Conditional Generative Adversarial Network for Monocular Image Depth Map Prediction

Abstract

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