Vision-Based 3D Reconstruction Using a Compound Eye Camera

The vision-based 3D reconstruction methods have various advantages and can be used in various applications such as navigation. Although various vision-based methods are being studied, it is difficult to reconstruct many parts at once with a general camera because of a small FOV. To solve this problem, we propose a coarse but lightweight reconstruction method using a camera with a unique structure called a compound eye with various advantages such as large FOV. In the process, we devise a network that performs depth estimation on a compound eye structure to obtain a depth image containing 3D information from an RGB image. We tested our methods by collecting data using a compound eye camera implemented in a Gazebo simulation and simulation scenes we created. As a result, our 3D reconstruction method using the data we collected and the confidence score from our depth estimation result, can capture the environment with a high recall of 97.51 %.