Image processing method, device, equipment, storage medium and program product

Abstract

The invention discloses an image processing method, device and equipment, a storage medium and a program product, and relates to the technical field of image processing. The specific implementation scheme is as follows: acquiring a to-be-processed image of at least one part of a target object from an image comprising the target object; taking the to-be-processed image of at least one part as a node element, and constructing an association graph of all parts of the target object by utilizing the node element; and inputting the association graph into a graph neural network model, and outputting texture complemented images of all parts of the target object by utilizing the graph neural network model. According to the embodiment of the invention, the high-quality complete three-dimensional texture map can be generated for the target object, the cost of three-dimensional texture reconstruction can be remarkably reduced, and the omnibearing simulation rendering of the target object is realized.

Description

technical field [0001] The present disclosure relates to the technical field of computers, and in particular to the technical field of image processing. Background technique [0002] The 3D model texture map completion technology is a necessary way to reconstruct a 3D model with a complete appearance, and it is also an important step to use the reconstructed model for simulation and provide training resources for deep neural networks. At present, in the monocular image texture reconstruction task, due to the single viewing angle of the image, it is impossible to obtain the complete texture map of the target object. Taking a vehicle as the target object as an example, if the vehicle is photographed from the front, the taillights of the vehicle cannot be photographed. Therefore, it is necessary to use the 3D model texture map completion technology to complete the missing part of the target object. [0003] At present, the solid color filling and bilinear interpolation method...

AI Technical Summary

Problems solved by technology

[0003] At present, the pure color filling and bilinear interpolation methods used in the completion of 3D model texture maps have poor visual effects and cannot reconstruct the semantic features of object textures. For example, components such as car lights and windows cannot be reconstructed.

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