You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
The joint bilateral filter, which enables feature-preserving signal smoothing according to the structural information from a guidance, has been applied for various tasks in geometry processing. Existing methods either rely on a static guidance that may be inconsistent with the input and lead to unsatisfactory results, or a dynamic guidance that is automatically updated but sensitive to noises and outliers. Inspired by recent advances in image filtering, we propose a new geometry filtering technique called static dynamic filter, which utilizes both static and dynamic guidances to achieve state-of-the-art results. The proposed filter is based on a nonlinear optimization that enforces smoothness of the signal within designated neighborhoods, while preserving signal variations that correspond to features of certain scales. We develop an efficient iterative solver for the problem, which unifies existing filters that are based on static or dynamic guidances. The filter can be applied to mesh face normals followed by vertex position update, to achieve scale-aware and feature-preserving filtering of mesh geometry. It also works well for other types of signals defined on mesh surfaces, such as texture colors. Extensive experimental results demonstrate the effectiveness of the proposed filter for various geometry processing applications such as mesh denoising, geometry feature enhancement, and texture color filtering.

Cited Abstract(s):
[1]: We present a general framework for performing geometry filtering through the solution of a screened Poisson equation. We show that this framework can be efficiently adapted to a changing Riemannian metric to support curvature-aware filtering and describe a parallel and streaming multigrid implementation for solving the system. We demonstrate the practicality of our approach by developing an interactive system for mesh editing that allows for exploration of a large family of curvature-guided, anisotropic filters.
In the past, various filtering approaches have been proposed to process mesh geometry. Early work from Taubin and applied low-pass filters on meshes, which remove high-frequency noises but also attenuate sharp features. Later, Taubin proposed a two-step approach that first performs smoothing on face normals, followed by vertex position updates using anisotropic filters. To enhance crease edges, applied anisotropic diffusion to mesh normals before updating vertex positions. Chuang and Kazhdan [1] developed a framework for curvature-aware mesh filtering based on the screened Poisson equation.