A Study on Occlusion Removal Technology Using Integral Imaging with Semantic Segmentation and Predictive Labeling

In this paper, we propose an improved occluded object visualization method using semantic segmentation and predictive labeling based on integral imaging (InIm) technology. InIm is a passive 3D visualization technique that can generate 3D information through elemental images with different perspectives of information about an object and, it can be used to remove occlusion. However, occlusion pixels in elemental images can degrade the quality of reconstructed images. Our proposed method can remove occlusion and visualize the object of interest. In addition, we propose a predictive labeling method for reducing processing time, which can generate accurate 3D object information without occlusion. Therefore, when the proposed method is commercialized, it is expected to contribute to increasing the safety of autonomous driving technology.