In several applications, such as scene interpretation and reconstruction, precise depth measurement from images is a significant challenge. Current depth estimate techniques frequently provide fuzzy, low-resolution estimates. With the use of transfer learning, this research executes a convolutional neural network for generating a high-resolution depth map from a single RGB image. With a typical encoder-decoder architecture, when initializing the encoder, we use features extracted from high-performing pre-trained networks, as well as augmentation and training procedures that lead to more accurate outcomes. We demonstrate how, even with a very basic decoder, our approach can provide complete high-resolution depth maps. A wide number of deep learning approaches have recently been presented, and they have showed significant promise in dealing with the classical ill-posed issue. The studies are carried out using KITTI and NYU Depth v2, two widely utilized public datasets. We also examine the errors created by various models in order to expose the shortcomings of present approaches which accomplishes viable performance on KITTI besides NYU Depth v2.
Boosting Monocular Depth Estimation Models to High-Resolution via Content-Adaptive Multi-Resolution Merging
