This work proposes a new method to accurately complete sparse LiDAR maps
guided by RGB images. For autonomous vehicles and robotics the use of LiDAR is
indispensable in order to achieve precise depth predictions. A multitude of
applications depend on the awareness of their surroundings, and use depth cues
to reason and react accordingly. On the one hand, monocular depth prediction
methods fail to generate absolute and precise depth maps. On the other hand,
stereoscopic approaches are still significantly outperformed by LiDAR based
approaches. The goal of the depth completion task is to generate dense depth
predictions from sparse and irregular point clouds which are mapped to a 2D
plane. We propose a new framework which extracts both global and local
information in order to produce proper depth maps. We argue that simple depth
completion does not require a deep network. However, we additionally propose a
fusion method with RGB guidance from a monocular camera in order to leverage
object information and to correct mistakes in the sparse input. This improves
the accuracy significantly. Moreover, confidence masks are exploited in order
to take into account the uncertainty in the depth predictions from each
modality. This fusion method outperforms the state-of-the-art and ranks first
on the KITTI depth completion benchmark. Our code with visualizations is
available.

提出了一种新的方法，利用 RGB 图像指导生成稀疏的 LiDAR 地图的精确深度预测，通过融合 RGB 图像引导可以提高深度预测的准确性。该方法的实现在 KITTI 深度完成基准测试中得到了最佳成绩。