Learning Regularizer for Monocular Depth Estimation with Adversarial Guidance

Monocular Depth Estimation (MDE) is a fundamental task in computer vision and multimedia. With the wide applications of deep Convolutional Neural Networks (CNNs), learning-based methods have achieved superior performance on MDE tasks in recent years. Because loss functions are important to train an accurate CNN with good generalization performance, nearly all previous efforts contribute to proposing powerful loss functions with careful hand-crafted regularizers(e.g., gradient loss and normal loss) added to the basic depth L1-Loss. However, the hand-crafted regularizers require rich domain knowledge, while their performance can still not be guaranteed. In this paper, we learn a new regularizer, approximated by a tiny CNN Regularizrer-Net(RN), and train it in an adversarial way. As demonstrated experimentally, our learned regularizer can notably outperform the current state-of-the-art methods by both quantitative evaluation and qualitative visualization on the benchmark NYU-Depth-v2 dataset, and well generalize to the new ScanNet dataset without any further training. Our code will be released soon.