# Monocular Depth Estimation Using Res-UNet with an Attention Model
## 1 Introduction  
Depth maps are created for respective RGB images to achieve distance information about the scene. Depth maps store the information of the scene in three-dimensional axes, i.e., xyz coordinates where z represents the distance of the object from the camera axes. Over the past few decades, extensive work has been conducted in 3D photogrammetry and depth mapping. Depth mapping has numerous applications in autonomous cars, robot navigation, remote sensing, 3D mapping, and obstacle detection. Depth estimation has been widely studied in computer vision and machine learning. A typical example of a depth map with its respective RGB image can be seen in Figure 1.  
Initially, depth sensing was tackled with stereovision and structure from motion systems. These methods use the concepts of triangulation and stereo correspondence to calculate the disparity and depth, as seen in Figure 1(a). Developing depth maps with these approaches requires calibrated cameras, a proper baseline, and template matching. Such approaches are complex and require extensive post-processing, making them less effective [1]. Similarly, some researchers have developed depth maps with special time-of-flight sensors (TOF) such as LIDAR, RADAR, and SONAR to calculate the depth of a scene [2,3]. Such devices use distance information and flight time to create depth maps. These TOF devices have many limitations, such as the amount of calibration required, lower resolution, difficulty of transport, and being bulky in nature. Additionally, these sensors are not energy efficient, which has reduced their application. A typical example of a TOF sensoris shown in Figure 2b. Recently, new depth cameras have been adopted to create depth maps [4]. Microsoft Kinect and 3D lidars are new devices used to estimate depth cues. These devices are also employed in creating datasets for machine learning and deep learning methodologies. Figure 2c shows a Microsoft Kinect 3D lidar depth sensor. Depth mapping is an ill-posed problem because it can be generated from many respective scenes. Therefore, recently, depth maps have been developed with deep-learning methods. Many researchers have tried to solve depth problems using deep convolutional neural networks [5; 6; 7]. Deep-learning techniques are adopted to create depth maps because they can solve scale invariance and distorted boundary problems. In deep learning, the depth-estimation task is divided into three domains. The learning can be supervised, semi-supervised, and unsupervised to train the model [8]. In general, supervised learning requires the whole available dataset during training [9]. The unsupervised learning methodology does not require labeled datasets; rather, the datasets are generated during or before the training. Generally, unsupervised depth-mapping datasets are generated with traditional stereovision systems [10; 11]. Similarly, semi-supervised learning is a hybrid type of learning where models are trained with partially labeled and partially unlabeled datasets. In recent years, depth-map estimation has been studied widely in deep learning. However, there exist some limitations. Some of the major limitations are discussed as follows. * Most of the depth maps suffer from inaccurate boundaries and edges which leads the model to a high level of errors and losses during training. * Sometimes, models create depth maps which have scale invariance problems. Figure 1: RGB image with its respective depth map. X and Y show the dimensions of the image, while z represents the depth of the scene. Figure 2: Different types of depth sensors. (**a**) stereovision system, (**b**) TOF sensor, (**c**) Microsoft Kinect/depth camera. * In semi-supervised and unsupervised learning, the model learns from a stereovision system which requires extensive calibration and post-processing. * Certain models are very complex and require a considerable amount of memory and training time to develop depth maps. In order to address such shortcomings, we propose a simple encoder-decoder network in this paper.