Title: CD-Depth: Unsupervised Domain Adaptation for Depth Estimation via Cross Domain Integration. Abstract: Despite the efficiency of data collecting for depth estimation in the synthetic environment, we cannot take full advantage of such benefit due to the distribution gap between the synthetic and the real world. In this paper, we introduce a new unsupervised domain adaptation framework, CD-Depth, for depth estimation to alleviate domain shift by extracting structure-consistent and domain-agnostic latents using following methods. (1) We propose domain-agnostic latent mapping which projects images from different domains to the shared latent space by removing redundant domain features for estimating monocular depth. (2) We also fuse visual signals from both RGB and latent domains to fully exploit multi domain information with adaptive-window-based cross-attention. Our proposed framework achieves state-of-the-art results in unsupervised domain adaptation for depth estimation both on indoor and outdoor datasets and produces better generalization performance on an unseen dataset.
