Depth maps inpainting based on adaptive generative models

Abstract

Important goals in the application of various technologies designed to increase crop yields are to reduce costs and reduce environmental impact. The advent of agricultural robots can improve the quality of fresh produce, reduce production costs, reduce manual labor and, in some countries, offset labor shortages in some agricultural sectors. Modern mobile robots use technologies for constructing the most optimal path for its movement. It uses simultaneous navigation and mapping techniques known as SLAM. The problem with all mapping methods is the presence of lost areas in the depth map. This paper presents an approach to reconstruct depth maps using a generative adversarial network (GAN) structure augmented with a two-path discriminator for separate texture and color analysis. The proposed GAN architecture consists of two main components: a generator and a discriminator. The generator strives to create an image that is as close to the original as possible, without aliases or extraneous objects, while the discriminator attempts to determine how successful the restoration was. As part of the study, an experiment was conducted on a dataset to test the effectiveness of the proposed architecture. The results showed that the new GAN model exhibits good depth map reconstruction performance, outperforming most existing methods.