# Monocular Depth Estimation Using Res-UNet with an Attention Model
## 5 Conclusions and Future Work  
The primary goal of this study was to implement, train, and evaluate a neural network that predicts the depth map from monocular RGB images. Our network predicts the depth map from monocular images with an accuracy comparable to state-of-the-art methods. We applied residuals block with an attention model in a traditional UNet. Our work was inspired by current trends in learning-based depth estimation research, which emphasizes accuracy improvement at the expense of larger models and more sophisticated computations. Modern depth-estimation techniques frequently use deep neural networks, which need considerable processing power and are just too complex to operate on real world data.  
Upon the completion of this study, we came up with some future work suggestions that focus on improving the network performance. In order to increase the network performance, more labeled datasets are required. Current depth datasets are very limited. New and more labeled datasets could help in training the networks more accurately. Additional data augmentations, such as spatial scaling, color, brightness, and contrast augmentations, could be used to provide more varied data to the network. This would train the network to better generalize to new scenes.  
Similarly, we built this model on the idea of semantic segmentation, as depth maps are very similar to semantic segmentation tasks. The network could be modified to perform semantic segmentation and depth estimation simultaneously because both the semantic labels and the depths share context information. Thus, semantic labels could be used to guide and presumably improve depth prediction. In addition, this work has focused on both the encoder and decoder parts of the network. We agreed on the residual block, as this performed well on classification tasks and it reduced the problem of vanishing gradient. However, the choice of one type of backbone is not limited to such a network. Currently, new and more efficient encoders such as MobileNets, EfficientNets, and vision transformers are being developed which can achieve state-of-the-art results on ImageNet classification tasks. Such encoders are possible to incorporate inside the UNets, but further study is required to check their performance. We will look for a more efficient and denser encoder and decoder to train on depth-estimation tasks. The transformers are becoming the new standard in image processing tasks, as they are fast and lightweight. Current transformers are smart features extractors which have fewer parameters. In future studies, we may experiment with vision transformers to verify whether they can develop dense depth maps. Similarly, a variety of attention models can be incorporated into new networks. There is also a chance that the network will become so lightweight that it can perform depth-estimation tasks in real time.  
Conceptualization, A.J. and S.S.; methodology, A.J.; software, A.J.; writing--original draft preparation, A.J.; writing--review and editing, S.S.; visualization, A.J.; supervision, S.S.; project administration, S.S.; funding acquisition, S.S. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2016R1D1A1B02011625).  
Not applicable.