Positional, Metric, and Curvature Control for Constraint‐Based Surface Deformation

Eigensatz, Michael; Pauly, Mark

doi:10.1111/j.1467-8659.2009.01395.x

Cited by 32 publications

(21 citation statements)

References 34 publications

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“…These possess complex geometric properties in common, but their variability cannot be reduced to perturbations of a template shape parameterised by a few quantities. [26][27][28][29] In this paper, we provide implicit formulae to recover the structure of a point cloud using overparameterised method. We conclude the paper providing experimental results, for both synthetic and real models evaluating the performance of the techniques and showing the feasibility of the approach.…”

Section: Related Workmentioning

confidence: 99%

Globally optimal estimate for variational surface reconstruction

Jin

Wan

2012

The Imaging Science Journal

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In this article, we tackle the problem of using globally optimal estimate for variational surface reconstruction. We give an overview of globally optimal method on variational surface when the three-dimensional (3D) surface is represented by a point-based surface and a triangular mesh-based surface, and we detail the variational surface used on surface reconstruction. It can be applied to derive a range of meaningful surface reconstructions from this high dimensional space. We show that using a progression of spatially varying anisotropic weights can achieve significant improvements in surface reconstruction. Simulated surfaces and real model are experimentally studied, and the results validate that the proposed approaches improved the reconstruction. The proposed method improved the reconstruction results significantly for the simulated and real data.

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Section: Related Workmentioning

confidence: 99%

Globally optimal estimate for variational surface reconstruction

Jin

Wan

2012

The Imaging Science Journal

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“…Note that this technique cannot only be used for the efficient computation of volume constraints. It can also be employed for other constraints that depend on a large number of mesh vertices, such as, for instance, constraining the area of a large surface patch in the framework of [EP09]. We remark that there are several ways to compute a factorization of a matrix with dense rows through matrix updates.…”

Section: Numerical Minimizationmentioning

confidence: 99%

Example‐Driven Deformations Based on Discrete Shells

Fröhlich

Botsch

2011

Computer Graphics Forum

162

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Despite the huge progress made in interactive physics‐based mesh deformation, manipulating a geometrically complex mesh or posing a detailed character is still a tedious and time‐consuming task. Example‐driven methods significantly simplify the modelling process by incorporating structural or anatomical knowledge learned from example poses. However, these approaches yield counter‐intuitive, non‐physical results as soon as the shape space spanned by the example poses is left. In this paper, we propose a modelling framework that is both example‐driven and physics‐based and thereby overcomes the limitations of both approaches. Based on an extension of the discrete shell energy we derive mesh deformation and mesh interpolation techniques that can be seamlessly combined into a simple and flexible mesh‐based inverse kinematics system.

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“…Our implementation of [EP09] cannot generate results with such strictly preserved metrics -see the comparison shown in Fig.8. All examples show less than 1% of error on different types of metrics.…”

Section: Validation and Comparisonmentioning

confidence: 99%

Deformation with enforced metrics on length, area and volume

Jinsp

Zhang

Wang

2014

Computer Graphics Forum

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Figure 1: Our framework for generating deformation with enforced metrics provides a user-friendly tool for designers to accurately control the metrics in a deformation while well preserving the shape of input models. This function is hard to be realized by constrained deformation. Progressive deformation (by gradually increasing the constrained length by 1% in each step) cannot generate results as good as ours. Moreover, the formulation of scale-driven deformation investigated in this work can converge in a few iterations. AbstractTechniques have been developed to deform a mesh with multiple types of constraints. One limitation of prior methods is that the accuracy of demanded metrics on the resultant model cannot be guaranteed. Adding metrics directly as hard constraints to an optimization functional often leads to unexpected distortion when target metrics differ significant from what are on the input model. In this paper, we present an effective framework to deform mesh models by enforcing demanded metrics on length, area and volume. To approach target metrics stably and minimize distortion, an iterative scale-driven deformation is investigated, and a global optimization functional is exploited to balance the scaling effect at different parts of a model. Examples demonstrate that our approach provides a user-friendly tool for designers who are used to semantic input.

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Positional, Metric, and Curvature Control for Constraint‐Based Surface Deformation

Cited by 32 publications

References 34 publications

Globally optimal estimate for variational surface reconstruction

Globally optimal estimate for variational surface reconstruction

Example‐Driven Deformations Based on Discrete Shells

Deformation with enforced metrics on length, area and volume

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