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.