Model simplification using vertex-clustering
Proceedings of the 1997 symposium on Interactive 3D graphics
View-dependent simplification of arbitrary polygonal environments
Proceedings of the 24th annual conference on Computer graphics and interactive techniques
Out-of-core simplification of large polygonal models
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Dual contouring of hermite data
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Efficient adaptive simplification of massive meshes
Proceedings of the conference on Visualization '01
A multiphase approach to efficient surface simplification
Proceedings of the conference on Visualization '02
Metro: measuring error on simplified surfaces
Metro: measuring error on simplified surfaces
Quadric-based polygonal surface simplification
Quadric-based polygonal surface simplification
Removing excess topology from isosurfaces
ACM Transactions on Graphics (TOG)
Dual Contouring with Topology-Preserving Simplification Using Enhanced Cell Representation
VIS '04 Proceedings of the conference on Visualization '04
IEEE Transactions on Visualization and Computer Graphics
Using enhanced shape distributions to compare CAD models
PCM'07 Proceedings of the multimedia 8th Pacific Rim conference on Advances in multimedia information processing
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Product developers have used a lot of polygon data, approximated from 3D-CAD data, as a collaboration tool on the Internet. It is difficult to deal with this data for example with respect to transmission, computational cost, or rendering, so simplification algorithms are required for data compression. In general, a vertex-clustering algorithm in simplification algorithms is very fast, although it has the problem that topology information is not preserved and for some applications, such as 3D-CAD, it is important to preserve topology information. We define topology information in this paper as genus on the polyhedron and 2-manifold. In this paper, we propose a topology-preserving simplification method using a depth-first search tree on a vertex-clustering algorithm. Our method does not lose the advantage that vertex-clustering algorithms are fast yet it solves the problem of lost topology information. Experimental results show that this method is effective and is easily adapted to space division algorithms of other vertex-clustering algorithms