基于多尺度球坐标变换与自适应层次聚类的三维点云可逆信息隐藏算法

任帅; 丛天煜

doi:10.11959/j.issn.1000-0801.DXKX260209

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基于多尺度球坐标变换与自适应层次聚类的三维点云可逆信息隐藏算法

更新时间：2026-06-24

- 基于多尺度球坐标变换与自适应层次聚类的三维点云可逆信息隐藏算法
- Reversible data hiding for 3D point clouds via multi-scale spherical coordinate and adaptive hierarchical clustering
- 电信科学 2026年
- 作者机构：
  
  长安大学信息工程学院，陕西西安 710064
- 作者简介：
  
  任帅（1982—），男，博士，长安大学信息工程学院教授，主要研究方向为信息隐藏技术。
  丛天煜，2024124131@chd.edu.cn
- 基金信息：
  
  国家自然科学基金资助项目(62372062)
- DOI：10.11959/j.issn.1000-0801.DXKX260209
  中图分类号： TP309.2
- 收稿：2026-04-01，
  
  修回：2026-06-23，
  
  录用：2026-06-24，
- 稿件说明：
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任帅, 丛天煜. 基于多尺度球坐标变换与自适应层次聚类的三维点云可逆信息隐藏算法[J/OL]. 电信科学, 2026.

Ren Shuai, Cong Tianyu. Reversible data hiding for 3D point clouds via multi-scale spherical coordinate and adaptive hierarchical clustering[J/OL]. Telecommunications Science, 2026.
任帅, 丛天煜. 基于多尺度球坐标变换与自适应层次聚类的三维点云可逆信息隐藏算法[J/OL]. 电信科学, 2026. DOI： 10.11959/j.issn.1000-0801.DXKX260209.

Ren Shuai, Cong Tianyu. Reversible data hiding for 3D point clouds via multi-scale spherical coordinate and adaptive hierarchical clustering[J/OL]. Telecommunications Science, 2026. DOI： 10.11959/j.issn.1000-0801.DXKX260209.

摘要

三维点云模型在数字版权保护、内容认证等领域具有重要应用价值。然而，其可逆信息隐藏研究仍存在嵌入容量与几何保真度难以兼顾的问题。提出了一种融合多尺度球坐标变换与自适应层次聚类的三维点云可逆信息隐藏算法，基于聚类局部点密度与尺度特征对点云进行三层分级处理，在各聚类的局部球坐标系下建立高斯加权径向距离预测器，并基于预测误差扩展（Prediction Error Expansion

PEE）技术实现水印的可逆嵌入与精确提取。实验结果表明，该算法在嵌入容量有效提升的同时，在阈值

时信噪比均保持在65dB以上，且在参数

的条件下误码率为零（恢复率100%），嵌入密度相较现有同类点云方法提升显著；消融实验进一步验证了球坐标变换、自适应分层策略及高斯加权预测器各模块的有效性。

Abstract

Three-dimensional point cloud models hold significant application value in fields such as digital copyright protection and content authentication. However

reversible data hiding for 3D point clouds still faces the challenge of balancing embedding capacity with geometric fidelity. A reversible data hiding algorithm for 3D point clouds was proposed by integrating multi-scale spherical coordinate transformation with adaptive hierarchical clustering. The point cloud was processed through a three-level hierarchical scheme based on local cluster point density and scale features. A Gaussian-weighted radial distance predictor was constructed within the local s

pherical coordinate system of each cluster

and reversible watermark embedding and accurate extraction were achieved based on Prediction Error Expansion (PEE) technique. Experimental results showed that the proposed algorithm achieved a significant improvement in embedding capacity

while the signal-to-noise ratio was maintained above 65 dB under the condition of threshold

and the bit error rate was zero (recovery rate 100%) when parameter

; ablation experiments further validated the effectiveness of each module

including the spherical coordinate transformation

the adaptive hierarchical strategy

and the Gaussian-weighted predictor. The embedding density of the proposed method demonstrates a marked improvement over existing plaintext-domain point cloud methods.

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references

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