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 …

Depth maps produced by LiDAR-based approaches are sparse. Even high-end LiDAR
sensors produce highly sparse depth maps, which are also noisy around the object …

Depth completion involves recovering a dense depth map from a sparse map and an RGB
image. Recent approaches focus on utilizing color images as guidance images to recover …

Image guided depth completion aims to recover per-pixel dense depth maps from sparse
depth measurements with the help of aligned color images, which has a wide range of …

This paper proposes a new approach to integrating image features for unsupervised depth
completion. Instead of resorting to the image as input like existing works, we propose to …