Depth Estimation using Weighted-loss and Transfer Learning

arXiv:2404.07686v1 Announce Type: new
Abstract: Depth estimation from 2D images is a common computer vision task that has applications in many fields including autonomous vehicles, scene understanding and robotics. The accuracy of a supervised depth estimation method mainly relies on the chosen loss function, the model architecture, quality of data and performance metrics. In this study, we propose a simplified and adaptable approach to improve depth estimation accuracy using transfer learning and an optimized loss function. The optimized loss function …

abstract accuracy applications architecture arxiv autonomous autonomous vehicles computer computer vision cs.ai cs.cv data fields function images loss metrics performance quality robotics transfer transfer learning type understanding vehicles vision