What makes the unsupervised monocular depth estimation (UMDE) model training better.

ABSTRACT: Current computer vision tasks based on deep learning require a huge amount of data with annotations for model training or testing, especially in some dense estimation tasks, such as optical flow segmentation and depth estimation. In practice, manual labeling for dense estimation tasks is very difficult or even impossible, and the scenes of the dataset are often restricted to a small range, which dramatically limits the development of the community. To overcome this deficiency, we propose a synthetic dataset generation method to obtain the expandable dataset without burdensome manual workforce. By this method, we construct a dataset called MineNavi containing video footages from first-perspective-view of the aircraft matched with accurate ground truth for depth estimation in aircraft naviga

SUBMITTER: Wang X 

PROVIDER: S-EPMC9768171 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature