  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 navigation application. We also provide
quantitative experiments to prove that pre-training via our MineNavi dataset
can improve the performance of depth estimation model and speed up the
convergence of the model on real scene data. Since the synthetic dataset has a
similar effect to the real-world dataset in the training process of deep model,
we also provide additional experiments with monocular depth estimation method
to demonstrate the impact of various factors in our dataset such as lighting
conditions and motion mode.
