Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Gaussian curvature is an invariant local descriptor of smooth surfaces.  present an object signature which is a condensed representation of the distribution of Gaussian curvature information at visible object points. An invariant related to Gaussian curvature at a point is derived from the covariance matrix of the photometric values in a neighborhood about that point. In addition,  introduce an albedo-normalization method that is capable of cancelling albedo on Lambertian surfaces.  use three illumination conditions, two of which are unknown. The three-tuple of intensity values at a point is related via a one-to-one mapping to the surface normal at that point. The determinant of the covariance matrix of the local three-tuples is invariant to albedo, rotation and translation. The collection of determinants over mutually illuminated object points is combined into a signature distribution which is albedo, rotation, translation, and scale invariant. An object recognition methodology using these signatures is proposed.
['Elli Angelopoulou', 'James P. Williams', 'Lawrence B. Wolff']
I am writing a research paper on a idea A curvature based descriptor invariant to pose and albedo derived from photometric data
[{"from": "human", "value": "I am writing a research paper on a idea A curvature based descriptor invariant to pose and albedo derived from photometric data"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Gaussian curvature is an invariant local descriptor of smooth surfaces.  present an object signature which is a condensed representation of the distribution of Gaussian curvature information at visible object points. An invariant related to Gaussian curvature at a point is derived from the covariance matrix of the photometric values in a neighborhood about that point. In addition,  introduce an albedo-normalization method that is capable of cancelling albedo on Lambertian surfaces.  use three illumination conditions, two of which are unknown. The three-tuple of intensity values at a point is related via a one-to-one mapping to the surface normal at that point. The determinant of the covariance matrix of the local three-tuples is invariant to albedo, rotation and translation. The collection of determinants over mutually illuminated object points is combined into a signature distribution which is albedo, rotation, translation, and scale invariant. An object recognition methodology using these signatures is proposed."}]