No abstract

International audienceWe propose a robust thickness estimation approach for 3D objects based on the Shape Diameter Function (SDF). Our method first applies a modified strategy to estimate the local diameter with increased accuracy. We then compute a scale-dependent robust thickness estimate from a point cloud, constructed using this local diameter estimation and a variant of a robust distance function. The robustness of our method is benchmarked against several operations such as remeshing, geometric noise and artifacts common in triangle soups. The experimental results show a more stable local thickness estimation than the original SDF, and consistent segmentation results on defect-laden inputs

Modulating the distances between the vertices and the center of mass of a triangular mesh is a popular approach to watermark 3D objects. Prior work has formulated this approach as a quadratic programming problem which minimizes the geometric distortion while embedding the watermark payload in the histogram of distances. To enhance this framework, we introduce two watermarking components, namely the spread transform and perceptual shaping based on roughness information. Benchmarking results showcase the benefits of these addons with respect to the fidelity-robustness trade-off.

No abstract

No abstract