Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The typical goal of surface remeshing consists in finding a mesh that is (1) geometrically faithful to the original geometry, (2) as coarse as possible to obtain a low-complexity representation and (3) free of bad elements that would hamper the desired application. ,  design an algorithm to address all three optimization goals simultaneously. The user specifies desired bounds on approximation error {\delta}, minimal interior angle {\theta} and maximum mesh complexity N (number of vertices). Since such a desired mesh might not even exist, our optimization framework treats only the approximation error bound {\delta} as a hard constraint and the other two criteria as optimization goals. More specifically,  iteratively perform carefully prioritized local operators, whenever they do not violate the approximation error bound and improve the mesh otherwise. In this way our optimization framework greedily searches for the coarsest mesh with minimal interior angle above {\theta} and approximation error bounded by {\delta}. Fast runtime is enabled by a local approximation error estimation, while implicit feature preservation is obtained by specifically designed vertex relocation operators. Experiments show that our approach delivers high-quality meshes with implicitly preserved features and better balances beten geometric fidelity, mesh complexity and element quality than the state-of-the-art.
['Kaimo Hu', 'Dong-Ming Yan', 'David Bommes', 'Pierre Alliez', 'Bedrich Benes']
I am writing a research paper on a idea Error-Bounded and Feature Preserving Surface Remeshing with Minimal Angle Improvement
[{"from": "human", "value": "I am writing a research paper on a idea Error-Bounded and Feature Preserving Surface Remeshing with Minimal Angle Improvement"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The typical goal of surface remeshing consists in finding a mesh that is (1) geometrically faithful to the original geometry, (2) as coarse as possible to obtain a low-complexity representation and (3) free of bad elements that would hamper the desired application. ,  design an algorithm to address all three optimization goals simultaneously. The user specifies desired bounds on approximation error {\\delta}, minimal interior angle {\\theta} and maximum mesh complexity N (number of vertices). Since such a desired mesh might not even exist, our optimization framework treats only the approximation error bound {\\delta} as a hard constraint and the other two criteria as optimization goals. More specifically,  iteratively perform carefully prioritized local operators, whenever they do not violate the approximation error bound and improve the mesh otherwise. In this way our optimization framework greedily searches for the coarsest mesh with minimal interior angle above {\\theta} and approximation error bounded by {\\delta}. Fast runtime is enabled by a local approximation error estimation, while implicit feature preservation is obtained by specifically designed vertex relocation operators. Experiments show that our approach delivers high-quality meshes with implicitly preserved features and better balances beten geometric fidelity, mesh complexity and element quality than the state-of-the-art."}]