  Virtual engines can generate dense depth maps for various synthetic scenes,
making them invaluable for training depth estimation models. However,
discrepancies between synthetic and real-world colors pose significant
challenges for depth estimation in real-world scenes, especially in complex and
uncertain environments encountered in unsupervised monocular depth estimation
tasks. To address this issue, we propose Back2Color, a framework that predicts
realistic colors from depth using a model trained on real-world data, thus
transforming synthetic colors into their real-world counterparts. Additionally,
we introduce the Syn-Real CutMix method for joint training with both real-world
unsupervised and synthetic supervised depth samples, enhancing monocular depth
estimation performance in real-world scenes. Furthermore, to mitigate the
impact of non-rigid motions on depth estimation, we present an auto-learning
uncertainty temporal-spatial fusion method (Auto-UTSF), which leverages the
strengths of unsupervised learning in both temporal and spatial dimensions. We
also designed VADepth, based on the Vision Attention Network, which offers
lower computational complexity and higher accuracy than transformers. Our
Back2Color framework achieves state-of-the-art performance on the Kitti
dataset, as evidenced by improvements in performance metrics and the production
of fine-grained details. This is particularly evident on more challenging
datasets such as Cityscapes for unsupervised depth estimation.
