论文标题
失败网络：通过同时无监督的代表学学习一般的单眼深度
DeFeat-Net: General Monocular Depth via Simultaneous Unsupervised Representation Learning
论文作者
论文摘要
在当前的单眼深度研究中，主要的方法是在扭曲的光度一致性驱动的大型数据集上采用无监督的培训。这种方法缺乏鲁棒性，无法推广到具有挑战性的领域，例如夜间场景或不利的天气条件，有关光度一致性的假设破裂。 我们提出了Dist-Net（深度和功能网络），这是一种同时学习跨域密集特征表示的方法，以及基于扭曲的功能一致性的强大深度估计框架。所得的特征表示以无监督的方式学习，而无需明确的地面真实对应。 我们表明，在一个域内，我们的技术在单眼深度估计和监督特征表示学习中都与当前的最新状态相媲美。但是，通过同时学习特征，深度和运动，我们的技术能够推广到具有挑战性的域，使失败 - 网络在所有更具挑战性的序列（例如夜间驾驶）上的所有错误度量的降低约为10％，均超过了当前的最新面积。
In the current monocular depth research, the dominant approach is to employ unsupervised training on large datasets, driven by warped photometric consistency. Such approaches lack robustness and are unable to generalize to challenging domains such as nighttime scenes or adverse weather conditions where assumptions about photometric consistency break down. We propose DeFeat-Net (Depth & Feature network), an approach to simultaneously learn a cross-domain dense feature representation, alongside a robust depth-estimation framework based on warped feature consistency. The resulting feature representation is learned in an unsupervised manner with no explicit ground-truth correspondences required. We show that within a single domain, our technique is comparable to both the current state of the art in monocular depth estimation and supervised feature representation learning. However, by simultaneously learning features, depth and motion, our technique is able to generalize to challenging domains, allowing DeFeat-Net to outperform the current state-of-the-art with around 10% reduction in all error measures on more challenging sequences such as nighttime driving.
