Propagated mesh normal filtering

Abstract Weighted average is one of the most common strategies used in various mesh filters, and its performance depends on the weight design. When computing the weight between the current face and one of its neighbours, existing methods consider only properties of the two faces, such as positions and normals. Although they generate some convincing results, they definitely tend to suffer from cross-region mixing. For example, assigning such a large weight between two nearby faces separated by some feature edges, even when their properties are close, will damage the local structure. In this paper, we present a novel mesh filter model, named as Propagated Mesh Normal Filtering. It estimates the weight between the current face and its neighbours based on the integral of two kinds of face normal differences along the geodesic path, connecting them. Therefore, prominent features are better preserved when removing noises or textures. Furthermore, in view of the sparseness of large normal difference for most of geometry shapes, the L1 norm is employed when integrating to further improve the filter. Experiments illustrate the enhanced efficacy of our propagated filter comparing with state-of-the-art methods.

[1]  Keenan Crane,et al.  A General Framework for Bilateral and Mean Shift Filtering , 2014, ArXiv.

[2]  Pierre Soille,et al.  Edge-preserving smoothing using a similarity measure in adaptive geodesic neighbourhoods , 2009, Pattern Recognit..

[3]  Jian Sun,et al.  Guided Image Filtering , 2010, ECCV.

[4]  Ligang Liu,et al.  Decoupling noise and features via weighted ℓ1-analysis compressed sensing , 2014, TOGS.

[5]  Yutaka Ohtake,et al.  Mesh smoothing via mean and median filtering applied to face normals , 2002, Geometric Modeling and Processing. Theory and Applications. GMP 2002. Proceedings.

[6]  Hans-Peter Seidel,et al.  Smoothing by Example: Mesh Denoising by Averaging with Similarity-Based Weights , 2006, IEEE International Conference on Shape Modeling and Applications 2006 (SMI'06).

[7]  Charlie C. L. Wang,et al.  Bilateral recovering of sharp edges on feature-insensitive sampled meshes , 2006, IEEE Transactions on Visualization and Computer Graphics.

[8]  Konrad Polthier,et al.  Mesh Denoising Based on Normal Voting Tensor and Binary Optimization , 2016, IEEE Transactions on Visualization and Computer Graphics.

[9]  Ralph R. Martin,et al.  Fast and Effective Feature-Preserving Mesh Denoising , 2007, IEEE Transactions on Visualization and Computer Graphics.

[10]  Hujun Bao,et al.  ℓ1-Based Construction of Polycube Maps from Complex Shapes , 2014, ACM Trans. Graph..

[11]  Yizhou Yu,et al.  Robust Feature-Preserving Mesh Denoising Based on Consistent Subneighborhoods , 2010, IEEE Transactions on Visualization and Computer Graphics.

[12]  Qi Zhang,et al.  Rolling Guidance Filter , 2014, ECCV.

[13]  Hans-Peter Seidel,et al.  Mesh Smoothing by Adaptive and Anisotropic Gaussian Filter Applied to Mesh Normals , 2002, VMV.

[14]  Bailin Deng,et al.  Guided Mesh Normal Filtering , 2015, Comput. Graph. Forum.

[15]  Ralph R. Martin,et al.  Random walks for feature-preserving mesh denoising , 2008, Computer Aided Geometric Design.

[16]  Baining Guo,et al.  Rolling guidance normal filter for geometric processing , 2015, ACM Trans. Graph..

[17]  Ligang Liu,et al.  Bi-Normal Filtering for Mesh Denoising , 2015, IEEE Transactions on Visualization and Computer Graphics.

[18]  Alberto Signoroni,et al.  Mesh Denoising with (Geo)Metric Fidelity , 2018, IEEE Transactions on Visualization and Computer Graphics.

[19]  Frédo Durand,et al.  Non-iterative, feature-preserving mesh smoothing , 2003, ACM Trans. Graph..

[20]  Xin Tong,et al.  Mesh denoising via cascaded normal regression , 2016, ACM Trans. Graph..

[21]  Richard Szeliski,et al.  Digital photography with flash and no-flash image pairs , 2004, ACM Trans. Graph..

[22]  Daniel Cohen-Or,et al.  ℓ1-Sparse reconstruction of sharp point set surfaces , 2010, TOGS.

[23]  Youyi Zheng,et al.  IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 1 Bilateral Normal Filtering for Mesh Denoising , 2022 .

[24]  Charlie C. L. Wang,et al.  Extracting Manifold and Feature-Enhanced Mesh Surfaces From Binary Volumes , 2008, J. Comput. Inf. Sci. Eng..

[25]  Daniel Cohen-Or,et al.  Bilateral mesh denoising , 2003 .

[26]  Konrad Polthier,et al.  Robust and High Fidelity Mesh Denoising , 2017, IEEE Transactions on Visualization and Computer Graphics.

[27]  Yutaka Ohtake,et al.  A comparison of mesh smoothing methods , 2003 .

[28]  Minh N. Do,et al.  Cross-based local multipoint filtering , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[29]  Lei He,et al.  Mesh denoising via L0 minimization , 2013, ACM Trans. Graph..

[30]  Roberto Manduchi,et al.  Bilateral filtering for gray and color images , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[31]  Arye Nehorai,et al.  Performance bounds for estimating vector systems , 2000, IEEE Trans. Signal Process..

[32]  Zhigang Deng,et al.  A Robust Scheme for Feature-Preserving Mesh Denoising , 2016, IEEE Transactions on Visualization and Computer Graphics.

[33]  Kuo-Young Cheng,et al.  A sharpness dependent filter for mesh smoothing , 2005, Comput. Aided Geom. Des..

[34]  M. Gross,et al.  Algebraic point set surfaces , 2007, SIGGRAPH 2007.

[35]  Frédo Durand,et al.  Flash photography enhancement via intrinsic relighting , 2004, SIGGRAPH 2004.

[36]  Yu-Chiang Frank Wang,et al.  Propagated image filtering , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[37]  Bailin Deng,et al.  Static/Dynamic Filtering for Mesh Geometry , 2017, IEEE Transactions on Visualization and Computer Graphics.