Mesh denoising is a classical problem and has made great progress, but it has not been solved perfectly. Our paper presents an adaptive and dynamic mesh denoising method, which can remove noise effectively while preserving sharp features and visually meaningful fine-scale components. Most state-of-the-art approaches still fall short of robustly handling various noisy 3D models, because optimal parameters are usually selected manually, and remain unchanged for the whole model and throughout the whole denoising procedure. Actually, the parameters should be adaptively adjusted according to the feature intensity of different regions in each iteration and dynamically changed in subsequent iterations to avoid over-smooth. In this paper, the parameters are determined automatically and adjusted dynamically, which allows the real shape of the surface to be restored as much as possible, especially in feature regions. Extensive qualitative and quantitative experiments on various noisy meshes have demonstrated the effectiveness and robustness of our approach.

网格去噪是一个经典的问题，已经取得了长足的进步，但是还没有得到很好的解决。我们的论文提出了一种自适应的和动态的网格去噪方法，该方法可以有效地去除噪声，同时保留清晰的特征和视觉上有意义的精细尺度分量。大多数最先进的方法仍无法有效地处理各种嘈杂的3D模型，因为通常是手动选择最佳参数，并且对于整个模型和整个降噪过程而言，这些参数均保持不变。实际上，应该在每次迭代中根据不同区域的特征强度来自适应地调整参数，并在随后的迭代中动态更改这些参数，以避免过度平滑。本文中的参数是自动确定并动态调整的，这样可以尽可能地恢复表面的真实形状，尤其是在特征区域。在各种嘈杂的网格物体上进行的大量定性和定量实验证明了我们方法的有效性和鲁棒性。