论文标题
N-QGN：使用Quadtree生成网络的单眼相机的导航图
N-QGN: Navigation Map from a Monocular Camera using Quadtree Generating Networks
论文作者
论文摘要
几年来，单眼深度估计一直是一个流行的研究领域，尤其是因为自我保护的网络在用监督和立体声方法弥合差距方面表现出越来越好的结果。但是，这些方法将它们的兴趣集中在密集的3D重建上，有时是在自动导航中多余的细节上。在本文中，我们建议通过估计Quadtree代表下的导航图来解决此问题。目的是创建一个自适应深度图预测，该预测仅提取避免障碍物必不可少的细节。离开大型导航的其他3D空间将有大约距离。 Kitti数据集上的实验表明，我们的方法可以显着减少输出信息的数量而不会严重损失。
Monocular depth estimation has been a popular area of research for several years, especially since self-supervised networks have shown increasingly good results in bridging the gap with supervised and stereo methods. However, these approaches focus their interest on dense 3D reconstruction and sometimes on tiny details that are superfluous for autonomous navigation. In this paper, we propose to address this issue by estimating the navigation map under a quadtree representation. The objective is to create an adaptive depth map prediction that only extract details that are essential for the obstacle avoidance. Other 3D space which leaves large room for navigation will be provided with approximate distance. Experiment on KITTI dataset shows that our method can significantly reduce the number of output information without major loss of accuracy.
