# Monocular Depth Estimation Using Res-UNet with an Attention Model
## 2 Related Work and Motivation
### Depth Estimation Using Deep Learning
#### 2.2.2 Semi-Supervised and Unsupervised Learning  
[PERSON] et al. [10] tried to solve the depth-estimation task in an unsupervised manner. They proposed a network and justified their work by comparing the weakness of supervised neural networks. They used an autoencoder setup to train on pairs of stereo images to construct depth maps. The encoder in their model was based on Alex-Net [21], in which the last connected layer is replaced with fully convolutional layers. Skip connections were introduced inside the network to obtain refined predictions. During training, the network predicts the inverse depth of the left image. Then, the right image, the disparity, and the predicted inverse depth of the left image are used by the network to reconstruct the left image. In the training phase, the reconstruction loss is reduced, and the reconstructed left image is matched to the input. [PERSON] et al. [11] also used unsupervised learning to estimated monocular depth. Their work was very similar to the method of [PERSON] et al. [10]. The main difference in their method is that they introduced a new loss with left-right consistency. They showed that solely using reconstruction of the left image will lead to a poor depth prediction. Therefore, they used epipolar geometric constrains to generate disparity maps for both left and right images. They enforced the disparity maps' consistency during the training phase, which improved the robustness and accuracy of the depth predictions. Their study demonstrated how a network could be trained in an unsupervised way for depth estimation using only stereo pairs of images. [PERSON] et al. [36] created an unsupervised learning framework to simultaneously estimate the depth and ego-motion from a monocular camera. The training dataset was extracted from video recordings captured with a single camera. The camera location moves slightly between frames, behaving similar to a stereo pair. They developed a separate network to estimate the movement between the frames; hence, the camera calibration is eliminated. This method performed better, but certain assumptions were necessary for it to hold true. In order to prevent bad pixels from moving objects, which have a negative effect on the training process, they weighted the loss from each pixel using the predicted value for those pixels. Following the trend, [PERSON] et al. [37] proposed a motion model in an unsupervised approach. Their method can simulate dynamic scenes by simulating object motion. Their proposed motion model was the same as the ego-motion model. The only difference in their model was that it could precisely predict the motion of each individual object in 3D space. They used a pre-segmented RGB image sequence dataset for training. Hence, the model predicts the transformation vectors for each object in 3D space. Their method not only model the objects in 3D but also learns their motion on the fly. Their approach was able to model the depth for every scene and each individual object. [PERSON] et al. [38] estimated the depth from RGB images at a very sparse set of pixels. They used lower-power depth sensors to develop high-quality, dense depth maps. They combined semantic segmentation with sensor data to enhance the performance of the model. Their model was trained in a semi-supervised manner with a stereo camera setup. Their framework relies on both depth estimation and semantic segmentation, which switch according to conditions. Region-aware depth estimation is performed using a novel left -right semantic consistency term. They claim that their sparse depth information is very flexible, which leads the model to generalize the multiple scenes--meaning it can simultaneously work for indoor and outdoor scenes. [PERSON] et al. [39] presented a new technique which uses the whole three-dimensional geometry of the scene, unlike previous depth-mapping methods which use the ego-motion, pixel-wise, or gradient-based losses in the small neighborhood.