Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  introduce several new functionals, inspired from variational image denoising models, for recovering a piecewise-smooth surface from a dense estimation of its normal field. In the ighted least-squares approach, the non-differentiable elements of the surface are a priori detected so as to ight the least-squares model. To avoid this detection step,  introduce reighted least-squares for minimising an isotropic TV-like functional, and split-Bregman iterations for L1 minimisation.
['Yvain Quéau', 'Jean-Denis Durou']
I am writing a research paper on a idea Edge-Preserving Integration of a Normal Field: Weighted Least-Squares, TV and L^1 Approaches
[{"from": "human", "value": "I am writing a research paper on a idea Edge-Preserving Integration of a Normal Field: Weighted Least-Squares, TV and L^1 Approaches"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  introduce several new functionals, inspired from variational image denoising models, for recovering a piecewise-smooth surface from a dense estimation of its normal field. In the ighted least-squares approach, the non-differentiable elements of the surface are a priori detected so as to ight the least-squares model. To avoid this detection step,  introduce reighted least-squares for minimising an isotropic TV-like functional, and split-Bregman iterations for L1 minimisation."}]