## 1 Introduction  
Estimating depth from a single image is a significant computer vision task in enabling computers to comprehend real scenes. It has been widely applied in AR (augmented reality) [1], robotics navigation [2], and self-driving cars [3] for generating high-quality depth from color instead of using expensive LiDAR sensors. Although the monocular camera is cheap and light weight, the depth estimation task is still challenging for the traditional SfM/SLAM algorithm [4]. In the deep learning method of monocular depth estimation, it can be coarsely classified into two categories, supervised and self-supervised learning. For the supervised learning task, the original view to be estimated and its ground-truth depth data are both the input of the neural network [5]. The depth map is obtained by expensive LiDAR sensors [6], which is sparse compared with the original image on the pixel level. In the case of poor ambient light, the capability of LiDAR will be greatly affected. Therefore, the generalization ability of the prediction network trained by the ground truth depth map obtained by the LiDAR sensor is insufficient. When the trained model is applied to a new camera or to predict the depth of a new scene, the reliability of the prediction will be greatly reduced. Meanwhile, obtaining significant ground-truth depth data for the dataset is challenging. However, unsupervised learning methods utilize geometric constraintsof stereo images as the only source of supervision, i.e., unsupervised learning takes the image obtained by a monocular camera as input and uses view synthesis to supervise the training network. This method is simpler than supervised learning and has a stronger generalization ability between different cameras and different scenes. However, self-supervised methods still do not perform as well as supervised methods on the standard benchmarks. The reason lies in the weak supervision of the photometric consistency, which refers to the pixel-level difference between the image from a perspective and the reconstructed image generated by another perspective. Most of the previous unsupervised monocular depth estimation methods rely on the color constancy hypothesis, which assumes that if the original image and the reconstructed image are identical at corresponding pixels, then they should have the same RGB values at each other's positions. However, in reality, as Figure 1 shows, many factors will cause color fluctuation, which may affect the color distribution. For example, the light intensity, noise and the reflection of light, etc., are different in two different views. Therefore, the unsupervised loss we use is easily affected by these common factors that interfere with the color. When the reconstructed image is generated from another image disturbed by the above factors, the losses incurred in the undisturbed areas are inaccurate, so that causing unreliable results when generating a depth map. The problem caused by color constancy hypothesis is referred to as the color fluctuation problem. At the same time, the low texture problem also affects the depth estimation. In the low texture region of the two images, if the feature information is ambiguous, then the disparity map generated by the model may not be accurate. For example, when calculating the photometric loss in those regions, the loss values could be very small for the model to represent, but in fact, the two areas may not be the same. To solve the above problems, we propose a novel self-supervised depth estimation network--PDANet, which contains two extra unsupervised signals: (1) the perceptual consistency uses the loss in the high-order semantics level rather than the pixel level, which brings abstract matching clues for the model to match the low texture and color fluctuation region. (2) The data augmentation consistency effectively enhances the robustness of the image to color fluctuation caused by illumination and other factors instead of applying color constancy hypothesis.