remained above 0.85, demonstrating robust performance against geometric attacks.

Abstract

3D point cloud models are being utilized more and more in computer vision, virtual reality, intelligent manufacturing, and cultural heritage preservation as a result of the quick advancements in computer graphics and 3D modeling technologies. Therefore, protecting the copyright and data integrity of 3D models has become an important issue. The study enhances the robustness of the model to rotation, translation, and scaling attacks by affine invariant processing. Moreover, the digital watermark is embedded into the processed 2D image by combining techniques such as principal component analysis, coordinate projection, redundant discrete wavelet transform, and singular value decomposition. The experimental results show that the peak signal‐to‐noise ratios of the four 3D models after embedding the watermark are all higher than 47 dB, among which the Happyᵣecon model reaches 56. 83 dB. The incremental 3D point cloud information hiding algorithm achieves an embedding capacity of 1643 bits in the Happyᵣecon model, which is 57. 22% higher than that of the label clustering method. In the watermark robustness test, the bit error rate of this method against geometric attacks is zero, and the normalized cross‐relation number is 1. Under a 40% sheet‐cutting attack, the bit accuracy rate still remained above 0. 85. The peak signal‐to‐noise ratio after embedding the watermark is on average approximately 18. 2% higher than that of the method based on geometric features and approximately 9. 5% higher than that of the method based on the transform domain. The information hiding algorithm based on incremental point cloud improves the embedding capacity by 57. 22% compared with the label clustering method and by 29. 98% compared with the distance feature method. In terms of robustness, the resistance to geometric attacks is completely immune, and the bit accuracy rate against noise attacks is approximately 5. 9% higher than that of deep learning methods. In conclusion, the proposed method effectively enhances the performance of 3D point cloud models in watermarking and information hiding tasks.

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