论文标题
DESC：通过语义一致性进行深度估算的域适应
DESC: Domain Adaptation for Depth Estimation via Semantic Consistency
论文作者
论文摘要
准确的真实深度注释很难获取，需要使用特殊设备，例如LiDAR传感器。自我监督的方法试图通过处理视频或立体声序列来克服此问题，这可能并不总是可用。取而代之的是，在本文中，我们提出了一种域适应方法，使用完全宣布的源数据集和未经许可的目标数据集训练单眼深度估计模型。我们通过利用语义预测和低级边缘特征来为目标域提供指导来弥合域间隙。我们在主模型和第二个模型之间执行一致性，该模型通过语义分割和边缘图训练，并以实例高度的形式引入先验。我们的方法对单眼深度估计的标准域适应基准进行了评估，并显示出最新技术的一致改进。
Accurate real depth annotations are difficult to acquire, needing the use of special devices such as a LiDAR sensor. Self-supervised methods try to overcome this problem by processing video or stereo sequences, which may not always be available. Instead, in this paper, we propose a domain adaptation approach to train a monocular depth estimation model using a fully-annotated source dataset and a non-annotated target dataset. We bridge the domain gap by leveraging semantic predictions and low-level edge features to provide guidance for the target domain. We enforce consistency between the main model and a second model trained with semantic segmentation and edge maps, and introduce priors in the form of instance heights. Our approach is evaluated on standard domain adaptation benchmarks for monocular depth estimation and show consistent improvement upon the state-of-the-art.
