Conditional generative adversarial-based three-dimensional model fuzzy texture feature saliency method

Abstract

The invention discloses a three-dimensional model fuzzy texture feature salification method based on conditional generative adversarial, and the method comprises the following steps: 1), generating the shape and texture of a three-dimensional geometric model, and obtaining the point cloud features of the three-dimensional geometric model; 2) extracting a fuzzy boundary point cloud; and 3) feature saliency of the fuzzy boundary based on the conditional generative adversarial network. According to the method, based on multi-scale voxelization fuzzy boundary frame selection, textures are generated through the conditional generative adversarial network for mapping, the extracted voxel blocks are embedded into the three-dimensional geometric model, global texture fidelity of the geometric model is achieved through a multi-scale voxel segmentation mode, global texture optimization is changed into local texture optimization, and the calculation amount is reduced.

Description

technical field [0001] The invention relates to the field of three-dimensional geometric model reconstruction, in particular to a three-dimensional model fuzzy texture feature salient method based on conditional generation confrontation. Background technique [0002] In the existing 3D geometric model reconstruction technology, the step of model fidelity is usually involved, and the reconstructed 3D geometric model only has shape information. Therefore, after the 3D geometric shape reconstruction, it is usually necessary to supplement the texture information on the basis of the geometric shape model. In order to make the whole 3D geometric model have a more realistic visual effect [0003] In 2019, Wang et al. from the University of Chinese Academy of Sciences proposed a re-identification method for supervised texture generation (Wang J, Zhong Y, Li Y, et al.Re-Identification Supervised Texture Generation[C] / / 2019IEEE / CVF Conference on Computer Vision and Pattern Recognitio...

AI Technical Summary

Problems solved by technology

The disadvantage is that the boundary information of the generated model is not processed, and there is blurring at the boundary of the generated texture

The disadvantage is that the texture is updated by replacing the fusion fixed operation, the result is limited by the clarity of the input data, and the boundary texture blurring cannot be fully processed

[0007] To sum up, the existing 3D reconstruction methods have a certain blurring phenomenon in the generated texture boundary, which makes the fidelity of 3D reconstruction have certain defects.

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