Journal
ELECTRONICS
Volume 11, Issue 11, Pages -Publisher
MDPI
DOI: 10.3390/electronics11111790
Keywords
bionic compound-eye system; 3D reconstruction; deep learning
Categories
Funding
- National Natural Science Foundation of China [61871034]
Ask authors/readers for more resources
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.
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available