# Vision-Based Distance Measurement in Advanced Driving Assistance Systems
## 2 Related Work
proposed a combination method in which a CNN is used to regress the depth of superpixels, and a conditional random field is used for post-processing [21]. The supervised methods require the real depth value of each pixel in the input image as the training labels, which are difficult to obtain. Therefore, as a result of the lack of sufficient training samples to train the supervised network, it is difficult for these methods to become popularly adopted in different application fields, such as V-DAS. Unsupervised methods are generally divided into two subcategories. One is referred to as the self-supervised method, which uses the temporal information from a monocular video as supervision information. Compared with supervised methods, the training samples for a self-supervised network can be easily obtained. However, self-supervised methods also have some shortcomings. Firstly, self-supervised methods have to complete pose estimation using other approaches that increase the complexity of these methods, as a result the depth estimation results are largely dependent on the accuracy of pose estimation. Secondly, because of the lack of scale information, these self-supervised methods can only obtain relative depth results, and cannot obtain the absolute depth values. This relative depth information does not meet the requirements of ADAS. The other subcategory, unsupervised methods, is based on the spatial constraint relationship from the stereo vision acting as the supervision information. This means that stereo vision is used during offline training and monocular vision is used to estimate depth maps online. Generally, since the relative pose of two cameras is known, the estimation results of this subcategory are better than the results of a self-supervised network. Moreover, different from self-supervised networks, because the relative location of two cameras is known, unsupervised methods can obtain the absolute depth value, which is very important for ADAS. [PERSON] et al. first used the spatial constraint of two views to propose a depth estimation method for unsupervised monocular vision based on convolutional neural networks [22]. [PERSON]'s method utilizes a network structure similar to a full convolutional neural network (FCN), including encoding and decoding. In the unsupervised network, the depth map is first obtained by inputting the left view into the CNN, and then the corresponding disparity map is calculated according to the relationship between the disparity and the depth in the stereo vision. Furthermore, both this disparity map and the right view are used to reconstruct the left view; the error between the original left view and reconstructed left view is used as the loss function of the encoding-decoding network. On the basis of this network structure, [PERSON] et al. proposed a loss function that contains appearance matching loss, disparity smoothness loss, and left-right disparity consistency loss [23]. However, the estimation accuracy of unsupervised methods would be further improved if new information, including real depths, was added to the loss functions during training. Generally speaking, compared to dense depth information corresponding to each pixel of a forward-looking image, sparse depth information corresponding to parts of pixels is easier to obtain. Therefore, semi-supervised methods using sparse and local depth information have been recently studied. [PERSON] et al. combined a sparse ground truth depth map with a calibrated stereo image pair to train a semi-supervised network [24], which demonstrated state-of-the-art performance using the KITTI dataset. Moreover, [PERSON] et al. proposed a novel semi-supervised adversarial learning framework that only utilizes a small number of image-depth pairs in conjunction with a large number of easily available monocular images to achieve depth estimation [25]. In summary, compared with unsupervised methods, semi-supervised methods can achieve better estimation results due to the introduction of local and sparse depth labels.