Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The radiosity problem can be expressed as a linear system, where light interactions beten patches of the scene are considered. Its resolution has been one of the main subjects in Computer Graphics, which has led to the development of methods focused on different goals. For instance, in inverse lighting problems, it is convenient to solve the radiosity equation thousands of times for static geometries. Also, this calculation needs to consider many (or infinite) light bounces to achieve accurate global illumination results. Several methods have been developed to solve the linear system by finding approximations or other representations of the radiosity matrix, because the full storage of this matrix is memory demanding. Some examples are hierarchical radiosity, progressive refinement approaches, or wavelet radiosity, which may become slow for many bounces. Recently, new direct methods have been developed based on matrix factorization.#R##N##R##N# introduces a novel and efficient error-bounded factorization method based on the use of multiple singular value decompositions and the Z-order curve to sort the patches of the model. This technique accelerates the factorization of in-core matrices, and allows to work with out-of-core matrices passing only one time over them. Using this method, the inverse of the radiosity matrix can be efficiently approximated, reducing the memory and time resources needed to compute radiosity with infinite bounces. In the experimental analysis, the presented method is applied to scenes up to 163 K patches. After a precomputation stage, it is used to solve the radiosity problem for fixed geometries at interactive times.
['José Pedro Aguerre', 'Eduardo Fernández']
I am writing a research paper on a idea A hierarchical factorization method for efficient radiosity calculations
[{"from": "human", "value": "I am writing a research paper on a idea A hierarchical factorization method for efficient radiosity calculations"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The radiosity problem can be expressed as a linear system, where light interactions beten patches of the scene are considered. Its resolution has been one of the main subjects in Computer Graphics, which has led to the development of methods focused on different goals. For instance, in inverse lighting problems, it is convenient to solve the radiosity equation thousands of times for static geometries. Also, this calculation needs to consider many (or infinite) light bounces to achieve accurate global illumination results. Several methods have been developed to solve the linear system by finding approximations or other representations of the radiosity matrix, because the full storage of this matrix is memory demanding. Some examples are hierarchical radiosity, progressive refinement approaches, or wavelet radiosity, which may become slow for many bounces. Recently, new direct methods have been developed based on matrix factorization.#R##N##R##N# introduces a novel and efficient error-bounded factorization method based on the use of multiple singular value decompositions and the Z-order curve to sort the patches of the model. This technique accelerates the factorization of in-core matrices, and allows to work with out-of-core matrices passing only one time over them. Using this method, the inverse of the radiosity matrix can be efficiently approximated, reducing the memory and time resources needed to compute radiosity with infinite bounces. In the experimental analysis, the presented method is applied to scenes up to 163\u00a0K patches. After a precomputation stage, it is used to solve the radiosity problem for fixed geometries at interactive times."}]