###### Abstract
Other studies using RNN (Recurrent Neural Network)-based models such as LSTM; Ref. [2] trained their models to predict an accurate disparity map in order to generate an image at a specific time point (t) based on time-series image data. They are often called the monocular-supervision models, but, in this paper, for clarity, we call these UVS (Unsupervised models based on Video Sequences) models. These models also do not suffer from the data-shortage issue of the supervised model, but the overall performance is lower than the USI models. Semi-supervised learning is a hybrid of the previous two methods. Some studies have employed pixel-level semantic segmentation or instance segmentation benchmarks to provide additional information for predicting depth [3,4]. In this paper, we propose a new USI model that integrates a simple network structure and several effective loss functions. Most of the existing related studies have focused on the development of learning networks and have shown the impact that specific network architectures can have on depth-prediction performance. They initially calculated the image-reconstruction loss through simple methods such as L1 and L2 loss, which are then combined with traditional computer vision-based image quality assessment (IQA) algorithms. Computer vision-based IQA algorithms such as SSIM [5] have been used to quantify the similarity between the reconstructed and target images in unsupervised learning-based monocular depth-estimation models [1,6,7]. However, these methods have limitations in modeling various patterns at the level of the human visual system. That is, they behave differently in some environments. IQA algorithms derive feature maps representing the image structure from the original image and a distorted image. Then, they measure how similar those images are. Since these methods do not tolerate texture resampling, they have shown poor performance in images with complex textures, such as grass [8]. If the loss function for image reconstruction does not accurately evaluate the image quality, there is a possibility of inducing inefficient learning of the network [6,9]. We argue that, in order to accurately predict monocular depth in an unsupervised model, it is essential to use a robust image-reconstruction loss function that accurately quantifies the difference between the reconstructed image and target images. Recently, perceptual IQA models that effectively mimic human visual mechanisms have shown that this challenge could be mitigated [8,10,11]. Therefore, we considered that a human-like perceptual ability would improve the reconstruction quality in the warping process of the USI model for monocular depth estimation based on stereo images. Consequently, we propose an unsupervised learning-based model using PieAPP [11], one of the representative perceptual IQA algorithms, as an image-reconstruction loss. To the best of our knowledge, there is no precedent for using these methods for monocular depth prediction. The proposed model is a simple ResNet50-based auto-encoder [12], which further highlights the contribution of the proposed effective loss functions. We trained our model to generate depth maps for 768 \(\times\) 384 images using stereo image pairs of the KITTI 2015 dataset [13]. We applied a variety of metrics to validate the performance of our approach. We also quantitatively and qualitatively compared our results with other studies. Our model performed well numerically in various standard evaluation indicators. Additional experimental results were also performed to verify our model's generalization capacity using the CityScapes dataset [14]. Although our model is based on a simple ResNet-based network, it outperforms other unsupervised learning-based state-of-the-art studies thanks to the image-reconstruction loss function based on the perceptual IQA model that effectively mimics the human visual system. The following sections first describe several studies related to our work.