  Depth estimation from monocular images is a challenging problem in computer
vision. In this paper, we tackle this problem using a novel network
architecture using multi scale feature fusion. Our network uses two different
blocks, first which uses different filter sizes for convolution and merges all
the individual feature maps. The second block uses dilated convolutions in
place of fully connected layers thus reducing computations and increasing the
receptive field. We present a new loss function for training the network which
uses a depth regression term, SSIM loss term and a multinomial logistic loss
term combined. We train and test our network on Make 3D dataset, NYU Depth V2
dataset and Kitti dataset using standard evaluation metrics for depth
estimation comprised of RMSE loss and SILog loss. Our network outperforms
previous state of the art methods with lesser parameters.
