Depth maps acquired with ToF cameras have a limited accuracy due to the high noise level and to the multi-path interference. Deep networks can be used for refining ToF depth, but their training requires real world acquisitions with ground truth, which is complex and expensive to collect. A possible workaround is to train networks on synthetic data, but the domain shift between the real and synthetic data reduces the performances. In this paper, we propose three approaches to perform unsupervised domain adaptation of a depth denoising network from synthetic to real data. These approaches are respectively acting at the input, at the feature and at the output level of the network. The first approach uses domain translation networks to transform labeled synthetic ToF data into a representation closer to real data, that is then used to train the denoiser. The second approach tries to align the network internal features related to synthetic and real data. The third approach uses an adversarial loss, implemented with a discriminator trained to recognize the ground truth statistic, to train the denoiser on unlabeled real data. Experimental results show that the considered approaches are able to outperform other state-of-the-art techniques and achieve superior denoising performances.

中文翻译：

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用于 ToF 深度细化的深度网络的无监督域自适应。

由于高噪声水平和多路径干扰，使用 ToF 相机获取的深度图精度有限。深度网络可用于细化 ToF 深度，但它们的训练需要在真实世界中获取具有基本事实的数据，这既复杂又昂贵。一种可能的解决方法是在合成数据上训练网络，但真实数据和合成数据之间的域转移会降低性能。在本文中，我们提出了三种方法来执行从合成数据到真实数据的深度去噪网络的无监督域自适应。这些方法分别作用于网络的输入、特征和输出级别。第一种方法使用域翻译网络将标记的合成 ToF 数据转换为更接近真实数据的表示，然后用于训练降噪器。第二种方法试图对齐与合成数据和真实数据相关的网络内部特征。第三种方法使用对抗性损失，使用经过训练以识别真实数据的鉴别器来训练未标记真实数据的降噪器。实验结果表明，所考虑的方法能够优于其他最先进的技术并实现卓越的去噪性能。