论文标题
带有语义和几何指导的天空高度预测和完善神经网络
Aerial Height Prediction and Refinement Neural Networks with Semantic and Geometric Guidance
论文作者
论文摘要
深度学习为许多计算机视觉任务提供了强大的新方法。从空中图像中预测的高度预测是从深度学习的部署中受益匪浅的任务之一，它取代了旧的多视觉几何技术。这封信提出了一种两阶段的方法，其中首先使用多任务神经网络来预测由单个RGB航空输入图像产生的高度图。我们还包括第二个改进步骤，其中使用Denoising AutoCodoter生成更高质量的高度图。两个公开可用数据集的实验表明，我们的方法能够产生最先进的结果。代码可在https://github.com/melhousni/dsmnet上找到。
Deep learning provides a powerful new approach to many computer vision tasks. Height prediction from aerial images is one of those tasks that benefited greatly from the deployment of deep learning which replaced old multi-view geometry techniques. This letter proposes a two-stage approach, where first a multi-task neural network is used to predict the height map resulting from a single RGB aerial input image. We also include a second refinement step, where a denoising autoencoder is used to produce higher quality height maps. Experiments on two publicly available datasets show that our method is capable of producing state-of-the-art results. Code is available at https://github.com/melhousni/DSMNet.
