Colorful 3D Reconstruction from Single Image Based on Deep Learning

The task of recovering the 3D shape and its surface color from a single image at the same time is extremely challenging. For this reason, an end-to-end network model is proposed to solve this problem, which uses an encoder and decoder structure. Taking a single image as input, first extract the features through the encoder, and then send them to the shape generator and the color generator at the same time to get the shape estimation and the corresponding surface color, and finally through the differentiable rendering framework to generate the fianl color three-dimensional model. In order to ensure the details of the reconstructed 3D model, an attention mechanism is introduced into the network encoder to further improve the reconstruction effect. The experimental results show that compared with the 3D reconstruction network models, the designed model has a 10% and 3% A increase in the real 3D model intersection ratio; compared with the open source project, the structural similarity of the designed model is improved by 3 A, and the mean square error is reduced by 1.2%.