Spatially Adaptive Regularizer for Mesh Denoising

IEEE Access

Fang Chen¹  Xuan Cheng¹  Yinglin Zheng¹  Yuhui Zheng¹  Kunhui Lin¹ 

[1] School of Informatics, Xiamen University, Xiamen, China;

关键词: Lₚ norm;    mesh denoising;    optimization;    regularizer;    spatially adaptive;   

DOI  :  10.1109/ACCESS.2020.2987046

来源: DOAJ

【 摘 要 】

Mesh denoising is a fundamental yet not well-solved problem in computer graphics. Many existing methods formulate the mesh denoising as an optimization problem, whereby the optimized mesh could best fit both the input and a set of constraints defined as an L_p norm regularizer. Instead of setting p as a static value for the whole surface, we adopt a dynamic L_p regularizer which imposes two different forms of regularization onto different surface patches for a better understanding of the local surface features. To help determine the appropriate p value for each facet, the guidance is constructed dynamically in a patch-based manner. We compare the proposed method with state-of-the-arts in both synthetic and real-scanned benchmark datasets, and show that the proposed method could produce comparable results to neural network based mesh denoising method, without collecting large training datasets.

【 授权许可】

Unknown