Conditional Generative Adversarial Network for Monocular Image Depth Map Prediction

Deep map prediction plays a crucial role in comprehending the three-dimensional structure of scene, which is essential for enabling mobile robots to navigate autonomously and avoid obstacles complex environments. However, most existing depth estimation algorithms based on deep neural networks rely heavily specific datasets, resulting poor resistance model interference. To address this issue, paper proposes implements an optimized monocular image algorithm conditional generative adversarial networks. The goal overcome limitations insufficient training data diversity overly blurred contours current proposed employs enhanced network with generator that adopts similar UNet novel feature upsampling module. discriminator uses multi-layer patchGAN incorporates original as input effectively utilize prior knowledge. loss function combines least squares L1 function. Compared traditional algorithms, optimization can restore contour information enhance visualization capability maps. Experimental results demonstrate our method expedite convergence NYU-V2 Make3D generate predicted maps contain more details clearer object contours.