Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  propose a new strategy to estimate surface normal information from highly noisy sparse data. Our approach is based on a tensor field morphologically adapted to infer normals. It acts as a three-dimensional structuring element of smooth surfaces. Robust orientation inference for all input elements is performed by morphological operations using the tensor field. A general normal estimator is defined by combining the inferred normals, their confidences and the tensor field. This estimator can be used to directly reconstruct the surface or give input normals to other reconstruction methods.  present qualitative and quantitative results to show the behavior of the original methods and ours. A comparative discussion of these results shows the efficiency of our propositions.
['Marcelo Bernardes Vieira', 'Paulo Martins', 'Arnaldo de Albuquerque Araújo', 'Matthieu Cord', 'Sylvie Philipp-Foliguet']
I am writing a research paper on a idea Smooth Surface Reconstruction Using Tensor Fields as Structuring Elements
[{"from": "human", "value": "I am writing a research paper on a idea Smooth Surface Reconstruction Using Tensor Fields as Structuring Elements"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  propose a new strategy to estimate surface normal information from highly noisy sparse data. Our approach is based on a tensor field morphologically adapted to infer normals. It acts as a three-dimensional structuring element of smooth surfaces. Robust orientation inference for all input elements is performed by morphological operations using the tensor field. A general normal estimator is defined by combining the inferred normals, their confidences and the tensor field. This estimator can be used to directly reconstruct the surface or give input normals to other reconstruction methods.  present qualitative and quantitative results to show the behavior of the original methods and ours. A comparative discussion of these results shows the efficiency of our propositions."}]