1 research outputs found
Fully convolutional multiāscale dense networks for monocular depth estimation
Monocular depth estimation is of vital importance in understanding the 3D geometry of a scene. However, inferring the underlying depth is illāposed and inherently ambiguous. In this study, two improvements to existing approaches are proposed. One is about a clean improved network architecture, for which the authors extend Densely Connected Convolutional Network (DenseNet) to work as endātoāend fully convolutional multiāscale dense networks. The dense upsampling blocks are integrated to improve the output resolution and selected skip connection is incorporated to connect the downsampling and the upsampling paths efficiently. The other is about edgeāpreserving loss functions, encompassing the reverse Huber loss, depth gradient loss and feature edge loss, which is particularly suited for estimation of fine details and clear boundaries of objects. Experiments on the NYUāDepthāv2 dataset and KITTI dataset show that the proposed model is competitive to the stateāofātheāart methods, achieving 0.506 and 4.977 performance in terms of root mean squared error respectively