With the increased use of depth information in computer vision, monocular depth estimation has been an emerging field of study. It is a challenging task where many deep convolutional neural network-based methods have been used for depth prediction. The problem with most of these approaches is that they use a repeated combination of max-pooling and striding in an encoder, which reduces spatial resolution. In addition, these approaches use information from all the channels directly from the encoder, which is prone to noise. Addressing these issues, we present a multigrid attention-based densenet-161 model. It consists of a multigrid densenet-161 encoder that increases the spatial resolution and an attention-based decoder to select the important information from low-level features. We achieved absolute relative error (Absrel) of 0.109 and 0.0724 on NYU v2 and KITTI, dataset respectively. Our proposed method exceeded most evaluation measures with fewer parameters compared to the state-of-the-art on standard benchmark datasets. We produce a dense depth map from a single RGB image which can be used to create a dense point cloud. The anticipated depth map is accurate and smooth, which can be used in several applications.

Convolutional Neural Network (CNN) depth estimation dilation rate multigrid attention mechanism depth map