2D to 3D Reconstruction: Neural Network Models for 3D Structure Reconstruction

Currently, commonly used neural network models for 2D to 3D structure reconstruction include the following:

1. VoxelNet: This model uses a convolutional neural network (CNN) to extract features from point clouds and then employs deconvolution layers to convert the feature map into a 3D voxel.

2. PointNet++: This model describes objects using point clouds. It reconstructs 3D structures by extracting multi-level features from point clouds.

3. PointCNN: This model leverages point clouds for feature extraction. It applies convolution operations on point clouds using a convolutional neural network and converts the convolution results into 3D voxels.

4. DeepSDF: This model utilizes a deep learning model to establish a mapping function. It maps 2D images to 3D space and optimizes the loss function to obtain the final 3D structure.

5. Multi-view CNN: This model uses multiple 2D images to represent objects. It extracts features from multiple images using a convolutional neural network and finally converts the feature map into a 3D structure.