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Adjacency Sampling: A Scalable Line Drawing Kernel with Artifact Reduced
Tang Chen,Li Sheng * #
School of Electronics Engineering and Computer Science, Peking University
*Correspondence author
#Submitted by
Funding: Specialized Research Fund for the Doctoral Program of Higher Education (No.No.20070001024)
Opened online:24 January 2011
Accepted by: none
Citation: Tang Chen,Li Sheng.Adjacency Sampling: A Scalable Line Drawing Kernel with Artifact Reduced[OL]. [24 January 2011] http://en.paper.edu.cn/en_releasepaper/content/4406859
In this paper we exploit sampling topology information in image space directly for visibility of line drawing and silhouette extraction. We propose a new line drawing kernel that depends on image-space adjacency test between primitives in GPU without any preprocessing step or extra adjacent information prestored. By this kernel, our visibility test acquires high accuracy in wireframe rendering and performs fairly well also in sketch and stylized line drawing, and our silhouette extraction method extracts visible portion of silhouette edges in image-space with clear and regular outlook. Our methods can be easily implemented and be controlled. The experiments show the privileges of our method in line drawing.
Keywords:computer graphics; line drawing; adjacency; GPU; visibility

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