Scene Target 3D Point Cloud Reconstruction Technology Combining Monocular Focus Stack and Deep Learning

In order to obtain the depth information of the target in the scene and realize three-dimensional (3D) reconstruction, in this paper, a target reconstruction method combining monocular focus stack image and deep neural network is proposed. This method makes full use of the advantages of light field imaging technology and can generate the all focus image. The method first collects multiple frames of continuous images at different focal lengths of the scene, using a divide and conquer algorithm strategy, uplink uses YOLO neural network to identify the target in 3D space and track the position information; the downlink reconstructs the four-dimensional (4D) light field data based on the focus stack image frequency domain back projection, and then uses light field imaging technology to invert the scene parallax; subsequently, achieve scene depth estimation and reconstruction of all focus image; finally, the uplink and downlink are merged to realize the reconstruction of the 3D point cloud of the space target. Experimental results on real scenes show the effectiveness of the proposed algorithm.