Improved appearance loss for deep estimation of image depth

Several recent deep network architectures tried to handle the depth estimation process as an image reconstruction problem, in order to overcome the shortfall that the ground truth depth data is not sufficiently available. The authors introduce and validate an efficient deeper network architecture for unsupervised depth estimation with an automated parameter optimisation. In addition, a hybrid appearance loss function is also proposed to improve the depth estimation accuracy and effectiveness. The authors' proposed model achieves the advantage that individual element of the loss function is weighted using normal distribution characteristics of a Gaussian model. The proposed ideas are validated on KITTI dataset achieving best reported results among recent state-of-the-art methods.