Time-sequence dynamic virtual images

We propose to reconstruct time-sequence dynamic virtual images. At different time of a day or different weather conditions the same scene is differently lighted. Using general regression neural networks the rule of lighting conditions changing with outside conditions can be infered, thus the virtual images in virtual outside conditions can be obtained from a real image of the scene. So using the method presented here we can reconstruct virtual images at any appointed virtual time or weather condition. Any geometrical information of the scene is not needed in generating virtual images. Combining IBMR techniques or panoramic image techniques, the model of scene is recovered. Further, combining the dynamic virtual images obtained by our method, time-sequence dynamic virtual scene can be reconstructed and revisited in virtual reality.