In order to enhance the sense of realism of the integration of virtual and real objects in augmented reality scenes， a variation-aware online dynamic illumination estimation method for indoor scenes was proposed. Unlike the existing methods that directly calculate lighting parameters or generate lightmaps， the lighting variation images of the scene under different lighting conditions are estimated by this method to implement the dynamic update of the scene illumination， which can obtain dynamic lighting of the scene more accurately and retain detailed information of the scene. The Convolutional Neural Network （CNN） of the proposed network includes two sub-networks， namely Low Dynamic Range （LDR） image feature extraction network and illumination estimation network. The whole network structure took a High Dynamic Range （HDR） panoramic lightmap with all the main light sources open in the scene as the initial lightmap， and this lightmap and the LDR image with limited field of view after lighting change were used as the input together. Firstly， the CNN was built based on AlexNet to extract the LDR image features， and these features were connected with the HDR lightmap features in illumination estimation network sharing encoder. Then， the U-Net structure was used to estimate the lighting variation image and the mask of light source by introducing the attention mechanism， so as to update the dynamic illumination of the scene. In the numerical evaluation of panoramic lightmap， the Mean Squared Error （MSE） indicator of the proposed method was improved by about 79%， 65%， 38%， 17%， and 87% compared with those of Gardner’s method， Garon’s method， EMLight， Guo’s method， and coupled dual StyleGAN panoramic synthesis network StyleLight， respectively， and other indicators were also improved. The above proves the effectiveness of the proposed method from both qualitative and quantitative aspects.
