Marching cubes: A high resolution 3D surface construction algorithm
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
Feature-oriented image enhancement using shock filters
SIAM Journal on Numerical Analysis
Surface reconstruction from unorganized points
SIGGRAPH '92 Proceedings of the 19th annual conference on Computer graphics and interactive techniques
A variational level set approach to multiphase motion
Journal of Computational Physics
The visualization toolkit (2nd ed.): an object-oriented approach to 3D graphics
The visualization toolkit (2nd ed.): an object-oriented approach to 3D graphics
A PDE-based fast local level set method
Journal of Computational Physics
Detecting undersampling in surface reconstruction
SCG '01 Proceedings of the seventeenth annual symposium on Computational geometry
Reconstruction and representation of 3D objects with radial basis functions
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
A hybrid particle level set method for improved interface capturing
Journal of Computational Physics
Regularized Shock Filters and Complex Diffusion
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Rough Surface Modeling Using Surface Growth
SMI '03 Proceedings of the Shape Modeling International 2003
Fast Surface Reconstruction Using the Level Set Method
VLSM '01 Proceedings of the IEEE Workshop on Variational and Level Set Methods (VLSM'01)
Geometric surface processing via normal maps
ACM Transactions on Graphics (TOG)
The power crust, unions of balls, and the medial axis transform
Computational Geometry: Theory and Applications
Hi-index | 0.00 |
We present a new level set method for reconstructing interfaces from point aggregations. Although level-set-based methods are advantageous because they can handle complicated topologies and noisy data, most tend to smooth the inherent roughness of the original data. Our objective is to enhance the quality of a reconstructed surface by preserving certain roughness-related characteristics of the original dataset. Our formulation employs the total variation of the surface as a roughness measure. The algorithm consists of two steps: a roughness-capturing flow and a roughness-preserving flow. The roughness capturing step attempts to construct a surface for which the original roughness is captured 驴 distance flow is well suited for roughness capturing. Surface reconstruction is enhanced by using a total variation preserving (TVP) scheme for the roughness-preserving flow. The shock filter formulation of Osher and Rudin is exploited to achieve this goal. In practice, we have found that better results are obtained by balancing the TVP term with a smoothing term based on curvature. The algorithm is applied to both fractal surface growth simulations and scanned data sets to demonstrate the efficacy of our approach.