The translated data: Doppelgangers is a method that uses learning algorithms to distinguish between similar but physically distinct 3D surface images. Researchers have constructed an image dataset containing both positive and negative samples, utilizing local feature points and the spatial distribution of matches for judgment. They have defined a visual disambiguation problem and designed a corresponding network architecture. This method holds broad application value, helping to avoid errors that people might make when dealing with similar images. The research findings of the paper provide a learning-based solution for handling visually similar but actually different 3D surface images.