Université catholique de Louvain Communication and Remote Sensing Lab.

Abstract

In this paper, we propose a blind watermarking scheme based on automatic feature points detection. The irregular sampling of 3D shapes is a challenging issue for extending well-known signal processing tools. 3D shape watermarking schemes have to resist to common resampling operations used for example in some compression applications. We propose an automatic selection of intrinsic feature points that are robust against surface remeshing. They are detected as multi-scale robust degeneracies of the shape curvature tensor field. The impact of the sampling on the curvature estimation is studied. These points are then used as seeds in the partition of the shape into fast approximated geodesic triangles. Each of them is then remeshed with a regular connectivity and watermarked in the mesh spectral domain. The watermark perturbations computed on the remeshed triangles are then projected on the original points of the 3D object. We discuss the robustness of the feature points and of the overall scheme under various watermarking attacks