Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  proposes an efficient method for the production of high quality radiosity solutions which uses an a priori knowledge of the dynamic properties of the scene to exploit temporal coherence. The method is based on a two-pass strategy that provides user-control on the final frame quality. In the first pass, it computes a coarse global solution of the radiosities along a time interval and then, in the second pass, it performs a frame-to-frame incremental gathering step using hardware graphic accelerators. Computing cost is thus reduced because the method takes advantage of frame-to-frame coherence by identifying the changes produced by dynamic objects and by decoupling them from computations that remain unchanged. The input data is a dynamic model of the environment through a period of time corresponding to the same camera recording. The method proceeds by incrementally updating two data structures: a space-time hierarchical radiosity solution for a given interval of time and a hierarchical tree of textures representing the space-time final illumination of the visible surfaces. These data structures are computed for a given viewpoint, either static or dynamic. The main contribution of this work is the efficient construction of the texture tree by identifying the changes produced by dynamic objects and by only recomputing these changes.
['Ignacio Martin', 'Xavier Pueyo', 'Dani Tost']
I am writing a research paper on a idea Frame-to-frame coherent animation with two-pass radiosity
[{"from": "human", "value": "I am writing a research paper on a idea Frame-to-frame coherent animation with two-pass radiosity"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  proposes an efficient method for the production of high quality radiosity solutions which uses an a priori knowledge of the dynamic properties of the scene to exploit temporal coherence. The method is based on a two-pass strategy that provides user-control on the final frame quality. In the first pass, it computes a coarse global solution of the radiosities along a time interval and then, in the second pass, it performs a frame-to-frame incremental gathering step using hardware graphic accelerators. Computing cost is thus reduced because the method takes advantage of frame-to-frame coherence by identifying the changes produced by dynamic objects and by decoupling them from computations that remain unchanged. The input data is a dynamic model of the environment through a period of time corresponding to the same camera recording. The method proceeds by incrementally updating two data structures: a space-time hierarchical radiosity solution for a given interval of time and a hierarchical tree of textures representing the space-time final illumination of the visible surfaces. These data structures are computed for a given viewpoint, either static or dynamic. The main contribution of this work is the efficient construction of the texture tree by identifying the changes produced by dynamic objects and by only recomputing these changes."}]