Fast and High Quality Suggestive Contour Generation with L0 Gradient Minimization

Line drawings are especially effective and natural in shape depiction. There are generally two ways to generate line drawings: object space methods and image space methods. Compared with object space methods, image space methods are much faster and independent of the 3D object, but easily affected by small noise in the rendered image. We suggest applying an edge-preserving L0 gradient smoothing step on the rendered image before line extraction. Experimental results show that our method can effectively alleviate unnecessary small scale lines, leading to results comparable to object space methods.

[1]  Michael D. Heath,et al.  Are edges sufficient for object recognition? , 1998 .

[2]  Zeev Farbman,et al.  Edge-preserving decompositions for multi-scale tone and detail manipulation , 2008, SIGGRAPH 2008.

[3]  Jürgen Döllner,et al.  Edge-Enhancement - An Algorithm for Real-Time Non-Photorealistic Rendering , 2003, WSCG.

[4]  Roberto Manduchi,et al.  Bilateral filtering for gray and color images , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[5]  Mario Costa Sousa,et al.  The edge buffer: a data structure for easy silhouette rendering , 2000, NPAR '00.

[6]  Adam Finkelstein,et al.  Suggestive contours for conveying shape , 2003, ACM Trans. Graph..

[7]  Ramesh Raskar,et al.  Non-photorealistic camera: depth edge detection and stylized rendering using multi-flash imaging , 2004, SIGGRAPH 2004.

[8]  Maneesh Agrawala,et al.  Multiscale shape and detail enhancement from multi-light image collections , 2007, SIGGRAPH 2007.

[9]  Philippe Decaudin,et al.  Cartoon-Looking Rendering of 3D-Scenes , 2003 .

[10]  Ilan Shimshoni,et al.  Demarcating curves for shape illustration , 2008, SIGGRAPH 2008.

[11]  Jitendra Malik,et al.  Scale-Space and Edge Detection Using Anisotropic Diffusion , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Roberto Cipolla,et al.  The visual motion of curves and surfaces , 1998, Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[13]  L. Rudin,et al.  Nonlinear total variation based noise removal algorithms , 1992 .

[14]  Ramesh Raskar,et al.  Image precision silhouette edges , 1999, SI3D.

[15]  Ying He,et al.  Real-Time Shape Illustration Using Laplacian Lines , 2011, IEEE Transactions on Visualization and Computer Graphics.

[16]  Tobias Isenberg,et al.  A Developer's Guide to Silhouette Algorithms for Polygonal Models , 2003, IEEE Computer Graphics and Applications.

[17]  Carsten Steger.  Subpixel-precise extraction of watersheds , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[18]  Cewu Lu,et al.  Image smoothing via L0 gradient minimization , 2011, ACM Trans. Graph..

[19]  Yunjin Lee,et al.  Line drawings via abstracted shading , 2007, ACM Trans. Graph..

[20]  Cassidy J. Curtis.  Loose and sketchy animation , 1998, International Conference on Computer Graphics and Interactive Techniques.

[21]  Peter-Pike J. Sloan,et al.  Interactive technical illustration , 1999, SI3D.

[22]  Holger Winnemöller,et al.  Real-time video abstraction , 2006, SIGGRAPH 2006.

[23]  Kevin W. Bowyer,et al.  Are real edges sufficient for object recognition , 1995 .

[24]  Frédo Durand,et al.  Apparent ridges for line drawing , 2007, SIGGRAPH 2007.

[25]  Steven W. Zucker,et al.  Logical/Linear Operators for Image Curves , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[26]  Joachim Weickert,et al.  Relations Between Regularization and Diffusion Filtering , 2000, Journal of Mathematical Imaging and Vision.

[27]  Takafumi Saito,et al.  Comprehensible rendering of 3-D shapes , 1990, SIGGRAPH.

[28]  Greg Turk,et al.  LCIS: a boundary hierarchy for detail-preserving contrast reduction , 1999, SIGGRAPH.

[29]  Hong Qin,et al.  An Effective Illustrative Visualization Framework Based on Photic Extremum Lines (PELs) , 2007, IEEE Transactions on Visualization and Computer Graphics.