Learning-based depth estimation requires a large amount of real-world training data, which can be both expensive and time-consuming to acquire. As a result, utilizing fully-annotated synthetic data from virtual environments has emerged as a promising alternative. However, networks trained with synthetic data typically exhibit sub-optimal performance due to the inherent distribution gap between virtual and real domain. To address this issue, we propose a new domain adaptation framework for depth estimation, DIVIDE, which learns a domain-invariant geometric space to minimize the domain shift. In particular, DIVIDE disentangles the domain-specific components of the input image and removes them while preserving its structural information for accurate depth estimation. Our proposed method outperforms state-of-the-art results in domain adaptation for depth estimation as well as achieves better style transfer with high image fidelity and structural consistency.

中文翻译：

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DIVIDE：学习域不变几何空间进行深度估计

基于学习的深度估计需要大量的现实世界训练数据，获取这些数据既昂贵又耗时。因此，利用来自虚拟环境的完全注释的合成数据已成为一种有前途的替代方案。然而，由于虚拟域和真实域之间固有的分布差距，使用合成数据训练的网络通常表现出次优的性能。为了解决这个问题，我们提出了一种用于深度估计的新域适应框架 DIVIDE，它学习域不变的几何空间以最小化域偏移。特别是，DIVIDE 解开输入图像的特定于域的组件并将其删除，同时保留其结构信息以进行准确的深度估计。我们提出的方法在深度估计的领域适应方面优于最先进的结果，并且通过高图像保真度和结构一致性实现了更好的风格转换。