发表机构
Peking University（北京大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对原始3D点云的所有权保护，提出一种基于八叉树架构的盲水印框架，直接在xyz坐标上联合学习嵌入与提取，在常见几何扰动下实现可靠消息恢复并保持低几何失真。
AI 中文摘要
原始3D点云是一种核心的几何表示。由于点集是不规则、非结构化的，并且经常受到重采样和几何预处理的改变，因此确立其所有权具有挑战性。我们提出了一种盲水印框架，该框架直接作用于xyz坐标，并支持对象级形状和场景级扫描。在验证时，嵌入的消息仅从观测到的点云中恢复，无需访问原始点云、颜色、法线或网格连通性。该方法通过前馈八叉树架构联合学习水印嵌入和提取，能够在大型点集上进行高效的多尺度几何推理。在训练期间，随机变换层使解码器暴露于常见的几何扰动，而渐进式姿态对齐提高了对姿态变化的鲁棒性。在对象级和场景级基准上的实验表明，在常见的几何处理下，消息恢复可靠，同时保持较低的几何失真。定性比较进一步表明，与手工设计的替代方案相比，学习到的扰动在视觉上不那么显眼，空间结构也更少。
英文摘要
Raw 3D point clouds are a core geometric representation. Establishing their ownership is challenging because point sets are irregular, unstructured, and frequently altered by resampling and geometric preprocessing. We present a blind watermarking framework that operates directly on xyz coordinates and supports both object-level shapes and scene-scale scans. At verification time, the embedded message is recovered from the observed point cloud alone, without access to the original point cloud, color, normals, or mesh connectivity. The method jointly learns watermark embedding and extraction through a feed-forward octree-based architecture, enabling efficient multi-scale geometric reasoning on large point sets. During training, a stochastic transformation layer exposes the decoder to common geometric perturbations, while progressive pose alignment improves robustness to pose changes. Experiments on object-level and scene-level benchmarks demonstrate reliable message recovery under common geometric processing while maintaining low geometric distortion. Qualitative comparisons further show that the learned perturbations are less visually conspicuous and less spatially structured than those of handcrafted alternatives.
