Spatially Adaptive Regularizer for Mesh Denoising

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.