The invention discloses a Monte Carlo path tracking noise reduction method and device based on importance feature map sharing, and computing equipment. The method comprises the following steps: performing preliminary filtering preprocessing on a real-time drawing frame generated by Monte Carlo path tracking; splicing the color feature corresponding to each preprocessed drawing frame and the auxiliary feature corresponding to the scene geometric data without noise as an input feature; obtaining a plurality of importance feature maps of input features through deep neural network prediction, and for each importance feature map, constructing a multi-channel filter with a specific window size on each pixel through a cross-pixel importance value sharing mode, wherein multi-channel filters with different window sizes constructed based on the multiple importance feature maps are used for multiple times of filtering of pixels; and fusing the multiple filtering processing results, and then superposing the basic material color to obtain a final noise reduction processing result. The filter is constructed by using the importance value predicted by the deep learning network to perform noise reduction on the drawing frame, so that the efficiency is high and the overhead is low.
Real-time Monte Carlo path tracking noise reduction method and device based on importance feature map sharing, and computer equipment