The depth completion task aims to predict a dense depth map from a sparse LiDAR point
cloud and an RGB image. This task is critical because an accurate depth map can be used …

Depth completion plays a crucial role in enabling real-world applications such as obstacle
avoidance and SLAM for robot navigation. This letter focuses on addressing the depth …

Transparent objects are commonly found in real life and industrial production. Unlike
opaque objects, transparent objects are not easily identifiable in RGB images and often …

Doctors perform limited one-way intestine endoscopy, in which advanced surgical robots
with depth sensors, such as stereo and ToF endoscopes, can only provide sparse and …

This paper addresses outdoor terrain mapping using overhead images obtained from an
unmanned aerial vehicle. Dense depth estimation from aerial images during flight is …

Point cloud patterns are hard to learn because of the implicit local geometry features among
the orderless points. In recent years, point cloud representation in 2D space has attracted …

In recent years, point cloud registration has achieved great success by learning geometric
features with deep learning techniques. However, existing approaches that rely on pure …

The recently proposed spacecraft three-dimensional (3D) structure recovery method based
on optical images and LIDAR has enhanced the working distance of a spacecraft's 3D …

Recovering dense depth maps from sparse depth sensors, such as LiDAR, is a recently
proposed task with many computer vision and robotics applications. Previous works have …

Depth completion is an important task in computer vision and robotics applications, which
aims at predicting accurate dense depth from a single RGB-LiDAR image. Convolutional …