# Monocular Depth Estimation Using Res-UNet with an Attention Model
## 2 Related Work and Motivation
### Traditional Methods  
Depth estimation is a very old concept in computer vision. It began with the theories of [PERSON] [13], who invented a stereoscope that proved that the brain uses horizontal disparity to find relative depth. Another researcher, [PERSON] et al. [14], discovered the depth from the structure of motion and proposed that objects at various distances move on the retinal surface at different speeds. This motion parallax allows the human brain to extract 3D shapes from motion. These theories were the foundation for multi-view depth mapping. For example, a depth map can be generated with the help of a stereovision system that uses template matching and horizontal disparity to calculate the depth. Similarly, a depth map can be created from the structure of motion where a single camera is moved with a constant baseline to develop a depth map [1].  
Similarly, several researchers have tried to solve the depth problem from a single image. The possible work known in traditional methods is the work of [PERSON] et al. [15]. They reconstructed a three-dimensional scene from a single RGB image for a virtual environment. Their approach was based on hand-crafted features, which categorized pixels into different classes based on their shapes and colors. Researchers such as [PERSON] et al. [16] and [PERSON] et al. [17] have used the theories of [PERSON] et al. [15] to conduct depth analysis, calling it depth from semantic segmentation.  
[PERSON] et al. [17] demonstrated the integration of monocular depth features with semantic object labels. They proposed a pixel-wise classifier that can jointly predict a semantic class and a depth label from a single image. Their model relied on manually created features and superpixels to segment the image. In their work, they require a proper alignment for semantic segmentation, which makes their runtime very slow. [PERSON] et al. (2016) used a SIFT flow-based mechanism on KNN transfer to estimate static background depths from single images. In their model, they augmented motion information to estimate better moving foreground subjects in videos. This can result in better alignment and depth labels, but it necessitates having the entire dataset available at runtime and per-forming costly alignment procedures.  
[PERSON] et al. (2018) used synthetic data to estimate depth maps. Synthetic datasets consist of RGB images with their depth maps. [PERSON] et al. (2018) applied custom-made filters to the input image in discrete sections. These filters calculated depth values for each small patch in a discrete section by processing the image information. Additionally, they used the Markov random field (MRF) model, which evaluated the absolute scales of several image patches and inferred the depth image.  
Other researchers such as [PERSON] et al. (2016) and [PERSON] et al. (2019) have used non-parametric methods to estimate the depth maps. In their work, the depth of a query image was calculated by fusing the depths of photos with related photometric content retrieved from a database. Depth images contain number of small features which are very useful in different applications. For rich feature extraction, a method of DFT can also be used; this method performs better in pattern recognition, artefact removal, and feature extraction ([PERSON] et al., 2018).  
When access to fast machines was limited, hand-crafted features were helpful. After years of progress, depth maps are tackled with high-speed computers and machine-learning techniques.