Domain Adaptive Monocular Depth Estimation With Semantic Information
This work addresses the challenge of generalizing depth estimation models across domains without costly labeled data, which is incremental as it builds on existing adversarial methods by incorporating semantic cues.
The paper tackles the problem of domain adaptation in monocular depth estimation by proposing an adversarial training model that uses semantic information to reduce domain shift, achieving state-of-the-art performance on datasets like KITTI and Cityscapes with improved results on boundaries and distant objects.
The advent of deep learning has brought an impressive advance to monocular depth estimation, e.g., supervised monocular depth estimation has been thoroughly investigated. However, the large amount of the RGB-to-depth dataset may not be always available since collecting accurate depth ground truth according to the RGB image is a time-consuming and expensive task. Although the network can be trained on an alternative dataset to overcome the dataset scale problem, the trained model is hard to generalize to the target domain due to the domain discrepancy. Adversarial domain alignment has demonstrated its efficacy to mitigate the domain shift on simple image classification tasks in previous works. However, traditional approaches hardly handle the conditional alignment as they solely consider the feature map of the network. In this paper, we propose an adversarial training model that leverages semantic information to narrow the domain gap. Based on the experiments conducted on the datasets for the monocular depth estimation task including KITTI and Cityscapes, the proposed compact model achieves state-of-the-art performance comparable to complex latest models and shows favorable results on boundaries and objects at far distances.