Computer-generated pen-and-ink illustration
SIGGRAPH '94 Proceedings of the 21st annual conference on Computer graphics and interactive techniques
Real-time nonphotorealistic rendering
Proceedings of the 24th annual conference on Computer graphics and interactive techniques
Image precision silhouette edges
I3D '99 Proceedings of the 1999 symposium on Interactive 3D graphics
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Non-photorealistic computer graphics: modeling, rendering, and animation
Non-photorealistic computer graphics: modeling, rendering, and animation
WYSIWYG NPR: drawing strokes directly on 3D models
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Suggestive contours for conveying shape
ACM SIGGRAPH 2003 Papers
Interactive rendering of suggestive contours with temporal coherence
Proceedings of the 3rd international symposium on Non-photorealistic animation and rendering
Ridge-valley lines on meshes via implicit surface fitting
ACM SIGGRAPH 2004 Papers
Estimating Curvatures and Their Derivatives on Triangle Meshes
3DPVT '04 Proceedings of the 3D Data Processing, Visualization, and Transmission, 2nd International Symposium
Smooth feature lines on surface meshes
SGP '05 Proceedings of the third Eurographics symposium on Geometry processing
A sharpness dependent filter for mesh smoothing
Computer Aided Geometric Design - Special issue: Geometry processing
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When detecting ridge-valley lines on 3D mesh model, estimation of the curvature and curvature derivatives may often yields to squiggly and noisy result, because the estimation is sensitive against unwanted surface noises. We present two algorithms to obtain smooth and noiseless ridge-valley lines. First, we apply an iterative procedure on ridge and valley vertices and their previous and next neighbors on connected feature lines, which leads to smooth lines. Secondly, we propose an algorithm to distinguish noises from meaningful feature lines based on graph theory model. Each separate feature line is considered as an undirected weighted graph which is called as Feature Graph. We can reasonably get rid of most noises and preserve meaningful feature lines through optimizing the minimal spanning tree of each feature graph.