An algorithm for automatically fitting digitized curves
Graphics gems
Fitting helices to data by total least squares
Computer Aided Geometric Design
Interactive multiresolution hair modeling and editing
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Super-helices for predicting the dynamics of natural hair
ACM SIGGRAPH 2006 Papers
ACM SIGGRAPH 2007 papers
Robust fitting of super-helices to parametric curves
ACM SIGGRAPH 2007 posters
Hair photobooth: geometric and photometric acquisition of real hairstyles
ACM SIGGRAPH 2008 papers
Algorithm note: HELFIT: Helix fitting by a total least squares method
Computational Biology and Chemistry
Sketch-based tree modeling using Markov random field
ACM SIGGRAPH Asia 2008 papers
International Journal of Bioinformatics Research and Applications
Example-based hair geometry synthesis
ACM SIGGRAPH 2009 papers
Proceedings of Graphics Interface 2009
Capturing hair assemblies fiber by fiber
ACM SIGGRAPH Asia 2009 papers
ACM SIGGRAPH Asia 2010 papers
3D inverse dynamic modeling of strands
ACM SIGGRAPH 2011 Posters
Inverse dynamic hair modeling with frictional contact
ACM Transactions on Graphics (TOG)
Technical note: Modeling piecewise helix curves from 2D sketches
Computer-Aided Design
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Curves are widely used in computer science to describe real-life objects such as slender deformable structures. Using only 3 parameters per element, piecewise helices offer an interesting and compact way of representing digital curves. In this paper, we present a robust and fast algorithm to approximate Bezier curves with G^1 piecewise helices. Our approximation algorithm takes a Bezier spline as input along with an integer N and returns a piecewise helix with N elements that closely approximates the input curve. The key idea of our method is to take N+1 evenly distributed points along the curve, together with their tangents, and interpolate these tangents with helices by slightly relaxing the points. Building on previous work, we generalize the proof for Ghosh@?s co-helicity condition, which serves us to guarantee the correctness of our algorithm in the general case. Finally, we demonstrate both the efficiency and robustness of our method by successfully applying it to various datasets of increasing complexity, ranging from synthetic curves created by an artist to automatic image-based reconstructions of real data such as hair, heart muscular fibers or magnetic field lines of a star.