Monocular Depth Prediction Using Generative Adversarial Networks

We present a technique for monocular reconstruction, i.e. depth map and pose prediction from input monocular video sequences, using adversarial learning. We extend current geometry-aware neural network architectures that learn from photoconsistency-based reconstruction loss functions defined over spatially and temporally adjacent images by leveraging recent advances in adversarial learning. We propose a generative adversarial network (GAN) that can learn improved reconstruction models, with flexible loss functions that are less susceptible to adversarial examples, using generic semi-supervised or unsupervised datasets. The generator function in the proposed GAN learns to synthesize neighbouring images to predict a depth map and relative object pose, while the discriminator function learns the distribution of monocular images to correctly classify the authenticity of the synthesized images. A typical photoconsistency-based reconstruction loss function is used to assist the generator function to train well and compete against the discriminator function. We demonstrate the performance of our method on the KITTI dataset in both, depth-supervised and unsupervised settings. The depth prediction results of the proposed GAN are shown to compare favorably with state-of-the-art techniques for monocular reconstruction.