Abstract
Mesh denoising is to recover high quality meshes from noisy inputs scanned from the real world. It is a crucial step in geometry processing, computer vision, computer-aided design, etc. Yet, state-of-the-art denoising methods still fall short of handling meshes containing both sharp features and fine details. Besides, some of the methods usually introduce undesired staircase effects in smoothly curved regions. These issues become more severe when a mesh is corrupted by various kinds of noise, including Gaussian, impulsive, and mixed Gaussian–impulsive noise. In this paper, we present a novel optimization method for robustly denoising the mesh. The proposed method is based on a triple sparsity prior: a double sparse prior on first order and second order variations of the face normal field and a sparse prior on the residual face normal field. Numerically, we develop an efficient algorithm based on variable-splitting and augmented Lagrange method to solve the problem. The proposed method can not only effectively recover various features (including sharp features, fine details, smoothly curved regions, etc), but also be robust against different kinds of noise. We testify effectiveness of the proposed method on synthetic meshes and a broad variety of scanned data produced by the laser scanner, Kinect v1, Kinect v2, and Kinect-fusion. Intensive numerical experiments show that our method outperforms all of the compared select-of-the-art methods qualitatively and quantitatively.
Highlights
With the development of consumer-grade scanner devices (e.g., Microsoft Kinect, Xtion Pro, Google Project Tango, and Intel RealSense), triangulated meshes can be acquired from the real world
To filter the face normals of the noisy input, we propose a normal filtering model containing three sparsity terms. It consists of a double sparsity prior on first order and second order variations of the face normal field to recover sharp features, fine details, and smooth regions and a third sparsity prior for handling different kinds of noise
The total variation (TV) regularization has been proven very successful in image processing for its excellent edge-preserving property [21]. We extend it to mesh denoising for preserving sharp features while removing noise
Summary
With the development of consumer-grade scanner devices (e.g., Microsoft Kinect, Xtion Pro, Google Project Tango, and Intel RealSense), triangulated meshes can be acquired from the real world. The above sparsity-based methods [20,21,22,23,24] can remove noise while preserving sharp features, they inevitably suffer undesired staircase effects in smoothly curved regions. This problem is even worse for the0 minimization [20] for its high sparsity requirement. The method first learns non-linear regression functions mapping filtered face normal descriptors to face normals of the clean mesh, and employs the learned functions for computing the filtered face normals This method can effectively remove noise and preserve geometric features.
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