# Monocular Depth Estimation Using Res-UNet with an Attention Model
## 2 Related Work and Motivation
### Depth Estimation Using Deep Learning
#### 2.2.1 Supervised Learning  
[PERSON] et al. [5] were one of the first groups of researchers who attempted to use convolutional neural networks to predict a depth map from a single set of images. They used multi-scale deep neural networks to create depth maps for a respective RGB image. Their network had two stages: coarse level and fine level. The coarse-level network generated depths at a global level from the entire image. Later, the image from the coarse network, along with the original RGB image, was passed into the fine network. The fine network refined the predicted depth using local information. The network of [PERSON] et al. [5] was pre-trained on the ImageNet [21] classification task dataset. Using a pre-trained weight worked better because the network performs well if the training is initialized with some existing weights values rather than random initialization weights. [PERSON] and [PERSON] [6] incorporated surface-normal and semantic labels to develop depth maps. They modified the model of [PERSON] et al. [5] by making their network deeper with more convolutional layers to estimate multi-channel feature maps. They used a single-scale, one-stack network. This combination resulted in combining depth and normal to share the computation load. Hence, they simultaneously predicted the depth and normal for a single RGB image. Their work shows that a VGG [22] based network performed better as compared to a small Alex-Net [21], but it failed to achieve sharp transitions. In image processing, edges and lines are important features because they are used to detect object shapes in a scene. The deficiency in the above work of [PERSON] and [PERSON] [6] can be overcome by post-processing of the output results. A method of signal-to-noise ratio (SNR) proposed by [PERSON] [23] can be applied to output depth maps to detect edges and lines. In his method, he analytically determined the SNR of the line and achieved quality results. We can enhance the output results by enhancing the lines and edges in the image. Similarly, for classification of each depth value, a method of wavelet transform can be performed on the predicted depth maps for smooth transitions during the post-processing [24; 25; 26]. [PERSON] et al. [7] used conditional random fields (CRF) with CNNs to develop depth maps from single images. The use of convolutional neural fields helped to develop the connection between neighboring parts of depth maps. CRF nodes modeled a probability distribution function to optimize the neural network. Their network consists of two parts (unary and pairwise). The unary convolutional part aims to regress the depth values, and the pairwise potential part encourages neighbor super-pixels with the same appearance to take on the same depth values. The output from their network was a regressed depth for super-pixel values. The qualitative results of [PERSON] et al. [7] have sharper transitions and aligned local structures. The drawback in their model was that scale invariance was not considered in their proposed work. [PERSON] et al. [27] proposed fully convolutional residual networks for depth estimation from a single image. They used a Res-Net 50 [28] encoder with pretrained weights in their model. Similarly, they proposed simple up-sampling and removed the last fully connected layers. This made their network simpler and more efficient. They used BerHu loss [29] during training, as this loss performed well compared to L2 loss when exploiting depth maps as ground truth. The outcomes of this study demonstrate that the value of applying up-projections in the decoder helps to produce dense output predictions with an improved resolution. Additionally, it demonstrates the possibility of employing Res-Net as the feature extractor rather than VGG or Alex-Net. Similarly, [PERSON] et al. [30] tried to use the global context CNN and regional CNN in a combined hierarchical conditional random field. They combined the work of [PERSON] et al. [7] and [PERSON] et al. [5].