Robust diameter-based thickness estimation of 3D objects

• We propose a robust diameter-based thickness estimation method for 3D objects.
• This method is based on the Shape Diameter Function and a robust distance function.
• Benchmark results against operations and artifacts show the robustness of our method.
• Results regarding the accuracy also show improvements.

We 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.

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