Circuit complexity and neural networks
Circuit complexity and neural networks
Fast and effective stripification of polygonal surface models
I3D '99 Proceedings of the 1999 symposium on Interactive 3D graphics
Optimal decomposition of polygonal models into triangle strips
Proceedings of the eighteenth annual symposium on Computational geometry
Neural Networks for Optimization and Signal Processing
Neural Networks for Optimization and Signal Processing
Technical Section: Comparison of triangle strips algorithms
Computers and Graphics
Optimal triangle stripifications as minimum energy states in hopfield nets
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
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The important task of generating the minimum number of sequential triangle strips (tristrips) for a given triangulated surface model is motivated by applications in computer graphics. This hard combinatorial optimization problem is reduced to the minimum energy problem in Hopfield nets by a linear-size construction. In particular, the classes of equivalent optimal stripifications are mapped one to one to the minimum energy states reached by a Hopfield network during sequential computation starting at the zero initial state. Thus, the underlying Hopfield network powered by simulated annealing (i.e., Boltzmann machine), which is implemented in the program HTGEN, can be used for computing the semioptimal stripifications. Practical experiments confirm that one can obtain much better results using HTGEN than by a leading conventional stripification program FTSG (a reference stripification method not based on neural nets), although the running time of simulated annealing grows rapidly near the global optimum. Nevertheless, HTGEN exhibits empirical linear time complexity when the parameters of simulated annealing (i.e., the initial temperature and the stopping criterion) are fixed and thus provides the semioptimal offline solutions, even for huge models of hundreds of thousands of triangles, within a reasonable time.