Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  formulates the Shape-from-Texture (SFT) problem of deriving the shape of an imaged surface from the distortion of its texture as a single-plane/multiple-view Structure-from-Motion (SFM) problem under full perspective projection. As in classical SFT formulations  approximate the surface as being piecewise planar. In contrast to many methods, our approach does not need a frontal view of the texture or the texture elements as reference, as it optimizes 3D patch positions and orientations from transformations beten texture elements in the image. The reconstruction results in minimizing a large sparse linear least squares cost function based on the reprojection error, a planarity constraint and the estimated rigid motion beten patches. Texture element positions in the image are estimated under the assumption of a regular texture from clustered feature points representing repeating appearances in the image.  present results obtained with synthetic data as ll as real data to evaluate our method.
['Anna Hilsmann', 'David C. Schneider', 'Peter Eisert']
I am writing a research paper on a idea Template-free Shape From Texture with Perspective Cameras.
[{"from": "human", "value": "I am writing a research paper on a idea Template-free Shape From Texture with Perspective Cameras."}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  formulates the Shape-from-Texture (SFT) problem of deriving the shape of an imaged surface from the distortion of its texture as a single-plane/multiple-view Structure-from-Motion (SFM) problem under full perspective projection. As in classical SFT formulations  approximate the surface as being piecewise planar. In contrast to many methods, our approach does not need a frontal view of the texture or the texture elements as reference, as it optimizes 3D patch positions and orientations from transformations beten texture elements in the image. The reconstruction results in minimizing a large sparse linear least squares cost function based on the reprojection error, a planarity constraint and the estimated rigid motion beten patches. Texture element positions in the image are estimated under the assumption of a regular texture from clustered feature points representing repeating appearances in the image.  present results obtained with synthetic data as ll as real data to evaluate our method."}]