REAL-NET: A Monochromatic Depth Estimation Using REgional Attention and Local Feature Mapping

Estimating the depth of a scene from a color image is an arduous task as it requires prior depth information to remove any uncertainty in the 3D interpretation. In recent works, many supervised depth estimation methods gave promising results, learning the priors based on end-to-end training. Unfortunately, inadequate addressing of actual physical constraints leads to inaccurate estimation. In this paper, we present REAL-Net, a supervised depth estimation encoder-decoder net by incorporating REgional constraints in the encoder through the Attention mechanism, and neighborhood feature constraints in the decoder taking care of the Local mapping. These make the output significantly clear and robust to the structural and color information. Results obtained upon conducting extensive experiments on benchmark datasets namely KITTI, Foggy Zurich, and Cityscapes establish the proposed method’s capability to outperform results obtained with the existing approaches.