ACM SIGGRAPH 2005 Papers
A roughness measure for 3D mesh visual masking
Proceedings of the 4th symposium on Applied perception in graphics and visualization
A local roughness measure for 3D meshes and its application to visual masking
ACM Transactions on Applied Perception (TAP)
Decimation of human face model for real-time animation in intelligent multimedia systems
Multimedia Tools and Applications
A feature-preserved simplification for autonomous facial animation from 3D scan data
ICCSA'03 Proceedings of the 2003 international conference on Computational science and its applications: PartIII
Mesh simplification with vertex color
GMP'08 Proceedings of the 5th international conference on Advances in geometric modeling and processing
Lossless 3D steganography based on MST and connectivity modification
Image Communication
Feature-preserving mesh denoising based on vertices classification
Computer Aided Geometric Design
Surface simplification with semantic features using texture and curvature maps
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part III
Extraction of ridges-valleys for feature-preserving simplification of polygonal models
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part II
A Robust Embedding Scheme and an Efficient Evaluation Protocol for 3D Meshes Watermarking
International Journal of Computer Vision and Image Processing
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In this paper we propose a new discrete differential error metric for surface simplification. Many surface simplification algorithms have been developed in order to produce rapidly high quality approximations of polygonal models, and the quadric error metric based on the distance error is the most popular and successful error metric so far. Even though such distance based error metrics give visually pleasing results with a reasonably fast speed, it is hard to measure an accurate geometric error on a highly curved and thin region since the error measured by the distance metric on such a region is usually small and causes a loss of visually important features.To overcome such a drawback, we define a new error metric based on the theory of local differential geometry in such a way that the first and the second order discrete differentials approximated locally on a discrete polygonal surface are integrated into the usual distance error metric. The benefits of our error metric are preservation of sharp feature regions after a drastic simplification, small geometric errors, and fast computation comparable to the existingmethods.