网格模型上基于线性插值的自适应保特征去噪算法

网格去噪是改善扫描设备所得数据并为后续数字几何处理提供理想模型的重要步骤.针对近年来涌现出的各种网格去噪算法难以平衡好时间消耗和去噪结果之间的关系,提出一种高效的基于线性插值的框架性保特征去噪算法,将体现结构特征的充分磨光模型与包含综合信息的噪声模型进行联合分析,通过自适应的线性插值算法逐步从噪声模型中提取出特征信息加到光滑模型中,从而实现保持不同程度特征(从显著特征到微弱特征)的去噪结果.同时提出迭代去噪算法的迭代终止判别条件,可为不同迭代去噪算法提供自动且可靠的终止条件.大量在不同复杂度和不同噪声程度的模型上的实验结果表明,与现有的局部迭代算法相比,该算法取得更好的视觉效果和较低的均方角度误差值,能够保持不同程度的特征且具有较好的时间性能.

Linear Interpolation Based Adaptive Feature-Preserving Filtering Method on Mesh Models

Mesh denoising is an important step to improve the data obtained by scanning devices and it provides an ideal model for subsequent digital geometry processing.The main challenge is to remove the noises while maintaining its geometric features,especially weak features.The denoising methods occurred in recent years oftentimes lack the capability to balance between time consumption and denoising results.Therefore,this paper proposes an efficient feature preserving denoising framework based on linear interpolation.In order to realize feature-preserving(for features of different levels,from salient features to weak features)denoising,this paper uses the interpolation method to jointly analyze the fully polished model that reflects the structural features and the noisy model that contains the comprehensive information.By designing an adaptive interpolation method,the features of different levels are extracted from the noisy model and added to the smooth model.At the same time,an automatic termination judgment for the iterative denoising algorithm is proposed,which provides automatic and reliable termination conditions for different iterative denoising algorithms.The quantitative and qualitative results on synthetic and real data with various levels of noises show that our method can achieve efficient and robust denoising results.