Three-Dimensional Reconstruction Method for Bionic Compound-Eye System Based on MVSNet Network

In practical scenarios, when shooting conditions are limited, high efficiency of image shooting and success rate of 3D reconstruction are required. To achieve the application of bionic compound eyes in small portable devices for 3D reconstruction, auto-navigation, and obstacle avoidance, a deep learning method of 3D reconstruction using a bionic compound-eye system with partial-overlap fields was studied. We used the system to capture images of the target scene, then restored the camera parameter matrix by solving the PnP problem. Considering the unique characteristics of the system, we designed a neural network based on the MVSNet network structure, named CES-MVSNet. We fed the captured image and camera parameters to the trained deep neural network, which can generate 3D reconstruction results with good integrity and precision. We used the traditional multi-view geometric method and neural networks for 3D reconstruction, and the difference between the effects of the two methods was analyzed. The efficiency and reliability of using the bionic compound-eye system for 3D reconstruction are proved.

Automated summary

This study investigates a deep learning method of 3D reconstruction using a bionic compound-eye system with partial-overlap fields to achieve high efficiency of image shooting and success rate of 3D reconstruction. By designing a neural network based on the MVSNet network structure, named CES-MVSNet, the system can generate 3D reconstruction results with good integrity and precision. The efficiency and reliability of using the bionic compound-eye system for 3D reconstruction are proven.