Autonomous vehicles were experiencing rapid development in the past few years. However,
achieving full autonomy is not a trivial task, due to the nature of the complex and dynamic …

Depth completion aims at predicting dense pixel-wise depth from an extremely sparse map
captured from a depth sensor, eg, LiDARs. It plays an essential role in various applications …

In this paper, we propose a robust and efficient end-to-end non-local spatial propagation
network for depth completion. The proposed network takes RGB and sparse depth images …

Image-guided depth completion aims to generate dense depth maps with sparse depth
measurements and corresponding RGB images. Currently, spatial propagation networks …

While deep neural networks have become the go-to approach in computer vision, the vast
majority of these models fail to properly capture the uncertainty inherent in their predictions …

Depth completion deals with the problem of recovering dense depth maps from sparse ones,
where color images are often used to facilitate this task. Recent approaches mainly focus on …

Depth completion aims to recover dense depth maps from sparse depth measurements. It is
of increasing importance for autonomous driving and draws increasing attention from the …

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 …

Dense depth perception is critical for autonomous driving and other robotics applications.
However, modern LiDAR sensors only provide sparse depth measurement. It is thus …

Depth completion aims to recover a dense depth map from the sparse depth data and the
corresponding single RGB image. The observed pixels provide the significant guidance for …