Non-iterative, feature-preserving mesh smoothing

By Thouis Raymond Jones, Frédo Durand, Mathieu Desbrun. ACM Transactions on Graphics, 2003

593 citations recorded by the source corpus.

With the increasing use of geometry scanners to create 3D models, there is a rising need for fast and robust mesh smoothing to remove inevitable noise in the measurements. While most previous work has favored diffusion-based iterative techniques for feature-preserving smoothing, we propose a radically different approach, based on robust statistics and local first-order predictors of the surface. The robustness of our local estimates allows us to derive a non-iterative feature-preserving filtering technique applicable to arbitrary "triangle soups". We demonstrate its simplicity of implementation and its efficiency, which make it an excellent solution for smoothing large, noisy, and non-manifold meshes.

Subfields: Computational Mechanics, Computer Graphics and Computer-Aided Design

View Paper: https://www.semanticscholar.org/paper/7bb56789abac7a89ff2e39d285b7de7f36f02636

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In Computational Mechanics

In Computer Graphics and Computer-Aided Design

Citations in the corpus

Face transfer with multilinear models (2005, methodology): “As a preprocess, the scans were smoothed using the bilateral filter [Jones et al. 2003] to eliminate some of the capture noise.”
Digital photography with flash and no-flash image pairs (2004, background): “…is a fast, non-iterative technique, and has been applied to a variety of problems beyond image denoising, including tonemapping [Durand and Dorsey 2002; Choudhury and Tumblin 2003], separating illumination from texture [Oh et al. 2001] and mesh smoothing [Fleishman et al. 2003; Jones et al. 2003].”
Robust moving least-squares fitting with sharp features (2005, methodology): “Our work is related to recent developments in feature-preserving smoothing [Clarenz et al. 2000; Fleishman et al. 2003b; Jones et al. 2003] but unlike smoothing, we define a surface rather than filtering the geometry.”
An Application of Markov Random Fields to Range Sensing (2005, methodology): “Most recently [10] proposed an efficient non-iterative technique for feature-preserving mesh smoothing, [9] adapted bilateral filtering for application to mesh denoising. and [7] developed anisotropic MRF techniques.”
3D Shape Perception from Monocular Vision, Touch, and Shape Priors (2018, unknown): “For visualization, bilateral ﬁlters are applied to remove aliasing [48].”
A Fast Approximation of the Bilateral Filter Using a Signal Processing Approach (2006, methodology): “It has been adopted for several applications such as texture removal [2], dynamic range compression [3], and photograph enhancement [4, 5]. It has also be adapted to other domains such as mesh fairing [ 6 , 7], volumetric denoising [8] and exposure correction of videos [9].”
Interpolating and approximating implicit surfaces from polygon soup (2004, background): “(For a single recent example, see [Jones et al., 2003].”
Nonlocal Discrete Regularization on Weighted Graphs: A Framework for Image and Manifold Processing (2008, background): “Bilateral and NLMeans filters have also been redefined to denoise meshes [49], [ 50 ], [51].”