Aiming at the impact of unstructured and rotational variability of three-dimensional information in point cloud on prediction results

a feature-supervised three-dimensional information encoding and decoding convolution deep learning network is proposed. The network is composed of feature monitoring coding and decoding modules in series. In the first part of the module

a three-dimensional convolution module is used in the form of hourglass structure to realize the coding and decoding of the feature map. In the second part

the residual blocks of different parameters are connected in parallel to realize the monitoring and fusion of feature maps. Feature monitored coding and decoding modules can build networks with different depths in series according to the size of data sets. At the same time

according to the data resolution

modules parameters can be set to realize feature learning from rough to fine

and ultimately obtain the best network. The experiment of ITOP database shows that the network achieves the end-to-end deep learning of three-dimensional information

significantly improves the system performance and has higher precision accuracy.