Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  present a method for computing  ambient occlusion  (AO) for a stack of images of a Lambertian scene from a fixed viewpoint. Ambient occlusion, a concept common in computer graphics, characterizes the local visibility at a point: it approximates how much light can reach that point from different directions without getting blocked by other geometry. While AO has received surprisingly little attention in vision,  show that it can be approximated using simple, per-pixel statistics over image stacks, based on a simplified image formation model.  use our derived AO measure to compute reflectance and illumination for objects without relying on additional smoothness priors, and demonstrate state-of-the art performance on the MIT Intrinsic Images benchmark.  also demonstrate our method on several synthetic and real scenes, including 3D printed objects with known ground truth geometry.
['Daniel Cabrini Hauagge', 'Scott Wehrwein', 'Kavita Bala', 'Noah Snavely']
I am writing a research paper on a idea Photometric Ambient Occlusion for Intrinsic Image Decomposition
[{"from": "human", "value": "I am writing a research paper on a idea Photometric Ambient Occlusion for Intrinsic Image Decomposition"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  present a method for computing  ambient occlusion  (AO) for a stack of images of a Lambertian scene from a fixed viewpoint. Ambient occlusion, a concept common in computer graphics, characterizes the local visibility at a point: it approximates how much light can reach that point from different directions without getting blocked by other geometry. While AO has received surprisingly little attention in vision,  show that it can be approximated using simple, per-pixel statistics over image stacks, based on a simplified image formation model.  use our derived AO measure to compute reflectance and illumination for objects without relying on additional smoothness priors, and demonstrate state-of-the art performance on the MIT Intrinsic Images benchmark.  also demonstrate our method on several synthetic and real scenes, including 3D printed objects with known ground truth geometry."}]