Three-dimensional grid model generation method and device based on single image

Abstract

The invention discloses a three-dimensional grid model reconstruction method and device based on a single image, and the single image can be a contour map of a natural image or an object, and a three-dimensional model consistent with the shape of the single image is completely automatically generated according to the single image provided by a user. The method comprises the steps of semantic segmentation, foreground extraction, shape feature extraction, view angle prediction, three-dimensional model training and point cloud packaging of surface grids so as to reconstruct and generate a three-dimensional grid model. According to the method, the three-dimensional model design time can be greatly shortened, so that the target model can be obtained only by fine trimming on the basis of the generated three-dimensional model when the three-dimensional model is designed.

Description

technical field [0001] The invention relates to a method for reconstructing a three-dimensional mesh model based on a single image, which uses an artificial neural network to automatically extract image information and restore surface information of a three-dimensional model, and belongs to the technical fields of artificial intelligence, computer vision and image processing. Background technique [0002] With the development of computer science and technology, 3D vision has been applied in more and more scenes, such as 3D movies. However, the design of 3D models requires designers to use 3D software to draw, which not only requires high professional quality of designers, but also takes a lot of time. Therefore, how to automatically construct a 3D model using 2D images has become an urgent problem to be solved. [0003] Existing traditional 3D model reconstruction methods are usually based on predefined templates, and generate 3D models by assembling similar parts or object...

AI Technical Summary

Problems solved by technology

Although this type of method can generate high-quality 3D models, it cannot generate models that are not in the 3D model database.

[0004] The method based on deep learning can model the uncertainty of the new 3D model, but limited by the representation format of the 3D model and the characteristics of the neural network, most of the work can only generate point clouds or voxels, and cannot directly obtain higher quality , a more general 3D mesh model

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