The three-dimensional reconstruction of a single image is a hotspot and a difficult point in the field of computer vision. The experimental results show that the proposed IMVD is better than the original MVD in the 3D object superresolution experiment and the high-resolution 3D reconstruction experiment of a single image. Moreover, we design a multiresidual dense block (MRDB) to optimize the depth map superresolution network, which allows the model to capture more object details and reduce the model parameters by approximately 25% when the number of network layers is doubled. Second, a 3D decoder using an effective subpixel convolutional neural network (3D ESPCN) improves the decoding speed in the decoding stage.

First, we use a 2D image encoder with multifeature fusion (MFF) to enhance the feature extraction capability of the model.

Eventually, we get an improved multiview decomposition (IMVD) network. To improve these two aspects and further enhance the high-resolution reconstruction capabilities of the 3D object generation network, we study and improve the low-resolution 3D generation network and the depth map superresolution network. On the other hand, a high-resolution reconstruction network maximizes fine low-resolution 3D objects. On the one hand, a low-resolution reconstruction network represents a good 3D object from a single RGB image. The results of high-resolution 3D object reconstruction are related to two aspects.

Recently, high-resolution representation of 3D objects using multiview decomposition (MVD) architecture is a fast reconstruction method for generating objects with realistic details from a single RGB image.

As a representative technology of artificial intelligence, 3D reconstruction based on deep learning can be integrated into the edge computing framework to form an intelligent edge and then realize the intelligent processing of the edge.

Reviews 3d object converter 6.50